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Events for the 3rd week of April
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ECE-S Seminar - Dr Stephen Tu
Mon, Apr 10, 2023 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr Stephen Tu, Research Scientist at Google Brain (Robotics at Google)
Talk Title: The foundations of machine learning for feedback control
Abstract: Recent breakthroughs in machine learning offer unparalleled optimism for the future capabilities of artificial intelligence. However, despite impressive progress, modern machine learning methods still operate under the fundamental assumption that the data at test time is generated by the same distribution from which training examples are collected. In order to build robust intelligent systems-”self-driving vehicles, robotic assistants, smart grids-”which safely interact with and control the surrounding environment, one must reason about the feedback effects of models deployed in closed-loop.
In this talk, I will discuss my work on developing a principled understanding of learning-based feedback systems, grounded within the context of robotics. First, motivated by the fact that many real world systems naturally produce sequences of data with long-range dependencies, I will present recent progress on the fundamental problem of learning from temporally correlated data streams. I will show that in many situations, learning from correlated data can be as efficient as if the data were independent. I will then examine how incremental stability-”a core idea in classical control theory-”can be used to study feedback-induced distribution shift. In particular, I will characterize how an expert policy's stability properties affect the end-to- end sample complexity of imitation learning. I will conclude by showing how these insights lead to practical algorithms and data collection strategies for imitation learning.
Biography: Stephen Tu is a research scientist at Robotics at Google in New York City. His research interests are focused on a principled understanding of the effects of using machine learning models for feedback control, with specific emphasis on robotics applications. He received his Ph.D. from the University of California, Berkeley in EECS under the supervision of Ben Recht.
Host: Dr Mahdi Soltanolkotabi, soltanol@usc.edu
Webcast: https://usc.zoom.us/j/92463220973?pwd=UHJEVmZFV2V2L25zOUo1aDY0cTFNQT09More Information: ECE Seminar Announcement 04.10.2023 Stephen Tu.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - EEB 248
WebCast Link: https://usc.zoom.us/j/92463220973?pwd=UHJEVmZFV2V2L25zOUo1aDY0cTFNQT09
Audiences: Everyone Is Invited
Contact: Miki Arlen
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Tabling Session with the Navy Talent Acquisition Group Pacific
Mon, Apr 10, 2023 @ 12:00 PM - 04:00 PM
Viterbi School of Engineering Career Connections
Workshops & Infosessions
Tabling Session with the Navy Talent Acquisition Group Pacific!
Date: Monday, April 10th
Time: 12:00 p.m. - 4:00 p.m.
Location: Epstein Family Plaza
Program overview: The Nuclear Propulsion Officer Candidate program (NUPOC) is open to graduates and students who are within 30 months of graduating. The collegiate programs provides students with a monthly salary so they can focus on completing their degrees, leading to an active duty Naval career in Nuclear power and engineering management. Upon graduation, NUPOC candidates are provided with valuable Nuclear power technical training. This career path has outstanding potential for growth, either in the Navy or the Civilian sector if they decide to separate at the end of their initial service obligation. Academic success is a students primary focus while in our collegiate program. Collegiate monthly pay works out to $6,500 per month, up to 30 months.
The following positions are part of the NUPOC program:
Naval Reactors Engineer (approximate GPA 3.8 - 4.0)
$15,000 sign-on bonus + an additional $2,000 upon completion of Nuclear Power School.
5 year position in Washington D.C. (DOES NOT DEPLOY).
Job entails approving, confirming, and planning the design, operation, and maintenance of over 100 nuclear reactors.
Supports the operational fleet (submarines and aircraft carriers).
Nuclear Power School Instructor (approximate GPA 3.6 - 4.0).
5 year position in Charleston, SC (DOES NOT DEPLOY)
Job entails training future Nuclear Propulsion Officers and Nuclear Field Enlisted personnel, while gaining valuable teaching experience in an exciting and technologically advanced curriculum.
Submarine Officer (GPA 3.0+)
$15,000 sign-on bonus + an additional $2,000 upon completion of Nuclear Power School.
Will be stationed on either a Ballistic Missile Trident, Fast Attack, or Guided Missile nuclear-powered submarine.
