My Research Highlights

My research spans on opportunities at the intersection of data science, computation, and biology. I have a broad range of computational modeling experience in diverse domains ranging from engineering, automation, medical diagnostics, agriculture, immunology, bioinformatics, epidemiology, and regulatory genomics. Over the period of last ten years, the primary focus of my research has been computational biology and bioinformatics. The following sections sketch some of the work in these areas that I have recently been involved in with colleagues, mentors, and the collaborators.

 

My primary research focuses on understanding the rich contextual information associated with most data sets in a variety of real-world domains and using it to infer complex patterns. A brief synopsis of my research work include:

 

  • 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.

  • Computational modeling and 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 and infectious diseases.

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

August 19, 2019

With outstanding teamwork and great efforts from my colleagues, 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...

February 1, 2016

By taking advantage of the availability of a large number of sequenced genomes in public depositories and the strength of modern computational methods, we have developed robust computational package named 'Fuzzy Inference of Gene-Sets (FIGS)' to understand context spec...

January 1, 2016

Advanced resources are important to improve our ability to extract knowledge from high-through put datasets. Ideally, these resources should facilitate data-integration, improve accessibility, and provide novel insights. We at Thakar Lab have tackled the above requirem...

August 1, 2014

Clustering organizes flow cytometry data into subpopulations with substantially homogeneous characteristics but does not directly address the important problem of identifying the salient differences in subpopulations between subjects and groups. Here, we address this p...

July 30, 2014

This advanced analysis is the continuity of previous work done on studying strain-specific immune responses among seasonal (A/New-Caledonia/20/1999 and A/Texas/36/1991) and pandemic (A/Brevig-Mission/1/1918 and A/California/4/2009) H1N1 influenza viruses. We developed...

March 1, 2014

This research was focused on data mining age related degenerative changes seen on human lumbar spine with the help of MRI scans and further designing intelligent system that can facilitate in medical decision making. This research was a part of an interdisciplinary pro...

August 1, 2013

This is a part of collaborative project in which supervised machine learning approach with wrapper method was used for classification and feature extraction. A support vector machine based recursive feature elimination (SVM_RFE) was used to find a small subsets of sign...

December 1, 2009

This project focused of designing fuzzy controllers, capable of controlling nonlinear systems like inverted pendulum where it is cumbersome to develop conventional controllers based on mathematical modelling. The self-erecting inverted pendulum (SEIP) system is an inhe...

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© 2019 by Atif Khan. 

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