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SUNMONTUEWEDTHUFRISAT
Conferences, Lectures, & Seminars
Events for January
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NL Seminar
Thu, Jan 12, 2023 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Arjun Subramonian, UCLA
Talk Title: Bias and Power in NLP
Abstract: REMINDER:
If you are an outside visitor, please inform us at nlg DASH seminar DASH host AT isi DOT edu beforehand so we are aware of your attendance and let you in.
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.
In person attendance will be held in CR 689 at ISI in Marina Del Rey, remote attendees can log on via Zoom.
NLP models are increasingly deployed as part of technological pipelines. However, these models reinforce the biased and unjust treatment of marginalized people. In this talk, I discuss the multiplicity and sociotechnical nature of bias, and sources of bias and harms in the NLP lifecycle, situating these concepts in the context of gender exclusivity. I further examine bias metrics, interventions, and their pitfalls. Finally, I connect bias and harms to the power structures in which NLP is embedded. Ultimately, I urge for more intersectional and reflexive approaches to NLP.
Biography: Arjun Subramonian is a Computer Science PhD student at the University of California, Los Angeles. Their research focuses on inclusive graph machine learning and natural language processing, including fairness, bias, ethics, and integrating queer perspectives. They are further a core organizer of Queer in AI.
Host: Jon May and Meryem M'hamdi
More Info: https://nlg.isi.edu/nl-seminar/
Webcast: https://usc.zoom.us/j/96014548234Location: Information Science Institute (ISI) - Virtual and ISI-Conf Rm#689
WebCast Link: https://usc.zoom.us/j/96014548234
Audiences: Everyone Is Invited
Contact: Pete Zamar
Event Link: https://nlg.isi.edu/nl-seminar/
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BME Seminar Speaker, Dr. Charles Henry
Fri, Jan 13, 2023 @ 11:00 AM - 12:00 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Charles Henry , Professor of Chemistry, Chemical & Biological Engineering, and Biomedical Engineering at Colorado State University
Talk Title: Advancing Point of Care Diagnostics Using Capillary Flow Microfluidics
Host: BME Professor Maral Mousavi - Zoom Link Available on Request.
More Information: AEM Seminar-Jan. 13th 2023-Chuck Henry.pdf
Location: Corwin D. Denney Research Center (DRB) - 145
Audiences: Everyone Is Invited
Contact: Michele Medina
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Epstein Institute - ISE 651 Seminar
Tue, Jan 17, 2023 @ 03:30 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Nian Si, Postdoctoral Principal Researcher, Chicago Booth
Talk Title: Distributionally Robust Policy Learning
Host: Prof. Maged Dessouky
More Information: Abstract_NS.pdf
Location: Ethel Percy Andrus Gerontology Center (GER) - GER 206
Audiences: Everyone Is Invited
Contact: Grace Owh
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ECE Seminar: Provenance Attestation: From Silicon Chips to Biological Cells and Beyond
Wed, Jan 18, 2023 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Prof. Yiorgos Makris, ECE Department, The University of Texas at Dallas
Talk Title: Provenance Attestation: From Silicon Chips to Biological Cells and Beyond
Abstract: Complex processes, whether natural or artificial, often exhibit inherent variability and result in slightly different products even when identical steps, equipment, materials and conditions are employed. Such variability typically consists of a random component, which is attributed to the endogenous stochasticity of the process itself, and a systematic component, which is attributed to the exogenous aspects of the production. In this presentation, we will discuss how this variability can be harnessed for the purpose of attesting both the process and each copy of the product, thereby facilitating trust, traceability and intellectual property protection. First, in the context of semiconductor manufacturing, using production test measurements from integrated circuits fabricated in two different Texas Instruments 65nm facilities, we will demonstrate the use of contemporary statistical and machine learning-based methods for determining whether a chip was produced by a ratified foundry. Then, using both physical and electrical measurements (a.k.a., metrology and wafer acceptance tests, respectively) from wafers manufactured using multiple copies of a mask-set in a GlobalFoundries 12nm facility, we will demonstrate the use of similar methods for determining whether a wafer was produced by a trusted mask-set and we will discuss the design of custom sensors for obtaining the relevant information from each die on the wafer. Lastly, in the context of synthetic biology, using amplicon sequencing data from multiple cell lines (i.e., HEK293, HCT116 and HeLa), we will demonstrate that the stochasticity of the non-homologous end-joining (NHEJ) DNA repair process can be leveraged as a mechanism for introducing a unique identifier (i.e., a Genetic Physical Unclonable Function (PUF)) in every legitimately produced copy of a cell line. Akin to their counterparts in the semiconductor industry, Genetic PUFs can be used for attesting the provenance and protecting the intellectual property of valuable, genetically-engineered cell lines.