You will oversee everything from nuclear propulsion plant operations to weapon systems and navigational duties.
Surface Warfare Officer (GPA 3.0+)
$15,000 sign-on bonus + an additional $2,000 upon completion of Nuclear Power School.
Will complete a first tour on a conventionally powered combat ship where you will receive your Surface Warfare Officer Qualification.
Following your first tour you will complete Nuclear Power Training and be stationed on an Nimitz-class nuclear powered aircraft carrier.
There you will oversee the operation and maintenance of the sophisticated nuclear propulsion plant.
Majors: STEM majors who have completed 1 year of Calculus and Physics and have a GPA of at least 3.0.
US Citizenship is required to apply. We are not able to offer Visa Sponsorship or hire students on CPT/OPT.Location: Epstein Family Plaza
Audiences: Everyone Is Invited
Contact: RTH 218 Viterbi Career Connections
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PhD Thesis Defense - Hikaru Ibayashi
Tue, Apr 11, 2023 @ 09:00 AM - 10:30 AM
Thomas Lord Department of Computer Science
University Calendar
PhD Thesis Defense - Hikaru Ibayashi
Title:
Sharpness Analysis of Neural-networks for Physics Simulation
Committee members:
Prof. Aiichiro Nakano (Chair), Prof. Yan Liu, Prof. Paulo Branicio (Department of Chemical Engineering and Materials Science)
Abstract:
Deep learning has attracted significant attention in recent years due to its remarkable achievements in various applications. However, building effective deep neural networks requires making crucial design choices such as the network architecture, regularization, optimization, and hyperparameter tuning.
In this dissertation, we focus on the concept of ``sharpness'' of neural networks,
which refers to neural networks' sensitivity against perturbation on weight parameters. We argue that sharpness is not only a theoretical notion but also has practical use cases that can lead to better generalization and robustness of neural models.
A major theoretical challenge of defining and measuring sharpness is its scale-sensitivity, i.e., the fact that sharpness can change to the scale transformation of neural networks. In this thesis, we propose a novel definition of sharpness that overcomes this limitation, with provable scale-invariance and extensive empirical validation. By analyzing the relationship between sharpness and model performance, We show how my definition can provide a more objective and accurate characterization of sharpness.
Another open question in the sharpness analysis is how training algorithms for machine learning models regularize sharpness. In this dissertation, we answer this question by showing that existing training algorithm methods regularize sharpness through what can be called "escaping" behavior, where the optimization process avoids sharp regions in the parameter space. This new explanation demystifies the connection between sharpness and training algorithms, paving the way for more effective and principled approaches to machine learning.
Finally, we demonstrate the practical benefits of sharpness regularization for physics simulations. We show that neural networks with small sharpness achieve high-fidelity fluid simulation and molecular dynamics. These findings include the significant implication that sharpness is not just a mathematical notion but also a practical tool for building physics-informed neural networks.
Location: Seaver Science Library (SSL) - 104
Audiences: Everyone Is Invited
Contact: Melissa Ochoa
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ECE-S Seminar - Dr Sabrina Neuman
Tue, Apr 11, 2023 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr Sabrina Neuman, Postdoctoral NSF Computing Innovation Fellow | Harvard University
Talk Title: Designing Computing Systems for Robotics and Physically Embodied Deployments
Abstract: Emerging applications that interact heavily with the physical world (e.g., robotics, medical devices, the internet of things, augmented and virtual reality, and machine learning on edge devices) present critical challenges for modern computer architecture, including hard real-time constraints, strict power budgets, diverse deployment scenarios, and a critical need for safety, security, and reliability. Hardware acceleration can provide high-performance and energy-efficient computation, but design requirements are shaped by the physical characteristics of the target electrical, biological, or mechanical deployment; external operating conditions; application performance demands; and the constraints of the size, weight, area, and power allocated to onboard computing-- leading to a combinatorial explosion of the computing system design space. To address this challenge, I identify common computational patterns shaped by the physical characteristics of the deployment scenario (e.g., geometric constraints, timescales, physics, biometrics), and distill this real-world information into systematic design flows that span the software-hardware system stack, from applications down to circuits. An example of this approach is robomorphic computing: a systematic design methodology that transforms robot morphology into customized accelerator hardware morphology by leveraging physical robot features such as limb topology and joint type to determine parallelism and matrix sparsity patterns in streamlined linear algebra functional units in the accelerator. Using robomorphic computing, we designed an accelerator for a critical bottleneck in robot motion planning and implemented the design on an FPGA for a manipulator arm, demonstrating significant speedups over state-of-the-art CPU and GPU solutions. Taking a broader view, in order to design generalized computing systems for robotics and other physically embodied applications, the traditional computing system stack must be expanded to enable co-design with physical real-world information, and new methodologies are needed to implement designs with minimal user intervention. In this talk, I will discuss my recent work in designing computing systems for robotics, and outline a future of systematic co-design of computing systems with the real world.
