Lexin Li

About

Lexin Li, Ph.D., is a Professor of Biostatistics and the Division Chair at the Department of Biostatistics and Epidemiology at the University of California, Berkeley. He also holds appointments in the Department of Statistics and the Helen Wills Neuroscience Institute at UC Berkeley. His research spans a broad range of topics including neuroimaging analysis, brain-computer interfaces, deep and reinforcement learning, functional and point process data analysis, tensor data analysis, and brain network analysis. Dr. Li's work integrates advanced statistical methodologies with applications in neuroscience and precision health, reflecting his interdisciplinary expertise and leadership in computational and theoretical foundations of learning and inference. He is recognized as a Fellow of several prestigious organizations including the American Association for the Advancement of Science, the Institute of Mathematical Statistics, the American Statistical Association, and the Asia-Pacific Artificial Intelligence Association, and is an elected member of the International Statistical Institute. He serves as the Editor-in-Chief of the Annals of Applied Statistics for the term 2025-2027.

Research topics

• Artificial Intelligence
• Computer Science
• Machine Learning
• Mathematics
• Statistics
• Algorithm
• Applied mathematics
• Data Mining
• Discrete mathematics

Selected publications

• Modeling interactive components by coordinate kernel polynomial models

  Mathematical Foundations of Computing · 2020 · 30 citations

  We proposed the use of coordinate kernel polynomials in kernel regression. This new approach, called coordinate kernel polynomial regression, can simultaneously identify active variables and effective interactive components. Reparametrization refinement is found critical to improve the modeling accuracy and prediction power. The post-training component selection allows one to identify effective interactive components. Generalization error bounds are used to explain the effectiveness of the algor…

  DOI

• Partially Observed Dynamic Tensor Response Regression

  Journal of the American Statistical Association · 2021 · 29 citations

  Senior authorCorresponding

  In modern data science, dynamic tensor data prevail in numerous applications. An important task is to characterize the relationship between dynamic tensor datasets and external covariates. However, the tensor data are often only partially observed, rendering many existing methods inapplicable. In this article, we develop a regression model with a partially observed dynamic tensor as the response and external covariates as the predictor. We introduce the low-rankness, sparsity, and fusion structu…

  DOI

• Generalized Connectivity Matrix Response Regression with Applications in Brain Connectivity Studies

  Journal of Computational and Graphical Statistics · 2022 · 21 citations

  Senior authorCorresponding

  Multiple-subject network data are fast emerging in recent years, where a separate connectivity matrix is measured over a common set of nodes for each individual subject, along with subject covariates information. In this article, we propose a new generalized matrix response regression model, where the observed network is treated as a matrix-valued response and the subject covariates as predictors. The new model characterizes the population-level connectivity pattern through a low-rank intercept…

  DOI

• Dynamic noise estimation: A generalized method for modeling noise fluctuations in decision-making

  Journal of Mathematical Psychology · 2024-02-27 · 17 citations

  articleOpen access

  Computational cognitive modeling is an important tool for understanding the processes supporting human and animal decision-making. Choice data in decision-making tasks are inherently noisy, and separating noise from signal can improve the quality of computational modeling. Common approaches to model decision noise often assume constant levels of noise or exploration throughout learning (e.g., the ϵ-softmax policy). However, this assumption is not guaranteed to hold – for example, a subject might…

  PublisherOA PDFDOI

• Image response regression via deep neural networks

  Journal of the Royal Statistical Society Series B (Statistical Methodology) · 2023-07-24 · 9 citations

  articleOpen access

  Delineating associations between images and covariates is a central aim of imaging studies. To tackle this problem, we propose a novel non-parametric approach in the framework of spatially varying coefficient models, where the spatially varying functions are estimated through deep neural networks. Our method incorporates spatial smoothness, handles subject heterogeneity, and provides straightforward interpretations. It is also highly flexible and accurate, making it ideal for capturing complex a…

  PublisherOA PDFDOI

Recent grants

• Sufficient Dimension Reduction for Missing, Censored, and Correlated Data

  NSF · $120k · 2007–2011

• Collaborative Research: Tensor Envelope Model - A New Approach for Regressions with Tensor Data

  NSF · $130k · 2016–2020

• New Dimension Reduction Approaches for Modern Scientific Data with High Dimensionality and Complex Structure

  NSF · $100k · 2011–2014

Frequent coauthors

• Bing Li

  20 shared

• Li Zhu

  University of Chinese Academy of Sciences

  18 shared

• Will Wei Sun

  Purdue University West Lafayette

  17 shared

• Xia Yin

  14 shared

• Chengchun Shi

  London School of Economics and Political Science

  14 shared

• Jian Kang

  University of Michigan–Ann Arbor

  13 shared

• Christopher J. Nachtsheim

  University of Minnesota

  13 shared

• Shuning Wang

  12 shared

Awards & honors

• Fellow of the American Statistical Association (ASA)
• Fellow of the Institute of Mathematical Statistics (IMS)
• Elected Member of the International Statistical Institute (I…
• Editor-in-Chief of the Annals of Applied Statistics for 2025…
• Lexin Li named fellow of American Association for the Advanc…

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