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Events for November 20, 2024
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EiS Communications Hub - Tutoring for Engineering Ph.D. StudentsWed, Nov 20, 2024 @ 10:00 AM - 12:00 PM
Viterbi School of Engineering Student Affairs
Workshops & Infosessions
Come to the EiS Communications Hub for one-on-one tutoring from Viterbi faculty for Ph.D. writing and speaking projects!
Location: Ronald Tutor Hall of Engineering (RTH) - 222A
Audiences: Viterbi Ph.D. Students
Contact: Helen Choi
Event Link: https://sites.google.com/usc.edu/eishub/home
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Computer Science General Faculty Meeting
Wed, Nov 20, 2024 @ 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 and staff only. Event details emailed directly to attendees.
Location: Ginsburg Hall (GCS) - 107
Audiences: Invited Faculty Only
Contact: Julia Mittenberg-Beirao
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Algorithmic Tools for Redistricting: Fairness via Analytics
Wed, Nov 20, 2024 @ 02:00 PM - 03:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Dr. David Shmoys, Laibe/Acheson Professor and Director of the Center for Data Science for Enterprise & Society - Cornell University
Talk Title: Algorithmic Tools for Redistricting: Fairness via Analytics
Abstract: The American winner-take-all congressional district system empowers politicians to engineer electoral outcomes by manipulating district boundaries. To date, most computational solutions focus on drawing unbiased maps by ignoring political and demographic input, and instead simply optimize for compactness and other related metrics. However, we maintain that this is a flawed approach because compactness and fairness are orthogonal qualities; to achieve a meaningful notion of fairness, one needs to model political and demographic considerations, using historical data. We will discuss a series of papers that explore and develop this perspective. We first present a scalable approach to explicitly optimize for arbitrary piecewise-linear definitions of fairness; this employs a stochastic hierarchical decomposition approach to produce an exponential number of distinct district plans that can be optimized via a standard set partitioning integer programming formulation. This enables a large-scale ensemble study of congressional districts, providing insights into the range of possible expected outcomes and the implications of this range on potential definitions of fairness. Further work extending this shows that many additional real-world constraints can be easily adapted in this framework (such as minimal county splits as was recently required in Alabama legislation in response to the US Supreme Court decision Milligan v. Alabama). In addition, one can adapt the same framework to heuristically optimize for other fairness-related objectives, such achieving a targeted number of majority minority districts (and in taking this approach, achieving stronger results than obtained by a prominent randomized local search approach known as “short bursts”).
We also show that our optimization infrastructure facilitates the study of the design of multi-member districts (MMDs) in which each district elects multiple representatives, potentially through a non-winner-takes-all voting rule (as was proposed in H.R. 4000 in an earlier session of Congress). We carry out large-scale analyses for the U.S. House of Representatives under MMDs with different social choice functions, under algorithmically generated maps optimized for either partisan benefit or proportionality. We find that with three-member districts using Single Transferable Vote, fairness-minded independent commissions can achieve proportional outcomes in every state (up to rounding), and this would significantly curtail the power of advantage-seeking partisans to gerrymander.
This is joint work with Wes Gurnee, Nikhil Garg, David Rothschild, Julia Allen, Cole Gaines, David Domanski, Rares-Stefan Bucsa, and Daniel Brous.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: David Shmoys is the Laibe/Acheson Professor and Director of the Center for Data Science for Enterprise & Society at Cornell University. He obtained his PhD in Computer Science from the University of California at Berkeley in 1984, and held postdoctoral positions at MSRI in Berkeley and Harvard University, and a faculty position at MIT before joining the faculty at Cornell University. He was Chair of the Cornell Provost’s “Radical Collaborations” Task Force on Data Science and was co-Chair of the Academic Planning Committee for Cornell Tech. His research has focused on the design and analysis of efficient algorithms for discrete optimization problems, with applications including scheduling, inventory theory, computational biology, computational sustainability, and data-driven decision-making in the sharing economy. His work has highlighted the central role that linear programming plays in the design of approximation algorithms for NP-hard problems. He was awarded the 2022 INFORMS Optimization Society Khachiyan Prize, the 2023 INFORMS Morse Lectureship, and the 2024 INFORMS Kimball Medal. His book (co-authored with David Williamson), The Design of Approximation Algorithms, was awarded the 2013 INFORMS Lanchester Prize and his work on bike-sharing (joint with Daniel Freund, Shane Henderson, and Eoin O’Mahony) was awarded the 2018 INFORMS Wagner Prize. David is a Fellow of the ACM, INFORMS, and SIAM, and was an NSF Presidential Young Investigator.
