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.

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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.

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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.   

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