I am a tenure-track assistant professor in the Department of Biostatistics and Bioinformatics at Duke University. My research is centered around Machine Learning, which spans the areas of Artificial Intelligence, Data Science, Optimization, Reinforcement Learning, and High Dimensional Statistics. My research goal is to develop computationally- and data-efficient machine learning algorithms with both strong empirical performance and theoretical guarantees.

Prior to joining Duke, I was a Postdoctoral Scholar Research Associate in the Department of Computing and Mathematical Science at the California Institute of Technology, working with Prof. Anima Anandkumar. At Caltech, I also work closely with Prof. Adam Wierman and Prof. Eric Mazumdar. I received my Ph.D. degree in the Department of Computer Science at the University of California, Los Angeles, where I was advised by Prof. Quanquan Gu.

[Fully funded PhD positions available] I am looking for highly motivated and self-driven students with strong mathematical backgrounds and/or programming skills in machine learning to work with me. If you are interested, please fill out this form and send me an email. For students already at Duke, feel free to drop me an email or stop by my office.

• [2022/01] I received the PIMCO Postdoctoral Fellowship in Data Science!
• [2021/06] I received the UCLA Outstanding Graduate Student Research Award!
• [2021/01] I am selected to the inaugural cohort of Rising Stars in Data Science by the Center for Data and Computing at the University of Chicago!
• [2020/09] I am selected as a top reviewer for the International Conference on Machine Learning (ICML) 2020.
• [2020/05] Our forecast on Combating COVID-19 has been adopted in the COVID-19 Forecasts by Centers for Disease Control and Prevention (CDC), California COVID Assessment Tool (CalCat) by California Department of Public Health (CDPH), and the COVID-19 Forecast Hub by the Reich Lab of the University of Massachusetts Amherst.
• [2020/03] We launched the project Combating COVID-19 to model and predict the spread of coronavirus using machine learning empowered epidemic models. Details can be found at covid19.uclaml.org
• [2020/02] One paper (P1) on rank aggregation is selected as Oral Presentation in AAAI 2020!
• [2019/09] I am selected as a top reviewer for the Conference on Advances in Neural Information Processing Systems (NeurIPS) 2019.
• [2019/06] One paper (P1) on reinforcement learning is selected as Oral Presentation in UAI 2019!
• [2019/05] I gave a tutorial on "Nonconvex Optimization for Knowledge Discovery and Data Mining" with Quanquan Gu and Zhaoran Wang in SIAM International Conference on Data Mining (SDM'19). Slides can be found at [Slides]
• [2018/09] Two papers (P1, P2) on nonconvex optimization are selected as Spotlight in NeurIPS 2018!
• [2018/05] One paper (P1) on high-dimensional statistics is selected as Long Oral Talk in ICML 2018!
• [2016/06] I received the Presidential Fellowship in Data Science from the University of Virginia.