Current Students and Postdocs
Postdocs
- Nanta Sophonrat, Eric and Wendy Schmidt AI in Science Postdoctoral Fellow: Finding Plastic Recycling Reaction Conditions using Chemist-in-the-loop Machine Learning (co-mentored with Prof. Anne McNeil)
- Soumi Tribedi, Eric and Wendy Schmidt AI in Science Postdoctoral Fellow: Machine Learning for Density Functional Theory (co-mentored with Prof. Paul Zimmerman)
Ph.D. Students
- Marc Brooks: LLMs in Healthcare
- Luke Francisco, Biomedical Informatics and Data Science Training Program (BIDS-TP) Fellow: Mobile Health, Risk Prediction, Missing Data, Synthetic Data Generation
- Kihyuk (Ki) Hong: Reinforcement Learning
- Chinmaya Kausik, Rackham International Student Fellow: Reinforcement Learning, Non-standard Rewards (co-advised with Prof. Martin Strauss)
- Preetham Mohan: Quantum Learning Theory (co-advised with Prof. Shravan Veerapaneni)
- Yash Patel, NSF Graduate Research Fellowship Honorable Mention: Conformal Prediction, Control Problems, AI for Science
- Joseph (Joe) Pennacchio: ML for Microplastics Research
- Gang Qiao: Pure Exploration
- Vinod Raman, NSF Graduate Research Fellow, 2025 Apple Scholar in AIML: Learning Theory
- Sahana Rayan: Uncertainty Quantification, Protein Language Models, Protein Structure
- Eduardo Ochoa Rivera: ML for Microplastics Research
- Unique Subedi, Rackham International Student Fellow, Rackham Predoctoral Fellow: Operator Learning, AI for Science
- Jacob (Jake) Trauger: Self-Attention and Transformers, Learning Theory with Sequences as Outputs
In addition to the above students, I am also working with the following Ph.D. students on an experimental basis:
Master's students
Undergraduate Students
- Rita Ionides: Private synthetic data generation (co-mentored with Luke Francisco)
- Cynthia (Cindy) Liu: Mobile Health (co-mentored with Luke Francisco)
- Hanzhen (Jenny) Zhu: Mobile Health (co-mentored with Luke Francisco). Next Step: Master's in Data Science at Harvard University.
Former Students and Postdocs
Postdocs
Ph.D. Students
- Saptarshi Roy, Ph.D. 2024, Department of Statistics Outstanding Dissertation Award Honorable Mention. Thesis: Statistics in the Modern Era: High Dimensions, Decision-Making, and Privacy. Postdoc with Prof. Alessandro Rinaldo and Prof. Purna Sarkar at UT Austin.
- Ziping Xu, Ph.D. 2023, Rackham Predoctoral Fellow, Department of Statistics Outstanding Dissertation Award. Thesis: On the Benefits of Multitask Learning: A Perspective Based on Task Diversity. Postdoc with Prof. Susan Murphy at Harvard University.
- Laura Niss, Ph.D. 2022. Thesis: Topics in Sequential Decision Making and Algorithmic Fairness (co-advised with Prof. Yuekai Sun). Technical Staff, AI Technology Group, MIT Lincoln Lab, Lexington, MA.
- 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. ML Engineer, Snap, Palo Alto, CA.
- Jonathan (Jack) Goetz, Ph.D. 2020: Thesis: Active Learning in Non-parametric and Federated Settings. Senior ML Research Scientist, Meta, Menlo Park, CA.
- Young Hun Jung, Ph.D. 2020. Thesis: New Directions in Online Learning: Boosting, Partial Information, and Non-Stationarity. Senior 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. Research Assistant Professor, Hong Kong University of Science and Technology.
- 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 Mathematics, Southern Methodist University, Dallas, TX.
- 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). Staff Data Scientist, Lyft, San Francisco, CA.
- Sougata Chaudhuri, Ph.D. 2016. Thesis: Learning to Rank: Online Learning, Statistical Theory and Applications. Lead Scientist, Salesforce, Palo Alto, CA.
Master's students (those who wrote papers with me are marked with a "*")
- Sreya Gogineni (2024): Deep Reinforcement Learning for Program Synthesis
- Chengcheng Wang (2022): Machine Learning for Detection and Characterization of Microplastics (co-mentored with Eduardo Ochoa Rivera)
- Shubham Pandey (2022): Conformer Generation (co-mentored with Yash Patel)
- Pengfei Liu (2022): Adversarial Bandits, Follow the Perturbed Leader
- Ziteng Pang (2020-2022): Machine Olfaction, Graph Neural Networks
- Alex Shen* (2020-2022): Federated Learning and Differential Privacy for Mobile Health. Next Step: Ph.D. in Statistics at Carnegie Mellon University.
- Anthony DiGiovanni* (2019-2021): Multi-Agent Reinforcement Learning. Next Step: Researcher, Center on Long-Term Risk, London, UK.
- Aravind Mantravadi (2020-2021): Machine Olfaction
- Tarun Gogineni* (2019-2020): Applications of Reinforcement Learning in Computational Chemistry (co-mentored with Prof. Paul Zimmerman). Next Steps: positions at Facebook, Atomic AI followed by Member of Technical Staff, OpenAI, San Francisco, CA.
