Current Students and Postdocs
Ph.D. Students
- Luke Francisco, Biomedical Informatics and Data Science Training Program (BIDS-TP) Fellow: Mobile Health, Missing Data, Synthetic Data Generation
- Kihyuk (Ki) Hong: Reinforcement Learning
- Chinmaya Kausik: Offline Reinforcement Learning (co-advised with Prof. Martin Strauss)
- Preetham Mohan: Quantum Learning Theory
- Vinod Raman, NSF Graduate Research Fellow: Boosting, Privacy, Adversarial Robustness, Distributed Learning
- Saptarshi Roy: High dimensional statistics (co-advised with Prof. Ziwei Zhu)
- Ziping Xu, Rackham Predoctoral Fellow: Reinforcement Learning, Transfer Learning, Multitask Learning, Curriculum Learning
In addition to the above students, I am also working with the following Ph.D. students on an experimental basis:
- Eduardo Ochoa Rivera: Machine Learning for Detection and Characterization of Microplastics
- Yash Patel: Conformer Generation
- Gang Qiao: Pure Exploration
- Jacob (Jake) Trauger: Graph Kernels, Graph Neural Networks, Machine Olfaction
Master's students
- Shubham Pandey: Conformer Generation (co-mentored with Yash Patel)
- Chengcheng Wang: Machine Learning for Detection and Characterization of Microplastics
Undergraduate Students
- Runxuan Jiang: Curriculum Learning for Reinforcement Learning (co-mentored with Ziping Xu)
- Yuhang Li: Reinforcement Learning (co-mentored with Kihyuk Hong)
- Alexander (Alex) Netzley: Conformer Generation (co-mentored with Yash Patel)
- Rui Nie: Machine Olfaction (co-mentored with Jake Trauger)
- Kevin Tan: Offline RL (co-mentored with Chinmaya Kausik)
- Chengsong Zhang: Conformer Generation (co-mentored with Yash Patel)
Former Students and Postdocs
Postdocs
Ph.D. Students
- Laura Niss, Ph.D. 2022. Thesis: Topics in Sequential Decision Making and Algorithmic Fairness (co-advised with Prof. Yuekai Sun).
- Aditya Modi, Ph.D. 2021. Thesis: Provably Efficient Reinforcement Learning Under Linear Model Structures: From Tabular to Feature Based Exploration (co-advised with Prof. Satinder Singh). ML Research Scientist, Microsoft, Sunnyvale, CA.
- Baekjin Kim, Ph.D. 2021, Department of Statistics Outstanding Dissertation Award. Thesis: Stability in Online Learning: From Random Perturbations in Bandit Problems to Differential Privacy. Machine Learning Engineer, Twitter, San Francisco, CA.
- Jonathan (Jack) Goetz, Ph.D. 2020. Thesis: Active Learning in Non-parametric and Federated Settings. Research Scientist, Facebook, Menlo Park, CA.
- Young Hun Jung, Ph.D. 2020. Thesis: New Directions in Online Learning: Boosting, Partial Information, and Non-Stationarity. ML Research Scientist, Microsoft, Sunnyvale, CA.
- Yitong Sun, Ph.D. 2019, ProQuest Distinguished Dissertation Awards Honorable Mention, Peter Smereka Award for Best AIM (Applied and Interdisciplinary Mathematics) Thesis: Random Features Methods in Supervised Learning (co-advised with Prof. Anna Gilbert). Research Engineer, Huawei, Shenzhen, China.
- Chansoo Lee, Ph.D. 2018. Thesis: Analysis of Perturbation Techniques in Online Learning (co-advised with Prof. Jacob Abernethy). Software Engineer, Google Brain, Pittsburgh, PA.
- Kam Chung Wong, Ph.D. 2017. Thesis: Lasso Guarantees for Dependent Data. Advisory Services Senior, Ernst and Young, New York, NY.
- Mohamad Kazem Shirani Faradonbeh, Ph.D. 2017. Thesis: Non-asymptotic Adaptive Control of Linear-Quadratic Systems (co-advised with Prof. George Michailidis). Assistant Professor, Department of Statistics, University of Georgia, Athens, GA.
- Huitian (Emmy) Lei, Ph.D. 2016. Thesis: An Online Actor Critic Algorithm and a Statistical Decision Procedure for Personalizing Intervention (co-advised with Prof. Susan Murphy). Senior Data Scientist, Lyft, San Francisco, CA.
- Sougata Chaudhuri, Ph.D. 2016. Thesis: Learning to Rank: Online Learning, Statistical Theory and Applications. Senior Machine Learning Scientist, DoorDash, Mountain View, CA.
Master's students
Undergraduate Students with Honors Thesis
Other Undergraduate Students