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Conferences, Lectures, & Seminars
Events for March
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Semiconductors & Microelectronics Technology Seminar - Andrew Mannix, Wednesday, 3/1 at 11am in EEB 248
Wed, Mar 01, 2023 @ 11:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Andrew Mannix, Stanford University
Talk Title: Automated assembly of synthetic van der Waals solids
Series: Semiconductors & Microelectronics Technology
Abstract: Synthetic van der Waals (vdW) solids assembled from two-dimensional (2D) materials yield unprecedented, atomic-scale control over their structure and properties, with profound implications for future quantum, electronic, and photonic devices. Within these vdW solids, moiré superlattices arising from lattice mismatch and interlayer twist angle can host novel quantum states (e.g., superconductivity), emergent ferroelectricity, and tunable quantum confinement. However,
the production of vdW solids remains a largely artisanal process,
limited in the size of the source material and the fabrication
throughput. In this talk, I will discuss our recent efforts to enhance the quality and speed of vdW solid fabrication. Our core approach, Robotic 4D Pixel Assembly, enables rapid manufacturing of designer vdW solids with unprecedented speed, area, patternability, and angle control. We utilize a high-vacuum robot to assemble prepatterned pixels made from 2D materials grown at the wafer scale. We fabricated vdW solids of up to 80 individual layers, consisting of (10 to 1000 μm)^2 areas with pre-designed patterned shapes, laterally/vertically programmed composition, and controlled interlayer angle. This enabled efficient optical spectroscopy assays of vdW solids and fabrication of twisted n-layer assemblies, where we observe atomic lattice relaxation
of twisted 4-layer WS_2 at unexpectedly high interlayer twist angles of greater than or equal to 4 degree. To conclude, I will outline ongoing efforts in my lab to understand and engineer high quality electronic interfaces, moiré superlattices, and point defects within vdW solids.
Biography: Andrew Mannix is an assistant professor of Materials Science and Engineering at Stanford University. He completed his B.S. in Materials Science and Engineering at the University of Illinois at Urbana-Champaign, and his Ph.D. in Materials Science and Engineering at Northwestern University as an NSF GRFP Fellow, where he worked on the growth and atomic-scale characterization of new 2D materials. Before moving to Stanford, Andy was a Kadanoff-Rice Postdoctoral Fellow in the James Franck Institute at the University of Chicago, where he developed new methods of atomically-thin nanomaterials growth, processing, and automated heterostructure assembly. His lab at Stanford focuses on the growth, assembly and atomic-scale characterization of 2D materials for new electronic and quantum information science applications.
Host: J Yang, H Wang, C Zhou, S Cronin, W Wu
More Information: Andrew_0301_new.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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CS Colloquium: Alexis E. Block (UCLA) - Towards Enhanced Social-Physical Human-Robot Interaction
Wed, Mar 01, 2023 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Alexis E. Block, UCLA
Talk Title: Towards Enhanced Social-Physical Human-Robot Interaction
Series: CS Colloquium
Abstract: Hugs are one of the first forms of contact and affection humans experience. Receiving a hug is one of the best ways to feel socially supported, and the lack of social touch can have severe adverse effects on an individual's well-being. Due to the prevalence and health benefits of hugging, we were interested in creating robots that can hug humans as seamlessly as humans hug other humans. However, hugs are complex affective interactions that need to adapt to the height, body shape, and preferences of the hugging partner, and they often include intra-hug gestures like squeezes. In this talk, I'll present the eleven design guidelines of natural and enjoyable robotic hugging that informed the creation of a series of hugging robots that use visual and haptic perception to provide enjoyable interactive hugs. Then, I'll share how each of the four presented HuggieBot versions is evaluated by measuring how users emotionally and behaviorally respond to hugging it. Next, I'll briefly touch on how HuggieBot 4.0 is explicitly compared to a human hugging partner using physiological measures. Finally, I'll share some other forms of physical human-robot interaction I've been working on during my post doc as well as future directions of my research in the area of social-physical human-robot interaction.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Alexis E. Block is currently a postdoctoral research fellow at the University of California, Los Angeles (UCLA), where she is funded by a competitive postdoctoral Computing Innovation Fellowship (CI Fellows) from the US National Science Foundation. She received her Bachelor's in Mechanical Engineering and Applied Science from the University of Pennsylvania in 2016, and her Master's in Robotics in 2017, also from Penn. Block received her Dr. Sc. in Computer Science from ETH Zürich in August 2021, as part of the Max Planck ETH Center for Learning Systems, supervised by Katherine Kuchenbecker, Otmar Hilliges, and Roger Gassert. She was awarded an Otto Hahn Medal from the Max Planck Society for her doctoral work and the Best Hands-On Demonstration at EuroHaptics 2022. Block is currently the General Chair for the Robotics Gordon Research Seminar 2024 and organized the 2022 Southern California Robotics Symposium that took place in September. Alexis's research has been featured in the New York Times, The Times, IEEE Spectrum (twice), NPR, and Nature Outlook to name a few.
Host: Heather Culbertson
Location: Ronald Tutor Hall of Engineering (RTH) - 109
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CEE Seminar Series
Wed, Mar 01, 2023 @ 02:00 PM - 03:00 PM
Sonny Astani Department of Civil and Environmental Engineering
Conferences, Lectures, & Seminars
Speaker: Dajiang Suo, Massachusetts Institute of Technology
Talk Title: Designing and Deploying Connected Infrastructure to Enable Secure and Safe Automated Transportation
Abstract: see attached
Host: CEE
Webcast: https://usc.zoom.us/j/95242807214More Information: Suo_Announcement.docx
Location: Kaprielian Hall (KAP) - 209
WebCast Link: https://usc.zoom.us/j/95242807214
Audiences: Everyone Is Invited
Contact: Salina Palacios
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AME Seminar
Wed, Mar 01, 2023 @ 03:30 PM - 04:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Junsoo Kim, Harvard University
Talk Title: Fracture of Highly Entangled Polymer Networks
Abstract: Polymers pollute our planet. Part of this pollution comes from tires. Every year, 0.8 kg of rubber particles are shed by tires per capita in the world.1 A recent study showed that rainstorms wash the rubber particles into rivers, where toxic chemical compounds leach out and kill fish.2 Despite its significant impact on the environment, the development of rubbers resistant to fracture has been stagnant for decades. In this talk, I will discuss how to improve the fracture properties of polymer networks, such as rubbers and gels. The key idea is that entanglements stiffen polymers but do not embrittle them, whereas crosslinks stiffen polymers and embrittle them (i.e., stiffness-toughness conflict). Therefore, highly entangled polymer networks in which entanglements greatly outnumber crosslinks can be both stiff and tough. Furthermore, whereas traditional toughening mechanisms are based on sacrificial bonds causing hysteresis and fatigue, highly entangled polymer networks achieve high toughness by stress deconcentration, leading to high strength, elasticity, and fatigue resistance. This toughening mechanism is based on the polymer topology, not chemistry, so it is generally applicable to many other polymer systems, such as various monomers, preexisting polymers,4 and filled rubbers.5 It is hoped that this work will reactivate the development of wear-resistant tires. Such materials can also be explored in other high-volume applications such as dampers and belts, as well as emerging applications such as soft robots, wearable devices, tissue replacements, bioprinting, and humanoids.
1 P. J. Kole, A. J. Löhr, F. G. A. J. V. Belleghem, A. M. J. Ragas, Int. J. Environ. Res. Public Health, 14(10), 1265 (2017)
2 Z. Tian et. al., Science, 371(6525), 185-189 (2020)
3 J. Kim*, G. Zhang*, M. Shi, Z. Suo, Science, 374(6564), 212-216 (2021)
4 G. Nian*, J. Kim*, X. Bao, Z. Suo, Adv. Mat., 34(50), 2206577 (2022)
5 J. Steck*, J. Kim*, Y. Kutsovsky, Z. Suo, under review
Biography: Junsoo Kim is a postdoctoral researcher at the John A. Paulson School of Engineering and Applied Sciences, Harvard University. He earned his Ph.D. in the Material Science and Mechanical Engineering department at Harvard University in 2022, where he studied fracture of soft materials. Before joining Harvard in 2017, he was a researcher at Electronics Telecommunications Research Institute since 2014. He earned his M.S. in 2013 and B.S. in 2011 at Seoul National University in South Korea. He co-authored 31 papers in peer-reviewed journals, registered six patents, and received fellowships, including the Ilun Science and Technology Foundation (2013) and Kwanjeong Educational Foundation (2017).
Host: AME Department
More Info: https://ame.usc.edu/seminars/
Webcast: https://usc.zoom.us/j/98775609685?pwd=a2lSd01oY0o2KzA4VWphbGxjWk5Qdz09Location: John Stauffer Science Lecture Hall (SLH) - 102
WebCast Link: https://usc.zoom.us/j/98775609685?pwd=a2lSd01oY0o2KzA4VWphbGxjWk5Qdz09
Audiences: Everyone Is Invited
Contact: Tessa Yao
Event Link: https://ame.usc.edu/seminars/
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CS Colloquium: Dakshita Khurana (University of Illinois, Urbana-Champaign) - Cryptographic Advances in Reasoning about Adversaries
Thu, Mar 02, 2023 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Dakshita Khurana , University of Illinois, Urbana-Champaign
Talk Title: Cryptographic Advances in Reasoning about Adversaries
Series: CS Colloquium
Abstract: A key challenge in cryptography is to ensure that a protocol resists all computationally feasible attacks, even when an adversary decides to follow a completely arbitrary and unpredictable strategy.
This often turns out to be notoriously difficult -- for example, proofs of security must typically extract an adversary's implicit input, but this is at odds with other goals like privacy, which require that inputs be hidden and difficult to extract.
In this talk, I will describe my work that reimagines how we reason about adversaries, thereby settling foundational questions in classical and quantum protocol design. On the classical front, these insights enable efficient verification of computations while preserving privacy, and immunize protocols against coordinated attacks on the internet. On the quantum front, these methods help exploit the "destructive" nature of measurements and open up fundamentally new possibilities for cryptography. I will discuss examples that leverage quantum information to (1) weaken the assumptions needed for core tasks like secure computation on distributed private data, and (2) allow outsourcing computations on sensitive data while also verifying that data was deleted after processing.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Dakshita Khurana is an Assistant Professor of Computer Science at the University of Illinois, Urbana-Champaign. Her research focuses on cryptography and its interactions with quantum information. She has made several contributions to secure protocol design, including to succinct and zero-knowledge proof systems, non-malleable protocols and secure computation. Her work has also impacted fields beyond cryptography, e.g., by establishing the hardness of finding Nash equilibria under standard lattice assumptions. Her recent research enabling secure computation from weak cryptographic structure in the quantum regime was invited as one of the (long) plenary talks at QIP.
Her research has also been recognized via invitations to the SIAM Journal on Computing, awarded to a select few papers at STOC and FOCS.
Dakshita is a recipient of the NSF CAREER award, Visa Research faculty award, and a Graduate of Last Decade (GOLD) Alumni award from IIT-Delhi. In addition, her work has been funded through grants and gifts from the NSF, DARPA, C3AI and Jump Arches. She was named to Forbes List of 30 under 30 in Science and awarded a Google Research Fellowship at the Simons Institute, Berkeley. Her thesis work was previously recognized with a UCLA Dissertation Year Fellowship, a UCLA CS Outstanding PhD Student Award and Outstanding Graduate Awards from Symantec and CISCO.
Host: Jiapeng Zhang
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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Viterbi Keynote Lecture: Learning to Communicate
Thu, Mar 02, 2023 @ 04:00 PM - 05:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Robert Calderbank, Charles S. Sydnor Distinguished Professor of Computer Science, Duke University
Talk Title: Learning to Communicate
Series: Viterbi Lecture
Abstract: It is common knowledge that a time-domain pulse is well adapted to pure delay channels, and that a frequency domain pulse is well adapted to pure Doppler channels. In this talk we will explain why the Zak-OTFS waveform, a pulse in the delay-Doppler domain, is well adapted to the doubly spread channels that arise in wireless communication.
We will describe how to design the Zak-OTFS waveform so that the input-output (IO) relation is predictable and non-fading, and we will explain how it is possible to learn the IO relation without needing to estimate the underlying channel. We will explore the possibility of a model-free mode of operation, which is especially useful when a traditional model-dependent mode of operation (reliant on channel estimation) is out of reach. We will also describe how the Zak-OTFS waveform supports combined communication and sensing by enabling unambiguous delay-Doppler estimation.
This is joint work with Saif Mohammed, Ananthanarayanan Chockalingam, and Ronny Hadani.
Biography: Dr. Calderbank directs the Rhodes Information Initiative at Duke University, where he is a Distinguished Professor. He is known for contributions to voiceband modem technology, to quantum information theory, and for co-invention of space-time codes for wireless communication. His research papers have been cited more than 50,000 times, and his inventions are found in billions of consumer devices. Dr. Calderbank was elected to the National Academy of Engineering in 2005, to the National Academy of Inventors in 2015, and to the American Academy of Arts and Sciences in 2022. He has received a number of awards, including the 2013 IEEE Hamming Medal for contributions to information transmission, and the 2015 Claude E. Shannon Award.
