Principal Investigator

Vikas Pejaver

Dr. Vikas Pejaver is an Assistant Professor at the Institute for Genomic Health and the Department of Genetics and Genomic Sciences in the Icahn School of Medicine at Mount Sinai. His research focuses on the development and application of machine learning methods to relate genetic variation to molecular function and disease phenotypes, with a particular emphasis on rare variants and diseases. His work utilizes a broad array of machine learning techniques on genomic, protein and electronic health record data sets. Dr. Pejaver has a Bachelor’s degree in Biotechnology from the People’s Education Society (PES) Institute of Technology (now PES University) in Bengaluru, India. After that, he received his Master’s degree in Bioinformatics and doctoral degree in Informatics from the School of Informatics and Computing (now School of Informatics, Computing and Engineering) at Indiana University, Bloomington. Dr. Pejaver then completed his postdoctoral training at the Department of Biomedical Informatics and Medical Education (BIME) and the eScience Institute at the University of Washington (UW), where he received the Moore/Sloan and Washington Research Foundation Innovation in Data Science Postdoctoral Fellowship. He also received a K99/R00 Pathway to Independence Award from the National Library of Medicine at the National Institutes of Health. At UW, he was also awarded the Fred Wolf Mentorship Award for his active roles in training and mentoring students in BIME.

Research Scientists and PostDocs

Tim Bergquist

As a Data Scientist at the Institute for Genomic Health in the Icahn School of Medicine at Mount Sinai, Tim focuses on using computational methods to interpret genetic variants for their role in disease. In his former role as a research scientist-biomedical informaticist at Sage Bionetworks, his work focused on planning and administering community challenges, a type of crowd-sourcing competition that engages the broader scientific community to solve open research questions. He led the benchmarking and evaluation of machine learning models in the EHR DREAM Challenge: Patient Mortality, the Pediatric COVID-19 Data Challenge, and the Long COVID Computational Challenge. Tim received his PhD in Biomedical Informatics from the University of Washington in 2021 and a Bachelors of Science in Biochemistry from Grove City College in 2016. During the evenings and weekends he can be found in the mountains of the Pacific Northwest hiking and camping.

Deepa Sarkar

Deepa is working as a Postdoctoral Fellow, with a research focus on developing machine learning approaches to link genetic variation with molecular function and disease using patient-derived datasets. Her work aims to identify meaningful patterns within electronic medical records and translate them into effective, cost-efficient strategies that enhance the quality of patient care. Prior to joining the Icahn School of Medicine at Mount Sinai, she earned her PhD in Biomedical Engineering from Chung Yuan Christian University in Taiwan.

Yile Chen

Yile Chen is a Bioinformatician at the Institute for Genomic Health in the Icahn School of Medicine at Mount Sinai. Her research focuses on developing computational methods for clinical variant interpretation, including gene-specific calibration of variant effect predictors. Yile received her PhD in Biomedical and Health Informatics from the University of Washington. As a trainee in the Impact of Genomic Variation on Function (IGVF) Consortium, she developed computational approaches for prioritizing genes for MAVE experiments and calibrating variant effect predictor scores, enabling more consistent and interpretable application of computational evidence in clinical variant interpretation.

Ph.D. Students

Matthew Neky

Matthew is a PhD student in the Pejaver Lab. The research areas he’s interested in are genomics, bioinformatics, pharmacology, biostatistics, and translational medicine. His research focuses on using computational methods to address pharmacology-related challenges for patients with genetic diseases. Matt received his Bachelor’s degree in Biochemistry and Master’s Degree in Biostatistics (Statistical Genetics) both from Columbia University. He previously did wet lab biophysics research, computational biophysics research, and translational cancer research. Outside of the lab, he enjoys books, movies, and going on walks with his dog, Potato.