Mental Health Data Science Staff
Melanie Wall, PhD
Division Chief, Professor
Dr. Wall has worked extensively with modeling complex multilevel and multimodal data on a wide array of psychosocial public health and psychiatric research questions in both clinical studies and large epidemiologic studies. She is an expert in longitudinal data analysis and latent variable modeling, including structural equation modeling focused on mediating and moderating (interaction) effects where she has made many methodological contributions. She has a long track record as a biostatistical mentor for Ph.D. students and NIH K awardees and regularly teaches graduate-level courses in the Department of Biostatistics in the Mailman School of Public Health attended by clinical Masters students, Ph.D. students, post-docs, and psychiatry fellows. Her current research mission is improving the accessibility and application of state-of-the-art and reproducible statistical methods across different areas of psychiatric research.
Yuanjia Wang, PhD
Dr. Yuanjia Wang is a tenured Professor in the Department of Biostatistics at the Mailman School of Public Health and the Department of Psychiatry at Columbia University. Her methodological expertise includes developing novel machine learning methods for precision medicine and developing analytic tools for risk prediction, early disease intervention, and prevention with multiple sources of large-scale data. She has extensive applied experience with analyzing clinical trials, electronic health records, high-dimensional biomarkers, latent variable modeling, and modeling of complex multilevel epidemiological data. She has served as principle investigator, co-investigator, and lead statistician on multiple major research projects, including several large multi-site clinical trials of national interest, with Columbia serving as the biostatistics core and data coordinating center. She also has extensive experience in mentoring master-level biostatisticians, doctoral students, and junior faculty. Her substantive research area of interest includes psychiatric disorders and neurological disorders.
Hanga Galfalvy, PhD
Dr. Galfalvy's areas of expertise include statistical methodology in psychiatric research, with a special focus on the prediction models for suicidal behavior from high-dimensional data, censored regression models, statistical genetics, and longitudinal data analysis in observational studies. Dr. Galfalvy received her Ph.D. in Statistics from the University of Illinois at Urbana-Champaign in 2000. She completed three years as a postdoctoral research scientist at the New York State Psychiatric Institute before her appointment in the Department of Psychiatry. Dr. Galfalvy recently successfully completed an NIMH-funded Mentored Quantitative Research Scientist Career Development Award (K-25 award), “Statistical Methods in Suicide Research”. While her primary appointment is in the Division of Biostatistics at the Department of Psychiatry at Columbia University, she has an interdisciplinary appointment in the Department of Biostatistics in the Mailman School of Public Health at Columbia.
Seonjoo Lee, PhD
Dr. Seonjoo Lee received her B.S. and M.S. degrees in Statistics from the Seoul National University, South Korea. She completed her Ph.D. in Statistics and Operations Research from the University of North Carolina at Chapel Hill in 2011. Her thesis focused on the development of independent component analysis with biomedical applications. After completion of her degree, she joined the Center for Neuroscience and Regenerative Medicine at National Institution of Health and Uniform Service University and is currently working on the development of statistical methodology for high-dimensional longitudinal data. She is also interested in multimodal data analysis and latent variable modeling.
Ying Liu, PhD
Dr. Ying Liu joined Mental Health Data Science in June 2019. Before that, she was an Assistant Professor at Medical College of Wisconsin for three years. She has dual expertise in biostatistics and machine learning. She has extensive experience in integrating machine learning methods to problem-solving in the medical domain, accompanied by open-source software packages in Python and R for these new methods. She has expertise in biostatistical methods in precision medical decision making, especially in statistical learning methods and experimental design for dynamic treatment regimes. Dr. Liu's projects utilize and adapt cutting-edge machine learning methods (such as deep learning, Bayesian generative modeling) to biostatistical practices. She has recently successfully introduced the deep generative model to the statistical community by proposing to solve a problem in False Discovery Rate controlled variable selection. This approach provides statistical inference to machine learning models such as LASSO, random forest, and neural networks. She also introduced deep reinforcement learning to mine medical registry data for sequential medical decisions. She is also interested in adopting reinforcement learning and randomization designs in mobile health. She is currently working on a variety of problems in projects ranging from drug addiction to finding patient subtypes with multiple modality brain imaging data.