Biography: Yiorgos Makris received the Diploma of Computer Engineering from the University of Patras, Greece, in 1995 and the M.S. and Ph.D. degrees in Computer Engineering from the University of California, San Diego, in 1998 and 2001, respectively. After spending a decade on the faculty of Yale University, he joined UT Dallas where he is now a Professor of Electrical and Computer Engineering, the Co-Founder and Site-PI of the NSF Industry University Cooperative Research Center on Hardware and Embedded System Security and Trust (NSF CHEST I/UCRC), as well as the Leader of the Safety, Security and Healthcare Thrust of the Texas Analog Center of Excellence (TxACE) and the Director of the Trusted and RELiable Architectures (TRELA) Research Laboratory. His research focuses on applications of machine learning and statistical analysis in the development of trusted and reliable integrated circuits and systems, with particular emphasis in the analog/RF domain. He has served as an Associate Editor of the IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, the IEEE Transactions on Information Forensics and Security and the IEEE Design & Test of Computers Periodical, and as a guest editor for the IEEE Transactions on Computers and the IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. He also served as the 2016-2017 General Chair and the 2013-2014 Program Chair of the IEEE VLSI Test Symposium. He is a recipient of the 2006 Sheffield Distinguished Teaching Award, Best Paper Awards from the 2013 IEEE/ACM Design Automation and Test in Europe (DATE'13) conference and the 2015 IEEE VLSI Test Symposium (VTS'15), as well as Best Hardware Demonstration Awards from the 2016 and the 2018 IEEE Hardware-Oriented Security and Trust Symposia (HOST'16 and HOST'18) and a recipient of the 2020 Faculty Research Award from the Erik Jonsson School of Engineering and Computer Science at UT Dallas.
Host: Prof. Sandeep Gupta, sandeep@usc.edu
Webcast: https://usc.zoom.us/j/99394637308?pwd=MlNnWDIvVEs2Mm1HRXR3Y2NXN1F6QT09Location: 248
WebCast Link: https://usc.zoom.us/j/99394637308?pwd=MlNnWDIvVEs2Mm1HRXR3Y2NXN1F6QT09
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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MHI Nano Science & Technology Seminar - Haozhe "Harry" Wang, Wednesday, January 18th at 10:30am in EEB 132
Wed, Jan 18, 2023 @ 10:30 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Haozhe "Harry" Wang, California Institute of Technology
Talk Title: Scalable Manufacturing for Quantum Materials in Angstrom Precision
Series: Nano Science & Technology
Abstract: The figures of merit of quantum devices based on semiconductor and quantum materials are increasingly limited by imperfections introduced in nanofabrication. Further advances in capabilities demand both additive and subtractive manufacturing methods with vastly improved precision compared to that of typical approaches. In this talk, I will describe our development of additive manufacturing of bilayer graphene leveraging chemical vapor deposition and 'smart' processes. While the number of exciting quantum effects observed in bilayer graphene increases, a significant gap persists in transforming these discoveries into practical applications, owing to the small-scale samples obtained via top-down approaches. We realized a layer-by-layer (that is, Frank-van der Merwe) growth mode in large-scale bilayer graphene, with no island impurities, which is unprecedented in any van der Waals-stacked materials. Machine learning is adopted to assist spectroscopy, enabling the 'smart' characterization following the
chemical vapor deposition. After growth, a transfer is necessary to move bilayer graphene from the growth substrate to a destination substrate with a mandatory sacrificial support layer. This process induces residuals, wrinkles, and cracks, thus deteriorating 2D materials from their intrinsic properties. We utilized the Marangoni effect, also known as the 'tears of wine', to enable 'smart' transfer by building a surface tension gradient in transfer liquids. We demonstrate our autonomous Marangoni-flow transfer technique can transfer bilayer graphene without a support layer, resulting in residue-free bilayer graphene. In addition, I will discuss our recent progress in the subtractive manufacturing of semiconductors and quantum hardware in Angstrom precision using the atomic layer etching technique.
