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Events for March 27, 2023

  • 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=dGQxNWRZVmE1bzZvRVVYRTd1Mk1VQT09

    More 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

  • PhD Thesis Proposal - Basel Shbita

    Mon, Mar 27, 2023 @ 10:30 AM - 12:00 PM

    Thomas Lord Department of Computer Science

    University Calendar

    Automatic Semantic Spatio-Temporal Interpretation of Historical Maps

    Craig A. Knoblock (chair), Cyrus Shahabi, John P. Wilson, Jay Pujara, Yao-Yi Chiang

    Friday, February 17th, 1:30pm-3pm PST

    Zoom Meeting Details:
    Meeting ID: 973 8753 9087
    Passcode: 312501

    Historical maps provide rich information for researchers in many areas, including the natural and social sciences. These maps include detailed documentation of a wide variety of natural and human-made features, their spatial extent, their changes over time, their geo-names, and additional metadata. Analyzing map collections that cover the same region at different points in time can be labor-intensive even for a scientist, often requiring further grounding and linking with external sources to contextualize the data. With rapidly increasing amounts of digitized map archives, we require methods to convert these maps into a machine-processable and machine-readable semantic form and do so automatically, efficiently, and at scale. Unfortunately, existing techniques are limited and do not leverage the vast landscape of information extracted from archives of historical maps.
    In this thesis proposal, we investigate how to convert the extracted geo-data and metadata to a dynamic knowledge graph representation that captures the data semantics, how the data can be interrelated across entire datasets, and how it can be grounded to real-world phenomena by leveraging external resources on the web. We explore approaches that benefit from the open and connective nature of linked data that can produce a spatio-temporal, semantic, and contextualized output that follows linked data principles, and that can be easily extended with further availability of contemporary maps while supporting backward compatible access. Once materialized in a dynamic knowledge graph, the output can hold the data in a semantic network, making it readily shared, accessible, visualized, standardized across domains, and scalable for effortless use by downstream tasks for analysis and expressive integration over time and space.

    WebCast Link: https://usc.zoom.us/j/97387539087?pwd=MWEwaHR0Z0FCOEdwdGdEcWxFSnorZz09

    Audiences: Everyone Is Invited

    Contact: Asiroh Cham

  • 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

  • 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=a0NFMS93VUhOaUhuc1JCMlQ3TUludz09

    Location: Hughes Aircraft Electrical Engineering Center (EEB) - 539

    WebCast Link: https://usc.zoom.us/j/96014499242?pwd=a0NFMS93VUhOaUhuc1JCMlQ3TUludz09

    Audiences: Everyone Is Invited

    Contact: Mayumi Thrasher

  • CS Teaching Faculty Meeting

    Mon, Mar 27, 2023 @ 12:00 PM - 02:00 PM

    Thomas Lord Department of Computer Science

    Receptions & Special Events

    Meeting for invited full-time Computer Science teaching faculty only. Event details emailed directly to attendees.

    Location: TBD - Hybrid

    Audiences: Invited Faculty Only

    Contact: Cherie Carter

  • 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

  • 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=aEtTRnQ2MmJ6UWE4dk9UMG9GdENLQT09

    Location: 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/