About

I am a Senior Applied Scientist at Amazon Style, where I help develop the NLP that powers Amazon’s first-ever physical store for clothing. Before Amazon, I was a PhD Student at Stanford University advised by Johan Ugander, where I focused on designing and analyzing learning algorithms for discrete choice and recommender systems. My work commonly falls at the intersection of Natural Language Processing, Machine Learning, and Recommender Systems.

I interned at Facebook CDS in the Summer of 2020, and helped develop ways to use People You May Know (PYMK) to catch spammers. In the Summer of 2019, I interned at Stitch Fix where I helped develop numerous approaches to better understand Client and Stylist preferences. Before my graduate work I received a BS in Electrical Engineering, Mathematics, and Economics from the University of Wisconsin, where I was advised by Barry Van Veen and Robert Blick. I have also interned at a number of companies in the past, including Microsoft and Silicon Labs.

Preprints

• A Seshadri, J Ugander Fundamental Limits of Testing the Independence of Irrelevant Alternatives in Discrete Choice (Journal Version) arXiv, January 2019.

Publications

• A Xu, M Vasileva, A Dave, A Seshadri HandsOff: Labeled Dataset Generation With No Additional Human Annotations Conference on Computer Vision and Pattern Recognition (CVPR), 2023. CVPR Highlight Award (Top 2% of Submissions)
• A Awadelkarim, A Seshadri, I Lo, I Ashlagi, J Ugander Rank-heterogeneous preference models for school choice Knowledge Discovery and Data Mining (KDD), 2023.
• S Verma, A Beniwal, N Sadagopan A Seshadri (2022) RecXplainer: Post-hoc Attribute Based Explainability for Recommender Systems, NeurIPS Trustworthy Embodied AI Workshop, 2022. Best Student Paper Award
• K Sevegnani, A Seshadri, T Wang, A Beniwal, J McAuley, A Lu, G Medioni Contrastive Learning for Interactive Recommendation in Fashion SIGIR Workshop On eCommerce, 2022.
• A Seshadri, S Ragain, J Ugander Learning Rich Rankings Neural Information Processing Systems (NeurIPS), 2020.
• A Seshadri, J Ugander Fundamental Limits of Testing the Independence of Irrelevant Alternatives in Discrete Choice ACM Conference on Economics and Computation (EC), 2019.
• A Seshadri, A Peysakhovich, J Ugander Discovering Context Effects from Raw Choice Data International Conference on Machine Learning (ICML), 2019.
• A Bhat, PV Gwozdz, A Seshadri, M Hoeft, RH Blick Tank Circuit for Ultrafast Single-Particle Detection in Micropores Physical Review Letters, 2018
• E Stava, HC Shin, M Yu, A Bhat, P Resto, A Seshadri, JC Williams, RH Blick Ultra-stable glass microcraters for on-chip patch clamping