Biography: Dr. Haozhe Wang is currently KNI Prize Postdoctoral Fellow at the
California Institute of Technology (Caltech). He is working on atomic layer etching (ALE) technology for quantum materials to remedy surface imperfections in electronic and optical quantum devices. Before joining Caltech, he obtained his Ph.D. in Electrical Engineering from the Massachusetts Institute of Technology (MIT) in 2020, working on the scalable synthesis and application of quantum materials.
Host: J Yang, H Wang, C Zhou, S Cronin, W Wu
More Information: Haozhe_0118.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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ECE-EP Seminar - Eli Levenson-Falk, Wednesday, January 18th at 12pm in EEB 248
Wed, Jan 18, 2023 @ 12:00 PM - 01:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Eli Levenson-Falk, Physics and Astronomy - USC
Talk Title: Building an Environmental Engineering Toolkit with Superconducting Circuits
Abstract: Many interesting systems, such as lasers and topological insulators, are dominated by both quantum effects and strong dissipation. Such systems can be characterized as sets of coherent quantum objects that interact with an uncontrolled quantum environment: an open quantum system. Such open quantum systems are a subject of intense theoretical research, but experimental tools have remained lacking. In this talk I cover some of my lab's work aimed at building experimental tools to customize quantum environments and so study open quantum systems effects. I will discuss how we can use noisy classical control, engineered quantum dissipation, and quantum weak measurement feedback in order to emulate desired environmental dynamics. I will also show how such techniques can be used in practical quantum computing and quantum simulation applications, suppressing errors and ensuring high-fidelity operation.
Biography: I received my bachelor's from Harvard in 2008 and then my PhD from UC Berkeley in 2013, where I worked in Irfan Siddiqi's Quantum Nanoelectronics Lab, conducting research on quasiparticles in superconducting circuits. I then worked as a postdoc at Stanford with Aharon Kapitulnik, researching unconventional superconductors and designing precision measurement experiments. In 2017 I began my appointment at USC in the Physics & Astronomy Department. I received Young Investigator awards from the AFOSR in 2018 and the ONR in 2021, and was named a Cottrell Scholar in 2021.
Host: ECE-EP
More Information: Eli Levenson Falk Flyer.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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AME Seminar
Wed, Jan 18, 2023 @ 03:30 PM - 04:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Jeff Moehlis, Professor and Chair, Department of Mechanical Engineering University of California at Santa Barbara
Talk Title: Controlling Populations of Neural Oscillators
Abstract: Many challenging problems that consider the analysis and control of neural brain rhythms have been motivated by the advent of deep brain stimulation as a therapeutic treatment for a wide variety of neurological disorders. In a computational setting, neural rhythms are often modeled using large populations of coupled, conductance-based neurons. Control of such models comes with a long list of challenges: the underlying dynamics are nonnegligibly nonlinear, high dimensional, and subject to noise; hardware and biological limitations place restrictive constraints on allowable inputs; direct measurement of system observables is generally limited; and the resulting systems are typically highly underactuated. In this talk, I highlight a collection of recent analysis techniques and control frameworks that have been developed to contend with these difficulties. Particular emphasis is placed on the problem of desynchronization for a population of pathologically synchronized neural oscillators, a problem that is motivated by applications to Parkinson's disease where pathological synchronization is thought to contribute to the associated motor control symptoms.