Biography: Sabrina M. Neuman is a postdoctoral NSF Computing Innovation Fellow at Harvard University. Her research interests are in computer architecture design informed by explicit application-level and domain-specific insights. She is particularly focused on robotics applications because of their heavy computational demands and potential to improve the well-being of individuals in society. She received her S.B., M.Eng., and Ph.D. from MIT. She is a 2021 EECS Rising Star, and her work on robotics acceleration has received Honorable Mention in IEEE Micro Top Picks 2022 and IEEE Micro Top Picks 2023.
Host: Dr Feifei Qian, feifeiqi@usc.edu | Dr Pierluigi Nuzzo, nuzzo@usc.edu
Webcast: https://usc.zoom.us/j/98275605184?pwd=NVBvL2hKdEZCRFRSTm1Hb1RWTSs2QT09More Information: ECE Seminar Announcement 04.11.2023 - Sabrina Neuman.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - EEB 132
WebCast Link: https://usc.zoom.us/j/98275605184?pwd=NVBvL2hKdEZCRFRSTm1Hb1RWTSs2QT09
Audiences: Everyone Is Invited
Contact: Miki Arlen
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CS Colloquium: Ruishan Liu (Stanford University) - Machine learning for precision medicine
Tue, Apr 11, 2023 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Ruishan Liu, Stanford University
Talk Title: Machine learning for precision medicine
Series: CS Colloquium
Abstract: Toward a new era of medicine, our mission is to benefit every patient with individualized medical care. This talk explores how machine learning can make precision medicine more effective and diverse. I will first discuss Trial Pathfinder, a computational framework to optimize clinical trial designs (Liu et al. Nature 2021). Trial Pathfinder simulates synthetic patient cohorts from medical records, and enables inclusive criteria and data valuation. In the second part, I will discuss how to leverage large real-world data to identify genetic biomarkers for precision oncology (Liu et al. Nature Medicine 2022), and how to use language models and causal inference to form individualized treatment plans.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Ruishan Liu is a postdoctoral researcher in Biomedical Data Science at Stanford University, working with Prof. James Zou. She received her PhD in Electrical Engineering at Stanford University in 2022. Her research lies in the intersection of machine learning and applications in human diseases, health and genomics. She was the recipient of Stanford Graduate Fellowship, and was selected as the Rising Star in Data Science by University of Chicago, the Next Generation in Biomedicine by Broad Institute, and the Rising Star in Engineering in Health by Johns Hopkins University and Columbia University. She led the project Trial Pathfinder, which was selected as Top Ten Clinical Research Achievement in 2022 and Finalist for Global Pharma Award in 2021.
Host: Yan Liu
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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Health Systems Management Engineering w/ Prof Belson
Tue, Apr 11, 2023 @ 11:00 AM - 12:00 PM
Viterbi School of Engineering Graduate Admission
Workshops & Infosessions
Join USC Viterbi School of Engineering for a webinar with Prof. David Belson who will highlight the MS in Health Systems Management Engineering program. The webinar will include program details, USC Viterbi's DEN@Viterbi online delivery option, admission requirements, a Q&A session and more!