Host: Dr. Richard M. Leahy, leahy@sipi.usc.edu
Webcast: https://usc.zoom.us/j/99839989058?pwd=MDFvNWxZNUg1VURjL3EyTDlJekViZz09More Information: 20230302 Calderbank Print.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - EEB 132
WebCast Link: https://usc.zoom.us/j/99839989058?pwd=MDFvNWxZNUg1VURjL3EyTDlJekViZz09
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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Semiconductors & Microelectronics Technology Seminar - Khaled Ahmed - Friday, 3/3 at 10am in EEB 248
Fri, Mar 03, 2023 @ 10:00 AM - 11:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Khaled Ahmed, Intel
Talk Title: Quo Vadis, MicroLEDs?
Series: Semiconductors & Microelectronics Technology
Abstract: It is estimated that the industry has spent ca. $7B on developing MicroLEDs for displays. At least one startup in the Silicon Valley is trying to leverage the MicroLEDs developed for display applications in chip-to-chip data communication. Recently, reports appeared on Apple's imminent implementation of MicroLED displays in smartwatches as evidenced by public announcements from Apple's MicroLED suppliers. Samsung has promised high volume production of MicroLED TV displays for about 5 years now. Google was reported to acquire a MicroLED startup in 2022 for estimated $1B. Hundreds of startups are trying to address one aspect or the other in the supply chain. For those who have been in the semiconductor industry for 10s of years relate to this pattern: we are on the verge of having innovative microscopic light emitters participate in making the lives of humans better. In this talk, the promise and challenge of MicroLED emitters are discussed based on the speaker's hands-on experience with the technology. A number of innovative technologies necessary for high volume manufacturing of MicroLED-based devices are highlighted, with specific problems to be solved. It is an opportunity for researchers to participate in the science and technology development for this important technology.
Biography: Dr. Ahmed received a B.S. degree and an M.S. degree in electrical engineering from Ain Shams University, Egypt in 1991 and 1994, respectively, and a PhD degree in electrical engineering in 1998 from North Carolina State University. Dr. Ahmed joined Intel Corporation in 2015 where he is currently a senior principal engineer and the CTO of Systems Supply Chain organization. Before joining Intel, Dr. Ahmed was with Advanced Micro Devices, Inc., Conexant Systems Inc., Applied Materials, Inc., and Intermolecular, Inc., all in California from 1997 to 2015. Dr. Ahmed serves as a technical program committee member on Display Week Conference since 2016 and won the Semiconductor Research Corporation Best Industry Liaison in 2008. Dr. Ahmed has authored 175+ patents (granted & pending) covering technologies such as semiconductor devices, semiconductors manufacturing equipment, MicroLED device architecture, MicroLED display architecture, metasurface optical elements for display and photonics applications, and optical interconnects technology. Dr. Ahmed was awarded Intel Top Inventor Awards in 2021 and 2022. Dr. Ahmed is known for his strategic thinking and entrepreneurial spirit. He co-founded a company along with others working at JPL/NASA and University of Southern California targeting the manufacturing of III-V photodetectors on 300mm silicon wafers for LIDAR applications.
Host: J Yang, H Wang, C Zhou, S Cronin, W Wu
More Information: Khaled Ahmed Flyer.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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Center of Autonomy and AI, Center for Cyber-Physical Systems and the Internet of Things, and Ming Hsieh Institute Seminar Series
Fri, Mar 03, 2023 @ 04:00 PM - 05:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Enrico Bini,
Talk Title: Zero-Jitter Task Chains via Algebraic Rings
Series: Center for Cyber-Physical Systems and Internet of Things
Abstract: In many embedded computing domains, such as the automotive one, complex functionalities are implemented by splitting their computation across multiple tasks, forming so-called task chains. The tasks in a chain are functionally dependent and communicate partial computations via shared memory slots. In the addressed automotive context, tasks are triggered according to their period, and communicate data at specific time instants, following the Logical Execution Time (LET) paradigm. This paper first presents a model that captures the fundamental behavior of an arbitrary pair of tasks in a chain, connected in a producer-consumer relationship. Exploiting basics of ring algebra, we analytically and fully characterize the timing of reading and writing events of such pair. The proposed characterization allows modeling the combined behavior of the pair as a single periodic task with clear properties. Finally, we apply these fundamental results to build a lightweight mechanism that eliminates the jitter of an entire chain of arbitrary size. This enables us to model the resulting chain as a single periodic LET task with zero jitter.
Biography: Enrico Bini is an Associate Professor at the Department of Computer Science, University of Turin and he has been holding positions at the Scuola Superiore Sant'Anna (Pisa, Italy) and Lund University, Dept. of Automatic Control (Sweden). In 2004, he completed the PhD on Real-Time Systems at the Scuola Superiore Sant'Anna (recipient of the "Spitali Award" for best PhD thesis of the whole university). In January 2010 he also completed a Master's degree in Mathematics with a thesis on optimal sampling for linear control systems.
He has published more than 100 papers (1 Test-of-Time award by the IEEE TCRTS, 4 best-paper awards) on real-time scheduling, operating systems, optimization methods for real-time and control systems, optimal management of distributed and parallel resources. He is Associate Editor of IEEE Transactions on Computers and Springer's Real-Time Systems journal.
Host: Pierluigi Nuzzo, nuzzo@usc.edu
More Info:
Webcast: : https://usc.zoom.us/j/92742577270?pwd=bEpXaWJudjZWRksyNk5lL1owUUdBQT09Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
WebCast Link: : https://usc.zoom.us/j/92742577270?pwd=bEpXaWJudjZWRksyNk5lL1owUUdBQT09
Audiences: Everyone Is Invited
Contact: Talyia White
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BME Seminar Speaker Dr. Donghui Zuo
Mon, Mar 06, 2023 @ 11:00 AM - 12:00 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Donghui Zhu , SUNY Empire Innovation Professor, Department of Biomedical Engineering and Neuroscience, Stony Brook University, SUNY
Talk Title: ZINC: a novel bioresorbable and bioactive material
Host: BME Professor Stacey Finley
More Info: Zoom Available Upon Request
Location: Corwin D. Denney Research Center (DRB) - 145
Audiences: Everyone Is Invited
Contact: Michele Medina
Event Link: Zoom Available Upon Request
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AME Seminar
Mon, Mar 06, 2023 @ 03:30 PM - 04:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Wen Chen, University of Massachusetts Amherst
Talk Title: Additive Manufacturing of Emerging Complex Alloys with Engineered Structures
Abstract: The increasing demands for materials serving under extreme environments call for the development of emerging classes of metal alloys with increasingly complex compositions. However, synthesis and processing of complex alloys via traditional routes are challenging. Additive manufacturing, also called 3D printing, is a disruptive technology for creating materials and components in a single print. Harnessing the vast compositional space of complex alloys and the far-from-equilibrium processing conditions (e.g., large thermal gradients and high cooling rates) of additive manufacturing provides a paradigm-shifting pathway for material design. In this talk, I will present the potential of utilizing laser additive manufacturing and direct ink writing to produce metal alloys with engineered structural hierarchy across multiple length scales. These unique microstructures give rise to exceptional mechanical and functional properties that extend far beyond those accessible by conventional manufacturing. In addition, I will discuss the abundant opportunities enabled by additive manufacturing for high-throughput materials discovery to accelerate the pace of future materials search for a wide range of applications in aerospace, biomedical, and renewable energy.
Biography: Wen Chen is an Assistant Professor in the Department of Mechanical and Industrial Engineering at University of Massachusetts Amherst. He completed his Ph.D. degree in Mechanical Engineering and Materials Science at Yale University in 2016. After his Ph.D., he worked as a postdoctoral research scientist at Lawrence Livermore National Laboratory, where he studied a variety of additive manufacturing techniques such as projection stereolithography, direct ink writing, and laser powder bed fusion. Dr. Chen's current research interests include advanced manufacturing, mechanical behavior of materials, physical metallurgy, and architected materials. He is the recipient of several prestigious awards including the SME Outstanding Young Manufacturing Engineer Award and NSF CAREER Award. He has served as an editorial board member of Scientific Reports since 2018.
Host: AME Department
More Info: https://ame.usc.edu/seminars/
Webcast: https://usc.zoom.us/j/95805178776?pwd=aEtTRnQ2MmJ6UWE4dk9UMG9GdENLQT09Location: John Stauffer Science Lecture Hall (SLH) - 102
WebCast Link: https://usc.zoom.us/j/95805178776?pwd=aEtTRnQ2MmJ6UWE4dk9UMG9GdENLQT09
Audiences: Everyone Is Invited
Contact: Tessa Yao
Event Link: https://ame.usc.edu/seminars/
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CS Colloquium: Matus Telgarsky (University of Illinois, Urbana-Champaign) - Searching for the implicit bias of deep learning
Tue, Mar 07, 2023 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Matus Telgarsky, University of Illinois, Urbana-Champaign
Talk Title: Searching for the implicit bias of deep learning
Series: CS Colloquium
Abstract: What makes deep learning special --- why is it effective in so many settings where other models fail? This talk will present recent progress from three perspectives. The first result is approximation-theoretic: deep networks can easily represent phenomena that require exponentially-sized shallow networks, decision trees, and other classical models. Secondly, I will show that their statistical generalization ability --- namely, their ability to perform well on unseen testing data --- is correlated with their prediction margins, a classical notion of confidence. Finally, comprising the majority of the talk, I will discuss the interaction of the preceding two perspectives with optimization: specifically, how standard descent methods are implicitly biased towards models with good generalization. Here I will present two approaches: the strong implicit bias, which studies convergence to specific well-structured objects, and the weak implicit bias, which merely ensures certain good properties eventually hold, but has a more flexible proof technique.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Matus Telgarsky is an assistant professor at the University of Illinois, Urbana-Champaign, specializing in deep learning theory. He was fortunate to receive a PhD at UCSD under Sanjoy Dasgupta. Other highlights include: co-founding, in 2017, the Midwest ML Symposium (MMLS) with Po-Ling Loh; receiving a 2018 NSF CAREER award; and organizing two Simons Institute programs, one on deep learning theory (summer 2019), and one on generalization (fall 2024).
Host: Vatsal Sharan
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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Epstein Institute - ISE 651 Seminar
Tue, Mar 07, 2023 @ 03:30 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Abdullah Alibrahim, Assistant Professor, Dept. of Industrial & Management Systems Engineering, Kuwait University
Talk Title: TBD
Host: Dr. Shinyi Wu
Location: Ethel Percy Andrus Gerontology Center (GER) - GER 206
Audiences: Everyone Is Invited
Contact: Grace Owh
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CAIS Seminar: Andrew Zolli (Planet) - Using Space and AI to Help Life on Earth: How AI and Satellites Are Transforming Our Stewardship of the Planet
Tue, Mar 07, 2023 @ 04:00 PM - 05:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Andrew Zolli, Planet
Talk Title: Using Space and AI to Help Life on Earth: How AI and Satellites Are Transforming Our Stewardship of the Planet
Series: CS Colloquium
Abstract: We're in the middle of two concurrent and convergent technological revolutions. The first is a sensor revolution, in which new streams of real-time data from the ground, the air, and space are making the change on Earth more transparent than ever before. New generations of satellites monitor every crop, every forest, every city, everywhere, every day - and provide unprecedented transparency. The second revolution is an AI summer, in which the wide availability of machine learning, cloud storage and computing are enabling the extraction of real-time indicators from these data sets. This is revealing real-time feedback loops that can show us how our actions impact the world -“ both positively and negatively - and enabling entirely new ways of seeing, analyzing, and responding to planetary change.
In this talk, Planet's Chief Impact Officer Andrew Zolli will share how these breakthrough approaches are transforming our stewardship of the planet, and where they are likely to go next.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: I currently oversee Sustainability and Global Impact initiatives at Planet, a breakthrough space and AI organization that has deployed the largest constellation of Earth-observing satellites in history. These satellites image our whole planet every day in high resolution, and my team makes sure this data is ethically used to its highest and best purposes to accelerate climate action, monitor the world's ecosystems, improve humanitarian action and disaster response, protect human rights, transform sustainable development, advance scientific discovery and artistic expression. We're even exploring how these tools can inform the next iteration of capitalism, where social and environmental externalities are more effectively measured and valued. I also currently serve on the International Board of Directors of Human Rights Watch.
Host: USC Center for Artificial Intelligence in Society (CAIS)
Location: Seeley G. Mudd Building (SGM) - 124
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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BME Seminar Speaker, Dr. Andy Tay Kah Ping
Wed, Mar 08, 2023 @ 11:00 AM - 12:00 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Andy Tay , Assistant Professor, College of Design and Engineering, National University of Singapore
Talk Title: Mechano-enhancement of wound regeneration and nanomedicine delivery
Host: Eun Ji Chung
More Information: bme seminar speaker andy tay.pdf
Location: Corwin D. Denney Research Center (DRB) - 145
Audiences: Everyone Is Invited
Contact: Michele Medina
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NL Seminar - • Enhancing Machine Translation with Large Language Models via Optimizing In Context Examples and Dictionary Based Prompting
Thu, Mar 09, 2023 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Marjan Ghazvininejad, FAIR, Facebook AI Research
Talk Title: Enhancing Machine Translation with Large Language Models via Optimizing In Context Examples and Dictionary Based Prompting
Abstract: REMINDER:
This Talk will be a Live Broadcast Only, It "Will Not" be recorded.
Meeting hosts only admit guests that they know to the Zoom meeting. Hence, you are highly encouraged to use your USC account to sign into Zoom.
