I am a Ph.D. candidate in the Department of Statistics and Data Science at Washington University in St. Louis, advised by Profs. Likai Chen and Todd Kuffner. I will be joining the Department of Statistics at the University of Chicago as a William H. Kruskal Instructor this fall.

As of 2023-2024, I will be a visiting student in the Department of Statistics at UChicago, advised by Prof. Wei Biao Wu. Prior to the graduate school, I received my B.S. in Mathematics at Soochow University in 2019. My undergrad thesis was advised by Prof. Rui Ding. I was also fortunate to be a research assistant with Profs. Chen Li and Shuang Zhao at UC Irvine in 2018.

My research interests include statistical learning theory, time series, high-dimensional data, change points, as well as their applications in neuroimaging and brain dynamics.

Here is a copy of my [CV].

Contact: lijiaqi at wustl dot edu [Google Scholar]

News

• Mar 2024: I was invited to visit the Department of Applied Mathematics at University of Twente and the Department of Economics, Econometrics & Finance at University of Groningen to give two seminar talks. Insightful conversations with the nice faculty members there!
• Mar 2024: I will be presenting our work on SGD theory from a nonlinear time series perspective at the 15th Workshop on Stochastic Models, Statistics and Their Applications (SMSA 2024) in TU Delft, Netherlands!
• Feb 2024: Our work on L^2 inference of change points in high-dimensional time series has been accepted to the Annals of Statistics! It's been my first paper during the PhD training! I greatly appreciate all the support from my coauthors Likai, Weining and Wei Biao, and the insightful feedback from the co-editor, AE and referees!
• Jan 2024: Our new paper on probablistic bounds for Stochastic Recursive Gradient in non-convex problems has been accepted to PAKDD 2024! A dimension-free Azuma-Hoeffding type inequality is introduced!
• Nov 2023: We introduce time series to machine learning community by conceptualizing the evolution of stochastic gradient descent (SGD) as an iterated random function and establishing its convergence in Euclidean distance! See this preprint for exciting new findings in SGD!
• Oct 2023: Our new paper on network-level enrichment for biological interpretations of machine learning models is now on bioRxiv! A useful guide for neuroscientists who are interested in machine learning algorithms.
• Oct 2023: I will be presenting our work on Asymptotic Theory of Constant Step Size SGD (on behalf of Wei Biao) at the Big Data and Machine Learning in Econometrics, Finance, and Statistics in Chicago, USA!
• Sep 2023: Our Github repo 'MachineLearning_NetworkLevelAnalysis' on machine learning pipelines for functional brain connectivity analysis is now public! First time provide biological interpretation for ML results by the network-level enrichment. Have a try!
• Aug 2023: I will be chairing an IMS-sponsored session and presenting our new work on Adaptive MOSUM: Inference for Multiscale Change Points in High-Dimensional Time Series at The Joint Statistical Meetings (JSM) in Toronto, Canada!
• Jul 2023: Our new R package 'L2hdchange' for high-dimensional change-point testing and estimation is now available on CRAN! Interesting examples such as COVID-19 outbreaks are included. Check them out!
• Jul 2023: I will be presenting our work on Asymptotic Theory of Constant Step Size Stochastic Gradient Descent at the Data Science and Dependence 2023 Conference in Heidelberg, Germany!
• Jan 2023: I will be visiting the Department of Statistics at the University of Chicago for 2023-2024 academic year!
• Dec 2022: I will be presenting our paper on L^2 inference of change points in high-dimensional time series at the 15th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2022) remotely!
• Dec 2022: Our new work on simultaneous inference of trends in partially linear time series is now on arXiv!
• Oct 2022: I will be chairing a session at Women in Statistics and Data Science Conference on Oct 8, 2022!
• Oct 2022: A new paper on change-point detection in high-dimensional time series with factor strutures has been submitted to ICASSP!
• Aug 2022: I will be presenting our paper on L^2 inference of change points in high-dimensional time series at the The Joint Statistical Meetings (JSM) in Washington, DC!
• Aug 2022: Our new work on L^2 inference of change points in high-dimensional time series is now on arXiv!
• Jun 2022: I will be presenting a poster on the network level analysis for biological interpretation of machine learning results at the Annual Meeting of The Organization for Human Brain Mapping (OHBM) in Scotland!
• May 2022: I will be attending the Mallinckrodt Institute of Radiology (MIR) Research Symposium in St. Louis!
• Feb 2021: I have been awarded the Best Talk in the Workshop: Machine Learning Tools Applied on Omics in Neuroscience organized by WashU!