About

Bin Nan is a Chancellor’s Professor of Statistics at UC Irvine. His research interests encompass various areas of statistics and biostatistics, including semiparametric inference, failure time and survival analysis, longitudinal data, missing data, two-phase sampling designs, high-dimensional data analysis, and machine learning methodology. He collaborates on projects in epidemiology, bioinformatics, and brain imaging, with a focus on identifying functional connectivity in the brain to understand how different regions interact, which may provide insights into brain function. His research activities are supported by grants from the National Science Foundation and the National Institutes of Health, primarily motivated by biomedical research collaborations. Currently, he is developing new methods and theories in survival time prediction, high-dimensional statistical inference, brain imaging data analysis, longitudinal data analysis, and regression with covariates subject to detection limits. A key goal of his work is to improve human health by developing statistical and machine learning methods to evaluate risk factors, biomarkers, improve diagnosis, and find cures for human diseases, with a particular focus on Alzheimer’s disease research. He collaborates closely with the UCI Alzheimer’s Disease Research Center and the UCI Center for the Neurobiology of Learning and Memory to identify biomarkers for earlier diagnosis.

Research topics

• Artificial Intelligence
• Internal medicine
• Computer Science
• Medicine
• Medical emergency
• Algorithm
• Economic growth
• Endocrinology
• Virology
• Applied mathematics
• Emergency medicine
• Econometrics
• Mathematics
• Statistics
• Pharmacology

Selected publications

• Optimal multiple-impulse fixed-time rendezvous via Newton’s method with analytical Hessian

  Advances in Space Research · 2026-04-01

  article
  PublisherDOI

• Evaluating and Correcting Human Annotation Bias in Dynamic Micro-Expression Recognition

  IEEE Transactions on Affective Computing · 2026-01-01

  article

  Existing manual labeling of micro-expressions is subject to errors in accuracy, especially in cross-cultural scenarios where deviation in labeling of key frames is more prominent. To address this issue, this paper presents a novel Global Anti-Monotonic Differential Selection Strategy (GAMDSS) architecture for enhancing the effectiveness of spatio-temporal modeling of micro-expressions through keyframe re-selection. Specifically, the method identifies Onset and Apex frames, which are characterized by significant micro-expression variation, from complete micro-expression action sequences via a dynamic frame reselection mechanism. It then uses these to determine Offset frames and construct a rich spatio-temporal dynamic representation. A two-branch structure with shared parameters is then used to efficiently extract spatio-temporal features. Extensive experiments are conducted on seven widely recognized micro-expression datasets. The results demonstrate that GAMDSS effectively reduces subjective errors caused by human factors in multicultural datasets such as SAMM and 4DME. Furthermore, quantitative analyses confirm that offset-frame annotations in multicultural datasets are more uncertain, providing theoretical justification for standardizing micro-expression annotations. These findings directly support our argument for reconsidering the validity and generalizability of dataset annotation paradigms. Notably, this design can be integrated into existing models without increasing the number of parameters, offering a new approach to enhancing micro-expression recognition performance.

  PublisherDOI

• Temporal–spatial cross-fusion for dynamic micro expression recognition

  Pattern Recognition · 2026-04-13

  article
  PublisherDOI

• Fast Symbolic Computation of Koopman Operator Approximation via Galerkin Method

  IFAC-PapersOnLine · 2025-01-01

  articleOpen access1st authorCorresponding

  The Koopman operator (KO) enables the projection of nonlinear dynamics into a high-dimensional linear system, making it a promising and emerging paradigm in the nonlinear systems research. The Galerkin approximation of the KO allows for large time-step state transitions, offering the potential to replace numerical integration methods for nonlinear extrapolation, and holds significant value for online applications involving nonlinear systems. The conventional Galerkin method often resorts to inefficient element-wise traversal for computation. Moreover, the computation of multiple integrals involves complex and cumbersome symbolic operations. These limitations make it difficult for the conventional Galerkin method to be applied to high-dimensional systems in KO approximations. This paper proposes an efficient algorithm for symbolic computation of KO approximation. By decomposing the symbolic dynamics, integrals are rewritten in a vector-input form, which significantly improves the efficiency of symbolic computation. Additionally, matrices are represented as Hadamard products of definite integrals, which enables symbolic computations of multiple integrals in high-dimensional systems. Finally, symbolic computation simulations of the KO approximation are conducted for the Duffing oscillator and attitude dynamics. The feasibility and computational efficiency of the proposed method were verified and analyzed.

