Office Phone: (404) 385-1687
My research lies at the intersection of statistics, optimization, and machine learning. I work on developing computationally efficient and statistically powerful algorithms with guarantees, for engineering problems arising from various real-world applications.
My work has generated societal and policy impact: see here for our work on data-driven policing.
For recent publications and preprints, please see here. My Erdős number is 3.
Graduate course: ISyE 6416, Computational Data Analysis. Spring 2022.
Graduate course: (Online) ISyE/OMSA 6740, Computational Data Analysis. Spring 2022.
I will give a lecture at the IEEE East Asian School of Information Theory 2022 (EASIT) in Summer 2022.
Sept. 2022, I will talk at a mini-symposium on "Geometric distances and robust data analysis" at the SIAM Data Science conference in San Diego.
August 2022, I will talk at JSM, in "The Best of AOAS" Session and "Statistical Advances in Learning Large-scale Networks from Massive Data Sets."
July 2022, I will talk at the ICML Workshop on Distribution-Free Uncertainty Quantification.
May 2022, I will give a talk in an invited session at the 2022 Statistical Methods in Imaging (SMI) conference, in the session "New statistical and machine learning methods for complex imaging data."
May 2022, talk at ITA workshop held by UCSD.
May 2022, talk at Conference in Honor of David Siegmund's Contribution to Statistical Sciences at Stanford University.
April 2022, Congratulations Woody Zhu for defending his Ph.D. thesis! He will join CMU Heinz School of Public Policy as a tenure-track Assistant Professor.
March 10, 2022, I give a keynote talk at Graduate Student Workshop at DIMACS, a NSF Tripod Center at Rutgers University.
March 9, 2022, organize special session at CISS (virtual) on "Recent advances in sequential analysis and hypothesis test".
March 2, 2022, speak at "Online seminar on spatial and spatio-temporal point processes".
I am excited to receive a new NSF grant as PI, in the SCALE MoDL Program "Bridging statistical hypothesis tests and deep learning for reliability and computational efficiency,'' 01/2022-12/2024.
I look forward to participating the newly annouced NSF AI Institute for Advances in Optimization.
At INFORMS 2021, I am organizing a Committee Choice Session "Analytics for policing and urban public service operations" (jointly with He Wang), and "Modern algorithms for threat detection."
Nov. 2021, talk at Monie A. Ferst Award Symposium, in honor of Professor Jeff Wu.
Nov. 2021, I will talk at Cornell ORIE Colloquium.
I serve as an Area Chair for NeurIPS 2021.
September 2021, give a tutorial entitled "Learning point process networks using discrete events data" at the Graduate International Workshop on Intelligent Signal Processing, organized by Zhejiang University.
Aug 2021, talk at IFDS-MADLab Workshop.
July 2021, give a short course on "Introduction to Machine Learning and Statistical Modeling" to Center for Disease Control (CDC), Statistical Advisory Group.
July 2021, talk at Bernoulli-IMS 10th World Congress in Probability and Statistics.
July 2021, talk at Online Seminar on Mathematical Foundations of Data Science.
Feb. 2021, virtual ECE seminar at University of California, Davis.
Feb. 2021, virtual SILO seminar at University of Wisconsin Madison.
Dec. 2020, virtual statistics seminar at University of Pennsylvania, Wharton School.
Nov. 2020, virtual seminar at CMU, Tepper School of Business.
Nov. 2020, "Woody" Shixiang Zhu won the Second Place in the Best Paper Competititon for the 15th INFORMS Workshop on Data Mining and Decision Analytics for paper entitled "Sequential Adversarial Anomaly Detection for Dependent Events". Congratulations!
Nov. 2020, co-found and co-organized the new Machine Learning/AI Cluster at 2020 INFORMS meeting.
Nov. 2020, virtual seminar at University of South Florida.
Oct. 2020, Liyan Xie and Henry Yuchi received the Georgia Tech IDEaS-TRIAD Research Scholarship, for their projects "Distributionally Robust Statistical Tests and Learning" and “Diffusion-based Gaussian Process Dynamic Model for Material Degradation and Prognostic Analysis”. Congratulations!
Oct. 2020, co-organizing a Special Issue at IEEE Journal on Selected Areas in Information Theory, on "Sequential, active, and reinforcement learning". Please see here for call-for-paper.
Oct. 2020, Liyan Xie was selected to participate the "Rising Stars 2020" Workshop. Congratulations!
Oct. 2020, virtual statistics seminar at Duke University.
July 2020, virtual seminar at CDC on "High-resolution spatio-temporal covariates models for county-level COVID-19 data in the US".
July 2020, Alexander Bukharin received the President’s Undergraduate Research Salary Award (PURA) for working on our project: COVID-19 spatial-temporal sequential modeling and prediction.
June 2020, I was selected to join the 2020-2021 Cohort of the Office of Provost's Emerging Leaders Program.
May 2020, give virtual statistics seminar at Rutgers University.
April 2020, Alexander Bukharin received the GT COE Outstanding Undergraduate Research Award for his work with us on Atlanta police districting.
March 2020, I organized a session on "Recent Advances in Sequential Analysis" at CISS, Princeton (canceled due to COVID-19).
I will serve as an Associate Editor for Sequential Analysis, 2020-2022.
Jan. 2020, our group has 3 papers accepted by IEEE ICASSP 2020: "Adversarial anomaly detection for marked spatio-temporal streaming data", "Online community detection by spectral CUSUM", and "Sequential vessel trajectory identification using truncated Viterbi algorithm."