Professor Susanta Tewari received his PhD in Statistics from the University of Georgia, Athens in 2008 and MSc from the University of Pune. For his doctoral thesis, he worked on a probabilistic framework of genetic mapping with Professor Jonathan Arnold at the Department of Genetics, the University of Georgia at Athens. Previously, he served as an Assistant Professor and Program Leader for the Department of Statistics at Amity University, Kolkata. He worked as a Postdoctoral Research Fellow at the University of Kentucky, Lexington in the Mosley Bioinformatics group. Previous to this, he was a Postdoctoral Research Fellow at the National Institutes of Health (NCBI), Bethesda from 2009-2014 working on population genetics.

Professor Tewari is broadly interested in computational and statistical aspects of biology, especially in genetics and evolution. His research in the past included the development of statistical models and efficient dynamic programming algorithms on genetic recombination,  and the development of Monte Carlo approaches for estimating mutation rates in evolutionary studies from sequence data. In recent times, he has worked on mining large public data repositories such as Gene-Expression-Omnibus (GEO) for integrating data from genetic to molecular to disease levels.

Professor Susanta Tewari is an Assistant Professor in the Mathematical and Physical Sciences division of the School of Arts and Sciences.

Professor Susanta Tewari is looking at ways to improve the inference for population-genetic samples under the model of infinite-sites mutation. He has proposed a class of algorithms that can significantly improve the accuracy of estimators. His current work explores this technique for models of migration with future interests for genetic recombination. His earlier research was on genetic recombination for experimental datasets.

At Ahmedabad, Professor Tewari is exploring the growing public datasets (such as GEO) for automated analysis of differential gene expressions. These repositories hold keys to finding many disease signatures. The approach involves ideas from sequence alignment and natural language processing.

• Tanmoy Dey, Amanda Saville, Kevin Myers, Susanta Tewari, David E. L. Cooke, Sucheta Tripathy, William E. Fry, Jean B. Ristaino & Sanjoy Guha Roy: Large sub-clonal variation in Phytophthora infestans from recent severe late blight epidemics in India. Scientific Reports, Nature. (2018) 8:4429. ARTICLE.
• S. Tewari, John L Spouge: Coalescent: an Open-Science framework for Importance Sampling in Coalescent theory. PeerJ. (2015). ARTICLE.
• S. Tewari, John L Spouge: An Open-Source and Scalable framework for Exact calculations in Coalescent theory. BMC Bioinformatics (2012) 13:257. ARTICLE.
• S. Tewari, J. Arnold, S.M. Bhandarkar: Likelihood of a Particular Order of Genetic Markers and Construction of Genetic Maps. Journal of Bioinformatics and Computational Biology (JBCB) Vol. 6, No. 1 (2008) 125–162. ARTICLE.
• S. Tewari, S.M. Bhandarkar, and J. Arnold: Design and Analysis of an Efficient Recursive Linking Algorithm for Constructing Likelihood Based Genetic Maps for a Large Number of Markers. Journal of Bioinformatics and Computational Biology (JBCB) Vol. 5, No. 2(a) (2007) 201–250. ARTICLE.
• S. Tewari, S.M. Bhandarkar, and J. Arnold: Efficient Recursive Linking Algorithm for Computing the Likelihood of an Order of a Large Number of Genetic Markers. Computational Systems Bioinformatics Conference (CSB); Series on Advances in Bioinformatics and Computational Biology. Vol. 4, (2006) 191–198. Stanford University, California, USA, 14-18 August 2006. ARTICLE.
• P. Ganguly, S. Dutta, M. Nasipuri and S. Tewari, Modeling Fraud in Residential Power Usage, 2022 IEEE 10th International Conference on Smart Energy Grid Engineering (SEGE), 2022, pp. 125-130, DOI: 10.1109/SEGE55279.2022.9889754.