  PublisherDOI

• A Visual Self-attention Mechanism Facial Expression Recognition Network beyond Convnext

  ArXiv.org · 2025-04-12 · 1 citations

  preprintOpen access1st authorCorresponding

  Facial expression recognition is an important research direction in the field of artificial intelligence. Although new breakthroughs have been made in recent years, the uneven distribution of datasets and the similarity between different categories of facial expressions, as well as the differences within the same category among different subjects, remain challenges. This paper proposes a visual facial expression signal feature processing network based on truncated ConvNeXt approach(Conv-cut), to improve the accuracy of FER under challenging conditions. The network uses a truncated ConvNeXt-Base as the feature extractor, and then we designed a Detail Extraction Block to extract detailed features, and introduced a Self-Attention mechanism to enable the network to learn the extracted features more effectively. To evaluate the proposed Conv-cut approach, we conducted experiments on the RAF-DB and FERPlus datasets, and the results show that our model has achieved state-of-the-art performance. Our code could be accessed at Github.

  PublisherOA PDFDOI

• Nonparametric Estimation of Conditional Survival Function with Time-Varying Covariates Using DeepONet

  ArXiv.org · 2025-05-28

  preprintOpen accessSenior author

  Traditional survival models often rely on restrictive assumptions such as proportional hazards or instantaneous effects of time-varying covariates on the hazard function, which limit their applicability in real-world settings. We consider the nonparametric estimation of the conditional survival function, which leverages the flexibility of neural networks to capture the complex, potentially long-term non-instantaneous effects of time-varying covariates. In this work, we use Deep Operator Networks (DeepONet), a deep learning architecture designed for operator learning, to model the arbitrary effects of both time-varying and time-invariant covariates. Specifically, our method relaxes commonly used assumptions in hazard regressions by modeling the conditional hazard function as an unknown nonlinear operator of entire histories of time-varying covariates. The estimation is based on a loss function constructed from the nonparametric full likelihood for censored survival data. Simulation studies demonstrate that our method performs well, whereas the Cox model yields biased results when the assumption of instantaneous time-varying covariate effects is violated. We further illustrate its utility with the ADNI data, for which it yields a lower integrated Brier score than the Cox model.

  PublisherOA PDFDOI

• A Robust Error-Resistant View Selection Method for 3D Reconstruction

  arXiv (Cornell University) · 2024-02-18

  preprintOpen access

  To address the issue of increased triangulation uncertainty caused by selecting views with small camera baselines in Structure from Motion (SFM) view selection, this paper proposes a robust error-resistant view selection method. The method utilizes a triangulation-based computation to obtain an error-resistant model, which is then used to construct an error-resistant matrix. The sorting results of each row in the error-resistant matrix determine the candidate view set for each view. By traversing the candidate view sets of all views and completing the missing views based on the error-resistant matrix, the integrity of 3D reconstruction is ensured. Experimental comparisons between this method and the exhaustive method with the highest accuracy in the COLMAP program are conducted in terms of average reprojection error and absolute trajectory error in the reconstruction results. The proposed method demonstrates an average reduction of 29.40% in reprojection error accuracy and 5.07% in absolute trajectory error on the TUM dataset and DTU dataset.

  PublisherOA PDFDOI

• Debiased lasso after sample splitting for estimation and inference in high‐dimensional generalized linear models

  Canadian Journal of Statistics · 2024-08-21 · 2 citations

  articleOpen accessSenior authorCorresponding

  We consider random sample splitting for estimation and inference in high dimensional generalized linear models, where we first apply the lasso to select a submodel using one subsample and then apply the debiased lasso to fit the selected model using the remaining subsample. We show that a sample splitting procedure based on the debiased lasso yields asymptotically normal estimates under mild conditions and that multiple splitting can address the loss of efficiency. Our simulation results indicate that using the debiased lasso instead of the standard maximum likelihood method in the estimation stage can vastly reduce the bias and variance of the resulting estimates. Furthermore, our multiple splitting debiased lasso method has better numerical performance than some existing methods for high dimensional generalized linear models proposed in the recent literature. We illustrate the proposed multiple splitting method with an analysis of the smoking data of the Mid-South Tobacco Case-Control Study.

