Atif Khan, PhD

My primary research focuses on the opportunities at the intersection of health data science, computation, and biology. I apply computational models for understanding the rich contextual information associated with most data sets in a variety of real-world domains and using them to infer complex patterns. 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. A brief synopsis of my research interests include:

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• Designing efficient inference and knowledge extraction pipelines for the models capable of handling uncertainties and interdependencies among large-scale data sets, a characteristic that is predominantly associated with large-scale biological data sets.  

• Using a combination of modern analytical and machine learning  skills for extracting the rich contextual information associated with complex data sets.

• Predictive modeling and computational analysis of disparate data sets, such as electronic health records, scientific texts, and high-throughput experiments.

• Developing bioinformatics strategies for mapping and understanding the interplay of genetic and environmental mechanisms of human disease.

• Investigating the multidimensional and integrated molecular data to understand the biological mechanism and the etiology of the diseases.

• Inferring the causes and consequences of environmental insult on human health with emphases on neuropsychiatric disorders, infectious diseases, and adverse birth outcomes.

• Connecting the dots from climate observations to disease prevalence with an overarching goal to formulate testable hypotheses from biomedical data.

• Improving human health by mining large country-scale clinical databases to predict disease outcomes and investigate existing drugs for new therapeutic purposes.

• Understanding the epidemiology of infectious diseases and assessing prevention and treatment programs for emerging and established infectious diseases.