If you are an outside visitor, please inform us at nlg DASH seminar DASH host AT isi DOT edu beforehand so we will be aware of your attendance and let you in.
Large language models LLMs have revolutionized natural language processing by demonstrating impressive abilities to perform a wide range of tasks, including machine translation MT. However, the quality and domain of the in-context examples used to prompt these models can significantly impact their performance for specific tasks. In this talk, I will discuss two recent papers that propose to optimize in-context examples and leverage bilingual dictionaries to enhance the quality and controllability of MT with LLMs. First, I will explore the impact of in-context examples on the translation quality of LLMs and highlight the challenges of selecting good examples in both in-domain and out-of-domain settings. Then, I will discuss how we can leverage bilingual dictionaries to provide fine-grained phrase-level control hints in the prompts of LLMs.
Biography: Marjan Ghazvininejad is a senior research scientist at Facebook AI Research. She received her Ph.D. at the University of Southern California on neural creative language generation. Her research interests include text representation, language generation, and machine translation. Her recent research has focused on how to optimize the use of large language models in various applications.
Host: Jon May and Justin Cho
More Info: https://nlg.isi.edu/nl-seminar/
Webcast: https://usc.zoom.us/j/96863901584Location: Information Science Institute (ISI) - Virtual and ISI-Conf Rm#689
WebCast Link: https://usc.zoom.us/j/96863901584
Audiences: Everyone Is Invited
Contact: Pete Zamar
Event Link: https://nlg.isi.edu/nl-seminar/
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CS Colloquium: Alexander Rodríguez (Georgia Tech) - AI for Public Health: Epidemic Forecasting Through a Data-Centric Lens
Thu, Mar 09, 2023 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Alexander RodrÃguez , Georgia Tech
Talk Title: AI for Public Health: Epidemic Forecasting Through a Data-Centric Lens
Series: CS Colloquium
Abstract: Epidemic forecasting is a crucial tool for public health decision making and planning. There is, however, a limited understanding of how epidemics spread, largely due to other complex dynamics, most notably social and pathogen dynamics. With the increasing availability of real-time multimodal data, a new opportunity has emerged for capturing previously unobservable facets of the spatiotemporal dynamics of epidemics. In this regard, my work brings a data-centric perspective to public health via methodological advances in AI at the intersection of time series analysis, spatiotemporal mining, scientific ML, and multi-agent systems. Toward realizing the potential of AI in public health, I addressed multiple challenges stemming from the domain such as data scarcity, distributional changes, and issues arising from real-time deployment to enable our support of CDC's COVID-19 response. This talk will cover methods to address these challenges with novel deep learning architectures for real-time response to disease outbreaks and new techniques for end-to-end learning with mechanistic epidemiological models-”based on differential equations and agent-based models-”that bridge ML advances and traditional domain knowledge to leverage individual merits. I will conclude by discussing challenges and opportunities in public health for data and computer scientists.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Alexander RodrÃguez is a PhD candidate in the College of Computing at Georgia Tech, advised by Prof. B. Aditya Prakash. His research is at the intersection of machine learning, time series, and scientific modeling, and his main application domains are public health and community resilience. He has published at top venues such as AAAI, NeurIPS, ICLR, KDD, WWW, AAMAS, PNAS and has organized workshops and tutorials at AAAI and KDD. His work won the best paper award at ICML AI4ABM 2022 and was awarded the 1st place in the Facebook/CMU COVID-19 Challenge and the 2nd place in the C3.ai COVID-19 Grand Challenge. He was also invited to the Heidelberg Laureate Forum in 2022, and named a 'Rising Star in Data Science' by the University of Chicago Data Science Institute in 2021 and a 'Rising Star in ML & AI' by the University of Southern California in 2022.
Host: Bistra Dilkina
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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BME Seminar Speaker, Dr. Nathan Shaner
Fri, Mar 10, 2023 @ 11:00 AM - 12:00 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Nathan Shaner, Associate Professor, Neurosciences, UCSD
Talk Title: Fluorescent protein engineering
Host: BME Chair Peter Wang - ZOOM link available on request
More Information: BMEseminar Nathan Shaner.pdf
Location: Corwin D. Denney Research Center (DRB) - 145
Audiences: Everyone Is Invited
Contact: Michele Medina
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***SPRING RECESS - No Epstein Institute - ISE 651 Seminar***
Tue, Mar 14, 2023 @ 03:30 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: ,
Talk Title:
Location: Ethel Percy Andrus Gerontology Center (GER) - GER 206
Audiences: Everyone Is Invited
Contact: Grace Owh
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USC Viterbi India Founders Series
Wed, Mar 15, 2023 @ 06:30 AM - 07:30 AM
USC Viterbi School of Engineering, Viterbi School of Engineering Alumni
Conferences, Lectures, & Seminars
Speaker: Mr. Srini Chinamilli, Co-Founder and CEO of Tessolve
Talk Title: Zero to $100M and Beyond
Abstract: Join us for an engaging USC Viterbi India Founders Series event that showcases Mr. Srini Chinamilli, Co-Founder and CEO of Tessolve.
Biography: Srini has over 25 years of experience in Semiconductor Engineering and Management. He held technical and management positions at Cirrus Logic and Centillium Communications prior to joining Tessolve as Co-Founder. He has extensive experience in Silicon Validation, Product Engineering, and has managed high volume productization of several complex System on Chip and Mixed Signal devices. He takes pleasure in building startup teams into world class organizations. Srini completed his Master in Electrical Engineering from the University of Southern California and Bachelors in Electronics from Birla Institute of Technology.
Host: Sudha Kumar
More Info: https://usc.zoom.us/meeting/register/tJEvfuygqDooEtdlbgnALn0q58eAqEvkukHI
Audiences: Everyone Is Invited
Contact: Sudha Kumae
Event Link: https://usc.zoom.us/meeting/register/tJEvfuygqDooEtdlbgnALn0q58eAqEvkukHI
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USC AI Futures Symposium Series
Thu, Mar 16, 2023
USC Viterbi School of Engineering, Viterbi School of Engineering Alumni
Conferences, Lectures, & Seminars
Speaker: Various, https://www.isi.edu/events/usc-ai-symposium/2023/ai-on-the-edge/schedule
Talk Title: USC AI Futures Symposium on AI on the Edge
Series: USC AI Futures Symposium Series
Abstract: Intelligent data processing in edge devices has become increasingly important with the rapid growth of ubiquitous sensors and their data generation rates. AI applications on the edge enable autonomous sensor steering, fast operations on smartphones, neuromorphic biodevices, and a vast array of Internet of Things (IoT) applications. For these kinds of applications, it is not possible or practical to convert all the analog data into digital and send to data centers to process in the cloud. There are many research challenges in terms of the design of algorithms and hardware required to support AI on the edge in terms of real-time processing at greater scale, storage capacity, and reduced energy consumption.
This symposium presents an overview of research innovations at the University of Southern California (USC) on AI on the Edge, from enabling hardware and algorithms to their co-design. Topics include AI and machine learning algorithms for edge devices and TinyML, bio-inspired and neuromorphic computing, new technologies and device materials beyond CMOS, and hardware-software co-design.
Host: Joshua Yang and Yolanda Gil
More Info: https://usc.zoom.us/webinar/register/WN_51GsYHBxT5uwd_RwBcG1Dw
Location: Virtual
Audiences: Everyone Is Invited
Contact: Sabrina Espinoza
Event Link: https://usc.zoom.us/webinar/register/WN_51GsYHBxT5uwd_RwBcG1Dw
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ECE-S Seminar - Dr Shiry Ginosar
Mon, Mar 20, 2023 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr Shiry Ginosar, Postdoctoral Fellow | University of California, Berkeley
Talk Title: Toward Artificial Social Intelligence
Abstract: As the covid pandemic made abundantly clear-”multi-faceted, face-to-face interaction is the most effective form of communication-”much more so than written text messages or phone calls. And yet, most current AI efforts focus primarily on text systems. In my work, I try to push the limits of machine perception systems toward artificially intelligent agents that can perceive and model the rich, multimodal signals of face-to-face human social interaction: speech and communicative gesture. I will cover several projects that take steps in this direction in the one-to-many scenario of lectures and monologues and one-on-one dyadic face-to-face communication. Through these examples, I will argue that it is possible to model minute, indescribable visual and auditory details of multi-faceted human communication using data-driven methods without relying on annotation. I will then broaden the discussion to questions in social intelligence, such as body language, abstract communicative motion, and spatiotemporal trends of social norms, and suggest directions for future inquiries.
Biography: Shiry Ginosar is a Computing Innovation Postdoctoral Fellow at UC Berkeley, advised by Jitendra Malik. She completed her Ph.D. in Computer Science at UC Berkeley, under the supervision of Alyosha Efros. Prior to joining the Computer Vision group, she was part of Bjoern Hartmann's Human-Computer Interaction lab at Berkeley. Earlier in her career, she was a Visiting Scholar at the CS Department of Carnegie Mellon University, with Luis von Ahn and Manuel Blum in the field of Human Computation. Between her academic roles, she spent four years at Endeca as a Senior Software Engineer. In her distant past, Shiry trained fighter pilots in F-4 Phantom flight simulators as a Staff Sergeant in the Israeli Air Force. Shiry's research has been covered by The New Yorker, The Wall Street Journal, and the Washington Post, amongst others. Her work has been featured on PBS NOVA, exhibited at the Israeli Design Museum and is part of the permanent collection of the Deutsches Museum. Her patent-pending research work inspired the founding of a startup. Shiry has been named a Rising Star in EECS, and is a recipient of the NSF Graduate Research Fellowship, the California Legislature Grant for graduate studies, and the Samuel Silver Memorial Scholarship Award for combining intellectual achievement in science and engineering with serious humanistic and cultural interests.
Host: Dr Antonio Ortega, aortega@usc.edu
Webcast: https://usc.zoom.us/j/93935933525?pwd=cVVWd2JoQzBhcXZuWDAzalp3eEZYUT09More Information: ECE Seminar Announcement 03.20.2023 - Shiry Ginosar.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - EEB 248
WebCast Link: https://usc.zoom.us/j/93935933525?pwd=cVVWd2JoQzBhcXZuWDAzalp3eEZYUT09
Audiences: Everyone Is Invited
Contact: Miki Arlen
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CS Colloquium: Rika Antonova (Stanford University) - Enabling Self-sufficient Robot Learning
Mon, Mar 20, 2023 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Rika Antonova, Stanford University
Talk Title: Enabling Self-sufficient Robot Learning
Series: CS Colloquium
Abstract: Autonomous exploration and data-efficient learning are important ingredients for helping machine learning handle the complexity and variety of real-world interactions. In this talk, I will describe methods that provide these ingredients and serve as building blocks for enabling self-sufficient robot learning.
First, I will outline a family of methods that facilitate active global exploration. Specifically, they enable ultra data-efficient Bayesian optimization in reality by leveraging experience from simulation to shape the space of decisions. In robotics, these methods enable success with a budget of only 10-20 real robot trials for a range of tasks: bipedal and hexapod walking, task-oriented grasping, and nonprehensile manipulation.
Next, I will describe how to bring simulations closer to reality. This is especially important for scenarios with highly deformable objects, where simulation parameters influence the dynamics in unintuitive ways. The success here hinges on finding a good representation for the state of deformables. I will describe adaptive distribution embeddings that provide an effective way to incorporate noisy state observations into modern Bayesian tools for simulation parameter inference. This novel representation ensures success in estimating posterior distributions over simulation parameters, such as elasticity, friction, and scale, even for scenarios with highly deformable objects and using only a small set of real-world trajectories.
Lastly, I will share a vision of using distribution embeddings to make the space of stochastic policies in reinforcement learning suitable for global optimization. This research direction involves formalizing and learning novel distance metrics on this space and will support principled ways of seeking diverse behaviors. This can unlock truly autonomous learning, where learning agents have incentives to explore, build useful internal representations and discover a variety of effective ways of interacting with the world.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Rika is a postdoctoral scholar at Stanford University and a recipient of the NSF/CRA Computing Innovation Fellowship for research on active learning of transferable priors, kernels, and latent representations for robotics. Rika completed her Ph.D. work on data-efficient simulation-to-reality transfer at KTH. Earlier, she obtained a research Master's degree from the Robotics Institute at Carnegie Mellon University, where she developed Bayesian optimization methods for robotics and for personalized tutoring systems. Before that, Rika was a software engineer at Google, first in the Search Personalization group and then in the Character Recognition team (developing open-source OCR engine Tesseract).
Host: Jesse Thomason
Location: Ronald Tutor Hall of Engineering (RTH) - 115
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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AME Seminar
Mon, Mar 20, 2023 @ 03:30 PM - 04:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Weiyu Li, Stanford
Talk Title: Battery Avatar: First-Principles Modeling and Data Analytics
Abstract: Rechargeable lithium batteries are electrochemical devices that are widely used in portable electronics and electric-powered vehicles. A breakthrough in battery performance requires advancements in battery cell configurations at the microscale level. This, in turn, places a premium on the ability to accurately predict complex multiphase thermoelectrochemical phenomena, e.g., migration of ions interacting with composite porous materials that constitute a battery cell microstructure. Optimal design of porous cathodes requires efficient quantitative models of microscopic (pore-scale) electrochemical processes and their impact on battery performance. In this talk, I will discuss effective properties (electrical conductivity, ionic diffusivity, reaction parameters) of a composite electrode comprising the active material coated with a mixture of the binder and conductor (the carbon binder domain or CBD). When used to parameterize the industry-standard pseudo-twodimensional (P2D) models, they significantly improve the predictions of lithiation curves in the presence of CBD. On the lithium anode, dendritic growth is a leading cause of degradation and catastrophic failure of lithium-metal batteries. Deep understanding of this phenomenon would facilitate the design of strategies to reduce, or completely suppress, the instabilities characterizing electrodeposition on the lithium anode. This would improve the safety of lithium-metal batteries with liquid electrolyte and all-solid-state lithium batteries. I will present the results of our analysis, which indicate that the use of anisotropic electrolytes and buffer layers can suppress dendritic growth of lithium metal.
