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Welcome to My Personal Website!


I am a seasoned research scientist with a focus on health data science, computational analysis, and epidemiology. My work is centered around harnessing advanced analytical techniques to meet the dynamic demands of the health sector, driving innovation through data-centric strategies, and utilizing real-world evidence to make a significant impact in health research.

I have expertise in integrating diverse health data sources, designing computational pipelines for knowledge inference, and analyzing various risk factors and predictors that influence disease outcomes. I am particularly adept at managing large-scale datasets, identifying hidden patterns, and gaining a comprehensive understanding of complex health phenomena.


My passion for data-driven healthcare extends beyond my research, as I am dedicated to benefiting underserved populations, bridging healthcare disparities, and contributing to global health initiatives. I am committed to advancing scientific knowledge, addressing critical challenges in public health, and improving patient outcomes through innovative approaches. With my work, I aim to make a substantial impact on the advancement of healthcare and the betterment of society as a whole.

Staff Scientist 

Department of Medicine

Section of Computational Biomedicine and Biomedical Data Science 

Knapp Center for Biomedical Discovery, The University of Chicago

MPH (Epidemiology), Harvard T.H. Chan School of Public Health, USA
Ph.D. (Intelligent Data Analysis), University of Warwick, UK

Peri- & Post-natal Risk Factors Associated with Health of Newborns
Pre-print available at
January 2023
Recreational Cannabis Legalization and the US Opioid Epidemic
Working Manuscript
Coming Soon!
Late 2023

Highlights of Recently Published Work

Risk of Preventable Injuries During Halloween Festivities
Published in 
Public Health
October 2020
Air Pollution and Risk of Psychiatric Disorders in the US and Denmark
Published in
PLOS Biology
August 2019
Machine Learning Approach for Predicting Past Occupational 
Published in Journal of Occupational and Environmental Medicine 
July 2018
FIGS Package for Meta-Analysis of Cell-Specific Transcriptomic Data
Published in BMC Bioinformatics Download package from GitHub
June 2017
Quantification of Age-Related Degenerative Changes Seen in Lumbar Spine 
Published in IEEE Journal of Biomedical and Health Informatics
June 2014
Incidence Measures for Schizophrenia in Medicaid and Commerical Enrollees
Pre-print available at
February 2023

Recent Collaborative Efforts (Team Science)

Gene-Environment Interactions and  Neuropsychiatric Disorders
Published in
Cell Reports Medicine
September 2022
Genetic and Environmental Contributions to Schizophrenia Risk
Published in
Nature Schizophrenia
May 2022
Repurposed Drug for the Treatment of Glioblastoma Multiforme 
Published in
Cell Reports

November 2021
Probing Seasonality of Psychiatric Disorders in US and Sweden 
Published in 
PLOS Biology
July 2021
Effects of Daylight Saving Time (DST) Shifts on Human Health
Published in
PLOS Computational Biology
June 2020
Department of Defense Study on Occupational Exposure Biomarkers and Health Effects 
Publsihed in Journal of Occupational and Environmental Medicine
December 2019
Humoral Alterations and Systematic Inflammation in Heroin Users
Published in
July 2017
Cell-Type Specific Pathogen
Response Network Explorer Tool
Link to PathCellNet Package
Journal of Immunological Methods
September 2016
Automatied Analysis of Flow Cytometry Datasets with Mixture Models 
Published in
Cytometry Part A
October 2015

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. 


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