Current Students and 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)

• 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

• Mihir Dhanakshirur: Reasoning and Test-Time Computation in LLMs
• Jiaxun (Jason) Li: Learning Theory

Former Students and Postdocs

1. Joshua (Josh) Kammeraad (2020-2022): Machine Learning for Chemistry (co-mentored with Prof. Paul Zimmerman)
2. Harish Guruprasad Ramaswamy (2015): Weakly Supervised Learning (jointly mentored with Prof. Clayton Scott). Assistant Professor, Department of Computer Science and Engineering, IIT Madras.

Ph.D. Students

1. 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.
2. 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. Assistant Professor, School of Data Science and Society, UNC Chapel Hill.
3. 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.
4. 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.
5. 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.
6. Jonathan (Jack) Goetz, Ph.D. 2020: Thesis: Active Learning in Non-parametric and Federated Settings. Senior ML Research Scientist, Meta, Menlo Park, CA.
7. 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.
8. 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.
9. 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.
10. Kam Chung Wong, Ph.D. 2017. Thesis: Lasso Guarantees for Dependent Data. Research Assistant Professor, Hong Kong University of Science and Technology.
11. 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.
12. 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.
13. 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 "*")

1. Neil Mankodi (2024-2025): Machine Learning for Diabetes Risk Prediction.
2. Yikun Han* (2023-2025): Machine Olfaction. Next Step: Ph.D. in Information Sciences at the University of Illinois Urbana-Champaign
3. Zehua Wang* (2022-2025): High Dimensional Statistics, Symbolic Regression, Machine Learning in Chemistry. Next Step: Ph.D. in Computer Science at the University of Virginia.
4. Sreya Gogineni (2024): Deep Reinforcement Learning for Program Synthesis
5. Chengcheng Wang (2022): Machine Learning for Detection and Characterization of Microplastics (co-mentored with Eduardo Ochoa Rivera)
6. Shubham Pandey (2022): Conformer Generation
7. Pengfei Liu (2022): Adversarial Bandits, Follow the Perturbed Leader
8. Ziteng Pang (2020-2022): Machine Olfaction, Graph Neural Networks
9. Alex Shen* (2020-2022): Federated Learning and Differential Privacy for Mobile Health. Next Step: Ph.D. in Statistics at Carnegie Mellon University.
10. Anthony DiGiovanni* (2019-2021): Multi-Agent Reinforcement Learning. Next Step: Researcher, Center on Long-Term Risk, London, UK.
11. Aravind Mantravadi (2020-2021): Machine Olfaction
12. 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.
13. 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.
14. Mengjie Shi (2018-2019): Mood Prediction Using Data from Smartphones and Wearables
15. 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)
16. Anurag Beniwal (2015-2016): Learning Analytics
17. Vatshank Chaturvedi (2014-2015): Matching Algorithms on Random Graphs

Undergraduate Students with Honors Thesis

1. Hanzhen (Jenny) Zhu (2024-2025): Hierarchical Modeling of mHealth Intervention Effects with Missing-Data Considerations in the Intern Health Study. Next Step: Master's in Data Science at Harvard University.
2. Guyang (Kevin) Cao (2023-2024): Non-parametric Conformal Distributionally Robust Optimization. Next Step: Ph.D. in Computer Science at the University of Wisconsin-Madison.
3. Rui Nie (2021-2023), Oustanding Undergraduate Major: Exploring Machine Olfaction, High Honors. Next Step: Ph.D. in Biostatistics at the University of Michigan.
4. Jack Finkel (2019-2020): Parameter Estimation in IRT Models using Matrix Completion with Applications to Intelligent Tutoring Systems, Highest Honors. Next Step: Data Engineer, Capital One, New York City.
5. Mingyuan (William) Zhang (2017-2018), Outstanding Undergraduate Major: Settling the Minimax Regret in Online Learning to Rank with Top-k Feedback . Next Step: Ph.D. in Computer Science at the University of Pennsylvania.
6. Mengjie Shi (2017-2018): Machine Learning Methods for Mood Prediction Using Data from Smartphones and Wearables. Next Step: Accelerated Masters in Applied Statistics at UM, then Ph.D. in Statistics at UC Davis
7. Haoyu Chen (2016-2018): Kernel Methods for Activation Energy Prediction. Next Step: MSE in Data Science at the University of Pennsylvania.
8. Zifan Li (2015-2017), Outstanding Undergraduate Major: Perturbation Algorithms for Adversarial Online Learning. Next Step: Ph.D. in Statistics at Yale University.
9. Xinyan Han (2014-2015): An Empirical Comparison of Various Online Binary Classification Algorithms. Next Step: MS in Computational Science and Engineering at Harvard University.