WebCast Link: https://uscviterbi.webex.com/weblink/register/r6b04fa55f20586ecc3b6123f81b92148
Audiences: Everyone Is Invited
Contact: Corporate & Professional Programs
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Epstein Institute - ISE 651 Seminar
Tue, Apr 11, 2023 @ 03:30 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Alexandre Jacquillat, Assistant Professor, Dept. of Operations Research and Statistics, MIT Sloan
Talk Title: Optimizing Relay Operations Toward Sustainable Logistics
Host: Dr. John Carlsson
More Information: April 11, 2023.pdf
Location: Ethel Percy Andrus Gerontology Center (GER) - GER 206
Audiences: Everyone Is Invited
Contact: Grace Owh
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Computer Science General Faculty Meeting
Wed, Apr 12, 2023 @ 12:00 PM - 02:00 PM
Thomas Lord Department of Computer Science
Receptions & Special Events
Bi-Weekly regular faculty meeting for invited full-time Computer Science faculty only. Event details emailed directly to attendees.
Location: Ronald Tutor Hall of Engineering (RTH) - 526- Hybrid
Audiences: Invited Faculty Only
Contact: Assistant to CS chair
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Systems Architecting & Engineering Master's & Grad Certificate Program w/ Prof. Madni
Wed, Apr 12, 2023 @ 12:00 PM - 01:00 PM
Viterbi School of Engineering Graduate Admission
Workshops & Infosessions
Join USC Viterbi School of Engineering for a webinar with Prof. Azad Madni who will highlight the Systems Architecting & Engineering graduate programs. The webinar will include program details, USC Viterbi's DEN@Viterbi online delivery option, admission requirements, a Q&A session and more!
WebCast Link: https://uscviterbi.webex.com/weblink/register/r53b5b32250ccdd7e35da0244f2455b1b
Audiences: Everyone Is Invited
Contact: Corporate & Professional Programs
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Astronautical Engineering Master's & Grad Certificate Program w/ Prof. Gruntman
Wed, Apr 12, 2023 @ 01:30 PM - 02:30 PM
Viterbi School of Engineering Graduate Admission
Workshops & Infosessions
Join USC Viterbi School of Engineering for a webinar with Prof. Mike Gruntman who will highlight the Astronautical Engineering graduate programs. The webinar will include program details, USC Viterbi's DEN@Viterbi online delivery option, admission requirements, a Q&A session and more!
WebCast Link: https://uscviterbi.webex.com/weblink/register/r6bdd87b02dd363933a53ade0ac077f75
Audiences: Everyone Is Invited
Contact: Corporate & Professional Programs
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CS Colloquium: C. Mohan (Tsinghua University) - Query Optimization and Processing: Trends and Directions
Wed, Apr 12, 2023 @ 02:00 PM - 03:30 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: C. Mohan, Tsinghua University
Talk Title: Query Optimization and Processing: Trends and Directions
Series: CS Colloquium
Abstract: Query optimization and processing (QOP) have been a dominant component of relational database management systems ever since such systems emerged in the research and commercial space more than four decades ago. Technologies related to QOP have received widespread attention and have evolved significantly since the days of IBM Research's System R, the project which gave birth to the concept of cost-based query optimization. Having worked on various database management topics at the birthplace of the relational model and the SQL language, until my retirement 2 years ago as an IBM Fellow at IBM Research in Silicon Valley, I have observed at close quarters a great deal of work in QOP. In this talk, I will give a broad overview of QOP's evolution. I will discuss not only research trends but also trends in the commercial world. Work done in various organizations across the world will be covered.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Dr. C. Mohan is currently a Distinguished Visiting Professor at Tsinghua University in China, a Member of the inaugural Board of Governors of Digital University Kerala, and an Advisor of the Kerala Blockchain Academy (KBA) and the Tamil Nadu e-Governance Agency (TNeGA) in India. He retired in June 2020 from being an IBM Fellow at the IBM Almaden Research Center in Silicon Valley. He was an IBM researcher for 38.5 years in the database, blockchain, AI and related areas, impacting numerous IBM and non-IBM products, the research and academic communities, and standards, especially with his invention of the well-known ARIES family of database locking and recovery algorithms, and the Presumed Abort distributed commit protocol.