Biography: Weiyu Li has received her M.Sc. degree in Mechanical and Aerospace Engineering from Princeton University and is scheduled to obtain her PhD in Energy Science and Engineering from Stanford University in the Spring of 2023. Her research focuses on modeling and simulation of electrochemical transport in energy storage systems, aiming to provide mechanistic insights into the optimal design of porous electrodes, electrolyte, etc. Her other research interests include data assimilation and biomedical modeling. Weiyu Li is the recipient of the Siebel Scholars Award in Energy Science, class of 2023, and of the Princeton University Fellowship in Natural Sciences and Engineering.
Host: AME Department
More Info: https://ame.usc.edu/seminars/
Webcast: https://usc.zoom.us/j/95805178776?pwd=aEtTRnQ2MmJ6UWE4dk9UMG9GdENLQT09Location: Olin Hall of Engineering (OHE) - 406
WebCast Link: https://usc.zoom.us/j/95805178776?pwd=aEtTRnQ2MmJ6UWE4dk9UMG9GdENLQT09
Audiences: Everyone Is Invited
Contact: Tessa Yao
Event Link: https://ame.usc.edu/seminars/
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ECE-S Seminar - Dr Jiaqi Gu
Tue, Mar 21, 2023 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr Jiaqi Gu, PhD Candidate | University of Texas at Austin
Talk Title: Light in Artificial Intelligence: Hardware/Software Co-Design for Photonic Machine Learning Computing
Abstract: The proliferation of big data and artificial intelligence (AI) has motivated the investigation of next- generation AI computing hardware to support massively parallel and energy-hungry machine learning (ML) workloads. Photonic computing, or computing using light, is a disruptive technology that can bring orders-of- magnitude performance and efficiency improvement to AI/ML with its ultra-fast speed, high parallelism, and low energy consumption. There has been growing interest in using nanophotonic processors for performing optical neural network (ONN) inference operations, which can make transformative impacts in future datacenters, automotive, smart sensing, and intelligent edge. However, the substantial potential in photonic computing also brings significant design challenges, which necessitates a cross-layer co-design stack where the circuit, architecture, and algorithm are designed and optimized in synergy.
In this talk, I will present my exploration to address the fundamental challenges faced by optical AI and to pioneer a hardware/software co-design methodology toward scalable, reliable, and adaptive photonic neural accelerator designs. First, I will delve into the critical area scalability issue of integrated photonic tensor units and present specialized photonic neural engine designs with domain-specific customization that significantly "compresses" the circuit footprint while realizing comparable inference accuracy. Next, I will present efficient on-chip training frameworks to show how to build a self-learnable photonic accelerator and overcome the robustness and adaptability bottlenecks by directly training the photonic circuits in situ. Then, I will introduce how to close the virtuous cycle between photonics and AI by applying AI/ML to photonic device simulation. In the end, I will conclude the talk with future research directions of emerging domain-specific photonic AI hardware with an intelligent end-to-end co-design & automation stack and deploying it to support real-world applications.
Biography: Jiaqi Gu is a final-year Ph.D. candidate in the Department of Electrical and Computer Engineering at The University of Texas at Austin, advised by Prof. David Z. Pan and co-advised by Prof. Ray T. Chen. Prior to UT Austin, he received his B.Eng. from Fudan University, Shanghai, China, in 2018. His research interests include emerging post-Moore hardware design for efficient computing, hardware/software co-design, photonic machine learning, and AI/ML algorithms.
He has received the Best Paper Award at the ACM/IEEE Asian and South Pacific Design Automation Conference (ASP-DAC) in 2020, the Best Paper Finalist at the ACM/IEEE Design Automation Conference (DAC) in 2020, the Best Poster Award at the NSF Workshop for Machine Learning Hardware Breakthroughs Towards Green AI and Ubiquitous On-Device Intelligence in 2020, the Best Paper Award at the IEEE Transaction on Computer-Aided Design of Integrated Circuits and Systems (TCAD) in 2021, the ACM Student Research Competition Grand Finals First Place in 2021, and Winner of the Robert S. Hilbert Memorial Optical Design Competition in 2022.
Host: Dr Pierluigi Nuzzo, nuzzo@usc.edu
Webcast: https://usc.zoom.us/j/99786583943?pwd=MnlmNGxQUUIwWXpWbk0wTUhrQWsxZz09More Information: ECE Seminar Announcement 03.21.2023 - Jiaqi Gu.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - EEB 248
WebCast Link: https://usc.zoom.us/j/99786583943?pwd=MnlmNGxQUUIwWXpWbk0wTUhrQWsxZz09
Audiences: Everyone Is Invited
Contact: Miki Arlen
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CS Colloquium: Yue Zhao (CMU) - Scalable and Automated Systems and Algorithms for Unsupervised ML
Tue, Mar 21, 2023 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Yue Zhao, Carnegie Mellon University
Talk Title: Scalable and Automated Systems and Algorithms for Unsupervised ML
Series: CS Colloquium
Abstract: Many real-world events do not have outcome labels. For example, the fraudulence of a transaction remains unknown until it is discovered. This is where unsupervised machine learning (ML) becomes crucial in real-world scenarios as it can make decisions based solely on observations. In this talk, I will address two key challenges in unsupervised ML: (i) developing scalable learning systems that can handle large amounts of data, and (ii) automating the selection of the best ML model. The first part of the talk will cover an ML system called TOD, which can "compile" a diverse group of ML algorithms for GPU acceleration. The second part will describe an automated algorithm called MetaOD, which can select top ML models for various applications without relying on labels or evaluations. Lastly, I will discuss my future plans, including the ML+X initiative, which aims to bring the advantages of ML systems and automation to other domains, and the creation of a fully automated ML pipeline that chooses hardware, systems, and models seamlessly.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Yue Zhao is a Ph.D. candidate at CMU, working with Prof. Leman Akoglu and Prof. Zhihao Jia. He focuses on creating scalable and automated ML systems and algorithms, and has published over 30 papers in top venues such as VLDB, MLSys, JMLR, and NeurIPS. His open-source systems (https://github.com/yzhao062) have been widely deployed in firms and industries such as Morgan Stanley and Tesla, and have received over 15,000 GitHub stars and 10 million downloads. Yue has received the CMU Presidential Fellowship and Norton Graduate Fellowship. More information about him can be found at https://www.andrew.cmu.edu/user/yuezhao2/.
Host: Robin Jia
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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Epstein Institute - ISE 651 Seminar
Tue, Mar 21, 2023 @ 03:30 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Brian Denton, Professor and Dept. Chair, Dept. of Industrial & Operations Engineering, University of Michigan, Ann Arbor
Talk Title: Optimization in the Presence of Model Ambiguity in Markov Decision Processes
Host: Dr. Sze-chuan Suen
More Information: March 21, 2023.pdf
Location: Ethel Percy Andrus Gerontology Center (GER) - GER 206
Audiences: Everyone Is Invited
Contact: Grace Owh
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ECE-S Seminar - Dr Ivan de Oliveira Nunes
Wed, Mar 22, 2023 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr Ivan de Oliveira Nunes, Assistant Professor | Rochester Institute of Technology (RIT)
Talk Title: Architectures for Verifiable Confidentiality, Integrity, and Availability in Resource-Constrained Embedded Devices
Abstract: Embedded devices are increasingly ubiquitous and their importance is hard to overestimate. While they often support safety-critical functions (e.g., in medical devices, industrial control systems, and sensor- alarm combinations), these devices are usually implemented under strict cost and energy budgets, using low-end microcontroller units (MCUs) that lack sophisticated security mechanisms. On the lower end of the scale, these devices are small, cheap, and specialized. They tend to host small CPUs, have very limited memory, and run simple software. Nonetheless, if such devices are left unprotected, consequences of forged sensor readings or ignored actuation commands can be catastrophic, particularly, in safety-critical settings. This prompts the following three questions: (1) how to trust data produced, or verify that commands were correctly performed, by a simple remote embedded device? (2) how to actively prevent malware that infects embedded devices from exfiltrating private sensor data? and (3) how to guarantee that safety-critical tasks are always performed in a timely manner, irrespective of malware infections?
Motivated by these questions, this talk will overview a set of architectures based on hardware/software (HW/SW) co-designs to provide provable guarantees about data confidentiality, software integrity, and availability in (potentially compromised) embedded devices. In particular, I will discuss three formally verified HW/SW co-designs, each realizing one of the aforementioned goals (namely APEX [SEC'20], GAROTA [SEC'22], and VERSA [S&P'22]) and how they have been securely implemented atop the popular TI MSP430 micro-controller at a relatively low-cost.
Biography: Ivan De Oliveira Nunes is an Assistant Professor of Computing Security at the Rochester Institute of Technology (RIT). Before RIT, he received his Ph.D. degree in 2021 from the University of California Irvine (UCI). Ivan also holds a Bachelor's degree in Computer Engineering from the Federal University of Espirito Santo (UFES), Brazil, and a Master's degree in Computer Science from the Federal University of Minas Gerais (UFMG), Brazil. In recent years, he has worked on several topics, including IoT Security, Hardware-assisted security, HW/SW Co-design, Network Security, and Applied Cryptography. His research interests span the fields of security and privacy, computing systems, computer networking, applied cryptography, and especially their intersection.
Host: Dr Bhaskar Krishnamachari, bkrishna@usc.edu
Webcast: https://usc.zoom.us/j/93387896454?pwd=MVdwL2NHS1hqSXFlaFhPaE91WHVGUT09More Information: ECE Seminar Announcement 03.23.23 - Ivan de Oliveira Nunes.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - EEB 248
WebCast Link: https://usc.zoom.us/j/93387896454?pwd=MVdwL2NHS1hqSXFlaFhPaE91WHVGUT09
Audiences: Everyone Is Invited
Contact: Miki Arlen
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CS Colloquium: Lindsay Sanneman (MIT) - Transparent Value Alignment: Foundations for Human-Centered Explainable AI in Alignment
Wed, Mar 22, 2023 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Lindsay Sanneman , MIT
Talk Title: Transparent Value Alignment: Foundations for Human-Centered Explainable AI in Alignment
Series: CS Colloquium
Abstract: Alignment of robot objectives with those of humans can greatly enhance robots' ability to act flexibly to safely and reliably meet humans' goals across diverse contexts from space exploration to robotic manufacturing. However, it is often difficult or impossible for humans, both expert and non-expert, to enumerate their objectives comprehensively, accurately, and in forms that are readily usable for robot planning. Value alignment is an open challenge in artificial intelligence that aims to address this problem by enabling robots and autonomous agents to infer human goals and values through interaction. Providing humans with direct and explicit feedback about this value learning process through approaches for explainable AI (XAI) can enable humans to more efficiently and effectively teach robots about their goals. In this talk, I will introduce the Transparent Value Alignment (TVA) paradigm which captures this two-way communication and inference process and will discuss foundations for the design and evaluation of XAI within this paradigm. First, I will present a novel suite of metrics for assessing alignment which have been validated through human subject experiments by applying approaches from cognitive psychology. Next, I will discuss the Situation Awareness Framework for Explainable AI (SAFE-AI), a human factors-based framework for the design and evaluation of XAI across diverse contexts including alignment. Finally, I will propose design guidance for XAI within the TVA context which is grounded in results from a set of human studies comparing a broad range of explanation techniques across multiple domains. I will additionally highlight how this research relates to real-world robotic manufacturing and space exploration settings that I have studied. I will conclude the talk by discussing the future vision of this work.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Lindsay Sanneman is a final year PhD candidate in the Department of Aeronautics and Astronautics at MIT and a member of the Interactive Robotics Group in the Computer Science and Artificial Intelligence Laboratory (CSAIL). Her research focuses on the development of models, metrics, and algorithms for explainable AI (XAI) and AI alignment in complex human-autonomy interaction settings. Since 2018, she has been a member of MIT's Work of the Future task force and has visited over 50 factories worldwide alongside an interdisciplinary team of social scientists and engineers in order to study the adoption of robotics in manufacturing. She is currently a Siegel Research Fellow and has presented her work in diverse venues including the Industry Studies Association and the UN Department of Economic and Social Affairs.
