Current Students and Postdocs

• Joshua (Josh) Kammeraad (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 Hong: Bandits and Reinforcement Learning
• Laura Niss: Sequential Decision Making, Fairness, Learning Analytics (co-advised with Prof. Yuekai Sun)
• Vinod Raman, NSF Graduate Research Fellow: Boosting, Privacy, Distributed Learning
• Saptarshi Roy: High dimensional statistics (co-advised with Prof. Ziwei Zhu)
• Ziping Xu, Rackham Predoctoral Fellow: Reinforcement Learning, Transfer Learning, Multitask Learning

Master's students

• Shubham Pandey

Undergraduate Students

• Runxuan Jiang: Reinforcement Learning for Conformer Generation (co-mentored with Josh Kammeraad)
• Yuhang Li: Reinforcement Learning (co-mentored with Kihyuk Hong)
• Rui Nie: Machine Olfaction (co-mentored with Ziteng Pang)
• Kevin Tan: Offline RL

Former Students and Postdocs

• 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

• 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

• Ziteng Pang (2020-2022): Machine Olfaction, Graph Neural Networks
• Alex Shen (2020-2022): Federated Learning and Differential Privacy for Mobile Health
• Anthony DiGiovanni (2019-2021): Multi-Agent Reinforcement Learning
• Aravind Mantravadi (2020-2021): Machine Olfaction
• Tarun Gogineni (2019-2020): Applications of Reinforcement Learning in Computational Chemistry (co-mentored with Prof. Paul Zimmerman)
• Jessica Liu (2018-2020): Federated Learning for Mobile Health
• 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

• Jack Finkel (2019-2020), Highest Honors: Parameter Estimation in IRT Models using Matrix Completion with Applications to Intelligent Tutoring Systems. Next Step: Data Engineer, Capital One, New York City
• Mingyuan (William) Zhang (2017-2018): 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 Class of 2017: 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

• Mohamad Bairakdar (2019-2021): Machine Learning in Aging Research (co-mentored with Matthias Truttman)
• 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
• 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)
• 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