This IBM (1997-2020), ACM (2002-) and IEEE (2002-) Fellow has also served as the IBM India Chief Scientist (2006-2009). In addition to receiving the ACM SIGMOD Edgar F. Codd Innovations Award (1996), the VLDB 10 Year Best Paper Award (1999) and numerous IBM awards, Mohan was elected to the United States and Indian National Academies of Engineering (2009), and named an IBM Master Inventor (1997). This Distinguished Alumnus of IIT Madras (1977) received his PhD at the University of Texas at Austin (1981). He is an inventor of 50 patents. During the last many years, he focused on Blockchain, AI, Big Data and Cloud technologies (https://bit.ly/sigBcP, https://bit.ly/CMoTalks). Since 2017, he has been an evangelist of permissioned blockchains and the myth buster of permissionless blockchains. During 1H2021, Mohan was the Shaw Visiting Professor at the National University of Singapore (NUS) where he taught a seminar course on distributed data and computing. In 2019, he became an Honorary Advisor to TNeGA for its blockchain and other projects.
In 2020, he joined the Advisory Board of KBA. Since 2016, Mohan has been a Distinguished Visiting Professor of China's prestigious Tsinghua University. In 2021, he was inducted as a member of the inaugural Board of Governors of the new Indian university Digital University Kerala (DUK). Mohan has served on the advisory board of IEEE Spectrum, and on numerous conference and journal boards. During most of 2022, he was a non-employee consultant at Google with the title of Visiting Researcher. He has also been a Consultant to the Microsoft Data Team. Mohan is a frequent speaker in North America, Europe and Asia. He has given talks in 43 countries. He is highly active on social media and has a huge network of followers. More information can be found in the Wikipedia page at https://bit.ly/CMwIkP and his homepage at https://bit.ly/CMoDUK
Host: Cyrus Shahabi
Location: Ronald Tutor Hall of Engineering (RTH) - 109
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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AME Seminar
Wed, Apr 12, 2023 @ 03:30 PM - 04:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Kristen Davis, UC Irvine
Talk Title: Large Amplitude Internal Wave Transformation Between 500m and the Surfzone
Abstract: Internal waves strongly influence the physical and chemical environment of coastal ecosystems worldwide. We report novel observations from a dense and rapidly-sampling array spanning depths from 500 m to shore near Dongsha Atoll in the South China Sea to track large amplitude internal solitary wave (ISW) shoaling, breaking, and runup. During the observational period incident ISW amplitudes ranged between 78 m and 146 m with propagation speeds between 1.40 m/s and 2.38 m/s. Fissioning ISWs generated larger trailing elevation waves when the thermocline was deep, and evolved into onshore propagating bores in depths near 100 m. Collapsing ISWs contained significant mixing and reduced upslope bore propagation. Bores on the shallow forereef drove bottom temperature variation in excess of 10 degrees Celsius and near-bottom cross-shore currents in excess of 0.4 m/s. Bores decelerated upslope, consistent with upslope two-layer gravity current theory, though runup extent, Xr, was offshore of the predicted gravity current location. Background stratification affected the bore runup, with Xr farther offshore when the Korteweg-de Vries nonlinearity coefficient, α, was negative. Fronts associated with the shoaling local internal tide, but equal in magnitude to the soliton-generated bores, were observed onshore of 20 m depth.
Biography: Kristen Davis is an Associate Professor of Civil & Environmental Engineering at the University of California, Irvine. She is an engineer and oceanographer who is interested in studying how physical processes shape coastal waters -“ combining principles of fluid mechanics, oceanography, and ecology. Kristen uses both field observations and numerical tools to examine circulation in the ocean, its natural variability, and influence on marine ecosystems and human-nature interactions. Kristen earned a Ph.D. in Civil and Environmental Engineering at Stanford University in 2009 and was a postdoctoral researcher at the Woods Hole Oceanographic Institution and the Applied Physics Laboratory at the University of Washington. Her recent research is focused on understanding nonlinear internal wave dynamics and the feasibility of the large-scale, offshore cultivation of macroalgae for the production of biofuels and as a strategy to sequester carbon dioxide.
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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NL Seminar -Drinking From The Firehose of Science.
Thu, Apr 13, 2023 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Waleed Ammar, Allen Inst of AI (AI2)
Talk Title: Drinking From The Firehose of Science.
