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Srinivas Tunuguntla

Srinivas Tunuguntla
PhD Student | Marketing
Job Candidate
Marketing
4185 Grainger Hall

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