Previous Rising Stars in Machine Learning
2023 Rising Stars
Megha Srivastava
Doctoral Candidate at Stanford University
"Challenges in Human-AI Interaction for Information-Seeking Tasks"
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Han Shao
Doctoral Candidate at Toyota Technological Institute at Chicago
“Trustworthy Machine Learning Under Social and Adversarial Data Sources”
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Wanrong Zhu
Doctoral Candidate at The University of California, Santa Barbara
“Towards Collaborative Generative AI for Vision-and-Language Studies”
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Laixi Shi
Postdoctoral Fellow at Caltech
"The Curious Data Price of Distributional Robustness in Reinforcement Learning: Minimax-Optimal Sample Efficiency”
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Sanae Lotfi
Doctoral Candidate at New York University
“Understanding, Quantifying, and Predicting Generalization in Deep Learning”
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Weijia Shi
Doctoral Candidate at the University of Washington
“Retrieval-augmented Language Models”
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Yutong Bai
Doctoral Student at Johns Hopkins University
"Less is More: Unleashing the Power of Pretraining"
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Zhijing Jin
Doctoral Student at Max Planck Institute and Federal Institute of Technology Zürich
"Causal Inference for Robust, Reliable, and Responsible NLP”
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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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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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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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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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