Host: Heather Culbertson
Location: Ronald Tutor Hall of Engineering (RTH) - 109
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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AME Seminar
Wed, Mar 22, 2023 @ 03:30 PM - 04:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Renee Zhao, Stanford University
Talk Title: Multifunctional Origami Robots
Abstract: In this talk, I will introduce our recent work on origami mechanisms and actuation strategies for applications spanning from biomedical devices to foldable space structures. The first topic is magnetically actuated millimeter-scale origami medical robots for effective amphibious locomotion in severely confined spaces or aqueous environments. The origami robots are based on the Kresling origami, whose thin shell structure 1) provides an internal cavity for drug storage, 2) permits torsion-induced contraction as a crawling mechanism and a pumping mechanism for controllable liquid medicine dispensing, 3) serves as propellers that spin for propulsion to swim, 4) offers anisotropic stiffness to overcome the large resistance from the severely confined spaces in biomedical environments. For the second part of my talk, the concept of hexagonal ring origami folding mechanism will be introduced as a strategy for deployable/foldable structures for space applications. The hexagonal rings can tessellate 2D/3D surfaces and each ring can snap to its stable folded configuration with only 10.6% of the initial area. Through finite-element analysis and the rod model, snap-folding of the hexagonal ring with slight geometric modification and residual strain are studied for easy folding of the ring to facilitate the design and actuation of hexagonal ring origami assemblies for functional foldable structures with extreme packing ratio.
Biography: Renee Zhao is an Assistant Professor of Mechanical Engineering at Stanford University. Renee received her PhD degree in Solid Mechanics from Brown University in 2016. She spent two years as a postdoc associate at MIT working on modeling of soft composites. Before Renee joined Stanford, she was an Assistant Professor at The Ohio State University from 2018 to 2021. Her research concerns the development of stimuli-responsive soft composites and shape morning mechanisms for multifunctional robotic systems. Renee is a recipient of the NSF Career Award (2020), AFOSR YIP (2023), ASME Journal of Applied Mechanics award (2021), the 2022 ASME Pi Tau Sigma Gold Medal, and the 2022 ASME Henry Hess Early Career Publication Award.
Host: AME Department
More Info: https://ame.usc.edu/seminars/
Webcast: https://usc.zoom.us/j/95805178776?pwd=aEtTRnQ2MmJ6UWE4dk9UMG9GdENLQT09Location: John Stauffer Science Lecture Hall (SLH) - 102
WebCast Link: https://usc.zoom.us/j/95805178776?pwd=aEtTRnQ2MmJ6UWE4dk9UMG9GdENLQT09
Audiences: Everyone Is Invited
Contact: Tessa Yao
Event Link: https://ame.usc.edu/seminars/
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ECE-S Seminar - Dr Stephen Xia
Thu, Mar 23, 2023 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr Stephen Xia, Postdoctoral Scholar | University of California, Berkeley
Talk Title: Embedded Intelligence Towards Smarter, Healthier and Safer Environments
Abstract: We have seen remarkable growth in smart devices and artificial intelligence in all aspects of our lives. Despite the ever-growing amount of AI around us, our environments are still far from truly intelligent. At the touch of a button, we have access to powerful AI that can easily outperform any human in complex tasks, yet our environments still cannot alert us to dangerous approaching vehicles, nor help us find our lost child in a busy grocery store, something all of us do regularly and intuitively. In this talk, I will present two lines of work that bridge the gap between AI and truly intelligent environments.
First, I will introduce my work on embedded acoustic intelligence. I will start by presenting my work on embedding acoustic intelligence into wearables we commonly carry, such as headphones and helmets, to create safer cities. These low-cost and long-lasting wearables leverage novel architectures that utilize a combination of physics-based models and machine learning techniques to alert pedestrians and construction workers of dangers from oncoming vehicles, ultimately acting as a second pair of ears that create a sphere of safety around us. Next, I will discuss how we can take lessons learned from urban safety to realize a generalized selective audio filtering architecture that allows us to embed robust acoustic intelligence into a diverse set of real-time and resource-constrained applications and platforms. This architecture dynamically leverages the physics of audio and a wide range of data-driven machine learning models to allow engineers and developers to enhance and suppress custom sounds in their applications.
Second, I will present my work on creating more configurable, adaptive, and evolving environments, which are three critical characteristics we need to realize to create truly intelligent environments. I will first touch on several works that allow anyone, regardless of their technical background, to easily deploy and configure complex sensing solutions, such as camera networks for indoor occupant tracking, without needing any domain or expert knowledge. Second, I will introduce my work on adaptive smart home systems that jointly consider human preferences and available resources within the environment to improve home automation and greatly reduce the barrier of entry for smart home technologies. Finally, I will present several works where we realize new dormant sensing and compute capabilities in several platforms, such as drones, by only leveraging processes already present, thereby "evolving" new capabilities completely for free.
Biography: Stephen Xia is a Postdoctoral Scholar in the Department of Electrical Engineering and Computer Sciences at UC Berkeley, advised by Dr. Prabal Dutta and Dr. Xiaofan (Fred) Jiang. Stephen received his Ph.D. in 2022 from Columbia University and his B.S. in 2016 from Rice University, all in Electrical Engineering. His research lies at the intersection between systems, embedded machine learning, and signal processing, spanning areas in mobile and embedded systems, Internet-of-Things, cyber-physical systems, artificial intelligence, and smart health. His work takes a joint physics-based and data-driven approach to realize truly intelligent and autonomous environments by embedding and dynamically utilizing compute, sensing, actuation, storage, and networking resources all around us. Stephen's research has been highlighted by many popular media outlets, including Mashable, Fast Company, and Gizmodo, and has received various distinctions, including Best Demo Awards at ACM SenSys 2021, ACM/IEEE IPSN 2020, ACM/IEEE IoTDI 2018, and the Best Presentation Award at IEEE VNC 2018.
Host: Dr Murali Annavaram, annavara@usc.edu
Webcast: https://usc.zoom.us/j/93387896454?pwd=MVdwL2NHS1hqSXFlaFhPaE91WHVGUT09More Information: ECE Seminar Announcement 03.23.2023 - Stephen Xia.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - EEB 248
WebCast Link: https://usc.zoom.us/j/93387896454?pwd=MVdwL2NHS1hqSXFlaFhPaE91WHVGUT09
Audiences: Everyone Is Invited
Contact: Miki Arlen
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NL Seminar-Designing and Evaluating Language Models for Human Interaction
Thu, Mar 23, 2023 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Mina Lee, Stanford University
Talk Title: Designing and Evaluating Language Models for Human Interaction
Abstract: REMINDER
Meeting hosts only admit guests that they know to the Zoom meeting. Hence, you are highly encouraged to use your USC account to sign into Zoom.
If you are an outside visitor, please inform us at nlg DASH seminar DASH host AT isi DOT edu beforehand so we will be aware of your attendance and let you in.
Despite the recent advancements in language models LMs, most LMs are not optimized for, nor are they evaluated on, real-world usage with human interaction. In this talk, I will present my research on designing and evaluating LMs for human LM interaction. Concretely, I will first describe how we can support human editing needs by enabling any LM to perform text infilling at any position in a document i.e., fill in the blanks. I will then introduce CoAuthor, a platform for capturing human LM interaction in collaborative writing as rich, replayable, keystroke level interaction traces. With the platform, I demonstrate how collecting a large interaction dataset and analyzing the traces provide unique insights into LM capabilities regarding language, ideation, and collaboration. Lastly, I will propose a new framework, HALIE Human AI Language based Interaction Evaluation, that defines the components of interactive systems and evaluation metrics for human LM interaction beyond writing. I will conclude by discussing open challenges and future directions in this field.
Biography: Mina Lee is a final year Ph.D. candidate at Stanford University, advised by Professor Percy Liang. Her research goal is to design and evaluate language models to enhance our productivity and creativity and understand how these models change the way we write. She has built various writing assistants, including an autocomplete system, a contextual thesaurus system, and a creative story writing system, as well as evaluated language models based on their ability to interact with humans and augment human capabilities.
She was named one of MIT Technology Reviews Korean Innovators under 35 in 2022, and her work has been published in top tier venues in natural language processing e.g., ACL and NAACL, machine learning e.g., NeurIPS, and human computer interaction e.g., CHI. Her recent work on human AI collaborative writing received an Honorable Mention Award at CHI 2022 and was featured in various media outlets including The Economist.
Host: Jon May and Justin Cho
More Info: https://nlg.isi.edu/nl-seminar/
Webcast: https://www.youtube.com/watch?v=rfl3_fa8eHQLocation: Information Science Institute (ISI) - Virtual and ISI-Conf Rm#689
WebCast Link: https://www.youtube.com/watch?v=rfl3_fa8eHQ
Audiences: Everyone Is Invited
Contact: Pete Zamar
Event Link: https://nlg.isi.edu/nl-seminar/
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Semiconductors & Microelectronics Technology Seminar - Heng Wang, Thursday, March 23 at 11am in EEB 132
Thu, Mar 23, 2023 @ 11:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Heng Wang, Illinois Institute of Technology
Talk Title: The Thermoelectric Effect under Photon Excitation
Series: Semiconductors & Microelectronics Technology
Abstract: Thermoelectric phenomena allow energy conversion between heat and electricity, which can be used in energy harvesting, solid state refrigeration, and temperature regulation. The physical origin of these phenomena are well understood with semi-classic theories such as the Boltzmann transport theory. Carefully conducted experiments often reveal results as predicted by such theories. Nonetheless, carrier transport not only happens when the system is near thermal equilibrium, as for the case of thermoelectric phenomena, but also happens in excited systems with electrons far from thermal equilibrium. And this draws our interest over the past a few years. In this talk we will discuss the characteristic, the physical origin, and measurement strategies of the thermoelectric effect under photon excitation (which is one version of the photo-thermoelectric phenomena). We will discuss a few case studies, what can these results tell us about the materials, and potential applications. There are still much to understand with this effect and we hope this discussion could stimulate more interest and applications as well.
Biography: Heng Wang is an assistant professor at department of Mechanical, Materials and Aerospace Engineering, Illinois Institute of Technology. He received his B.S. in materials science and engineering from Tsinghua University, China, and his PhD in materials science from California Institute of Technology. Before joining IIT he worked as a postdoctoral researcher at the Molecular Foundry, Lawrence Berkeley National Lab. He has over ten years of research experience in thermoelectric materials, physics, and devices, with more than 13000 citations. His current research interests include high-performance thermoelectric materials, as well as device design, manufacturing, and new applications. In addition, he is particularly interested in the interplay of photoelectric and thermoelectric phenomena.
Host: J Yang, H Wang, C Zhou, S Cronin, W Wu, J. Ravichandran
More Information: HengWang_0323.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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CS Colloquium: Benjamin Eysenbach (CMU) - Self-Supervised Reinforcement Learning
Thu, Mar 23, 2023 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Benjamin Eysenbach , CMU
Talk Title: Self-Supervised Reinforcement Learning
Series: CS Colloquium
Abstract: Reinforcement learning (RL) promises to harness the power of machine learning to solve sequential decision making problems, with the potential to enable applications ranging from robotics to chemistry. However, what makes the RL paradigm broadly applicable is also what makes it challenging: only limited feedback is provided for learning to select good actions. In this talk, I will discuss how we have made headway of this challenge by designing self-supervised RL methods, ones that can learn representations and skills for acting using unsupervised (reward-free) experience. These skill learning methods are practically-appealing and have since sparked a vibrant area of research. I will also share how we have answered some open theoretical questions in this area.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Benjamin Eysenbach is a final-year PhD student at Carnegie Mellon University. His research has developed machine learning algorithms for sequential decision making. His algorithms not only achieve a high degree of performance, but also carry theoretical guarantees, are typically simpler than prior methods, and draw connections between many areas of ML and CS. Ben is the recipient of the NSF and Hertz graduate fellowships. Prior to the PhD, he was a resident at Google Research and studied math as an undergraduate at MIT.
Host: Jyo Deshmukh
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Colloquium: Dr. Zhou Li (University of California Irvine) - Debugging the Fragmented DNS Infrastructure at Scale
Thu, Mar 23, 2023 @ 04:00 PM - 05:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Dr. Zhou Li, University of California Irvine
Talk Title: Debugging the Fragmented DNS Infrastructure at Scale
Abstract: Domain Name System (DNS) is a fundamental infrastructure that supports almost all sorts of Internet activities. However, service failures and breach of DNS are not rare, and some even led to the shutdown of large data centers, though DNS was designed under the goals like resiliency from the very beginning. We argue that the root causes are that DNS infrastructure has become too fragmented and its protocols have become much more complex, so new research efforts are needed to harden the DNS infrastructure. In this talk, I'll describe our efforts in this direction. First, I'll talk about two new DNS attacks we identified under the settings of domain revocation and conditional resolution, and their implications. Second, I'll talk about how we measure the operational status of DNS-over-Encryption at a large scale. Finally, I'll conclude the talk with an outlook for DNS-related research.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Zhou Li is an Assistant Professor at UC Irvine, EECS department, leading the Data-driven Security and Privacy Lab. Before joining UC Irvine, he worked as Principal Research Scientist at RSA Labs from 2014 to 2018. His research interests include Domain Name System (DNS), Graph Security analytics, Privacy Enhancement Technologies and Side-channel analysis. He received the NSF CAREER award, Amazon Research Award, Microsoft Security AI award and IRTF Applied Networking Research Prize.