- Jessica Liu* (2018-2020): Federated Learning for Mobile Health. Next Step: positions at American Express followed by Data Scientist, Federal Reserve Board, Washington, D.C.
- Mengjie Shi (2018-2019): Mood Prediction Using Data from Smartphones and Wearables
- Jin Seok (Andy) Lee (2014-2017): HeartSteps and NIDA challenge "Addiction Research: There's an app for that" (co-mentored with Profs. Predrag Klasnja and Susan Murphy)
- Anurag Beniwal (2015-2016): Learning Analytics
- Vatshank Chaturvedi (2014-2015): Matching Algorithms on Random Graphs
Undergraduate Students with Honors Thesis
Other Undergraduate Students (those who wrote papers with me are marked with a "*")
- Qianruixi (Erica) Wang (2024): Learning Theory (co-mentored with Jake Trauger)
- Yufan Zhang* (2024): Reinforcement Learning (co-mentored with Kihyuk Hong)
- Yiwen (Oliver) Wu (2022-2023): Mobile Health (co-mentored with Luke Francisco)
- Yuhang Li* (2022-2023): Reinforcement Learning (co-mentored with Kihyuk Hong): Next Step: M.S. in Computer Science at the University of Illinois Urbana-Champaign.
- Heyi Xu (2023): Machine Learning for Microplastics (co-mentored with Eduardo Ochoa Rivera)
- Andy Gu (2023): Mobile Health (co-mentored with Luke Francisco)
- Manheng Wang (2023): High dimensional statistics (co-mentored with Saptarshi Roy)
- Kevin Tan* (2021-2023), Oustanding Undergraduate Major: Offline RL (co-mentored with Chinmaya Kausik). Next Step: Ph.D. in Statistics at the Wharton School of the University of Pennsylvania.
- Chengsong Zhang (2022): Conformer Generation (co-mentored with Yash Patel)
- Runxuan Jiang* (2019-2022): Conformer Generation, Curriculum Learning for Reinforcement Learning (co-mentored with Ziping Xu). Next Step: Software Engineer, Citadel, Chicago, IL.
- Alexander (Alex) Netzley (2022): Conformer Generation (co-mentored with Yash Patel)
- Rahul Gupta (2022): Curriculum Learning for Reinforcement Learning (co-mentored with Ziping Xu)
- Zhiyu (Ted) Yuan (2022): Curriculum Learning for Reinforcement Learning (co-mentored with Ziping Xu)
- Zhong Zheng (2022-2023): Algorithmic fairness (co-mentored with Laura Niss) and Protein Reconstruction from Cryo-EM (co-mentored with Yash Patel)
- Ruoyu Wang (2022): Algorithmic fairness (co-mentored with Laura Niss)
- Yunze Wei (2022): Conformer Generation (co-mentored with Yash Patel)
- Mohamad Bairakdar* (2019-2021): Machine Learning in Aging Research (co-mentored with Matthias Truttman). Next Step: Associate Researcher, Icahn School of Medicine at Mount Sinai.
- Anh Tuan (Alan) Tran (2020-2021): Reinforcement Learning (co-mentored with Ziping Xu)
- Yifei He (2020-2021): Reinforcement Learning for Conformer Generation
- Zhongqi (Kevin) Ma (2020-2021): AI in education (co-mentored with Laura Niss)
- Zhihao (Patrick) Su (2020-2021): AI in education (co-mentored with Laura Niss)
- Vinod Raman* (2020-2021): Online Boosting with Partial Feedback. Next step: Ph.D. in Statistics at the University of Michigan.
- Alexandria (Alex) Pawlik (2019-2020): Privacy of Contact Tracing (co-mentored with Baekjin Kim)
- Haonan Sun (2019-2020): Learning Analytics (co-mentored with Laura Niss)
- Tarun Gogineni (2019): Active Learning (co-mentored with Jonathan (Jack) Goetz)
- Yeqing Lin (2019): Active Learning (co-mentored with Jonathan (Jack) Goetz)
- Daniel Zhang* (2017-2019): Online Boosting (co-mentored with Young Hun Jung). Next Steps: Software Engineer, Meta, Menlo Park, CA.
- Michael Kovalcik (2015-2017): Mobile Health (co-mentored with Prof. Susan Murphy)
- Joshua Song (2016): NIDA challenge "Addiction Research: There's an app for that" (co-mentored with Prof. Predrag Klasnja, Prof. Susan Murphy, and Prof. Maureen Walton)
- Steven Zheng (2016): NIDA challenge "Addiction Research: There's an app for that" (co-mentored with Prof. Predrag Klasnja, Prof. Susan Murphy, and Prof. Maureen Walton)
- Cyrus Anderson (2014-2015): Development of HeartSteps, a mobile phone app for encouraging physical activity (co-mentored with Prof. Predrag Klasnja)
- Ning Niu (2015): Improving importance weighted active learning (co-mentored with Prof. Barzan Mozafari)
- Scott Bommarito (2013-2014): Development of a pilot version of an Android app for mobile health; Simulations with bandit learning algorithms
- Bingjie Xu (2013): Development of a pilot version of an Android app for mobile health