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EECS Rising Stars 2021

EECS Rising Stars 2021

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Author: Myung-Hee Vabulas

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Participant

Zahra Ghodsi, UCSD: “Secure Frameworks for Outsourced Deep Learning”

September 3, 2021October 5, 2021Myung-Hee Vabulas
Participant

Mor Geva Pipek, Tel Aviv University: “Robust and Interpretable Machine Reasoning Over Text”

September 3, 2021October 5, 2021Myung-Hee Vabulas
Participant

Sijia Geng, University of Michigan: “Safety and Stability of Future Smart Grids: Managing Uncertainties of Distributed Energy Resources”

September 3, 2021October 7, 2021Myung-Hee Vabulas
Participant

Saadia Gabriel, University of Washington: “Probing Implications of News Headlines”

September 3, 2021October 5, 2021Myung-Hee Vabulas
Participant

Yaqi Duan, Princeton University: “Minimax-Optimal Off-Policy Evaluation with Linear Function Approximation”

September 3, 2021October 5, 2021Myung-Hee Vabulas
Participant

Priya Donti, Carnegie Mellon University: “Incorporating Power System Physics into Deep Learning via Implicit Layers”

September 3, 2021October 5, 2021Myung-Hee Vabulas
Participant

A. Feder Cooper, Cornell University: “(MC)^3: An Empirical Argument for Massively Capable MCMC”

September 3, 2021October 5, 2021Myung-Hee Vabulas
Participant

Xinyun Chen, UC Berkeley: “Deep Learning for Program Synthesis from Diverse Specifications”

September 3, 2021October 5, 2021Myung-Hee Vabulas
Participant

Beidi Chen, Stanford University: “Algorithms and Frameworks for Efficient Neural Network Training”

September 3, 2021October 5, 2021Myung-Hee Vabulas
Participant

Maria Brbic, Stanford University: “Bridging labeled and unlabeled data in biomedicine”

September 2, 2021October 5, 2021Myung-Hee Vabulas

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