  PublisherOA PDFDOI

• Bivariate functional patterns of lifetime medicare costs among ESRD patients

  The Annals of Applied Statistics · 2024-08-06

  articleOpen access

  In this work we study the lifetime Medicare spending patterns of patients with end-stage renal disease (ESRD). We extract the information of patients who started their ESRD services in 2007-2011 from the United States Renal Data System (USRDS). Patients are partitioned into three groups based on their kidney transplant status: 1-unwaitlisted and never transplanted, 2-waitlisted but never transplanted, and 3-waitlisted and then transplanted. To study their Medicare cost trajectories, we use a semiparametric regression model with both fixed and bivariate time-varying coefficients to compare groups 1 and 2, and a bivariate time-varying coefficient model with different starting times (time since the first ESRD service and time since the kidney transplant) to compare groups 2 and 3. In addition to demographics and other medical conditions, these regression models are conditional on the survival time, which ideally depict the lifetime Medicare spending patterns. For estimation, we extend the profile weighted least squares (PWLS) estimator to longitudinal data for the first comparison and propose a two-stage estimating method for the second comparison. We use sandwich variance estimators to construct confidence intervals and validate inference procedures through simulations. Our analysis of the Medicare claims data reveals that waitlisting is associated with a lower daily medical cost at the beginning of ESRD service among waitlisted patients which gradually increases over time. Averaging over lifespan, however, there is no difference between waitlisted and unwaitlisted groups. A kidney transplant, on the other hand, reduces the medical cost significantly after an initial spike.

  PublisherOA PDFDOI

• Kernel Estimation of Bivariate Time-Varying Coefficient Model for Longitudinal Data with Terminal Event

  Figshare · 2024-01-01

  datasetOpen access

  We propose a nonparametric bivariate time-varying coefficient model for longitudinal measurements with the occurrence of a terminal event that is subject to right censoring. The time-varying coefficients capture the longitudinal trajectories of covariate effects along with both the followup time and the residual lifetime. The proposed model extends the parametric conditional approach given terminal event time in recent literature, and thus avoids potential model misspecification. We consider a kernel smoothing method for estimating regression coefficients in our model and use cross-validation for bandwidth selection, applying undersmoothing in the final analysis to eliminate the asymptotic bias of the kernel estimator. We show that the kernel estimates follow a finite-dimensional normal distribution asymptotically under mild regularity conditions, and provide an easily computed sandwich covariance matrix estimator. We conduct extensive simulations that show desirable performance of the proposed approach, and apply the method to analyzing the medical cost data for patients with end-stage renal disease. Supplementary materials for this article are available online.

  PublisherDOI

Recent grants

• Cutting Edge Survival Methods for Epidemiological Data

  NIH · $1.3M · 2018–2023

• High-Dimensional Inference beyond Linear Models

  NSF · $200k · 2019–2022

• Statistical Methods for Alzheimer's Research

  NIH · $1.7M · 2022–2027

• Estimation Theory for Semiparametric Models with Bundled Parameters

  NSF · $200k · 2010–2013

• Emerging Issues in Modeling Longitudinal Observations with Censoring

  NSF · $107k · 2017–2018

Frequent coauthors

• Yue Wang

  85 shared

• Joshua D. Stein

  University of Michigan–Ann Arbor

  61 shared

• Norman E. Breslow

  University of Washington

  52 shared

• Daniel M. Green

  St. Jude Children's Research Hospital

  51 shared

• David Childers

  39 shared

• Shahzad I. Mian

  37 shared

• John A. Kalapurakal

  Northwestern Medicine

  26 shared

• Giulio J. D’Angio

  University of Pennsylvania

  26 shared

Awards & honors

• Professors Nan and Gillen Receive $1.8M Grant to Study Stati…