Host: CAIS
More Info: https://cais.usc.edu/events/usc-cais-seminar-with-dr-david-shmoys/
Location: Michelson Center for Convergent Bioscience (MCB) - 101
Audiences: Everyone Is Invited
Contact: Hailey Winetrobe Nadel, MPH, CHES
Event Link: https://cais.usc.edu/events/usc-cais-seminar-with-dr-david-shmoys/
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PhD Thesis Proposal - Hayley Song
Wed, Nov 20, 2024 @ 02:15 PM - 03:15 PM
Thomas Lord Department of Computer Science
University Calendar
Title: Riemannian-Geometric Fingerprints of Generative Models
Date: November 20, 2024
Time: 2:15 pm - 3:15 pm
Location: KAP 209
Committee: Laurent Itti, Chair, Emilio Ferrara, Kyler Siegel, Robin Jia, and Willie Neiswanger
Abstract: Recent breakthroughs and rapid integration of generative models (GMs) have sparked interest in the problem of model attribution and their fingerprints.For instance, service providers need reliable methods of authenticating their models to protect their IP, while users and law enforcement seek to verify the source of generated content for accountability and trust. In addition, a growing threat of model collapse is arising, as more model-generated data are being fed back into sources (e.g., YouTube) that are often harvested for training ("regurgitative training''), heightening the need to differentiate synthetic from human data. Yet, a gap still exists in understanding generative models' fingerprints, we believe, stemming from the lack of a formal framework that can define, represent, and analyze the fingerprints in a principled way. To address this gap, we take a geometric approach and propose a new definition of artifact and fingerprint of generative models using Riemannian geometry, which allows us to leverage the rich theory of differential geometry.Our new definition generalizes previous work (Song et al, 2024) to non-Euclidean manifolds by learning Riemannian metrics from data and replacing the Euclidean distances and nearest-neighbor search with geodesic distances and kNN-based Riemannian center of mass. We apply our theory to a new gradient-based algorithm for computing the fingerprints in practice. Results show that it is more effective in distinguishing a large array of generative models, spanning across 4 different datasets in 2 different resolutions (64x64, 256x256), 27 model architectures, and 2 modalities (Vision, Vision-Language). Using our proposed definition can significantly improve the performance on model attribution, as well as a generalization to unseen datasets, model types, and modalities, suggesting its efficacy in practice.Location: Kaprielian Hall (KAP) - 209
Audiences: Everyone Is Invited
Contact: Ellecia Williams
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AME Seminar
Wed, Nov 20, 2024 @ 03:30 PM - 04:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Adrian Lew, Stanford
Talk Title: The Art and the Science of Metal 3D Printing
Abstract: This is the title of a class I teach at Stanford on metal 3D printing, and it reflects my perspective on where metal 3D printing is today: part art and part science, because of the complexities and multiple physical processes at play. Printing strategies are inspired in science, but when it comes time to print a new alloy or a complex geometry, the art storms in to help bridge the gaps in understanding. A goal in metal 3D printing research is to shift this balance towards science.
In this talk I will first describe the main physical processes involved one of the most widely adopted metal 3D printing technologies, Laser Powder Bed Fusion (LPBF), and then showcase three vignettes of contributions we made: (a) in-situ alloying and printing of tantalum-tungsten alloys, (b) the “surprising” behavior of some martensitic steels under 3D printing conditions, (c) two ways to alter the optical absorptivity of highly-reflective metallic powders to facilitate printing of copper in some standard printers. The art and the science are interweaved in the three contributions.
Biography: Adrian J. Lew is a Professor of Mechanical Engineering and the Institute for Computational and Mathematical Engineering at Stanford University. He graduated with the degree of Nuclear Engineer from the Instituto Balseiro in Argentina, and received his master of science and doctoral degrees in Aeronautics from the California Institute of Technology. He is a fellow of the International Association for Computational Mechanics, and has been awarded Young Investigator Award by the International Association for Computational Mechanics, the ONR Young Investigator Award, the NSF Career Award, and the Ferdinand P. Beer & Russel Johnston, Jr., Outstanding New Mechanics Educator Award from the American Society of Engineering Education. He has also received an honorable mention by the Federal Communication Commission for the creation of the Virtual Braille Keyboard. He was the first USACM Technical Thrust Area Lead for Manufacturing, and still serves it as a member. He is currently member of the Technical Advisory Board for Velo 3D, a metal 3D printing start-up located in Campbell, CA, and consultant to other metal 3D printing companies.
Host: AME Department
More Info: https://ame.usc.edu/seminars/
Webcast: https://usc.zoom.us/j/96060458816?pwd=8LmoG2q6vBCQubqqWpcizd2F1bxqsH.1Location: Seaver Science Library (SSL) - 202
WebCast Link: https://usc.zoom.us/j/96060458816?pwd=8LmoG2q6vBCQubqqWpcizd2F1bxqsH.1
Audiences: Everyone Is Invited
Contact: Tessa Yao
Event Link: https://ame.usc.edu/seminars/
