News

Assistant Professor Ziyi Li is PI (MPI: Jing Ning, Ph.D., and Co-I: Yu Shen, Ph.D.) for NIH/NCI grant R21CA312466, Addressing Unmeasured Covariates in Source Cohorts with Transfer Learning for Survival Outcomes. This project will develop novel transfer-learning-based statistical methods to address unmeasured covariates and missing data challenges, with the goal of improving risk estimation and survival inference for patients with rare cancers.

Associate Professor Jian Wang is Biostatistics Core Leader for CPRIT (RP260740) AYA LASSO: A statewide platform to assess and improve long-term health outcomes in adolescents and young adults (AYA) cancer survivors (PI Mike Roth). AYA LASSO aims to establish the nation’s largest and most comprehensive AYA survivorship research platform to identify risk factors for poor long-term health outcomes, develop interventions to improve survivorship outcomes, and ultimately reduce long-term morbidity and mortality among AYA cancer survivors.

Professor Liang Li received a U01 grant from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) for the Chronic Pancreatitis Clinical Research Consortium (CPCRC) Data Coordinating Center (CPCRC-DCC), for which he is PI and Associate Professor Suyu Liu is Co-PI. CPCRC was established to undertake a comprehensive clinical, epidemiological and biological characterization of patients with chronic pancreatitis, including those with acute recurrent pancreatitis.

Professor Yisheng Li is director of the Biostatistics and Bioinformatics Core for the Sarcoma SPORE (PI: Richard Gorlick). The goal of this grant is to reduce sarcoma morbidity and mortality through innovative translational research focused on immunologic and targeted treatments.

Assistant Professor Ziyi Li is the PI of R35 grant, focused on developing statistical methods to delineate spatial and temporal patterns of cell-cell interactions in spatial transcriptomics data.

Associate Professor Christine Peterson is director of the Biostatistics Core for the new P01 titled InHANCE: Imaging Innovation for Head and Neck Cancer Evaluation & Treatment Delivery. The goal of this grant is to leverage advanced imaging and predictive modeling to better detect, prevent and manage normal tissue injuries caused by radiotherapy.

Professor Peng Wei is Biostatistics and Bioinformatics Core Director for a P01 titled Mechanisms of Bladder Cancer Development and its Therapeutic Vulnerabilities to Preventive and Interventive Therapy (PI: Bogdan Czerniak, UT MD Anderson Pathology; David McConkey, Johns Hopkins University).

Associate Professor Ruitao Lin is PI on an NIH/NLM R21 grant (1R21LM014699) to develop innovative design strategies to further advance platform trials that can handle multiple arms with multiple doses and multiple interim decision making. 

Assistant Professor Lulu Shang is PI on the awarded institutional research grant to develop computational strategies and tools to integrate multi-dimensional spatial multi-omics data to elucidate the mechanisms of cancer progression and therapeutic response. 

Professor Yisheng Li is dore director of the Biostatistics and Bioinformatics Core, which recently gained the CPRIT-MIRA (RP240440) grant, Novel Therapies for Osteosarcoma. The goal of this grant is to validate the proposed research across all projects and cores. This includes development of a database that integrates the results from all POSRP investigations (across projects), institutional databases, public “-omics” and drug databases for biomarker discovery and validation.

NCI funding was renewed for the UT MD Anderson Brain Cancer SPORE for five more years. Ying Yuan and James Long serve as director and co-director of the Biostatistics and Bioinformatics Core, and Ziyi Li serves as co-investigator. The objective of the SPORE is to improve outcomes for brain cancer patients.

Dr. Mien-Chie Hung and Mrs. Kinglan Hung Professor Xuelin Huang (PI) and department chair ad interim and Conversation with a Living Legend Professor Yu Shen (co-I, among others) were awarded an NIH/NCI 1 R01 (CA272806-01A1) titled "Optimizing treatment decision by accounting for longitudinal biomarker trajectories and competing risks of each individual."

Professor Peng Wei was awarded a grant from the Cancer Prevention and Research Institute of Texas (CPRIT) in support of cancer research, titled "Integrative modeling of spatially resolved multi-omics data to identify bladder cancer mucosal field effects."

Professors Ying Yuan and Liang Li are co-PIs and Assistant Professor Ziyi Li is co-I on an NCI U24 grant for a Coordinating and Data Management Center (CDMC) for the Translational and Basic Science Research in Early Lesions (TBEL) Program. This program aims to integrate basic and translational cancer research studies to understand biological precancer and early cancer drivers/restraints and facilitate biology-based precision prevention approaches. This new grant expands the ongoing NIH-funded CDMC for the Consortium for the Study of Chronic Pancreatitis, Diabetes, and Pancreatic Cancer, also managed by Drs. Yuan and Li.

