Biography
Srinivas is a doctoral candidate at the Wisconsin School of Business, majoring in Quantitative Marketing. His research focuses on the development of methods for measuring and improving marketing impact, with a particular interest in digital ecosystems. Relying on theories of machine learning, psychology, and microeconomics, Srinivas develops generalizable causal inference methods. He extends these approaches to produce machine learning based decision support systems and explore the game theoretic implications of their widespread adoption. Substantively, his research interests include effectiveness of digital advertising, exploring moderators of ad response, and attribution. His research has appeared in the Journal of Marketing Research and the proceedings of Association for the Advancement of Artificial Intelligence. Prior to joining the PhD program, Srinivas received his bachelor’s degree in Electrical Engineering from the Indian Institute of Technology Madras, and a master’s degree in Computer Science from the University of Wisconsin-Madison.
Research
Selected Published Journal Articles
Tunuguntla, S. & Hoban, P. (2021). A Near Optimal Bidding Strategy for Real-Time Display Advertising Auctions Journal of Marketing Research
Maleeha, Q. & Tunuguntla, S. & Lee, P. & Kanchinadam, T. & Fung, G. & Arora, N. (2019). Discovering Temporal Patterns from Insurance Interaction Data Association for the Advancement of Artificial Intelligence
Selected Submitted Journal Articles
Tunuguntla, S. (2021). Display Ad Measurement using Observational Data: A Reinforcement Learning Approach Marketing Science
Working Papers
Tunuguntla, S. (2021). Causal Inference with Reinforcement Learning: A Novel Observational Approach
Tunuguntla, S. (2021). Scalable Bayesian Hierarchical Models using Generative Neural Networks
Arora, N. & Fung, G. & Tunuguntla, S. (2017). T-Patterns in Business Marketing Science
Presentations
ISMS Marketing Science Conference (2021) Display Ad Measurement using Observational Data
ISMS Marketing Science Conference (2020) Causal Inference with Reinforcement Learning
Haring Symposium (2020) Causal Inference with Reinforcement Learning
Marketing Dynamics Conference (2019) A Near Optimal Bidding Strategy for Real-Time Display Advertising Auctions
Marketing Science (2018) T-patterns in Business
A.C. Nielsen Annual External Advisory Board Meeting (2018) Discovering Temporal Patterns from Insurance Interaction Data