Soham Phade

I am a Research Scientist in the AI Economist team at Salesforce Research. Prior to joining Salesforce, I graduated with a PhD degree in the EECS Department at UC Berkeley. You can reach me at:

Email: soham underscore phade at berkeley dot edu

Here is my curriculum vitae.

Projects

AI + Markets: Interactive Learning, Smart Pricing, and Optimal Allocations

We study the joint effect of pricing and recommendations on total customer satisfaction and market stability in markets such as AirBnB, Uber, Doordash, Amazon, eBay, etc. We develop an efficient algorithm to minimize customer dissatisfaction and market instability in a repeated learning setting by integrating techniqes such as collaborative filtering, explore and exploit, and bidding protocols for optimal resource allocation.

Collaborators: YE Eringbas, K Ramchandran (UC Berkeley)

Reinforcement Learning for Economic Policymaking

Deep reinforcement learning has shown promising results in games with a limited number of agents such as Chess, Go, Poker, etc. We develop methods to go beyond few agent settings and apply it to microeconomic simulations for finding general equilibria and optimal agent policies.

Collaborators: Stephan Zheng, Sunil Srinivasa, Stefano Ermon (Salesforce Research)

Behavioral Network Economics

As part of my PhD thesis, I focused on the role of behavioral models from psychology and decision theory (such as Cumulative Prospect Theory) in building efficient commercial systems that interact with humans.

Lottery-based optimal resource allocation

We develop the theory behind lotteries to incentivize people (eg. flight upgrades and middle-seat lotteries) and invent a lottery-based version of the TCP/IP protocol to design optimal lotteries.

Collaborators: V Anantharam (UC Berkeley)

Distributed Optimization

We develop a distributed variant of the Boyle-Dykstra-Han Scheme to find the projection of a point on the intersection of several convex sets where each agent can compute projections only its associated convex set.

Collaborators: VS Borkar (IIT Bombay)

About

In am interested in the design of networks and markets primarily from an economic and algorithmic point of view. My topics of interest include game theory, economics, behavioral psychology, decision theory, machine learning, reinforcement learning, and generative AI.

In Summer 2019, I interned as an Applied Scientist at Amazon Search Science and AI. Before joining UC Berkeley, I graduated from the Indian Institute of Technology, Bombay with a B.Tech. in Electrical Engineering with Honors and a Minor in Computer Science and Engineering.

Selected Publications

Learning in Games with Cumulative Prospect Theoretic Preferences.
Soham Phade and Venkat Anantharam
Dynamic Games and Applications (2021): 1-42
[link] [ArXiv]
Optimal Resource Allocation over Networks via Lottery-Based Mechanisms.
Soham Phade and Venkat Anantharam
International Conference on Game Theory for Networks (pp 51-70), Springer, Cham (2019)
Best Paper Award at the 9th EAI International Conference on Game Theory for Networks, GameNets 2019, April 25-26, Paris.
[link] [ArXiv] [Short Video Presentation]
On the Geometry of Nash and Correlated Equilibria with Cumulative Prospect Theoretic Preferences.
Soham Phade and Venkat Anantharam
Decision Analysis 16(2), 142-156 (2019)
[link] [ArXiv]
A Distributed Boyle-Dykstra-Han Scheme.
Soham Phade and Vivek Borkar
SIAM journal on Optimization 27(3): 1880-1897 (2017)
[link]