Using AI and Econometrics in enhancing shopping experience

Title of the Talk: Using AI and Econometrics in enhancing shopping experience
Speakers: Dr. Tanmoy Das
Host Faculty: Dr.Sandipan
Date: Feb 04, 2026
Time: 9:00 AM to 10:00 AM
Venue: Seminar Hall 1, Department of EE

Abstract: In the competitive landscape of online retail, optimizing how product information is displayed to customers has become a critical challenge for enhancing shopping experiences. This talk explores the intersection of artificial intelligence and econometric methods in solving the complex problem of determining optimal display orders for product-related media content, including images and videos, on retail websites. We present a sophisticated approach utilizing multi-armed bandit algorithms, an advanced machine learning technique that adaptively learns customer preferences and dynamically adjusts content presentation to maximize engagement. However, algorithmic optimization alone is insufficient—rigorous validation through A/B experimentation is essential to ensure that the ML-driven solutions genuinely resonate with customers and deliver measurable improvements in their shopping experience. Through this case study, we demonstrate how combining cutting-edge AI methodologies with robust econometric testing frameworks creates a powerful toolkit for data-driven decision-making in e-commerce, ultimately bridging the gap between theoretical optimization and real-world customer satisfaction.

Bio: Dr. Tanmoy Das is a Senior Applied Scientist at Amazon, where he designs innovative solutions at the intersection of machine learning, econometrics, and large-scale systems. His work spans A/B testing platforms, ranking and recommendation models that process terabytes of data daily. Prior to Amazon, he contributed to critical networking features at Microsoft that shipped in Windows 8.1 and Windows Server 2012 R2, and interned at Google optimizing power telemetry systems for millions of Pixel devices worldwide.

Dr. Das earned his Ph.D. in Computer Science from Ohio State University, where his research focused on wireless communication systems and RFID technology. He holds an M.Tech from IIT Madras and a B.E. from Jadavpur University. His research has been published in premier venues including NeurIPS, MobiCom, MobiSys, and CoNEXT, addressing challenges in A/B testing evaluation and signal processing.

With a unique blend of industry impact and academic rigor, Dr. Das bridges theoretical innovation with practical deployment, tackling problems that affect millions of users while advancing the state-of-the-art in applied machine learning and systems research.