Host: Weihang Wang
More Info: https://usc.zoom.us/j/92035174335?pwd=VzhKZ0xjM3A2SzFwOWsyRG1SQWpqUT09
Location: Seeley G. Mudd Building (SGM) - 124
Audiences: Everyone Is Invited
Contact: Melissa Ochoa
Event Link: https://usc.zoom.us/j/92035174335?pwd=VzhKZ0xjM3A2SzFwOWsyRG1SQWpqUT09
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ECE-EP seminar - David Burghoff, Friday, March 24th at 10am in EEB 132
Fri, Mar 24, 2023 @ 10:00 AM - 11:30 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: David Burghoff, Notre Dame
Talk Title: Broadband quantum and nonlinear photonics at long wavelengths
Series: ECE-EP Seminar
Abstract: While the longwave infrared and terahertz ranges have potential to revolutionize disease detection and environmental monitoring, there is currently a lack of compact broadband sources and integrated photonics platforms. I will discuss some of the work of my group that seeks to address this grand challenge. First, I will discuss our development of quantum cascade laser-based frequency combs, light sources that fill the gap between broadband incoherent sources and lasers. I will showcase how we created the first combs in the terahertz range and how our experimental investigations of these combs led to our discovery of a new fundamental comb state that manifests in any laser at any wavelength. Next, I will delve into our development of ultra-low-loss platforms for long wavelengths based on hybrid photonic integration, which allowed us to create optical resonators in the longwave infrared with quality factors two orders of magnitude better than the state-of-the-art. Finally, I will discuss our creation of ptychoscopy, a new sensing modality that allows for ultra-precise measurements of optical spectra. This measurement enables the measurement of remote signals with quantum-limited frequency resolution over the entire bandwidth of a comb, for the first time allowing incoherent spectra to be characterized with the precision techniques of combs.
Biography: David Burghoff is an Assistant Professor at Notre Dame, where his lab blends photonics with quantum devices to develop novel sensing and computing modalities. Prior to this, he was a postdoctoral fellow and research scientist at the Massachusetts Institute of Technology, where he led a team working in DARPA's SCOUT program. He also received his Ph.D. from MIT, where he won the J.A. Kong Award for MIT's Outstanding Electrical Engineering Thesis. He co-chaired the 2022 and 2020 International Quantum Cascade Laser School and Workshop, and he was one of only five faculty nationally named as a 2022 Moore Inventor's Fellow. His other awards include the ONR Young Investigator Program Award, the NSF CAREER Award, the AFOSR Young Investigator Program Award, and the Intelligence Community Postdoctoral Fellowship.
Host: ECE-Electrophysics
More Information: David Burghoff Seminar Announcement.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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Photonics Seminar - Stefan Badescu, Friday, March 24th at 10:30am in EEB 248
Fri, Mar 24, 2023 @ 10:30 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Stefan Badescu, Sensors Directorate, AFRL
Talk Title: The role of gain-loss distribution in topological laser arrays
Series: Photonics Seminar Series
Abstract: Motivated by earlier demonstrations of III-V topological lasers, I will present insights from modeling of ring arrays with engineered distributions of gain and loss. In addition, I will discuss the influence of Corbino geometrical parameters on the bulk density of states and on the properties of topological states, including the interplay between disorder, quality factors, and gain contrast. In the second part I will present progress with fabrication of device structures as part of a collaboration between Air Force Research Laboratory and the Ohio State University.
Biography: Stefan C. Badescu received his PhD in theoretical condensed matter physics in 2002 from Brown University, with work in quantum diffusion and in computational material science. From 2002 he was a National Research Council fellow at Naval Research Laboratory, with work in quantum computing. From 2005 he was a research faculty with University of Maryland at College Park with work on spin qubits and on carbon materials. He joined the Air Force in 2011 with computational work on wide bandgap materials for electronics and on III-V semiconductors. More recently he led a Topological Photonics subproject on 'Topologically Enabled Devices'.
Host: Mercedeh Khajavikhan, Michelle Povinelli, Constantine Sideris; Hossein Hashemi; Wade Hsu; Mengjie Yu; Wei Wu; Tony Levi; Alan E. Willner; Andrea Martin Armani
More Information: Stefan Badescu Flyer.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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BME Seminar Speaker, Dr. Alexander Hoffmann
Fri, Mar 24, 2023 @ 11:00 AM - 12:00 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Alexander Hoffmann, Professor of Microbiology and Immunology at UCLA
Talk Title: Systems biology, immune cell signaling
Host: BME Professor Stacey Finley - ZOOM link available on request
Location: Corwin D. Denney Research Center (DRB) - 145
Audiences: Everyone Is Invited
Contact: Michele Medina
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ECE-S Seminar Announcement: Dr. Christian Cuba Samaniego
Fri, Mar 24, 2023 @ 01:00 PM - 02:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Christian Cuba Samaniego, Research Fellow, Department of Immunology, Harvard Medical School
Talk Title: Adapting feedback control and pattern recognition paradigms for biotechnological applications
Abstract: Engineering synthetic genetic networks with desired behavior for robust adaptation or complex decision-making is challenging. Current approaches rely on different negative regulation techniques or logic-based operators, which suffer from suboptimal performance. To address this limitation, we introduce two design principles: (1) ultrasensitive input-output behavior and (2) tunable thresholds. Here, we engineer ultrasensitive-based networks to both achieve adaptive behavior through feedback control and build synthetic genetic programs for molecular pattern recognition by implementing neural computing networks in living cells.
Biography: Christian Cuba Samaniego received his BS degree in Mechatronic Engineering from "Universidad Nacional de Ingenieria" in Lima-Peru in 2009. He obtained his PhD in Mechanical Engineering from University of California Riverside in 2017 under the supervision of Prof. Elisa Franco. He joined the Biological Engineering Department at Massachusetts Institute of Technology as a postdoc under the supervision of Prof. Ron Weiss (2019), and Mechanical and Aerospace Engineering Department in the lab of Prof. Elisa Franco (2022). Currently, Christian is a research fellow in the Department of Immunology at Harvard Medical School in the lab of Prof. Ming-Ru Wu. His current research is at the interface of Control Theory, Systems and Synthetic Biology, and Machine Learning. I am specially interested in the design, analysis and applications of biomolecular feedback control systems and molecular neural networks for decision-making (molecular pattern recognition) in living cells.
Host: Dr. Urbashi Mitra (ubli@usc.edu)
Webcast: https://usc.zoom.us/j/93768871353?pwd=c0haOXhxREVBY05VbUs0cDh4YTMzdz09More Information: ECE Seminar Announcement-Cuba-Samaniego-032423.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
WebCast Link: https://usc.zoom.us/j/93768871353?pwd=c0haOXhxREVBY05VbUs0cDh4YTMzdz09
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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ECE-S Seminar - Dr Alireza Fallah
Mon, Mar 27, 2023 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr Alireza Fallah, PhD Candidate | Department of Electrical Engineering and Computer Science | Laboratory for Information and Decision Systems (LIDS), MIT
Talk Title: Data Markets and Learning: Privacy Mechanisms and Personalization
Abstract: The fuel of machine learning models and algorithms is the data usually collected from users, enabling refined search results, personalized product recommendations, informative ratings, and timely traffic data. However, increasing reliance on user data raises serious challenges. A common concern with many of these data-intensive applications centers on privacy -” as a user's data is harnessed, more and more information about her behavior and preferences is uncovered and potentially utilized by platforms and advertisers. These privacy costs necessitate adjusting the design of data markets to include privacy-preserving mechanisms.
This talk establishes a framework for collecting data of privacy-sensitive strategic users for estimating a parameter of interest (by pooling users' data) in exchange for privacy guarantees and possible compensation for each user. We formulate this question as a Bayesian-optimal mechanism design problem, in which an individual can share her data in exchange for compensation but at the same time has a private heterogeneous privacy cost which we quantify using differential privacy. We consider two popular data market architectures: central and local. In both settings, we use Le Cam's method to establish minimax lower bounds for the estimation error and derive (near) optimal estimators for given heterogeneous privacy loss levels for users. Next, we pose the mechanism design problem as the optimal selection of an estimator and payments that elicit truthful reporting of users' privacy sensitivities. We further develop efficient algorithmic mechanisms to solve this problem in both privacy settings. Finally, we consider the case that users are interested in learning different personalized parameters. In particular, we highlight the connections between this problem and the meta-learning framework, allowing us to train a model that can be adapted to each user's objective function.
Biography: Alireza Fallah is a Ph.D. candidate at the department of Electrical Engineering and Computer Science (EECS) and the Laboratory for Information and Decision Systems (LIDS) at Massachusetts Institute of Technology (MIT). His research interests are machine learning theory, data market and privacy, game theory, optimization, and statistics. He has received a number of awards and fellowships, including the Ernst A. Guillemin Best MIT EECS M.Sc. Thesis Award, Apple Scholars in AI/ML Ph.D. fellowship, MathWorks Engineering Fellowship, and Siebel Scholarship. He has also worked as a research intern at the Apple ML privacy team. Before joining MIT, he earned a dual B.Sc. degree in Electrical Engineering and Mathematics from Sharif University of Technology, Tehran, Iran.
Host: Dr Mahdi Soltanolkotabi, soltanol@usc.edu
Webcast: https://usc.zoom.us/j/93606233291?pwd=dGQxNWRZVmE1bzZvRVVYRTd1Mk1VQT09More Information: ECE Seminar Announcement 03.27.2023 - Alireza Fallah.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - EEB 248
WebCast Link: https://usc.zoom.us/j/93606233291?pwd=dGQxNWRZVmE1bzZvRVVYRTd1Mk1VQT09
Audiences: Everyone Is Invited
Contact: Miki Arlen
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CS Colloquium: Pavel Izmailov (New York University) - Deconstructing models and methods in deep learning
Mon, Mar 27, 2023 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Pavel Izmailov, New York University
Talk Title: Deconstructing models and methods in deep learning
Series: CS Colloquium
Abstract: Machine learning models are ultimately used to make decisions in the real world, where mistakes can be incredibly costly. We still understand surprisingly little about neural networks and the procedures that we use to train them, and, as a result, our models are brittle, often rely on spurious features, and generalize poorly under minor distribution shifts. Moreover, these models are often unable to faithfully represent uncertainty in their predictions, further limiting their applicability. In this talk, I will present works on neural network loss surfaces, probabilistic deep learning, uncertainty estimation and robustness to distribution shifts. In each of these works, we aim to build foundational understanding of models, training procedures, and their limitations, and then use this understanding to develop practically impactful, interpretable, robust and broadly applicable methods and models.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: I am a final year PhD student in Computer Science at New York University, working with Andrew Gordon Wilson. I am primarily interested in understanding and improving deep neural networks. In particular my interests include out of distribution generalization, probabilistic deep learning, representation learning and large models. I am also excited about generative models, uncertainty estimation, semi-supervised learning, language models and other topics. Recently, our work on Bayesian model selection was recognized with an outstanding paper award at ICML 2022.
Host: Robin Jia
Location: Ronald Tutor Hall of Engineering (RTH) - 115
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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MoBI Seminar: Measuring Attention Control: Oscillations, Connectivity, ADHD
Mon, Mar 27, 2023 @ 11:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Agatha Lenartowicz, PhD, Associate Professor, Department of Psychiatry and Biobehavioral Sciences, UCLA
Talk Title: Measuring Attention Control: Oscillations, Connectivity, ADHD
Abstract: In this talk I will discuss our efforts to qualify and quantify the mechanisms of attention control. I will review neuroimaging measures - oscillations as measured by EEG, connectivity estimated by fMRI - that track attention-related processes, including how they may go awry in ADHD. I will also discuss the emerging questions in the measurement and conceptualization of these processes, their measurement, and their application to real-world settings.
Biography: Agatha Lenartowicz, Ph.D., is Associate Professor in the Department of Psychiatry and Biobehavioral Sciences at UCLA. She holds a Ph.D. degree in Psychology and Neuroscience from Princeton University, and has over 15 years' experience in cognitive neuroscience of attention and its deficits. Over the past seven years, she has worked to develop a translational arm to her research, including basic mechanisms and rehabilitative approaches to attention deficits in ADHD, and is a past Klingenstein Third Generation Fellow and a NARSAD Young Investigator in recognition of this translational work. She is a pioneer in the use of concurrent EEG-fMRI recordings in the study of the attention system and especially its disorders in ADHD. She is also actively building a mobile-EEG research program to assess attention in the real-world, in particular in the classroom. Dr. Lenartowicz is the Operations Director at the Staglin OneMind IMHRO Center for Cognitive Neuroscience and is the director of the EEG Analysis Core at the Semel Institute of Neuroscience and Human Behavior.
Host: Dr. Karim Jerbi, karim.jerbi.udem@gmail.com and Dr. Richard M. Leahy, leahy@sipi.usc.edu
Webcast: https://usc.zoom.us/j/96014499242?pwd=a0NFMS93VUhOaUhuc1JCMlQ3TUludz09Location: Hughes Aircraft Electrical Engineering Center (EEB) - 539
WebCast Link: https://usc.zoom.us/j/96014499242?pwd=a0NFMS93VUhOaUhuc1JCMlQ3TUludz09
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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ECE-EP seminar - Eric Pollmann, Monday, March 27th at 2pm in EEB 248
Mon, Mar 27, 2023 @ 02:00 PM - 03:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Eric Pollmann, Columbia University
Talk Title: Implantable CMOS Optoelectronics for Bidirectional Neural Interfacing
Series: ECE-EP Seminar
Abstract: Optical neurotechnologies use light to interface with neurons and overcome the limitations associated with penetrating electrodes and glial scarring in electrophysiology. Miniaturized microscopes monitor and manipulate neural activity with high spatial-temporal precision over large cortical extents; however, current implementations still require a chronic opening in the dura and skull that matches or exceeds the field-of-view of the implant. Viable translation of these technologies to human clinical use will require a much more noninvasive, fully implantable form factor. In my talk, I will introduce the first subdural CMOS optical probe (SCOPe) for bidirectional optical stimulation and recording in mouse and nonhuman primates. This radical improvement in implantability is achieved through the design of a CMOS ASIC consisting of monolithically integrated SPADs for low-light-intensity imaging and dual color flip-chip bonded micro-LEDs for light emission. Along with a fully flexible electronic packaging, I will present the heterogeneous integration of the light sources, filters, and lens-less computational imaging masks required for a high-performance optical neural interface. This transformative, ultrathin, miniaturized device was validated in a sequence of in vivo mouse and nonhuman primate experiments and defines a path for the eventual human translation of a new generation of brain-machine interfaces based on light.