Assistant Professor Ziyi Li is PI on an R03 award from NIH/NCI (1R03CA270725) titled "Statistical Models for Intratumor Heterogeneity of Tumor-infiltrated Leukocytes in Lung Cancer."

Associate Professor Jing Ning and department chair ad interim and Conversation with a Living Legend Professor Yu Shen (MPI) were awarded an R01 grant from NCI (CA269696-01) titled "Statistical Methods for Integration of Multiple Data Sources toward Precision Cancer Medicine."

Assistant Professor Christine Peterson is PI for the NIH/NHLBI R01 grant titled "New Data Science Approaches to Visualize and Understand the Impact of the Microbiome on Risk of Graft-versus-host Disease."

Associate Professor Suyu Liu received an R01 grant (2R01CA160254-10) subaward via the University of Michigan titled "Serum Glyco-Markers of Early Hepatocellular Carcinoma Using a Mass Spec Approach," for which she is PI.

Professor Peter Thall and Assistant Professor Ruitao Lin (MPI) were awarded an R01 grant (1R01CA261978-01) titled "Bayesian Methods for Complex Precision Biotherapy Trials in Oncology."

Professor Liang Li received a CPRIT grant for Data Management and Analysis Core for Comparative Effectiveness Research on Cancer in Texas, for which he is Co-PI.

Assistant Professor Christine Peterson received the National Science Foundation Division of Mathematical Sciences DMS- 2113602 and DMS- 2113557 grants for Collaborative Research: Covariate-driven Approaches to Network Estimation, for which she is Co-PI.

Professor J. Jack Lee will serve as the core director for the UT MD Anderson Cancer Center SPORE in Melanoma, Core 3 Biostatistics and Bioinformatics. The goal is to provide comprehensive service to guide experiment design to optimize quantitative data analysis, while maintaining statistical justification and results interpretation through sound experimental design principles tailored to each project. This will include data analyses and contributions to the interpretation of results via written reports and interactions with investigators.

The Department at JSM 2026

Biostatistics participated in the Joint Statistical Meetings (JSM) 2026 event August 1–6 with roles presenting research, leading sessions and more. Our presence included nine contributed papers, two invited paper sessions, four contributed posters and 11 topic-contributed paper sessions. Read more on the online JSM program.

JSM Invited Sessions

• Jing Ning, Ph.D., A Likelihood-Based Framework for Adaptive Integration of External Aggregate Data in Survival Analysis
• Ziyi Li, Ph.D., Accommodating time-varying heterogeneity in risk estimation under the Cox model: a transfer learning approach

JSM Topic Contributed Sessions

• J. Jack Lee, Ph.D., Generalized Bayesian Optimal Phase II (G-BOP2) Multi-stage Designs with Particle Swarm Optimization
• Chenqi Fu and J. Jack Lee, Ph.D., Next Generation Model-Assisted Phase I Clinical Trials with Global Optimality
• Yisheng Li, Ph.D., A Bayesian semi-mechanistic dose-finding design for phase I oncology trials with multiple schedules
• Liang Li, Ph.D., Dynamic prediction of terminal clinical events using longitudinal continuous, categorical and recurrent event data 
• Ruitao Lin, Ph.D., Novel adaptive factorial designs for evaluating contributions of components for combination therapies
• Yu Shen, Ph.D., and Jing Ning, Ph.D., Modeling Disease-specific Survival in Observational Studies with Missing Cause of Death: Leveraging Information from Clinical Trial Data
• Satabdi Saha, Ph.D., Bayesian sparse regression for microbiome–metabolite data integration
• Lulu Shang, Ph.D., Prototype-driven fusion of pathology and spatial transcriptomics for interpretable survival prediction
• Can Xie, Ph.D., and Xuelin Huang, Ph.D., Flexible hazards and cure models for dynamic prediction based on longitudinal biomarker measurements
• Ying Yuan, Ph.D., Dose optimization strategies and practice based on the industry survey and dose optimization whitepaper from a KOL panel discussion
• Ying Yuan, Ph.D., Seamless Phase II/III Design: A Useful Strategy to Reduce the Sample Size for Dose Optimization
  

