
Suhas Srinivasan
Affiliated with:
Education:
Ph.D., Data Science, Worcester Polytechnic Institute, MA, 2015 - Present
M.E., Software Systems, Birla Institute of Technology and Science, Pilani, India, 2013
B.E., Computer Science, Visvesvaraya Technological University, Belgaum, India, 2010
I am a Data Science Ph.D. Candidate pursuing “biological data science” research in Korkin Lab. My Ph.D. advisor is Prof. Dmitry Korkin.
My research focus is in developing artificial intelligence to identify novel patterns in next-generation sequencing data and psychometric & MRI data; structural bioinformatics and computational epidemiology. Additional research interests include anomaly detection in time-series data and machine learning for detection of communities in network biology.
I was a teaching assistant in the Computer Science department and have taught courses in Artificial Intelligence, Distributed Computing Systems, Object-oriented Design and Computer Graphics.
Office Location
Fuller Labs 318
Contact
Research Lab
Awards
Academic Excellence Award, Data Science Program. WPI 2020.
2nd Place Award in Computer Science, Data Science & Cybersecurity category. WPI GRIE 2019.
1st Place Award in Computer Science, Data Science & Cybersecurity category, GRIE. WPI 2018.
Conference Scholarship. Gordon Research Conferences 2018.
Graduate Student Travel Scholarship. WPI 2018.
BITS Pilani Merit Scholarship, 2011-13
Research Interests
Research Interests:
Data Science
Artificial intelligence
Machine Learning
Bioinformatics
Scholarly Work
Scholarly Work:
Srinivasan S, Cui H, Gao Z, Liu M, Lu S, Mkandawire W, Narykov O, Sun M, Korkin D. Structural genomics of SARS-CoV-2 indicates evolutionary conserved functional regions of viral proteins. Viruses. 2020 Apr;12(4):360.
Srinivasan S, Leshchyk A, Johnson N, Korkin D. A Hybrid Deep Clustering Approach for Robust Cell Type Profiling Using Single-cell RNA-seq Data. doi: 10.1261/rna.074427.119
Choobdar S, Ahsen ME, Crawford J, Tomasoni M, Fang T, Lamparter D, Lin J, Hescott B, Hu X, Mercer J, Natoli T, The DREAM Module Identification Challenge Consortium. Assessment of network module identification across complex diseases. Nature methods. 2019
Cui H, Srinivasan S, Korkin D. Enriching human interactome with functional mutations to detect highimpact network modules underlying complex diseases. Genes. 2019 Nov;10(11):933.