Rising Stars in Machine Learning Workshop 2024

The University of Maryland Center for Machine Learning is hosting an academic workshop featuring our 2024 Rising Stars in Machine Learning at the Brendan Iribe Center for Computer Science and Engineering on Thursday, December 5, 2024. Workshop attendance is by invitation only.

Join the workshop virtually on Zoom here. Meeting ID: 917 6672 6196 Passcode: 027729



Agenda for Thursday, December 5, 2024

8:30–8:55 a.m.

Breakfast
8:55–9 a.m.

Welcoming Remarks

  • Tom Goldstein, Volpi-Cupal Endowed Professor of Computer Science and Director of the UMD Center for Machine Learning
9–9:30 a.m. Ying Fan Ph.D. Candidate, University of Wisconsin–Madison "Looped Transformers for Length Generalization"
9:30–10 a.m. Angelina Wang Postdoctoral Fellow, Standford University “Equality is Not Equity: Recognizing Group Differences in AI Fairness”
10–10:30 a.m.

Coffee Break
10:30–11 a.m. Swati Padmanabhan Postdoctoral Researcher, Massachusetts Institute of Technology "A Gradient Sampling Method with Complexity Guarantees for Lipschitz Functions in High and Low Dimensions"
11-11:30 a.m. Chulin Xie Ph.D. Candidate, University of Illinois at Urbana-Champaign “Assessing and Addressing Trustworthiness Challenges in Language Models”
11:30 a.m.–12:30 p.m.

Lunch
12:30–1 p.m. Maya Varma Ph.D. Candidate, Stanford University “Towards Accurate and Reliable Artificial Intelligence Methods for Medical Image Interpretation”
1—1:30 p.m. Sarah A. Wiegreffe Postdoctoral Researcher, Allen Institute for AI "Demystifying the Inner Workings of Language Models"
1:30–2 p.m.

Coffee Break
1:55 p.m.

Final Remarks

    Matthias Zwicker, Professor and Chair of Department of Computer Science
2:00 p.m.

Dessert

2024 Rising Stars

Ying Fan

Ph.D. Candidate, University of Wisconsin–Madison

"Looped Transformers for Length Generalization"
Personal Page

Angelina Wang

Postdoctoral Fellow, Stanford University

“Equality is Not Equity: Recognizing Group Differences in AI Fairness”
Personal Page

Swati Padmanabhan

Postdoctoral Researcher, Massachusetts Institute of Technology

“A Gradient Sampling Method with Complexity Guarantees for Lipschitz Functions in High and Low Dimensions”
Personal Page

Chulin Xie

Ph.D. Candidate, University of Illinois at Urbana-Champaign

"Assessing and Addressing Trustworthiness Challenges in Language Models”
Personal Page

Maya Varma

Ph.D. Candidate, Stanford University

“Towards Accurate and Reliable Artificial Intelligence Methods for Medical Image Interpretation”
Personal Page

Sarah A. Wiegreffe

Postdoctoral Researcher, Allen Institute for AI

"Demystifying the Inner Workings of Language Models"
Personal Page
Workshop Sponsors