JSM Topic Contributed Sessions

• Muxuan Liang, Ph.D., A General Framework for Incorporating Identification Uncertainty in Individualized Treatment Rules
• Peng Yang, Ph.D.; Ruitao Lin, Ph.D.; Ying Yuan, Ph.D.; Suyu Liu, Ph.D., A Robust Bayesian Approach to Estimating Treatment Effects in Partially Decentralized Clinical Trial
• Suyu Liu, Ph.D., A Phase I Dose-finding Design Incorporating Intra-patient Dose Escalation
• Yung-Han Chang and Ryan Sun, Ph.D., Configuration-based Bayesian Mediation Model for Survival Outcomes
• Junhyoun Sung and Peng Wei, Ph.D., Imputing Proteomic Profiles from Gene Expression and Genetic Data for R²-Based Mediation Analysis
• Xingyu Li, Ph.D., and Peng Wei, Ph.D., M-high-learner: High-Dimensional Heterogeneous Mediation via M-Learner in Genetic Epidemiology
• Xuelin Huang, Ph.D., and Ziyi Li, Ph.D., SurvFM: Adapting Tabular Foundation Model for Survival Prediction
• Satabdi Saha, Ph.D., Graph-Adaptive Shrinkage for Compositional Regression
• Chong Wu, Ph.D., Semantic reasoning enables leakage-resistant forecasting of target–indication clinical development success

JSM Chairs / Speakers / Discussants / Courses

• Kim-Anh Do, Ph.D., Statistical Composition and Its Implications in Omics Research (chair)
• J Jack Lee, Ph.D., Recent Advances in AI-Assisted Methods and Software for Modern Biopharmaceutical Development (discussant)
• Ruitao Lin, Ph.D., Bayesian Methods in Clinical Trials After the FDA Draft Guidance: When, How, and How Much? (roundtable). ASA BIOP Student Paper Competition Awards Session (organizer)
• Suprateek Kundu, Ph.D., ASA Statistics in Imaging Section Student Paper Competition (organizer)
• Ying Yuan, Ph.D., Integrating biomarker discovery and dose-optimization to drive drug development success (organizer); Pharmacometrically Based Dose-Optimization Designs: Statistical Innovations for Modern Drug Development (discussant)

JSM Posters

• Li-Ting Ku; Ying Yuan, Ph.D.; Liang Li, Ph.D.; Ziyi Li, Ph.D., Accounting for Cellular Mixture in Spatially Aware Cell-Cell Interaction Analysis
• Jian Wang, Ph.D., and Jing Ning, Ph.D., The Bayesian Monitoring Using the Win Ratio for Phase II Trials with Multiple Time-to-Event Endpoint
• Shun Rao, Indication-specific Bayesian Hierarchical Model to Predict Long-term Endpoints in Oncology Trials
• Chenxuan Zang, Ph.D.; Ziyi Li, Ph.D.; Peng Wei, Ph.D., Copy Number Variation and Tumor Subclone Analysis for High-Resolution Spatial Transcriptomics
  

The Department at ENAR 2026

Biostatistics faculty, analysts, postdoctoral fellows and graduate researchers organized sessions, gave presentations and/or had coauthored work presented at the ENAR 2026 Spring Meeting, March 15–18. 

UT MD Anderson Biostatistics talks at the ENAR 2026 Spring Meeting included

• Embracing Next-Generation Clinical Trials: A Patient-Centric Approach, discussant J. Jack Lee, Ph.D.
• Recent Methodological Advances in Spatial Omics in an AI-Augmented Era, speaker, Lulu Shang, Ph.D.
• Using AI Generated Predictions for Statistical Inference, chaired by Muxuan Liang, Ph.D.

Read more in the ENAR Program.

Suprateek Kundu, Ph.D., was elected Program Chair-Elect 2026 for the ASA Statistics in Imaging Section

The American Statistical Association (ASA) Statistics in Imaging Section promotes statistics and statisticians’ work in all areas of the imaging sciences.

Chair ad interim elected Mathematical Statistics fellow

Yu Shen, Ph.D., was granted the Institute of Mathematical Statistics’ Fellowship in April 2024. This fellowship honors her novel contributions to the methodology of complex survival data analysis, adaptive clinical trial designs and cancer screening data modeling, as well as substantial collaborations impacting the practice of medicine and public health recommendations.

Two faculty elected AAAS fellows

J. Jack Lee, Ph.D., and Liang Li, Ph.D., were elected as fellows in the American Association for the Advancement of Science (AAAS). A process of selection by peers in the organization since 1874, this honor recognizes invaluable contributions to science and technology.

Two biostatistics faculty are fellows of the Society for Clinical Trials

Professors Peter F. Thall (2014) and J. Jack Lee (2017) are Fellows of the Society for Clinical Trials. This fellowship honors society members who have made significant contributions to the advancement of clinical trials and to the society.

Nine biostatistics faculty are American Statistical Association fellows

Nine of the department's faculty members have attained the prestigious honor of being named a Fellow in the American Statistical Association (ASA): Donald A. Berry (1986), Kim-Anh Do (2006), Yu Shen (2007), J. Jack Lee (2008), Sanjay Shete (2012), Peter F. Thall (2015), Xuelin Huang (2017), Ying Yuan (2017), Peng Wei (2022) and Jing Ning (2023). This honor recognizes their outstanding contributions to the profession of statistics and as members of ASA.