Research details

My research is mainly in the area of studying evolutionary history of humans and other species using machine learning techniques. In the last few years, the amount of data available for such analyses has increased exponentially.The use of suitable machine learning methods can enable us to utilize these large amounts of data well to produce interpretable and meaningful results. The objective of my research is to develop methodology to model the processes that shape evolutionary history of populations using expressive and interpretable models.

Two main focus areas of my thesis research are:


S. Shringarpure, D. Won and E. P. Xing, StructHDP: Automatic inference of number of clusters from admixed genotype data. The Nineteenth International Conference on Intelligence Systems for Molecular Biology (ISMB 2011). Bioinformatics 2011. (Journal Link)
Airoldi, EM., Erosheva, EA., Fienberg, SE., Joutard, CJ., Love, TM., & Shringarpure S. (2010). Re-conceptualizing the classification of PNAS articles. Proceedings of the National Academy of Sciences.(Journal Link)
Shringarpure S, Xing EP. (2009) mStruct: inference of population structure in light of both genetic admixing and allele mutations. Genetics. 2009;182:575-593.(Journal Link)
S. Shringarpure and E. P. Xing. (2008) mStruct: A New Admixture Model for Inference of Population Structure in Light of Both Genetic Admixing and Allele Mutations, Proceedings of the 25th International Conference on Machine Learning (ICML 2008).(pdf)
P. Ray, S. Shringarpure, M. Kolar and E. P. Xing, CSMET: Comparative Genomic Motif Detection via Multi-Resolution Phylogenetic Shadowing, PLoS Computational Biology (2008).(pdf)

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