Biography: Eric H. Pollmann received the Ph.D. degree in 2023 advised by Kenneth Shepard in the Department of Electrical Engineering at Columbia University. Previously, he received the B.S. degree in Electrical Engineering from the Georgia Institute of Technology in 2017, and the M.S. degree in Electrical Engineering from Columbia University in 2018. His research lies at the intersection of integrated circuit and system design, applied optics, and neurotechnology and has resulted in multiple publications in top-tier IEEE conferences and journals. In addition to research work, he was the recipient of the 2021 IEEE CASS Predoctoral Fellowship.
Host: ECE-Electrophysics
More Information: Eric Pollmann Seminar Announcement.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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AME Seminar
Mon, Mar 27, 2023 @ 03:30 PM - 04:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Dennis Kim, UCLA
Talk Title: Finding Order in Disorder: Atomic-Scale Understanding of Phase Transformations
Abstract: Crystalline imperfections and their dynamics are essential in phase transformations and structure-property relationships in materials. Classical methods for determining atomic structures average over many unit cells. As a result, such methods cannot correctly capture atomic-level information on amorphous packing, point defects, chemical ordering, strain, and interfaces. I will first present my recent work extending atomic electron tomography (AET) to overcome the limitations of conventional methods to obtain 3D atomic packing information with picometer precision in amorphous materials. With every atom accounted for, we can understand how atoms in amorphous solids arrange in short- to medium-range order and the implications of these findings for metallic glasses. I will then discuss other systems where chemical ordering and crystalline imperfections of point defects, strain, and interfaces play an essential role in phase transformations and atomic-scale structure-property relationships. I will also present recent efforts in developing an electron thermal diffuse scattering method to determine spatially resolved lattice dynamics. The diffuse patterns are highly sensitive to differences in phonon energies. Combining high-reciprocal space sampling and high-dynamic-range imaging methods, and machine-learned interatomic potential-based dynamical simulations, we are able to observe temperature-dependent soft phonon mode dynamics and nuclear quantum effects. These findings have far-reaching implications in understanding heat transport. Finally, I will show how feedback loops powered by experimental coordinates with picometer accuracy, scattering spectroscopy, and ab initio computational methods will guide future materials discovery and design.
Biography: Dennis Kim is a research scientist at the University of California Los Angeles and holds a PhD in Materials Science from the California Institute of Technology. Prior to his current position, he was a postdoctoral associate in the Department of Materials Science and Engineering at the Massachusetts Institute of Technology and a STROBE postdoctoral fellow in the Department of Physics and Astronomy at the University of California Los Angeles. His research background is in materials thermodynamics and understanding phase transformations through state-of-the-art scattering, imaging, and quantum mechanical computational techniques. He is interested in developing and optimizing materials for various applications in thermal, energy, and quantum sciences through a fundamental understanding from the atom up.
Host: AME Department
More Info: https://ame.usc.edu/seminars/
Webcast: https://usc.zoom.us/j/95805178776?pwd=aEtTRnQ2MmJ6UWE4dk9UMG9GdENLQT09Location: Olin Hall of Engineering (OHE) - 406
WebCast Link: https://usc.zoom.us/j/95805178776?pwd=aEtTRnQ2MmJ6UWE4dk9UMG9GdENLQT09
Audiences: Everyone Is Invited
Contact: Tessa Yao
Event Link: https://ame.usc.edu/seminars/
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ECE-S Seminar - Dr Yupeng Zhang
Tue, Mar 28, 2023 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr Yupeng Zhang, Assistant Professor | Department of Computer Science and Engineering, Texas A&M University
Talk Title: Zero-Knowledge Proofs: from Theory to Practice
Abstract: A zero-knowledge proof is a powerful cryptographic tool to establish trust without revealing any sensitive information. It allows one party to convince others that a claim about the properties of secret data is true, while the data remains confidential. Zero-knowledge proofs have been widely used in blockchains and crypto-currencies to enhance privacy and improve scalability. They can also be applied to prove the fairness and integrity of machine learning inferences and the correctness of program analysis.
In this talk, I will present my research in this area to bring zero-knowledge proofs from theory to practice with new efficient algorithms. In the first part, I will talk about a new framework to build general-purpose zero-knowledge proofs for any computations.
In this framework, we were able to develop the first zero- knowledge proof scheme with a linear proof generation time. In the second part, I will talk about our recent works on new applications of zero-knowledge proofs in machine learning and program analysis. The scalability and efficiency of the schemes can be further improved with new sublinear algorithms. Finally, I will discuss my future research plans, including memory-efficient and distributed algorithms for scalable blockchains and smart contracts, privacy-preserving machine learning, and cloud computing with full security and privacy.
Biography: Yupeng Zhang is an assistant professor in the Computer Science and Engineering department at the Texas A&M University. His research is in the area of cybersecurity and applied cryptography, developing efficient and scalable cryptographic protocols to enhance the security and privacy of data and computations in real-world applications. He has been working on zero-knowledge proofs, secure multiparty computations, and their applications in blockchain, machine learning and program analysis. He has published many papers in top security and cryptography conferences including S&P, CCS, USENIX Security and Crypto. He is the recipient of the NSF CAREER award, the Facebook Faculty award, the ACM SIGSAC best dissertation award runners-up and the Google PhD fellowship. Before joining Texas A&M, he was a postdoctoral researcher at UC Berkeley, and he obtained his Ph.D. from the University of Maryland.
Host: Dr Sandeep Gupta, sandeep@usc.edu | Dr Murali Annavaram, annavara@usc.edu
More Information: ECE Seminar Announcement 03.27.2023 - Yupeng Zhang.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - EEB 248
Audiences: Everyone Is Invited
Contact: Miki Arlen
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CS Colloquium: Kexin Pei (Columbia University) - Analyzing and Securing Software via Robust and Generalizable Learning
Tue, Mar 28, 2023 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Kexin Pei, Columbia University
Talk Title: Analyzing and Securing Software via Robust and Generalizable Learning
Series: CS Colloquium
Abstract: Software is powering every aspect of our society, but it remains plagued with errors and prone to critical failures and security breaches. Program analysis has been a predominant technique for building trustworthy software. However, traditional approaches rely on hand-curated rules tailored for specific analysis tasks and thus require significant manual effort to tune for different applications. While recent machine learning-based approaches have shown some early promise, they, too, tend to learn spurious features and overfit to specific tasks without understanding the underlying program semantics.
In this talk, I will describe my research on building machine learning (ML) models toward learning program semantics so they can remain robust against transformations in program syntax and generalize to various program analysis tasks and security applications. The corresponding research tools, such as XDA, Trex, StateFormer, and NeuDep, have outperformed commercial tools and prior arts by up to 117x in speed and by 35% in precision and have helped identify security vulnerabilities in real-world firmware that run on billions of devices. To ensure the developed ML models are robust and generalizable, I will briefly describe my research on building testing and verification frameworks for checking the safety properties of deep learning systems. The corresponding research tools, such as DeepXplore, DeepTest, ReluVal, and Neurify, have been adopted and followed up by the industry, been covered in media such as Scientific American, IEEE Spectrum, Newsweek, and TechRadar, and inspired over thousands of follow-up projects.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Kexin Pei is a Ph.D. candidate in Computer Science at Columbia University, advised by Suman Jana and Junfeng Yang. His research lies at the intersection of security, software engineering, and machine learning, with a focus on building machine-learning tools that utilize program structure and behavior to analyze and secure software. His research has received the Best Paper Award in SOSP, an FSE Distinguished Artifact Award, been featured in CACM Research Highlight, and won CSAW Applied Research Competition Runner-Up. He was part of the learning for code team when he interned at Google Brain, building program analysis tools based on large language models.
Host: Jiapeng Zhang
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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Epstein Institute - ISE 651 Seminar
Tue, Mar 28, 2023 @ 03:30 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Gino Lim, Professor and Dept. Chair, Department of Industrial Engineering, University of Houston
Talk Title: A Chance Constrained Programming Framework to Handle Uncertainties in Radiation Therapy Treatment Planning
Host: Dr. Sze-chuan Suen
More Information: March 28, 2023.pdf
Location: Ethel Percy Andrus Gerontology Center (GER) - GER 206
Audiences: Everyone Is Invited
Contact: Grace Owh
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ECE-S Seminar - Dr Corey Baker
Wed, Mar 29, 2023 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr Corey Baker, Assistant Professor | Department of Computer Science, University of Kentucky
Talk Title: Tolerable Delay: Overcoming Intermittent Connectivity With Entity Centered Systems and Applications
Abstract: Reliance on Internet connectivity is detrimental where modern networking technology is lacking, power outages are frequent, or network connectivity is expensive, sparse, or non-existent (i.e., underserved urban communities, rural areas, natural disasters). Though there has been much research conducted around 5G and 6G serving as the conduit for connecting any and everything; scalability issues are a major concern and real-world deployments have been limited. Realization of the limitations resulting from reliance on Internet and cellular connectivity are prevalent in mHealth applications where remote patient monitoring has improved the timeliness of clinical decision making, decreased the length of hospital stays, and reduced mortality rates everywhere in the nation except in medically underserved and rural communities in the US like Appalachian Kentucky, where chronic disease is approximately 20% more prevalent than other areas. As an alternative, deploying resilient networking technology can facilitate the flow of information in resource-deprived environments to disseminate non-emergency, but life saving data. In addition, leveraging opportunistic communication can supplement cellular networks to assist with keeping communication channels open during high-use and extreme situations. This talk will discuss the pragmatic applications of designing opportunistic systems for particular entities (patients, citizens, etc.); specifically applied to healthcare and empowering low-cost smart cities, permitting any community to become smart and connected while simultaneously keeping network connectivity costs to a minimum.
Biography: Corey E Baker, PhD, is an Assistant Professor in the Department of Computer Science at the University of Kentucky (UK). His work centers around making data accessible in the midst of intermittent and limited connectivity while minimizing delay. He currently a directs the Network Reconnaissance (NetRecon) Lab [https://www.cs.uky.edu/~baker/research/ ] where his research investigates full stack systems for distributing, protecting, and authenticating data in opportunistic networking scenarios for rural remote patient monitoring, smart cities, and natural disasters to improve the livelihood of people. Professor Baker received a B.S. degree in Computer Engineering (CE) from San Jose State University (SJSU), a M.S. in Electrical and Computer Engineering (ECE) from California State University, Los Angeles (CSULA), and M.S. and Ph.D. degrees in CE from the University of Florida (UF). After the completion of his graduate studies, Baker was a University of California Presidents Postdoctoral Fellow in the ECE department at the University of California San Diego (UCSD) and a Visiting Scholar in the ECE department at the University of Southern California (USC). In 2019, Dr. Baker received the UK Inclusive Excellence Award [http://uknow.uky.edu/campus-news/office-institutional-diversity-awards-five-inclusive-excellence-awards?j=121590&sfmc_sub=129146772&l=18687_HTML&u=3630624&mid=10966798&jb=0]for his work in creating a graduate campus visit program and diversifying Computer Science and the College of Engineering at the doctoral level. Baker is currently the Region 6 (West Coast) Advisory Board Chairperson for the National Society of Black Engineers.
Host: Dr Massoud Pedram, pedram@usc.edu | Dr Sandeep Gupta, sandeep@usc.edu
Webcast: https://usc.zoom.us/j/94295584258?pwd=VzlITkJaa1FBQ05ERFYvRXZ2MUwvUT09More Information: ECE Seminar Announcement 03.29.2023 - Corey Baker.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - EEB 132
WebCast Link: https://usc.zoom.us/j/94295584258?pwd=VzlITkJaa1FBQ05ERFYvRXZ2MUwvUT09
Audiences: Everyone Is Invited
Contact: Miki Arlen
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CS Colloquium: Paul Gölz (Harvard) - Fair, Representative, and Transparent Algorithms for Citizens’ Assemblies
Wed, Mar 29, 2023 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Paul Gölz, Harvard
Talk Title: Fair, Representative, and Transparent Algorithms for Citizens' Assemblies
Series: CS Colloquium
Abstract: Globally, an alternative approach to democracy is gaining momentum: citizens' assemblies, in which randomly selected constituents discuss policy questions and propose solutions. Domain experts have two conflicting requirements on the selection of these assemblies: (1) assemblies should reflect the demographics of the population, and (2) all constituents should have equal chances of being selected. In this talk, I will describe work on designing and analyzing randomized selection algorithms that favorably trade off these objectives. I will share experiences with deploying these algorithms on our online platform Panelot and discuss what we learned from practitioners in the process of adoption. Finally, I will explore how these lessons sparked work on other aspects of citizens' assemblies, such as making the random selection process transparent and managing the discussions within the assembly.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Paul Gölz is a postdoctoral researcher at the School of Engineering and Applied Sciences at Harvard. He received his Ph.D. in computer science from Carnegie Mellon University under the supervision of Ariel Procaccia. Paul studies democratic decision-making and the fair allocation of resources, using tools from algorithms, optimization, and artificial intelligence. Algorithms developed in his work are now deployed to select citizens' assemblies around the world and to allocate refugees for a major US resettlement agency.
