We are a part of the Scripps Research Translational Institute and the Department of Integrative Structural and Computational Biology at Scripps Research.
We are interested in the design of probabilistic machine learning methods and statistical models that incorporate known biochemical principles to facilitate decision-making from limited data and to allow for asking smarter questions. We have a major focus on quantitative analysis of regulatory variation in the human genome from large scale functional genomics data and its application to rare diseases and precision medicine.
PI, Associate professor
Pejman is a computational biologist with a background primarily in statistical machine learning and Bayesian data modeling. His current research interests span over practical and theoretical issues that arise in genomics and personalized medicine, focusing on quantitative analysis of regulatory variation in the genome and its application to rare diseases.
Pejman joined Scripps Research as a tenure track Assistant Professor in 2018 and was promoted to Associate Professor in 2021. He holds a Ph.D. in Computational Biology from ETH Zurich and carried out postdoctoral work at the NY Genome Center and Columbia University from 2015 to 2017.
Pam coordinates, plans, and supports daily operational and administrative functions within the lab. Her current role brings her joy in interacting with and assisting the lab members. In her 21+ years here at Scripps Research, she has worked 10 years as an Accounts Payable Specialist and 10 years as a Grants Administrator. When she not working, she enjoys traveling, camping, and doing crafts with her grandchildren.
Nava joined the lab in 2019 as a postdoctoral research associate. She is working on statistical methods for quantifying the effect of genetic variants on gene regulation. She is interested in challenging problems that improve human health. She received both her PhD and MSc in Computer Engineering from University of Tehran, Iran.
Robert joined the lab as a postdoc in 2021. He is interested in developing and applying computational methods for uncovering novel biological insights from genomic and transcriptomic data. Prior to joining the lab, he worked on developing and applying nonlinear dynamic and statistical models for understanding cellular response variability observed in single-cell measurements and in binary classification tasks. He holds a Ph.D. in Physiology, Biophysics, and Systems Biology from the Weill Cornell Graduate School of Medical Sciences and completed postdoctoral work at IBM Research and the Icahn School of Medicine at Mount Sinai.
Daniel joined the lab in 2020 and has a joint position in the Palmer Lab at UC San Diego. He is developing methods to identify regulatory variation in outbred rats with applications in psychiatry, as well as tools for extracting biological features from RNA-Seq. He did his PhD in Quantitative and Computational Biology at Princeton University with Mona Singh. Personal website
Shiyi joined the lab in 2020 and is a member of Torbett Lab at the University of Washington, Seattle. She is a graduate student in the Molecular Medicine and Mechanisms of Disease (M3D) Ph.D. Program. Her thesis project focuses on sequencing and analyzing HIV RNA present in virions and studying the development of drug resistance mutations in HIV from HIV-infected patient sera isolates.
Kaushik Ram Ganapathy
Kaushik joined the lab in 2021 and is a second-year graduate student. His thesis project involves developing methods for rare variant outlier detection. He is interested in using data science to help improve human health. Before joining Scripps, he co-founded and led GeoACT, a project to model COVID-19 spread in schools working at the San Diego Supercomputer Center with Dr. Ilya Zaslavsky. Kaushik holds a B.S. in Data Science from the Halicioglu Data Science Institute, UC San Diego. Google Scholar | LinkedIn
Mahdi is a current PhD student who joined the lab in 2022. He has a background in Biostatistics and aims to engage in biomedical discovery and pattern recognition through data mining and to elevate the accuracy of the criteria in our hands for better decision and policymaking. Mahdi is interested in using the power of computers efficiently through modern tools like AI and Machine learning to solve problems in healthcare and medicine. He finished his B.S. in Statistics at Allameh Tabataba’i University and received his MSc in Biostatistics from Tehran University of Medical Science, Iran.
Sarah is a fourth-year medical student at Rutgers New Jersey Medical School and current PhD student with the Rutgers-NIH graduate partnership program. She joined the lab of Carsten G Bönnemann MD at the NNDCS/NIH and David Adams MD PhD at the Undiagnosed Diseased Program/NIH in 2020. Her thesis project focuses on using whole transcriptomic sequencing to improve molecular diagnosis in genome and exome negative rare disease patients. Upon graduation, she plans on pursuing clinical training in pediatric neurology with a focus in neurogenetics.
Eric joined the lab in 2020. He is a third year undergraduate student at UC San Diego studying Data Science and Mathematics-Computer Science. At the lab he is developing statistic models for allelic specific expression analysis and quality control pipelines.
Eric is a fourth-year undergraduate researcher working concurrently in the Yao Lab at the University of Arizona. His work at Scripps focuses on analyzing short-read RNA-seq data for allele specific expression in gene isoforms.
Now: Red Hat
Now: Columbia University MS in Data Science Program
Now: Johns Hopkins MD-PhD Program