Mendoza School of Business

Sriram Somanchi

Assistant Professor
IT, Analytics, and Operations
  344 Mendoza College of Business
  • Biography
  • Background
  • Publications

Sriram Somanchi is an Assistant Professor of Business Analytics at Mendoza College of Business. His research focuses on bridging the gap between machine learning and social science problems. His interests include developing computationally efficient statistical machine learning algorithms for pattern detection in massive, complex data and demonstrating the practical utility of applying these approaches to real-world problems. He has worked in the area of event and pattern detection in the domains of healthcare, digital experimentation, economic development, crowdsourcing, and social media. Somanchi also is interested in leading the development of machine learning and data-mining methods to enable data-driven decision making in organizations and public policy agencies. His research has been published in Journal of Machine Learning Research (JMLR), Journal of Computational and Graphical Statistics (JCGS), ACM Transactions of Information Systems (ACM TOIS), Manufacturing and Service Operations Management (M&SOM), Production and Operations Management (POM), Journal of Americal Medical Association (JAMA) Network Open, Statistics and Medicine, as well as leading conferences.

Somanchi has a Ph.D. in Information Systems and Management from Heinz College at Carnegie Mellon University. He is a graduate of the Machine Learning Department at CMU and earned an M.E. in computer science from the Indian Institute of Science, Bangalore, India.

Ph D, Carnegie Mellon University
MS, Carnegie Mellon University
M.Phil, Carnegie Mellon University
Master of Engineering, Indian Institute of Science
Bachelor of Technology, Jawaharlal Nehru Technological University

Areas of Expertise
Machine Learning
Business Analytics

"Business Analytics in Healthcare: Past, Current, and Future Trends", (With Kaitlin Wowak, John Lalor, Corey Angst), Manufacturing and Service Operations Management, 25, 2023

"Exploiting Discovered Regression Discontinuities to Debias Conditioned-on-Observable Estimators", (With Benjamin Jakubowski, Edward McFowland III, Daniel Neill), Journal of Machine Learning Research, 24, 2023

"Examining User Heterogeneity in Digital Experiments", (With Ahmed Abbasi, Ken Kelley, David Dobolyi, Ted Yuan), ACM Transactions on Information Systems, 41, 2023

"Racial and gender inequities in the utilization of and outcomes after left ventricular assist devices among Medicare patients: A retrospective cohort study.", (With Thomas Cascino, Jeffrey McCullough), Journal of American Medical Association (JAMA) Network Open, 5, 2022

"To Predict or Not to Predict: The Case of Inpatient Admissions from the Emergency Department", (With Idris Adjerid, Ralph Gross), Production and Operations Management Journal, 32, 2022

"Discovering Anomalous Patterns In Large Digital Pathology Images", (With Daniel Neill, Anil Parwani), Statistics in Medicine, 2018