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CV: See here. It is being updated regularly.

Research Interest: Distribution-free Uncertainty Quantification, Spatial-temporal Data Modeling, Optimization, Anomaly Detection, Change Detection.

Introduction: I am a 2nd-year Operations Research (OR) Ph.D. student in Georgia Tech ISyE, advised by Professor Yao Xie. I am particularly interested in modeling data with complex spatio-temporal dependency through statistical and optimization techniques and subsequently, provide uncertainty quantification for the estimates with provable guarantees. Please see my Google Scholar page for a list of works. Feel free to contact me at if you share similar research interests and/or find my work interesting and inspiring.

Before my PhD, I received my joint BS/MS degrees during 2016-2020 from the University of Chicago. My MS Thesis advisor is Professor Rina Foygel Barber and my MS degree is in Statistics. My BS degrees are Computational and Applied Mathematics (CAM) and Economics (specialized in data science).

Hobby: weightlifting, basketball, reading.


  • Septembter 2021: Passed the Ph.D. Comprehensive Exam in Operations Research.
  • August 2021: To work as a part-time intern for Healthcare Xplorers @ Roche Diagnostics on “Develop machine learning-based methods for early anomaly detection in diagnostics instrument and assay data”. The position starts in early 2022.
  • July 2021: To work as a full-time intern in Fall 2021 for the Energy Systems @ Argonne National Laboratory and I am supervised by Dr. Feng Qiu. The job involves building statistical models for assessing wildfire risks and subsequence consequences and eventually leads to journal paper publication.
  • June 2021: Conformal Anomaly Detection on Spatio-Temporal Observations with Missing Data is accepted by ICML 2021 workshop on Distribution-free Uncertainty Quantification.
  • May 2021: Conformal Prediction Interval for Dynamic Time-Series is accepted by ICML 2021 as a long talk/oral presentation (3% of all submissions).
  • May 2021: Become a TA for Prof. Yao Xie’s OMSA ISyE 6740: Computational Data Analysis during Summer 2021, a highly well-regarded machine learning course by past students.
  • April 2021: Invited to be a Presenter in the INFORMS 2021 Annual Meeting, General Session entitled Adaptive online learning of high-dimensional data in October 2021. I will talk about Online Prediction For High-dimensional Discrete Event Data and provide slides after the presentation.
  • Sep 2020: Predictive inference is free with the jackknife+-after-bootstrap is accepted by NeurIPS 2020 as a poster presentation.
  • June 2020: Graduated from the University of Chicago as a joint BS/MS student.
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