People
Steering Committee
Ying Wei, PhD
- Director
- Professor of Biostatistics
Dr. Ying Wei serves as the director of TRAIL. Dr. Wei is a Professor of Biostatistics and Vice Chair of Research in the Department of Biostatistics at Columbia University. She is an elected Fellow of the American Statistical Association, Institute of Mathematical Statistics, and International Statistical Institute. As the Director of the TRAIL4Health at the Mailman School of Public Health, Dr. Wei leads efforts to bridge cutting-edge artificial intelligence methodologies with their applications in public health, emphasizing on robust, scalable, and inferential AI solutions. Her methodological expertise in quantile regression and tools to address challenges like missing data, measurement errors, and high-dimensional confounders ensures that large-scale and multi-modal observational datasets are leveraged effectively for accurate and reproducible applications in public health and medicine.
Tian Gu, PhD
- Assistant Professor of Biostatistics
Dr. Tian Gu is an Assistant Professor of Biostatistics at Columbia Mailman School of Public Health. She specializes in developing statistical and machine learning methods to support precision medicine and enhance patient outcomes. Her work includes data integration using advanced technologies like transfer and federated learning, and addressing health disparities by utilizing multi-center and EHR-linked biobank data to improve disease prediction and diagnosis in underrepresented populations. The software and methods she developed support studies in various health fields, such as infectious disease epidemiology, pulmonary diseases, cardiovascular diseases, and cancer.
Zhonghua Liu, ScD
- Assistant Professor of Biostatistics
Dr. Zhonghua Liu’s main research interests lie in the intersections of causal inference, deep learning and genetics/genomics with applications to emerging challenges in public health and medicine.
Linda Valeri, PhD
- Associate Professor of Biostatistics
Linda Valeri is an expert biostatistician specializing in causal inference, with a focus on biostatistical methodology and statistical learning. Her research encompasses causal mediation analysis, measurement error, missing data, and the integration of data from multiple sources, such as smartphone and wearable devices, life-course cohort studies, and electronic medical records, in diverse populations. Dr. Valeri has developed widely utilized open-access computational tools for causal inference, benefiting scientists across biomedical and social sciences. She collaborates with interdisciplinary teams to advance our understanding of mental health across the life-course, environmental determinants of health, and health disparities, contributing to informed policy-making.She completed a PhD in Biostatistics from Harvard University. Dr. Valeri is a K01 awardee from the National Institute of Mental Health and an R01 awardee from the National Institute of Aging.
Senior Advisor
Jeff Goldsmith, PhD
- Associate Dean for Data Science
- Associate Professor of Biostatistics
For several years, Jeff Goldsmith has worked to advance the state-of-the-art in functional data analysis by developing methods for understanding patterns in large, complex datasets in neuroscience, physical activity monitoring, and other areas.
Working closely with clinicians and neuroscientists around the world, he and his collaborators have focused on improving the understanding skilled movements. This work involves reaching movements made by stroke patients: in these experiments, a patient's fingertip position is recorded hundreds of time per second for the duration of the reach. Dr. Goldsmith has developed new statistical methods to understand the impact of stroke on movement quality, and applied these to large, longitudinal datasets. In parallel, he has proposed methods for wearable device research, especially focusing on accelerometers. These devices can produce minute-by-minute (or even finer) resolution observations of activity for hundreds of participants over several days, weeks, or months. The methods developed include approaches for regression with activity trajectories as outcomes; for interpretable dimension reduction; and for aligning major patterns (like wake from sleep, mid-day dips in activity, and sleep onset) across subjects.
Dr. Goldsmith has worked to incorporate data science techniques for transparency and reproducibility into biostatistical analyses. Research projects are accompanied by robust, publicly available software and analytical pipelines that ensure the reproducibility of the results. This approach is informed by his work in teaching data science.
Faculty Investigators
Daniel Malinsky, PhD
- Assistant Professor of Biostatistics
Daniel Malinsky's methodological research focuses mostly on causal inference: developing statistical methods and machine learning tools to support inference about the consequences of (e.g.) medical decisions, environmental & social exposures, and policies. Current research topics include graphical structure learning (a.k.a. causal discovery or causal model selection), mediation analysis, semiparametric inference, time series analysis, and missing data. Application areas of particular interest include environmental determinants of health (especially air pollution) and health disparities. Dr. Malinsky also studies algorithmic fairness: understanding and counteracting the biases introduced by data science tools deployed in socially-impactful settings. Finally, Dr. Malinsky has interests in the philosophy of science and the foundations of statistics.
