Rising Stars in Machine Learning

The University of Maryland Center for Machine Learning invites graduate students and postdocs from underrepresented groups to apply to its Rising Stars in Machine Learning program. People who identify as women, Black, Latinx, Indigenous, LGBTQIA or living with disabilities are underrepresented in computer science and machine learning. The goal of this program is to support machine learning researchers from these underrepresented groups as they pursue new scientific discoveries and academic opportunities. Three winners will be selected to present their research as part of our virtual distinguished ML seminar series and will receive an honorarium payment.

For announcements regarding this program, signup for our email list. Rising Stars in Machine Learning is organized and led by assistant professor of computer science Soheil Feizi with the help of other CML faculty. Questions can be directed to ml at umd dot edu.

2021 Rising Stars

Tian Li

Doctoral Student at Carnegie Mellon University

"On Heterogeneity in Federated Settings"

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Xinyun Chen

Doctoral Student at the University of California, Berkeley

“Deep Learning for Program Synthesis: Towards Human-like Reasoning”

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Qi Lei

Associate Research Scholar at Princeton University

“Provable Representation Learning: The Importance of Task Diversity and Pretext Tasks"

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2020 Rising Stars

Diana Cai

Doctoral Student at Princeton University

“Probabilistic Inference Under Model Misspecification”

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Irene Chen

Doctoral Student at Massachusetts Insititute of Technology

“Machine Learning for Equitable Healthcare”

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Presentation

Mahsa Ghasemi

Doctoral Student at The University of Texas at Austin

“Efficient Data Processing and Trustworthy Decision Making through Structured Task Representation”

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Nan Rosemary Ke

Doctoral Student at University of Montreal

"From 'What' to 'Why': Towards Causal Deep Networks”

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2019 Rising Stars

Surbhi Goel

Doctoral Student at The University of Texas at Austin

“Provably Efficient Algorithms for Learning Neural Networks”

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Adji Bousso Dieng

Doctoral Student at Columbia University

“Learning with Deep Probabilistic Generative Models”

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Karolina Gintare Dziugaite

Fundamental Research Scientist at Element AI

“PAC-Bayesian Approaches to Understanding Generalization in Deep Learning”

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