Host: David Kempe
Location: Ronald Tutor Hall of Engineering (RTH) - 109
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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AME Seminar
Wed, Mar 29, 2023 @ 03:30 PM - 04:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Thomas Hou, Caltech
Talk Title: Recent progress on singularity formation of 3D incompressible Euler and Navier-Stokes equations
Abstract: Abstract: Whether the 3D incompressible Euler and Navier equations can develop a finite time singularity from smooth initial data is one of the most challenging problems in fluid dynamics. In this talk, I will present a recent result with Dr. Jiajie Chen in which we prove finite time blowup of the 2D Boussinesq and 3D Euler equations with smooth initial data. There are several essential difficulties in establishing such blowup result. We overcome these difficulties by decomposing the solution operator into a leading order operator that enjoys sharp stability estimates plus a finite rank perturbation operator that can be estimated by using computer assisted proof. This enables us to establish nonlinear stability of the approximate self-similar profile and prove nearly self-similar blowup of the 2D Boussinesq and 3D Euler equations. I will also report some recent progress on potentially singular behavior of the 3D incompressible Navier-Stokes equations.
Biography: Thomas Yizhao Hou is the Charles Lee Powell professor of applied and computational mathematics at Caltech. His research interests include 3D Euler singularity, interfacial flows, multiscale problems, and adaptive data analysis. He received his Ph.D. from UCLA in 1987, and became a tenure track assistant professor at the Courant Institute in 1989, and a tenured associate professor in 1992. He moved to Caltech in 1993 and was named the Charles Lee Powell Professor in 2004. Dr. Hou has received a number of honors and awards, including Fellow of American Academy of Arts and Sciences in 2011, a member of the inaugural class of SIAM Fellows in 2009 and AMS Fellows in 2012, the SIAM Ralph E. Kleinman Prize in 2023, the SIAM Outstanding Paper Prize in 2018, the SIAM Review SIGEST Award in 2019, the Computational and Applied Sciences Award from USACM in 2005, the Morningside Gold Medal in Applied Mathematics in 2004, the SIAM Wilkinson Prize in Numerical Analysis and Scientific Computing in 2001, the Frenkiel Award from the Division of Fluid Mechanics of American Physical Society in 1998, the Feng Kang Prize in Scientific Computing in 1997, a Sloan fellow from 1990 to 1992. He was also the founding Editor-in-Chief of the SIAM Journal on Multiscale Modeling and Simulation from 2002 to 2007.
Host: AME Department
More Info: https://ame.usc.edu/seminars/
Webcast: https://usc.zoom.us/j/95805178776?pwd=aEtTRnQ2MmJ6UWE4dk9UMG9GdENLQT09Location: John Stauffer Science Lecture Hall (SLH) -
WebCast Link: https://usc.zoom.us/j/95805178776?pwd=aEtTRnQ2MmJ6UWE4dk9UMG9GdENLQT09
Audiences: Everyone Is Invited
Contact: Tessa Yao
Event Link: https://ame.usc.edu/seminars/
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CS Colloquium: Emilio Ferrara (USC) - AI & Social Manipulation
Wed, Mar 29, 2023 @ 04:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Emilio Ferrara, USC Annenberg / CS
Talk Title: AI & Social Manipulation
Series: CS Colloquium
Abstract: In this talk, I will overview my decadelong journey into understanding the implications of online platform manipulation. I'll start from detecting malicious bots and other forms of manipulation including troll accounts, coordinated campaigns, and disinformation operations. The impact of my work will be corroborated with examples of findings enabled by our technology, e.g., our unveiling of the "Russian bots" operation prior to the 2016 U.S. Presidential election, which informed official Senate investigations and new regulations. I will then illustrate similar issues with the 2020 U.S. Election, as well as COVID-related conspiracies and public health misinformation. I'll conclude by discussing the ML tools we developed to model online mis/disinformation, reveal the malicious adversaries behind the curtains, and characterize their activity, behavior, and strategies, suggesting how they are changing the way researchers and study online platforms in the era of automation and artificial intelligence.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Emilio Ferrara is a professor of communication and computer science at USC Annenberg and at the USC Viterbi Department of Computer Science, professor (by courtesy) of Preventive Medicine at the Keck School, and co-director of the Machine Intelligence and Data Science (MINDS) group at USC ISI. His research focus has been at the intersection between developing theory and methods in network science, machine learning and NLP, and applying them to study socio-technical systems and networks. He is concerned with understanding the implications of AI and networks on human behavior, and their effects on society at large. Ferrara has published 230+ articles that have appeared on venues like the Proceeding of the National Academy of Sciences, Communications of the ACM, Physical Review Letters, and the top ACM, IEEE and AAAI conferences and journals. As a PI at USC, he has received $20M+ in research funding from DARPA, IARPA, NSF, NIH, AFOSR and ONR. Ferrara received accolades including the 2016 DARPA Young Faculty Award and DARPA Director's Fellowship, the 2016 Complex Systems Society Junior Scientific Award, the 2019 USC Viterbi Research Award and the 2022 Research.com Rising Stars award. Until He also served as associate director of the USC Data Science programs.
Host: CS Department
Location: Ronald Tutor Hall of Engineering (RTH) - 526
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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ECE-S Seminar: Dr. Priyanka Raina
Thu, Mar 30, 2023 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Priyanka Raina, Assistant Professor of Electrical Engineering, Stanford University
Talk Title: Agile Design of Domain-Specific Accelerators and Compilers
Abstract: With the slowing of Moore's law, computer architects have turned to domain-specific hardware accelerators to improve the performance and efficiency of computing systems. However, programming these systems entails significant modifications to the software stack to properly leverage the specialized hardware. Moreover, the accelerators become obsolete quickly as the applications evolve. What is needed is a structured approach for generating programmable accelerators and for updating the software compiler as the accelerator architecture evolves with the applications. In this talk, I will describe a new agile methodology for co-designing programmable hardware accelerators and compilers. Our methodology employs a combination of new programming languages and formal methods to automatically generate the accelerator hardware and its compiler from a single specification. This enables faster evolution and optimization of accelerators, because of the availability of a working compiler. I will showcase this methodology using Amber, a coarse-grained programmable accelerator for imaging and machine learning (ML) we designed and fabricated using our flow in TSMC 16 nm technology. I will show how we agilely evolved Amber into Onyx, our next generation accelerator, using an application-driven design space exploration framework called APEX enabled by our hardware-compiler co-design flow.
Biography: Priyanka Raina is an Assistant Professor of Electrical Engineering at Stanford University. She received her B.Tech. degree in Electrical Engineering from the IIT Delhi in 2011 and her S.M. and Ph.D. degrees in Electrical Engineering and Computer Science from MIT in 2013 and 2018. Priyanka's research is on creating high-performance and energy-efficient architectures for domain-specific hardware accelerators in existing and emerging technologies. She also works on methodologies for agile hardware-software co-design. Her research has won best paper awards at VLSI, ESSCIRC and MICRO conferences and in the JSSC journal. She has also won the NSF CAREER Award, the Intel Rising Star Faculty Award, Hellman Faculty Scholar Award and is a Terman Faculty Fellow.
Host: Dr. Murali Annavaram, annavara@usc.edu
Webcast: https://usc.zoom.us/j/93842345540?pwd=V3U1TUgwK2pyTE9BWThDeCtxbDJOdz09More Information: ECE Seminar Announcement-Raina, Priyanka-033023.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
WebCast Link: https://usc.zoom.us/j/93842345540?pwd=V3U1TUgwK2pyTE9BWThDeCtxbDJOdz09
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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NL Seminar-Getting AI to Do Things I Can't
Thu, Mar 30, 2023 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Ruiqi Zhong, Cal-Berekely
Talk Title: Getting AI to Do Things I Can't
Series: NL Seminar
Abstract: REMINDER
Meeting hosts only admit guests that they know to the Zoom meeting. Hence, you are highly encouraged to use your USC account to sign into Zoom.
If you are an outside visitor, please inform us at nlg DASH seminar DASH host AT isi DOT edu beforehand so we will be aware of your attendance and let you in.
Is it possible to tame powerful AI systems even when we struggle to determine the ground truth ourselves? In this talk, I will cover two example NLP tasks 1. automatically searching for goal-relevant patterns in large text collections and explaining them to humans in natural language 2. labeling complex SQL programs using non-programmers with the aid of our AI system and achieving accuracy on par with database experts. In both cases, we build tools that help humans scrutinize the AI's behavior with high effectiveness but low effort, bringing new insights that human experts have not anticipated.
Biography: Ruiqi Zhong is a 4th year Ph.D. student advised by Jacob Steinhardt and Dan Klein, working on NLP and AI Alignment.
Ruiqi Zhong attends th Univ. of California Berkeley, he is working on NLP and AI Alignment. His research aims to enable humans to effectively supervise AI systems on tasks where the ground truth is hard to obtain. He reads about epistemology and labor economy in his spare time.
Host: Jon May and Justin Cho
More Info: https://nlg.isi.edu/nl-seminar/
Webcast: https://www.youtube.com/watch?v=dHkYN33TtLMLocation: Information Science Institute (ISI) - Virtual and ISI-Conf Rm#689
WebCast Link: https://www.youtube.com/watch?v=dHkYN33TtLM
Audiences: Everyone Is Invited
Contact: Pete Zamar
Event Link: https://nlg.isi.edu/nl-seminar/
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CS Colloquium: Shuang Li (MIT) - Enabling Compositional Generalization of AI Systems
Thu, Mar 30, 2023 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Shuang Li, Massachusetts Institute of Technology (MIT)
Talk Title: Enabling Compositional Generalization of AI Systems
Series: CS Colloquium
Abstract: A vital aspect of human intelligence is the ability to compose increasingly complex concepts out of simpler ideas, enabling both rapid learning and adaptation of knowledge. Despite their impressive performance, current AI systems fall short in this area and are often unable to solve tasks that fall outside of their training distribution. My research aims to bridge this gap by incorporating compositionality into deep neural networks, thereby enhancing their ability to generalize and solve novel and complex tasks, such as generating 2D images and 3D assets based on complicated specifications, or enabling humanoid agents to perform a diverse range of household activities. The implications of this work are far-reaching, as compositionality has numerous applications across fields such as biology, robotics, and art production. By significantly improving the compositionality ability of AI systems, this research will pave the way for more data-efficient and powerful models in different research areas.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Shuang Li is a Ph.D. Candidate at MIT, advised by Antonio Torralba. She is interested in developing AI systems that generalize to a wide range of novel tasks and continually learn from the environment. Her research explores methods to incorporate compositionality into deep learning models, giving rise to stronger generalization abilities for solving more challenging novel tasks. Her research involves Generative Modeling, Embodied AI, and Vision-Language Understanding. Shuang is a recipient of the Meta Research Fellowship, Adobe Research Fellowship, MIT Seneff-Zue CS Fellowship, EECS Rising Star, ICML Outstanding Reviewer, and best and outstanding paper awards at NeurIPS workshops.
Host: Swabha Swayamdipta
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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Munushian Seminar - Jun Ye, Friday, March 31st at 9am in EEB 132
Fri, Mar 31, 2023 @ 09:00 AM - 10:30 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Jun Ye, JILA, National Institute of Standards and Technology and University of Colorado Boulder
Talk Title: Coherence, entanglement, and clock: from emergent phenomena to fundamental physics
Series: Munushian Seminar Series
Abstract: Precise quantum state engineering, many-body physics, and innovative laser technology are revolutionizing the performance of atomic clocks and metrology, providing opportunities to explore emerging phenomena and probe fundamental physics. Recent advances include measurement of gravitation time dilation across a few hundred micrometers, and employment of quantum entanglement for clock comparison.
Biography: Jun Ye is a Fellow of JILA, a Fellow of NIST, and a member of the National Academy of Sciences. His research focuses on the development of new tools for light-matter interactions and their applications in precision measurement, quantum science, and frequency metrology. He has co-authored over 400 scientific papers and delivered 600 invited talks. Among his many awards and honors are N.F. Ramsey Prize (APS), I.I. Rabi Award (IEEE), I.I. Rabi Prize (APS), and W.F. Meggers Award (OSA). His recent 2022 honors include Breakthrough Prize in Fundamental Physics, Niels Bohr Institute Medal of Honour, Herbert Walther Award, and Vannevar Bush Fellowship.
Host: ECE-Electrophysics
More Information: Flyer Munushian seminar Jun Ye.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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Alfred E.Mann Department of Biomedical Engineering Fred S. Grodins Keynote Lecture , Igor Efimov
Fri, Mar 31, 2023 @ 03:00 PM - 04:00 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Igor Efimov, Professor of Biomedical Engineering, Northwestern University Chicago,IL
Host: BME Associate Professor and Associate Chair Megan McCain - ZOOM link available upon request
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Carla Stanard