Other Undergraduate Students (those who wrote papers with me are marked with a "*")

1. Rita Ionides (2023-2025): Private synthetic data generation (co-mentored with Luke Francisco)
2. Cynthia (Cindy) Liu (2024-2025): Mobile Health (co-mentored with Luke Francisco)
3. Qianruixi (Erica) Wang (2024): Learning Theory (co-mentored with Jake Trauger)
4. Yufan Zhang* (2024): Reinforcement Learning (co-mentored with Kihyuk Hong)
5. Yiwen (Oliver) Wu (2022-2023): Mobile Health (co-mentored with Luke Francisco)
6. 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.
7. Heyi Xu (2023): Machine Learning for Microplastics (co-mentored with Eduardo Ochoa Rivera)
8. Andy Gu (2023): Mobile Health (co-mentored with Luke Francisco)
9. Manheng Wang (2023): High dimensional statistics (co-mentored with Saptarshi Roy)
10. 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.
11. Chengsong Zhang (2022): Conformer Generation (co-mentored with Yash Patel)
12. Runxuan Jiang* (2019-2022): Conformer Generation, Curriculum Learning for Reinforcement Learning (co-mentored with Ziping Xu). Next Step: Software Engineer, Citadel, Chicago, IL.
13. Alexander (Alex) Netzley (2022): Conformer Generation (co-mentored with Yash Patel)
14. Rahul Gupta (2022): Curriculum Learning for Reinforcement Learning (co-mentored with Ziping Xu)
15. Zhiyu (Ted) Yuan (2022): Curriculum Learning for Reinforcement Learning (co-mentored with Ziping Xu)
16. Zhong Zheng (2022-2023): Algorithmic fairness (co-mentored with Laura Niss) and Protein Reconstruction from Cryo-EM (co-mentored with Yash Patel)
17. Ruoyu Wang (2022): Algorithmic fairness (co-mentored with Laura Niss)
18. Yunze Wei (2022): Conformer Generation (co-mentored with Yash Patel)
19. 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.
20. Anh Tuan (Alan) Tran (2020-2021): Reinforcement Learning (co-mentored with Ziping Xu)
21. Yifei He (2020-2021): Reinforcement Learning for Conformer Generation
22. Zhongqi (Kevin) Ma (2020-2021): AI in education (co-mentored with Laura Niss)
23. Zhihao (Patrick) Su (2020-2021): AI in education (co-mentored with Laura Niss)
24. Vinod Raman* (2020-2021): Online Boosting with Partial Feedback. Next step: Ph.D. in Statistics at the University of Michigan.
25. Alexandria (Alex) Pawlik (2019-2020): Privacy of Contact Tracing (co-mentored with Baekjin Kim)
26. Haonan Sun (2019-2020): Learning Analytics (co-mentored with Laura Niss)
27. Tarun Gogineni (2019): Active Learning (co-mentored with Jonathan (Jack) Goetz)
28. Yeqing Lin (2019): Active Learning (co-mentored with Jonathan (Jack) Goetz)
29. Daniel Zhang* (2017-2019): Online Boosting (co-mentored with Young Hun Jung). Next Steps: Software Engineer, Meta, Menlo Park, CA.
30. Michael Kovalcik (2015-2017): Mobile Health (co-mentored with Prof. Susan Murphy)
31. 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)
32. 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)
33. Cyrus Anderson (2014-2015): Development of HeartSteps, a mobile phone app for encouraging physical activity (co-mentored with Prof. Predrag Klasnja)
34. Ning Niu (2015): Improving importance weighted active learning (co-mentored with Prof. Barzan Mozafari)
35. Scott Bommarito (2013-2014): Development of a pilot version of an Android app for mobile health; Simulations with bandit learning algorithms
36. Bingjie Xu (2013): Development of a pilot version of an Android app for mobile health