Computational Biologist | Data Scientist | Public Health Enthusiast
Welcome to My Personal Website!
I am a research scientist with experience in computational modeling and knowledge inference from large and complex biomedical data-sets using modern machine learning and data mining techniques. My primary research spans on the opportunities at the intersection of health data science, computation, and biology. Throughout my career, I have tried to define my research interests by the demands of health care and how they could be satisfied by the modern computing approaches.
I primarily do research in data-intensive biology by building innovative tools and approaches for addressing a wide variety of real-world problems. My interest lies in understanding the rich contextual information associated with most data sets in a variety of real-world domains and using it to infer novel hidden patterns.
To this end, I have worked on designing efficient inference and learning pipelines for the models capable of handling uncertainties and interdependencies, a characteristic that is predominantly associated with large-scale data sets. I have computational modeling experience in diverse domains ranging from real-world engineering problems to medical diagnostics, immunology, epidemiology, bioinformatics, regulatory genomics, and clinical and health informatics.
Department of Medicine
Section of Computational Biomedicine and Biomedical Data Science
University of Chicago
Masters in Public Health
Epidemiology Concentration (2021 - current)
Harvard T.H. Chan
School of Public Health
Understanding the Etiology of Adverse Birth Outcomes
Recreational Cannabis Legalization and the US Opioid Epidemic
Highlights of Recently Published and Ongoing Work
Risk of Preventable Injuries During Halloween Festivities
A Novel Quantitative Approach for Lumbar Spine
Air Pollution and Risk of Psychiatric Disorders in the US and Denmark
Machine Learning Approach for Predicting Past Occupational
Published in Journal of Occupational and Environmental Medicine
FIGS Package for Meta-Analysis of Cell-Specific Transcriptomic Data
Published in BMC Bioinformatics Download package from GitHub
Quantification of Age-Related Degenerative Changes Seen in Lumbar Spine
Recent Collaborative Efforts (Team Science)
Genetic and Environmental Contributions to Schizophrenia Risk
Repurposed Drug for the Treatment of Glioblastoma Multiforme
Probing Seasonality of Psychiatric Disorders in US and Sweden
Effects of Daylight Saving Time (DST) Shifts on Human Health
Department of Defense Study on Occupational Exposure Biomarkers and Health Effects
Publsihed in Journal of Occupational and Environmental Medicine
Cell-Type Specific Pathogen
Response Network Explorer Tool
Automatied Analysis of Flow Cytometry Datasets with Mixture Models
Featured Study: Environmental Pollution and Risk of Psychiatric Disorders
Thanks to my colleagues and collaborators (much obliged to my mentor Professor Andrey Rzhetsky, Edna K. Papazian Professor of Medicine at the University of Chicago), we recently published a trailblazing study on the association between environmental pollution and psychiatric disorders in the United States and Denmark. We did a computational investigation to study the complex interactions of environmental risk factors that are predictive of neuropsychiatric conditions.
This study is notable for its breadth, we analyzed over 150 million patients in the US and applied our model to Denmark to study the entire population of the country born between 1979 and 2002. The analyses showed that air and land pollution were significant predictors for the clinical frequency of several psychiatric disorders. An in-depth understanding of the environmental influence on mental health is needed to better characterize the health effects of exposure to pollutants. Evidence from most recent animal studies shows that air pollution causes neuroinflammation, which specifically supports our findings from massive clinical data mining.
The study was published in PLOS Biology on August 20, 2019.