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.
Five years ago, I visited ISI to talk about our progress taming the scientific literature in the Semantic Scholar team at the Allen Institute for Artificial Intelligence. In this talk, I will highlight some of the exciting developments in Semantic Scholar over the past few years, then share with you how we've enabled a wide variety of users and partners to "drink" from the firehose of scientific publications by interfacing with the Semantic Scholar APIs. I will end with an interactive discussion of how we can increase the participation of underrepresented groups in science.
Biography: Waleed Ammar currently leads the Semantic Scholar APIs effort at the Allen Institute for Artificial intelligence (AI2), which enables researchers, practitioners and decision makers to do various computations on the scientific literature in a wide variety of research fields. Before rejoining AI2 this year, Waleed was a senior research scientist at Google, where he helped develop transformer-based models for generating DNA sequences based on PacBio long-reads which significantly reduced variant-calling errors [Nature Biotech'22]. He also helped develop task-oriented dialog systems which are more robust to disfluencies, code-switching and user revisions [arXiv'23]. Prior to joining Google, Waleed led the Semantic Scholar research team's efforts to develop ML-based methods to facilitate access to the literature [e.g., NAACL 19], build a knowledge graph of the scientific literature [NAACL'18], and use this wealth of information to identify systemic social problems in science [JAMA'19]. He also occasionally teaches courses at UW linguistics as an affiliate faculty member. In 2016, Waleed received a Ph.D. degree in artificial intelligence from Carnegie Mellon University. Before pursuing the Ph.D., Waleed was a research engineer at Microsoft Research and a web developer at eSpace Technologies. Outside work, Waleed spends most of his time on the water or in dancing studios.
Host: Jon May and Justin Cho
More Info: https://nlg.isi.edu/nl-seminar/
Webcast: https://www.youtube.com/watch?v=SsJNCkPEDu8Location: Information Science Institute (ISI) - Virtual and ISI-Conf Rm#689
WebCast Link: https://www.youtube.com/watch?v=SsJNCkPEDu8
Audiences: Everyone Is Invited
Contact: Pete Zamar
Event Link: https://nlg.isi.edu/nl-seminar/
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CS Colloquium: Ibrahim Sabek (MIT) - Building Better Data-Intensive Systems Using Machine Learning
Thu, Apr 13, 2023 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Ibrahim Sabek, MIT
Talk Title: Building Better Data-Intensive Systems Using Machine Learning
Series: CS Colloquium
Abstract: Database systems have traditionally relied on handcrafted approaches and rules to store large-scale data and process user queries over them. These well-tuned approaches and rules work well for the general-purpose case, but are seldom optimal for any actual application because they are not tailored for the specific application properties (e.g., user workload patterns). One possible solution is to build a specialized system from scratch, tailored for each use case. Although such a specialized system is able to get orders-of-magnitude better performance, building it is time-consuming and requires a huge manual effort. This pushes the need for automated solutions that abstract system-building complexities while getting as close as possible to the performance of specialized systems. In this talk, I will show how we leverage machine learning to instance-optimize the performance of query scheduling and execution operations in database systems. In particular, I will show how deep reinforcement learning can fully replace a traditional query scheduler. I will also show that-”in certain situations-”even simpler learned models, such as piece-wise linear models approximating the cumulative distribution function (CDF) of data, can help improve the performance of fundamental data structures and execution operations, such as hash tables and in-memory join algorithms.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Ibrahim Sabek is a postdoc at MIT and an NSF/CRA Computing Innovation Fellow. He is interested in building the next generation of machine learning-empowered data management, processing, and analysis systems. Before MIT, he received his Ph.D. from University of Minnesota, Twin Cities, where he studied machine learning techniques for spatial data management and analysis. His Ph.D. work received the University-wide Best Doctoral Dissertation Honorable Mention from University of Minnesota in 2021. He was also awarded the first place in the graduate student research competition (SRC) in ACM SIGSPATIAL 2019 and the best paper runner-up in ACM SIGSPATIAL 2018.