Biography: Jeff Moehlis received a Ph.D. in Physics from UC Berkeley in 2000, and was a Postdoctoral Researcher in the Program in Applied and Computational Mathematics at Princeton University from 2000-2003. He joined the Department of Mechanical Engineering at UC Santa Barbara in 2003, and is currently Chair of this department. He was also recently the Chair of the Program in Dynamical Neuroscience at UC Santa Barbara. He has been a recipient of a Sloan Research Fellowship in Mathematics and a National Science Foundation CAREER Award, and was Program Director of the SIAM Activity Group in Dynamical Systems from 2008-2009. Jeff's current research includes applications of dynamical systems and control techniques to neuroscience, cardiac dynamics, and collective behavior. He has published over 100 journal / conference proceedings articles on these and other topics including shear flow turbulence, microelectromechanical systems, energy harvesting, and dynamical systems with symmetry.
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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BME Seminar Speaker, Dr. Matthew Brown
Fri, Jan 20, 2023 @ 11:00 AM - 12:00 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Matthew Brown , Professor-in-Residence, Radiological Sciences, University of California Los Angeles
Talk Title: Medical Image Analysis
Host: BME Professor Brent Liu - Zoom Link Available Upon Request
More Information: bme seminar flier for dr. matthew brown.pdf
Location: Corwin D. Denney Research Center (DRB) - 145
Audiences: Everyone Is Invited
Contact: Michele Medina
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Epstein Institute - ISE 651 Seminar
Tue, Jan 24, 2023 @ 03:30 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Xiao Liu, John L. Imhoff Endowed Chair and Assistant Professor - Department of Industrial Engineering, University of Arkansas
Talk Title: Domain-Aware Statistical Learning --- Harnessing the Convergence of Engineering Knowledge and Data-Driven Methods
Host: Prof. Suvrajeet Sen
More Information: Abstract_XL.pdf
Location: Ethel Percy Andrus Gerontology Center (GER) - GER 206
Audiences: Everyone Is Invited
Contact: Grace Owh
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AME Seminar
Wed, Jan 25, 2023 @ 03:30 PM - 04:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Steven L. Brunton, Professor of Mechanical Engineering Department of Mechanical Engineering University of Washington Seattle, WA
Talk Title: Machine Learning for Scientific Discovery, with Examples in Fluid Mechanics
Abstract: Accurate and efficient nonlinear dynamical systems models are essential to understand, predict, estimate, and control complex natural and engineered systems. In this talk, I will explore how machine learning may be used to develop these models purely from measurement data. We explore the sparse identification of nonlinear dynamics (SINDy) algorithm, which identifies a minimal dynamical system model that balances model complexity with accuracy, avoiding overfitting. This approach tends to promote models that are interpretable and generalizable, capturing the essential physics of the system. We also discuss the importance of learning effective coordinate systems in which the dynamics may be expected to be sparse. This sparse modeling approach will be demonstrated on a range of challenging modeling problems, for example in fluid dynamics. Because fluid dynamics is central to transportation, health, and defense systems, we will emphasize the importance of machine learning solutions that are interpretable, explainable, generalizable, and that respect known physics.
Biography: Steven L. Brunton is a Professor of Mechanical Engineering at the University of Washington. He is also Adjunct Professor of Applied Mathematics and Computer science, and a Data Science Fellow at the eScience Institute. Steve received the B.S. in mathematics from Caltech in 2006 and the Ph.D. in mechanical and aerospace engineering from Princeton in 2012. His research combines machine learning with dynamical systems to model and control systems in fluid dynamics, biolocomotion, optics, energy systems, and manufacturing. He received the Army and Air Force Young Investigator Program (YIP) awards and the Presidential Early Career Award for Scientists and Engineers (PECASE). Steve is also passionate about teaching math to engineers as co-author of three textbooks and through his popular YouTube channel, under the moniker eigensteve.
