I am a PhD Student at Stanford University advised by Johan Ugander and a part-time Data Scientist at Stitch Fix. I am broadly interested in the theory and applications of Machine Learning in graphical settings. One of my main focuses has been designing and analyzing learning algorithms for discrete choice, where the aim is to understand preferences revealed from choices individuals make when presented with a set of alternatives. My work commonly falls at the intersection of Machine Learning, Statistics, Optimization, and Graph Theory.
I interned at Stitch Fix in the Summer of 2019, 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.
- A Seshadri, J Ugander Fundamental Limits of Testing the Independence of Irrelevant Alternatives in Discrete Choice (Journal Version) arXiv, January 2019.
- 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