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Events for April 10, 2025
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PhD Thesis Proposal - Jiahao Wen
Thu, Apr 10, 2025 @ 12:00 PM - 01:00 PM
Thomas Lord Department of Computer Science
University Calendar
Title of Thesis Proposal: Optimal r-Adaptive In-Timestep Remeshing for Elastodynamics
Date and Time: April 10th, 12 pm - 1pm
Location: SAL 213
Committee Members: Prof. Jernej Barbic, Prof. Yong Chen, Prof. Oded Stein, Prof. Satyandra Gupta, and Prof. Stefanos Nikolaidis.
Abstract: This work is about finding optimal degrees of freedom for FEM simulation of nonlinear deformable objects with frictional contacts. This is done by moving the vertices in the undeformed (reference) mesh to improve the match to the true analytical solution of the underlying PDE. I.e., get closer to the true solution with a fewer number of mesh vertices by optimally repositioning those vertices in the undeformed mesh. More broadly, the work tries to improve how partial differential equations are solved by adapting the FEM solution space.Location: Henry Salvatori Computer Science Center (SAL) - 213
Audiences: Everyone Is Invited
Contact: Jiahao Wen
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
PhD Thesis Proposal - Robby Costales
Thu, Apr 10, 2025 @ 03:00 PM - 04:30 PM
Thomas Lord Department of Computer Science
University Calendar
Title: The Three-Tiered Exploration Problem in Open-Ended Adaptive Learning
Committee members: Stefanos Nikolaidis (chair), Erdem Biyik, Stephen Tu, Willie Neiswanger, Daniel Seita
Abstract: A central challenge in training adaptive decision-making agents via meta-reinforcement learning (meta-RL) is meta-exploration—the search for an efficient exploration strategy that generalize to new, unseen tasks. Another bottleneck is the significant expense of manually designing training task distributions. While autocurricula methods—which automatically generate appropriately challenging training tasks for the learning agent—are well-studied in the standard RL setting, their application to meta-RL has been underexplored. These autocurricula approaches are a promising route for both (1) reducing the difficulty of meta-exploration and (2) removing the need for hand-designing tasks for meta-RL training, but the emergent training dynamics are complex—with each component mutually exacerbating each others' separate instabilities. In this talk, I outline a preliminary framework for understanding this combined learning problem, and present a research trajectory for addressing the associated challenges, building on my ongoing PhD work.Location: Ginsburg Hall (GCS) - 502C - 5th Floor
Audiences: Everyone Is Invited
Contact: Ellecia Williams
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor.
