Department of Medicine
University of Chicago
I am a computational scientist with experience in algorithm design and computational modeling of complex biological data-sets using machine learning and modern data mining techniques. My primary research focuses on opportunities at the intersection of 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 am interested in doing research in data-intensive biology, where I can build tools and approaches that can directly address a variety of biological 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 complex 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, clinical and health informatics.
Fuzzy Inference of Gene-Sets (FIGS) package is now available online
Paper Published in BMC Bioinformatics Download package from GitHub
June 06, 2017
PathCellNet Paper published in Journal of Immunological Methods
September 20, 2016
Air Pollution and Risk of Psychiatric Disorders in US and Denmark
August 20, 2019
DoD Occupational Exposure Biomarker & Health Effects Study
Accepted in Journal of Occupational and Environmental Medicine
July 08, 2019
Featured Study: Environmental Pollution and Psychiatric Disorders
With outstanding teamwork and great efforts from my colleagues (special thanks to my mentor Prof. Andrey Rzhetsky), we recently published a trailblazing study on the association between environmental pollution and psychiatric disorders in the United States and Denmark. We did advance computational investigation to study the complex interactions of environmental 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.