Events for May 08, 2023
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ECE Seminar: Robust Classification under Sparse Adversarial Attacks
Mon, May 08, 2023 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Payam Delgosha, Research Assistant Professor, Computer Science Department, University of Illinois at Urbana Champaign
Talk Title: Robust Classification under Sparse Adversarial Attacks
Abstract: It is well-known that machine learning models are vulnerable to small but cleverly-designed adversarial perturbations that can cause misclassification. While there has been major progress in designing attacks and defenses for various adversarial settings, many fundamental and theoretical problems are yet to be resolved. In this talk, we consider classification in the presence of L0-bounded adversarial perturbations, a.k.a. sparse attacks. This setting is significantly different from other Lp-adversarial settings, with p >= 1, as the L0-ball is non-convex and highly non-smooth. In this talk, we discuss the fundamental limits of robustness in the presence of sparse attacks. In order to find an upper bound on the robust error, we introduce novel classification methods that are based on truncation. Furthermore, in order to find a lower bound on the robust error, we design a specific adversarial strategy which tries to remove the information about the true label given the adversary's budget. We discuss scenarios where the bounds match asymptotically. Motivated by the theoretical success of the proposed algorithm, we discuss how to incorporate truncation as a new component into a neural network architecture, and verify the robustness of the proposed architecture against sparse attacks through several experiments. Finally, we investigate the generalization properties and sample complexity of adversarial training in this setting.
Biography: Payam Delgosha received his B.Sc. in Electrical Engineering and Pure Mathematics in 2012, and his M.Sc. in Electrical Engineering in 2014, both from Sharif University of Technology, Tehran, Iran. He received his Ph.D. in Electrical Engineering and Computer Sciences from the University of California at Berkeley in 2020. He joined the computer science department at the University of Illinois at Urbana Champaign as a research assistant professor in 2020. He received the 2020 IEEE Jack Keil Wolf ISIT best student paper award.
Host: Dr. Richard M. Leahy, leahy@sipi.usc.edu
Webcast: https://usc.zoom.us/j/97124212376?pwd=NTd0QzRzSXk3OGlzL0dIdFdXMmZYZz09More Information: ECE Seminar-Delgosha-050823.pdf
WebCast Link: https://usc.zoom.us/j/97124212376?pwd=NTd0QzRzSXk3OGlzL0dIdFdXMmZYZz09
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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MoBI Seminar: Dr Bradley Voytek
Mon, May 08, 2023 @ 11:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr Bradley Voytek, Department of Cognitive Science, HalıcıoÄlu Data Science Institute, UC San Diego
Talk Title: The physiology and function of aperiodic neural activity
Series: MoBI Seminar Series
Abstract: Perception, action, and cognition depend upon coordinated neural activity. This coordination operates within noisy, distributed neural networks, which themselves change with development, aging, and disease. Extensive field potential and EEG research shows that neural oscillations interact with neuronal spiking. This interaction has been proposed to be a mechanism for implementing dynamic coordination between brain regions, placing oscillations at the forefront of neuroscience research. Our work challenges our conception of what an oscillation even is. Beginning from basic theory and modeling, we show that traditional analyses conflate non-oscillatory, aperiodic activity with oscillations. To do this, we leverage neural modeling and a breadth of empirical data-”spanning human iPSC-derived cortical organoids, animal electrophysiology, invasive human EEG, and large-scale data mining. We show that, while not all things that appear oscillatory are so, the physiological information we can extract from the local field potential and EEG may nevertheless be far richer than previously thought, including nonsinusoidality of oscillation waveform shape and the aperiodic signal.
Biography: Bradley Voytek is a Professor in the Department of Cognitive Science, the HalıcıoÄlu Data Science Institute, and the Neurosciences Graduate Program at UC San Diego. He's an Alfred P. Sloan Neuroscience Research Fellow and a Kavli Fellow of the National Academies of Sciences, as well as a founding faculty member of the UC San Diego HalıcıoÄlu Data Science Institute and the Undergraduate Data Science program. After his PhD at UC Berkeley, he joined Uber as their first data scientist-”when it was a 10-person startup-”where he helped build their data science strategy and team. His research lab combines large-scale data science and machine learning to study how brain regions communicate with one another, and how that communication changes with aging and disease. He is an advocate for promoting science to the public and speaks extensively with students at all grade levels about the joys of scientific research and discovery. In addition to his academic publications, his outreach work has appeared in outlets ranging from Scientific American and NPR to the San Diego Comic-Con. He is currently writing a book with neuroscientist Ashley Juavinett regarding the powerful future of data science in neuroscience discovery, though his most important contribution to science is his book with fellow neuroscientist Tim Verstynen, "Do Zombies Dream of Undead Sheep?", by Princeton University Press.
Host: Dr Richard Leahy, leahy@sipi.usc.edu | Dr Karim Jerbi, karim.jerbi.udem@gmail.com
Webcast: https://usc.zoom.us/j/97647013783?pwd=d1h2N3hxYUpJVU9CWlduYTZzMWNGQT09More Information: MoBI Seminar Flyer - 05.08.2023 Bradley Voytek.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - EEB 132
WebCast Link: https://usc.zoom.us/j/97647013783?pwd=d1h2N3hxYUpJVU9CWlduYTZzMWNGQT09
Audiences: Everyone Is Invited
Contact: Miki Arlen
