• Ph.D., Wharton School of Business, University of Pennsylvania, 2020
• M.P.P., Honors, Public Policy, University of Chicago, 2012
• B.A. in Applied Mathematics, High Honors, Operations Research, University of California at Berkeley, 2006

• Assistant Professor of Business Administration, University of Illinois at Urbana-Champaign, 2020 to present
• Instructor of Business Administration, University of Illinois at Urbana-Champaign, 2019-2020

• Kaaua, D., & Virudachalam, V. Forthcoming. Going the Distance: The Impact of Commute on Gender Diversity in Public Service. Manufacturing & Service Operations Management.  link >
• Virudachalam, V., Savin, S., & Steinberg, M. (2023). Too Much Information: When Does Additional Testing Benefit Schools? Management Science, 70 (9), 6220-6233.  link >
• Bavafa, H., Örmeci, L., Savin, S., & Virudachalam, V. (2022). Surgical Case-Mix and Discharge Decisions: Does Within-Hospital Coordination Matter? Operations Research, 70 (2), 990-1007.  link >

• Teachers Ranked as Excellent, UIUC Center for Innovation in Teaching & Learning, 2021, 2023
• RC Evans Data Analytics Scholar, University of Illinois at Urbana-Champaign, 2021-2022

• Junior Faculty Council Grant, Gies College of Business, Department of Business Administration, 2021-2022

Service Operations, Public Sector Operations, Education Operations, Healthcare Operations

• Business Analytics II (BADM 211) Builds on the foundation from the Business Analytics I (BADM 210), synthesizes concepts through hands-on application and project-based learning. Focuses on data acquisition, organization, analysis and visualization in a business setting. Expanding on the use of statistics in generating basic inferences to predictive modeling Identify opportunities for improving business decisions using data, conduct relevant analysis of the gathered and cleaned data, and finally, interpret and present analysis outcomes to decision makers. Using statistical tools and software applications to identify business problems, acquire relevant data, and generate analytic solutions using advanced analytics techniques and tools for generating insights. Introduces the students to analyzing, learning, and prediction using advanced analytics techniques and tools for generating business insights. This course will provide a practical introduction to various techniques regarding clustering, text mining, classification and decision trees, and time series analysis. Finally, the course will introduce advanced and emerging topics in predictive analytics.