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Events for the 5th week of March

  • 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

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  • PhD Thesis Proposal - Basel Shbita

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

    Thomas Lord Department of Computer Science

    University Calendar


    Title:
    Automatic Semantic Spatio-Temporal Interpretation of Historical Maps

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

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

    Zoom Meeting Details:
    https://usc.zoom.us/j/97387539087?pwd=MWEwaHR0Z0FCOEdwdGdEcWxFSnorZz09
    Meeting ID: 973 8753 9087
    Passcode: 312501

    Abstract:
    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

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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=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

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  • 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

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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=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/

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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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  • Lockheed Martin Hypersonics Tech Talk

    Tue, Mar 28, 2023 @ 04:00 PM - 07:00 PM

    Viterbi School of Engineering Career Connections

    Workshops & Infosessions


    Lockheed Martin is showing Top Gun Maverick! Come join us for movie night and a Hypersonics Tech Talk!

    Date: Tuesday, March 28th
    Time: 4:00 p.m. - 7:00 p.m.
    Location: Michelson Hall (MCB) 101

    Lockheed Martin is a world-leader in the field of Hypersonics. Please join us to learn about our exciting work, and our collaboration with Paramount Pictures on the blockbuster movie Top Gun Maverick.

    There will be popcorn and candy!

    Please RSVP on gateway and also HERE

    What majors and class levels are you interested in connecting with? All levels, AE, ME, EE/Comp E, Materials, IE/SysE, and CS majors.

    Are you recruiting for internships, full time, or both? Pipelining for 2024 events.

    Can you offer Visa sponsorship? Are you able to hire a student on CPT or OPT? No, sorry.

    Location: Michelson Center for Convergent Bioscience (MCB) - 101

    Audiences: Everyone Is Invited

    Contact: RTH 218 Viterbi Career Connections

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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=VzlITkJaa1FBQ05ERFYvRXZ2MUwvUT09

    More 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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  • Computer Science General Faculty Meeting

    Wed, Mar 29, 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: Michelson Center for Convergent Bioscience (MCB) - 101- Hybrid

    Audiences: Invited Faculty Only

    Contact: Assistant to CS chair

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  • Min Family Challenge Semi Finals

    Wed, Mar 29, 2023 @ 02:00 PM - 04:00 PM

    Viterbi Technology Innovation and Entrepreneurship

    Receptions & Special Events


    The Min Family Challenge is a program for innovative technologies that have societal impact solutions.
    Come and hear from this year's Min Family Challenge teams about their solutions.

    Location: Michelson Center for Convergent Bioscience (MCB) - 101

    Audiences: Everyone Is Invited

    Contact: Viterbi TIE

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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=aEtTRnQ2MmJ6UWE4dk9UMG9GdENLQT09

    Location: 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=V3U1TUgwK2pyTE9BWThDeCtxbDJOdz09

    More 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=dHkYN33TtLM

    Location: 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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  • 2023 Mork Family Department Student Research Symposium

    Fri, Mar 31, 2023 @ 09:00 AM - 03:30 PM

    Mork Family Department of Chemical Engineering and Materials Science

    University Calendar


    Location: Michelson Center for Convergent Bioscience (MCB) - First Floor

    Audiences: Everyone Is Invited

    Contact: Candy Escobedo

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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

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