Ruiqi Liu

Ruiqi Liu is an Assistant Professor in the Department of Mathematics and Statistics at Texas Tech University. His research focuses on econometrics, statistical machine learning, and nonparametric methods, with emphasis on developing statistical procedures for real-world applications and foundational theory.

He holds a Ph.D. in Mathematics from SUNY Binghamton (2018), an M.A. in Mathematics from the same institution (2015), a B.S. in Mathematics and a B.B.A. in Management from Sun Yat-sen University (2013/2012). Prior to Texas Tech, he served as a PostDoc in the Department of Mathematical Sciences at Indiana University-Purdue University Indianapolis (2018-2020).

His research spans topics including reinforcement learning, spatial statistics, deep learning applications in statistics, and optimization algorithms. Recent work emphasizes theoretical guarantees for machine learning methods and statistical inference under complex data structures such as panel data and hypergraphs.

Notable contributions include advancements in distributed learning algorithms, nonparametric inference under quantization constraints, and statistical analysis of ODE calibration using neural networks. His work has been published in top journals like the Annals of Statistics, Journal of Econometrics, and Journal of Machine Learning Research.

Liu is affiliated with professional organizations such as the Econometric Society and American Statistical Association. He actively reviews for leading journals including Annals of Statistics and NeurIPS.