Cale Basaraba, MPH
Cale Basaraba received his MPH in Epidemiology and Applied Biostatistics from Columbia University’s Mailman School of Public Health in 2016 and holds a BA and BS from Stanford University. As part of the Mental Health Data Science Group, he collaborates as a data analyst on projects concerning the treatment of first episode psychosis in young adults, substance abuse research, and also provides statistical support for the HIV Center for Behavioral and Clinical Studies.
Ying Chen, MD, MS, MA
Dr. Chen received her MD in Medicine in China and an MS in Biostatistics, MA in Medical Informatics from Columbia University. Dr. Chen is interested in applied statistics in Psychiatry, especially in depression treatment and precision medicine.
Jean Choi, MS
Jean Choi earned her master’s degree in biostatistics at the University of Pittsburgh, Graduate School of Public Health and her BS in psychology and statistics at Carnegie Mellon University. She has collaborated as the primary data analyst with Richard Sloan in Behavioral Medicine working on several clinical trials examining the effects on cardiovascular responses due to aerobic conditioning as well as varying emotions and participated in one paper being published in Psychophysiology. Jean has been involved in ongoing development of new statistical methods for relating the high dimensional cardiac monitoring data to the momentary assessment of emotions.
Jongwoo Choi, MA
Jongwoo Choi received his MA in Statistics from Columbia University in 2019. In the Division of Mental Health Data Science, he collaborates on projects pertaining to biomarker identification of late-life depression and Alzheimer’s disease. His research interests include causal inference, machine learning, and high-dimensional data analysis.
Tse Choo, MPH
Tse Choo has an MPH in Biostatistics from Columbia's Mailman School of Public Health and is currently a Biostatistician in the Division of Biostatistics in Psychiatry at Columbia. His research interests include mediation, spatial data analysis, latent variables, and resampling techniques.
Thomas Corbeil, MPH
Tom Corbeil holds a BA from UC Davis, an MCS from Regent College, and an MPH in Biostatistics from Columbia University’s Mailman School of Public Health. He currently provides data analysis collaboration on projects including the study of psychological development in Puerto Rican youth, mental health and hazardous drinking in sexual minority women, the promotion and acceptance of HIV testing among high school students in the Bronx, and predictors of successful transitional care after psychiatric hospitalization.
Tianshu Feng, MS
Tianshu Feng joined the Division of Biostatistics in Psychiatry in October 2012. Her primary collaboration is with Dr. Monk in behavioral medicine working on several studies looking at stress markers and birth outcomes in teen pregnancy. She also works with Dr. Wall on the project related to impact of state-level medical marijuana law passage on adolescent marijuana use. Her other collaborations include anxiety disorder and cognitive regulation of addictive behaviors.
Jun Liu, PhD
Dr. Liu received his Ph.D. in Statistics from Rutgers University at New Brunswick. He was focused on MRI-related statistical research for several years and is now doing research on generalized linear modeling and machine learning methods for general psychiatric data, such as hospital discharge data and appnero data.
Jennifer Scodes, MS
Jennifer Scodes received her MS in Biostatistics from Columbia University's Mailman School of Public Health and earned her BA in Computational Biology and Statistics from Cornell University. She has been working in the Division of Mental Health Data Science since May 2015. Her current research projects involve the use of both observational and clinical trial data, and span across multiple areas of psychiatry including behavioral medicine, substance abuse, and first-episode psychosis.
Eileen Shea, MPH
Eileen Shea received her BS from the University of Wisconsin – Madison and her MPH from Columbia University’s Mailman School of Public Health. During her MPH program, she split her time between the Department of Environmental Health Sciences and the Department of Biostatistics. Her interests include depression and anxiety disorders, psychosocial stressors, adverse childhood experiences (ACEs), and mental health throughout the lifecourse.
Qing Xu, MS
Qing Xu has an MS in Biostatistics from Columbia University’s Mailman School of Public Health. She worked as a part-time data analyst with the New York State Psychiatric Institute before joining the Division of Biostatistics. Her collaborations include psychological development and disorder among Puerto Rican Youth, building predictive models for conversion to psychosis, violent behavior, and longitudinal data analysis for suicide attempts.