Yuan Zhang, PhD
- Assistant Professor of Sociomedical Sciences (in the Robert N. Butler Columbia Aging Center)
Yuan Zhang’s research examines how social factors across different life stages influence aging-related outcomes, with an emphasis on populations in less economically developed countries. She also studies population health trends to uncover how disease burden, dementia, and mortality are unfolding in the population, and how they are linked to other structural changes such as increasing education.
![Profile Headshot]( "Yuan Zhang")
Kaizheng Wang, PhD
- Assistant Professor of Industrial Engineering and Operations Research
Kaizheng Wang works at the intersection of optimization, machine learning, and statistics. He develops and studies scalable algorithms for analyzing massive data that are unstructured, incomplete, and heterogeneous. The methods have wide applications in revenue management, signal processing, distributed computing, etc.
A main focus of Wang’s research is data integration for learning and decision-making. This is a methodology for solving new tasks based on limited direct information and rich auxiliary data from other sources. Their unknown relevance and reliability, distributed storage, and data privacy requirements pose significant challenges. Wang leverages cutting-edge tools in optimization, statistics, and related fields to design principled approaches that faithfully output high-quality solutions.
Orson Xu, PhD
- Assistant Professor, Biomedical Informatics
Xuhai “Orson” Xu is an assistant professor at Columbia University, Department of Biomedical Informatics, and a visiting faculty researcher at Google. He received his PhD at the University of Washington in 2023 and was a postdoc at MIT until 2024. Specializing in human-computer interaction, applied machine learning, and health, Xu develops deployable behavior modeling algorithms to monitor various health and well-being conditions using everyday sensor data and health records. He further designs and deploys intelligent intervention & interaction techniques that help users achieve personal health and well-being goals and support health experts in making decisions. Xu has earned several awards, including several Best Paper, Best Paper Honorable Mention, and Best Artifact awards. His research has been covered by media outlets such as the Washington Post and ACM News. He was recognized as the Outstanding Student Award Winner at UbiComp 2022, the 2023 UW Distinguished Dissertation Award, and the 2024 Innovation and Technology Award at the Western Association of Graduate Schools.
Xiao Wu, PhD
- Assistant Professor of Biostatistics
Xiao Wu is an Assistant Professor of Biostatistics at Columbia University. His research interests lie in developing statistical and causal inference methods to address methodological needs in climate and health research. The key goal of his research is to provide scientific evidence on the health impacts of environmental factors and their mitigation and adaptation in an age of rapidly changing climate. Contact me if you are interested in using data science to build a healthier, environmentally sustainable world!
He completed his Ph.D. in Biostatistics at Harvard University, where he was advised by Dr. Francesca Dominici and Dr. Danielle Braun. His dissertation focuses on developing causal inference methods to handle error-prone, continuous, and time-series exposures. He was a Data Science Postdoctoral Fellow at Stanford University, where he worked with Dr. Trevor Hastie and Dr. Stefan Wager during 2021-2022. He is also working on collaborative projects to design Bayesian clinical trials, meta-analyses, and real-world evidence studies.
He has been named to Forbes 30 Under 30 list. His research has been published in prestigious scientific venues such as Science Advances, New England Journal of Medicine, the Lancet Planetary Health, and the Journal of the American Statistical Association, and it has attracted the attention of international journalism, including at the New York Times, the Guardian, National Geographic, USA Today, and Scientific American.
Research Staff
Weijie Xia
- Data Engineer
Weijie Xia is the data engineer at the TRAIL. With an MA in Statistics from Columbia University, Weijie specializes in curating and managing databases of electronic health records (EHR) data. He actively participates in data analysis projects and develops query UI applications for databases. Additionally, Weijie conducts tutorial sessions to guide researchers in utilizing data resources effectively.
Qi Liu
- Postdoctoral Research Scientist
Dr. Qi Liu is a Postdoctoral Research Scientist. Her research focuses on closing the loop between AI and scientific discovery, particularly in developing AI applications for healthcare and biomedical fields. She obtained her Ph.D. in Electrical Engineering from City University of Hong Kong.
Rujula Pradeep, MS
- Data Scientist
Students and Post-docs
Qi Zhang, PhD
- Post-doc research fellow
Yiling Song, PhD
- Post-doc research fellow
Won Eui Hong, PhD
- Post-doc research fellow
Xinyi Yang, PhD
- Post-doc research fellow
Will Ke Wang, PhD
- Post-doc research fellow
Yiming Li
- Ph.D student
Yiheng Wei
- Ph.D. student
Yirou Hu
- Master student
Liliang Su
- Master student
