I am a fourth-year Ph.D. candidate in Statistics at Purdue University, where I am fortunate to be coadvised by Prof. Qifan Song and Prof. Jean Honorio. I have obtained an MS degree in Computer Science and Statistics during my Ph.D. study. I received my B.Sc. in Mathematics from Chinese University of Hong Kong, with double stream in Computational Applied Mathematics (CAM) and Enrichment Mathematics. My academic advisor was Prof. Ronald Lok Ming Lui. I was also an exchange student at University of Pennsylvania. I was an intern at Amazon Ads and Sensetime Research.
My research interests include:
- Optimization Theory
- Bandit Algorithms
- Federated Learning
- High Dimensional Statistics
- Deep Learning Theory and Practice
I am actively building and maintaining an open-source Python library for X-armed bandit algorithms and benchmarks. Check out this repository.
- [Jul, 2022]. "Variance Reduction on General Adaptive Mirror Descent" is accepted by the Machine Learning Journal!
- [May, 2022]. "A Simple Unified Framework for High Dimensional Bandit Problems" is accepted by ICML2022!
- [Apr, 2022]. I will be an Applied Scientist Intern at Amazon this summer!
- [Mar, 2022]. I have passed the preliminary exam! Many thanks to my advisors and my committee members.
- [Oct, 2021]. "Optimum-statistical Collaboration Towards Efficient Black-box Optimization" is accepted by the NeurIPS2021 OPT Workshop!
- [Oct, 2020]. "Variance Reduction on Adaptive Mirror Descent" is accepted by the NeurIPS2020 OPT Workshop with Spotlight presentation!
- [Aug, 2020]. I have passed the qualifying exams!