David Burstein, Ph.D.
David Burstein, PhD is a data scientist and independent investigator at the VISN 2 Mental Illness Research, Education and Clinical Center (MIRECC). He received his PhD in mathematics from the University of Pittsburgh and holds a dual appointment as an Assistant Professor in the Department of Psychiatry at the Icahn School of Medicine at Mount Sinai.
Dr. Burstein develops and applies artificial intelligence and machine learning methods to large-scale biobank and electronic health record data to improve how neuropsychiatric traits are defined, measured and studied at population scale. His research focuses on computational phenotyping and the integration of clinical and genetic data to support reproducible, scalable analyses, including genome-wide association studies. Through this work, he advances AI-driven approaches for studying complex psychiatric conditions across multiple patient populations.
In support of his research, Dr. Burstein has received a VA Merit Award and has published in peer reviewed journals including JAMA Psychiatry and Nature Genetics.
Research Interests
artificial intelligence, machine learning, computational phenotyping, genetics
Grants
Mitigating Genomic Research Disparities in the Million Veteran Program (2024 - 2028)
Role: Principal Investigator. Funding Source: VA.
Publications (Selected)
Fullard, J. F., Nm, P., Lee, D., Mathur, D., Therrien, K., Hong, A., ... & Roussos, P. (2025). Population-scale cross-disorder atlas of the human prefrontal cortex at single-cell resolution. Scientific Data, 12(1), 954.
Goleva, S. B., Leu, C., Feng, Y. C. A., Burstein, D., Venkatesh, S., Birnbaum, R., ... & Davis, L. K. (2025). Multi-site, multi-ancestry, genome-wide association study meta-analysis of functional seizure disorder in a hospital sample of 675,680 patients. Biological Psychiatry Global Open Science, 100604.
Burstein, D., Griffen, T. C., Therrien, K., Bendl, J., Venkatesh, S., Dong, P., ... & Roussos, P. (2023). Genome-wide analysis of a model-derived binge eating disorder phenotype identifies risk loci and implicates iron metabolism. Nature Genetics, 55(9), 1462-1470.
Bigdeli, T. B., Voloudakis, G., Barr, P. B., Gorman, B. R., Genovese, G., Peterson, R. E., ... & Gill, S. (2022). Penetrance and pleiotropy of polygenic risk scores for schizophrenia, bipolar disorder, and depression among adults in the US veterans affairs health care system. JAMA psychiatry, 79(11), 1092-1101.
