Research
Publications
A Near-Optimal Bidding Strategy for Real-Time Display Advertising Auctions
Srinivas Tunuguntla and Paul R. Hoban · 2021
Journal of Marketing Research, 58(1), 1–21
Develops a budget-constrained learning algorithm for real-time display-ad bidding that approaches the value achievable with perfect foresight.
Discovering Temporal Patterns from Insurance Interaction Data
Maleeha Qazi, Srinivas Tunuguntla, Peng Lee, Teja Kanchinadam, Glenn Fung, and Neeraj Arora · 2019
Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 9573–9580
Applies scalable temporal-pattern discovery to insurance interactions, identifying event sequences linked to customer satisfaction and fraud.
Working Papers
Designing Ad Auctions with Targeting Information
Srinivas Tunuguntla, Carl F. Mela, and Jason Pratt · 2026
Develops an auction mechanism that uses targeting information while preserving competition, with theoretical revenue guarantees and an empirical application to retail media.
Display Ad Measurement Using Observational Data
Srinivas Tunuguntla · 2026
Develops a sequential framework for estimating campaign-level advertising effects from auction histories and evaluates its estimates against randomized holdout experiments.
Work in Progress
Consumer Search and Beliefs
Studies how consumers interpret product rankings and how their beliefs evolve as they gain experience with a ranking policy, with implications for evaluating and improving search.
LLM Conversations, Search, and Demand
Incorporates the text of LLM queries and responses into models of consumer search and demand to study how conversational AI changes information gathering, product discovery, and purchase decisions.
Consumer Digital Twins
Develops consumer representations from longitudinal behavioral and survey data to predict responses in new contexts, with uncertainty that reflects the relevance and coverage of past observations.