Current Students and Postdocs

• Soumi Tribedi: Machine Learning for Density Functional Theory (co-mentored with Prof. Paul Zimmerman)

• 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 (co-advised with Prof. Shravan Veerapaneni)
• Yash Patel, NSF Graduate Fellowship Honorable Mention: Conformer Generation, Cryo-EM
• Gang Qiao: Pure Exploration
• Vinod Raman, NSF Graduate Research Fellow: Boosting, Privacy, Adversarial Robustness, Distributed Learning
• Saptarshi Roy: High dimensional statistics (co-advised with Prof. Ziwei Zhu)
• Unique Subedi: Learnability of Vector-valued Functions
• Ziping Xu, Rackham Predoctoral Fellow, Department of Statistics Outstanding Dissertation Award: Reinforcement Learning, Transfer Learning, Multitask Learning, Curriculum Learning

• Marc Brooks
• Joseph (Joe) Pennacchio
• Sahana Rayan
• Eduardo Ochoa Rivera
• Jacob (Jake) Trauger

Master's students

• Zehua Wang: New Computational Approches for Best Subset Selection (co-mentored with Saptarshi Roy)

Undergraduate Students

• Kevin Cao: Protein Reconstruction from Cryo-EM (co-mentored with Yash Patel)
• Yuhang Li: Reinforcement Learning (co-mentored with Kihyuk Hong)
• Oliver Wu: Mobile Health (co-mentored with Luke Francisco)
• Heyi Xu: Machine Learning for Microplastics (co-mentored with Eduardo Ochoa Rivera)

Former Students and Postdocs

• Joshua (Josh) Kammeraad (2020-2022): Machine Learning for Chemistry (co-mentored with Prof. Paul Zimmerman)
• 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

• 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. ML Data Scientist, Moloco, Redwood City, 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.
• 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. Applied Scientist, Amazon, Palo Alto, CA.

Master's students (those who wrote papers with me are marked with a "*")

• 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

• Rui Nie (2021-2023), Oustanding Undergraduate Major: Exploring Machine Olfaction, High Honors. Next Step: Ph.D. in Biostatistics at University of Michigan.
• 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.
• 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 University of Pennsylvania.
• 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
• Haoyu Chen (2016-2018): Kernel Methods for Activation Energy Prediction. Next Step: MSE in Data Science at University of Pennsylvania.
• Zifan Li (2015-2017), Outstanding Undergraduate Major: Perturbation Algorithms for Adversarial Online Learning. Next Step: Ph.D. in Statistics at Yale University.
• 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 "*")

• 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