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Events for April 14, 2025
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EiS Communications Hub - Tutoring for Engineering Ph.D. StudentsMon, Apr 14, 2025 @ 10:00 AM - 12:00 PM
Viterbi School of Engineering Student Affairs
Workshops & Infosessions
Viterbi Ph.D. students are invited to drop by the Hub for instruction on their writing and speaking tasks! All tutoring is one-on-one and conducted by Viterbi faculty.
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
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. -
DREAM Industry Mentorship speaker series- special event with Teague Egan
Mon, Apr 14, 2025 @ 10:00 AM - 11:00 AM
USC Viterbi School of Engineering
University Calendar
DREAM connects students with experienced industry professionals from a variety of tech and destination companies who help them create a vision for their futures, align their careers around purpose, and build character in the context of growth, reinvention, and constant change. Industry mentors discuss how professional challenges present opportunities for character and leadership development. This event features visionary USC alum Teague Egan, the Founder and CEO of EnergyX, discussing his remarkable career as an entrepreneur and energy futurist developing cutting-edge lithium and battery technology.https://eis.usc.edu/dream/
More Information: DREAM Flyer 4-14 Teague Egan talk.png
Location: Ronald Tutor Hall of Engineering (RTH) - 217
Audiences: Everyone Is Invited
Contact: Elisabeth Arnold Weiss
Event Link: https://cglink.me/2nB/r403917
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. -
"Keys to Life" series at USC ORSL
Mon, Apr 14, 2025 @ 12:00 PM - 01:00 PM
USC Viterbi School of Engineering
University Calendar
"Keys to Life" with Prof. Weiss is a motivational discussion series designed to promote student success and well-being. This series is for students who want to develop their "keys" in a small group setting and a peaceful, reflective environment. Finding purpose is essential to living a meaningful life and key to personal fulfillment. This series will help students identify and articulate their purpose and provide group motivation to work towards it. A unique feature of the series will be its peripatetic "Purpose Walks" through campus.
More Information: Keys to Life with Prof. Weiss.jpg
Location: University Religious Center (URC) - courtyard
Audiences: Everyone Is Invited
Contact: Elisabeth Arnold Weiss
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 - Changzhi Xie
Mon, Apr 14, 2025 @ 02:30 PM - 03:30 PM
Thomas Lord Department of Computer Science
University Calendar
Title of Presentation: On the Dynamics of Learning Linear Functinos with Neural Networks
Date and Time: 4.14 2:30-3:30PM
Location: EEB 203
Committee Members: Mahdi Soltanolkotabi(committee chair), Haipeng Luo, Robin Jia, Vatsal Sharan, Adel Javanmard.
Abstract: We study the gradient descent training dynamics of fitting a one-hidden-layer network with multi-dimensional outputs to linear target functions. That is, we focus on a realizable model where the inputs are drawn i.i.d. from a Gaussian distribution and the labels are generated according to a planted linear model with multiple outputs. This framework serves as a good model for a variety of interesting problems including end-to-end training in inverse problems and various auto-encoder models in machine learning. Despite the seemingly simple formulation, understanding training dynamics is a challenging unresolved problem. This is in part due to the fact that the training landscape contains multiple local optima and it is completely unclear why gradient descent from random initialization is able to escape such bad optima. In this work, we develop the first comprehensive analysis of the gradient descent dynamics for learning linear target functions with ReLU networks. We show that gradient descent with moderately small random initialization converges to a global minimizer at a linear rate. To rigorously show that GD avoids local optima, we develop intricate techniques to decompose the loss and control the GD trajectory, which may have broader implications for the analysis of non-convex optimization problems involving local optima. We corroborate our theoretical results with extensive experiments with various configurations.Location: Hughes Aircraft Electrical Engineering Center (EEB) - 203
Audiences: Everyone Is Invited
Contact: Changzhi Xie
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 - Tejas Srinivasan
Mon, Apr 14, 2025 @ 04:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
University Calendar
Title of Thesis Proposal: Facilitating Reliable Human-AI Collaboration Under Uncertainty
Date and Time: April 14, 2025, 4--5pm
Location: GCS 402C
Committee Members: Jesse Thomason (Chair), Robin Jia, Heather Culbertson, Morteza Dehghani, Diyi Yang
Abstract: AI systems are increasingly assisting humans with decision-making tasks. Effective human-AI collaboration requires AI assistants to be reliable by not only being accurate but also knowing when they don’t know and acting appropriately when uncertain. Popular strategies for handling uncertainty include abstaining from answering, providing prediction sets using conformal prediction, communicating uncertaintyto users, and asking clarification questions to resolve uncertainty. However, these mechanisms do not always facilitate appropriate reliance on and utilization of AI systems by users. In this thesis, we explore methods for proactively mitigating under- and over-reliance in human-AI collaboration under uncertainty. In selective prediction, always abstaining when uncertain can lead to under-utilization by the user, so we develop an algorithm to reduce over-abstention in multimodal selective prediction systems without increasing the error rate of the system’s predictions. When communicating uncertainty, we find that user trust can bias how users rely on AI confidence estimates and lead to inappropriate reliance, which we mitigate by adapting AI assistants’ behavior to user trust levels. Finally, we propose reducing over-reliance on LLM agents by modeling and proactively resolving uncertainty about user goals through frictive dialogue. Our works highlight the importance of modeling uncertainty about AI predictions and the user-AI interaction itself, and the benefits of responding to uncertainty through AI introspection and adaptive AI behaviorsLocation: Ginsburg Hall (GCS) - 402C
Audiences: Everyone Is Invited
Contact: Tejas Srinivasan
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.