Host: AME Department
More Info: https://ame.usc.edu/seminars/
Webcast: https://usc.zoom.us/j/95805178776?pwd=aEtTRnQ2MmJ6UWE4dk9UMG9GdENLQT09More Information: Screenshot 2023-01-11 140424.jpg
Location: 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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BME Speaker, Dr. Kay Chung
Fri, Jan 27, 2023 @ 11:00 AM - 12:00 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Kay Chung , Postdoctoral Research Associate at Salk Institute for Biological Studies
Talk Title: Immune Cell Programming
Host: BME Chair Peter Wang - Zoom Link Available Upon Request
Location: Corwin D. Denney Research Center (DRB) - 145
Audiences: Everyone Is Invited
Contact: Michele Medina
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CS Colloquium: David Held (CMU) - Relational Affordance Learning for Robot Manipulation
Fri, Jan 27, 2023 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: David Held, Carnegie Mellon University
Talk Title: Relational Affordance Learning for Robot Manipulation
Series: CS Colloquium
Abstract: Robots today are typically confined to interact with rigid, opaque objects with known object models. However, the objects in our daily lives are often non-rigid, can be transparent or reflective, and are diverse in shape and appearance. I argue that, to enhance the capabilities of robots, we should develop perception methods that estimate what robots need to know to interact with the world. Specifically, I will present novel perception methods that estimate "relational affordances": task-specific geometric relationships between objects that allow a robot to determine what actions it needs to take to complete a task. These estimated relational affordances can enable robots to perform complex tasks such as manipulating cloth, articulated objects, grasping transparent and reflective objects, and other manipulation tasks, generalizing to unseen objects in a category and unseen object configurations. By reasoning about relational affordances, we can achieve robust performance on difficult robot manipulation tasks.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: David Held is an assistant professor at Carnegie Mellon University in the Robotics Institute and is the director of the RPAD lab: Robots Perceiving And Doing. His research focuses on perceptual robot learning, i.e. developing new methods at the intersection of robot perception and planning for robots to learn to interact with novel, perceptually challenging, and deformable objects. Prior to coming to CMU, David was a post-doctoral researcher at U.C. Berkeley, and he completed his Ph.D. in Computer Science at Stanford University. David also has a B.S. and M.S. in Mechanical Engineering at MIT. David is a recipient of the Google Faculty Research Award in 2017 and the NSF CAREER Award in 2021.
Host: Stefanos Nikolaidis
Location: Ronald Tutor Hall of Engineering (RTH) - 115
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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MHI Photonics Seminar - Koby Scheuer, Friday, January 27th at 3pm in EEB 248
Fri, Jan 27, 2023 @ 03:00 PM - 04:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Koby Scheuer, Tel-Aviv University
Talk Title: White Light Cavities, Exceptional Points, and their applications
Series: Photonics Seminar Series
Abstract: We consider the deep relations between concepts which apparently belong to distinct fields: Exceptional points in optical PT-symmetric systems, White light cavities and superluminal group velocity. It is also shown that this relationship is a key for understanding the underlying physics of these concepts as well as for the development of many important practical applications such as flat-top filters, broad band impedance matching and perfect absorption (anti-lasing).
Biography: Koby Scheuer received the Ph.D. degree in Electrical Engineering from the Technion-”Israel Institute of Technology in 2001. He was a Chief Designer with Lambda Crossing-”an optical component startup specializing in microring resonators for two years. Between 2003-2006 he was a research associate with the Department of Applied Physics at the California Institute of Technology, after which he joined the school of Electrical Engineering at Tel-Aviv University. Currently, he is a full professor with the School of Electrical Engineering at Tel-Aviv University. His research interests include nanophotonics, metasurfaces and metamaterials, slow & fast light, and optics in soluble materials.
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: Koby Scheuer Flyer.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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Epstein Institute - ISE 651 Seminar
Tue, Jan 31, 2023 @ 03:30 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Krishna Balasubramanian, Assistant Professor, Department of Statistics, University of California
Talk Title: Stochastic Compositional Optimization for Machine Learning
Host: Prof. Suvrajeet Sen
More Information: Abstract_KB.pdf
Location: Ethel Percy Andrus Gerontology Center (GER) - GER 206
Audiences: Everyone Is Invited
Contact: Grace Owh