Host: Cyrus Shahabi
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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Semiconductors & Microelectronics Technology Seminar - Qiushi Guo, Thursday, April 13th at 11am in EEB 248
Thu, Apr 13, 2023 @ 11:00 AM - 12:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Qiushi Guo, CUNY Advanced Science Research Center (ASRC)
Talk Title: Lithium niobate integrated nonlinear photonics: new devices and systems on an old material
Series: Semiconductors & Microelectronics Technology
Abstract: Despite being an old material in optical and microwave technologies in its bulk form, thin-film lithium niobate (TFLN) has recently emerged as one of the most promising integrated photonic platforms owing to its strong electro-optic (EO) coefficient, quadratic optical nonlinearity, and broadband optical transparency ranging from 250 nm to 5 um. In this talk, I will first overview the basic optical properties of LN, and how LN nanophotonics can grant us new regimes of nonlinear photonics. Then I will present some of our recent experimental results on the realization and utilization of dispersion-engineered and quasi-phase-matched ultrafast photonic devices in both classical and quantum domains. I will discuss the realization of 100 dB/cm optical parametric amplification, 1.5-3 um widely tunable optical parametric oscillator (OPO), ultra-wide bandwidth quantum squeezing, femtosecond and femtojoule on chip all-optical switching, and the integrated mode-locked lasers based on TFLN with watt-level peak power.
Biography: Qiushi Guo is an assistant professor at the Advanced Science Research Center, City University of New York. Prior to joining the ASRC and the CUNY Graduate Center, Qiushi was a postdoctoral research associate at the California Institute of Technology. He received his Ph.D. in Electrical Engineering from Yale University in Dec. 2019, advised by Prof. Fengnian Xia. He received his M.S. degree in Electrical Engineering from the University of Pennsylvania in 2014, and his B.S. degree in Electrical Engineering from Xi'an Jiaotong University in 2012. Qiushi is the finalist of the 2022 Rising Star of Light, and the winner of the 2021 Henry Prentiss Becton Graduate Prize for his exceptional research achievements at Yale University. His research interests include integrated nonlinear and quantum photonics, mid-infrared photonics, and 2-D materials optoelectronics. He has published more than 40 peer-reviewed research papers in leading scientific journals with citations more than 300 times. He is serving on the editorial board of the journal Micromachines.
Host: J Yang, H Wang, C Zhou, S Cronin, W Wu
More Information: Qiushi Guo_2023-4-13.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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Biomedical Engineering Master's Programs w/ Prof. Wang
Thu, Apr 13, 2023 @ 11:00 AM - 12:00 PM
Viterbi School of Engineering Graduate Admission
Workshops & Infosessions
Join USC Viterbi School of Engineering for a webinar with Prof. Peter Yingxiao Wang who will highlight the Biomedical Engineering master's degree programs.
The webinar will discuss program details, USC Viterbi's DEN@Viterbi online delivery option, admission requirements and more!WebCast Link: https://uscviterbi.webex.com/weblink/register/r00f4fe97748670d8408f2c257b6343bc
Audiences: Everyone Is Invited
Contact: Corporate & Professional Programs
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Industrial & Systems Engineering Master's Programs w/ Prof. Dessouky
Thu, Apr 13, 2023 @ 02:00 PM - 03:00 PM
Viterbi School of Engineering Graduate Admission
Workshops & Infosessions
Join USC Viterbi School of Engineering for a webinar with Prof. Dessouky who will highlight the Master's degree programs in Industrial & Systems Engineering. The webinar will include program details, USC Viterbi's DEN@Viterbi online delivery option, admission requirements, a live Q&A session and more!
WebCast Link: https://uscviterbi.webex.com/weblink/register/r1bb001c4f4fd4e3ac264214ef504f256
Audiences: Everyone Is Invited
Contact: Corporate & Professional Programs
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CS Colloquium: Michael Oberst (MIT) - Rigorously Tested & Reliable Machine Learning for Health
Thu, Apr 13, 2023 @ 04:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Michael Oberst, MIT
Talk Title: Rigorously Tested & Reliable Machine Learning for Health
Series: CS Colloquium
Abstract: How do we make machine learning as rigorously tested and reliable as any medication or diagnostic test?
Machine learning (ML) has the potential to improve decision-making in healthcare, from predicting treatment effectiveness to diagnosing disease. However, standard retrospective evaluations can give a misleading sense for how well models will perform in practice. Evaluation of ML-derived treatment policies can be biased when using observational data, and predictive models that perform well in one hospital may perform poorly in another.
In this talk, I will introduce methods I have developed to proactively assess and improve the reliability of machine learning models. A central theme will be the application of external knowledge, including guided review of patient records, incorporation of limited clinical trial data, and interpretable stress tests. Throughout, I will discuss how evaluation can directly inform model design.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Michael Oberst is a final-year PhD candidate in Computer Science at MIT. His research focuses on making sure that machine learning in healthcare is safe and effective, using tools from causal inference and statistics. His work has been published at a range of machine learning venues (NeurIPS / ICML / AISTATS / KDD), including work with clinical collaborators from Mass General Brigham, NYU Langone, and Beth Israel Deaconess Medical Center. He has also worked on clinical applications of machine learning, including work on learning effective antibiotic treatment policies (published in Science Translational Medicine). He earned his undergraduate degree in Statistics at Harvard.
Host: Yan Liu
Location: Ronald Tutor Hall of Engineering (RTH) - 526
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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The Knowledge Shop, Volunteer Info Session
Thu, Apr 13, 2023 @ 07:00 PM - 07:30 PM
USC Viterbi School of Engineering
Conferences, Lectures, & Seminars
The KNOWLEDGE SHOP was created so that families in disadvantaged communities could get access to educational services that would assist in the empowerment and educational development of their children. Join the info session to learn how you can get involved by volunteering your time, knowledge, and skills.
Learn more about The Knowledge Shop by visiting:
https://www.theknowledgeshopla.com/
Location: Online Event
Audiences: Everyone Is Invited
Contact: Noe Mora
Event Link: https://engage.usc.edu/viterbi/rsvp?id=389323
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ShowCAIS 2023
Fri, Apr 14, 2023 @ 09:00 AM - 05:00 PM
Thomas Lord Department of Computer Science, USC Viterbi School of Engineering
University Calendar
The USC Center for AI in Society is hosting an all-day, in-person symposium on Friday, April 14th on the USC campus. This event will highlight the work of students and faculty using AI for good, and will include lunch and refreshments.
Registration: https://sites.google.com/usc.edu/showcais2023/registration?authuser=0
PLEASE REACH OUT TO THE SHOWCAIS ORGANIZING COMMITTEE WITH ANY QUESTIONS: USCCAIS@USC.EDULocation: Ronald Tutor Hall (RTH) 526
Audiences: Everyone Is Invited
Contact: Caitlin Dawson
Event Link: https://sites.google.com/usc.edu/showcais2023/home?authuser=0
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BME Speaker, Michael Cho
Fri, Apr 14, 2023 @ 11:00 AM - 12:00 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Michael Cho , Professor of Bioengineering, University of Texas at Austin
Talk Title: Stem cells, biomechanics, traumatic brain injury
Host: BME Professor, Qifa Zhou - ZOOM link available upon request
Location: Corwin D. Denney Research Center (DRB) - DRB 145
Audiences: Everyone Is Invited
Contact: Michele Medina
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Das Family Student Innovation Competition
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Fri, Apr 14, 2023 @ 06:00 PM - 09:00 PM
Sonny Astani Department of Civil and Environmental Engineering
Student Activity
The Sonny Astani Department of Civil and Environmental Engineering (CEE), with the generous support of the Das Family, has launched its inaugural Das Family CEE Student Innovation Competition themed Innovate LA. Focusing on some of the most pressing challenges facing the City of Los Angeles, student teams must develop innovative solutions using civil and environmental engineering concepts. This competition is designed to provide students with guidance for launching a startup and creating compelling pitches for their ideas. Student teams have a chance to win a grand prize of $15,000!
Location: Ronald Tutor Campus Center (TCC) - Tommy's Place
Audiences: Everyone Is Invited
Contact: Maral Mardirossians
Event Link: https://www.eventbrite.com/e/innovatela-competition-final-pitch-day-tickets-560903215617
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