About me

Hello there! I’m a 5th year Ph.D. candidate in Operations Research at Stanford University MS&E, where I’m very fortunate to be advised by Itai Ashlagi. My research interests are in design and analysis of marketplaces, such as online market platforms and kidney allocation waitlists. Broadly speaking, I use tools from mechanism design, optimization, and machine learning.

In Summer 2021, I interned at Facebook as a Data Scientist, where I conducted causal inference studies for Ads. In Summer 2020, I interned at Lyft as a Research Scientist, where I developed machine learning models to predict driver ETAs. In academic year 2020-2021, I was the student organizer of the RAIN (Research on Algorithms and Incentives in Networks) seminar.

Research

  • Counterbalancing Learning and Strategic Incentives in Allocation Markets (pdf)
    • with Faidra Monachou, Moran Koren and Itai Ashlagi, working paper
    • Preliminary version to appear at NeurIPS 2021
    • Presented at ACM EC 2021 Workshop on Operations of People-Centric Systems
    • Abstract
      Motivated by the high discard rate of donated organs in the United States, we study an allocation problem in the presence of learning and strategic incentives. We consider a setting where a benevolent social planner decides whether and how to allocate a single indivisible object to a queue of strategic agents. The object has a common true quality, good or bad, which is ex-ante unknown to everyone. Each agent holds an informative, yet noisy, private signal about the quality. To make a correct allocation decision the planner attempts to learn the object quality by truthfully eliciting agents' signals. Under the commonly applied sequential offering mechanism, we show that learning is hampered by the presence of strategic incentives as herding may emerge. This can result in incorrect allocation and welfare loss. To overcome these issues, we propose a novel class of incentive-compatible mechanisms. Our mechanism involves a batch-by-batch, dynamic voting process using a majority rule. We prove that the proposed voting mechanisms improve the probability of correct allocation whenever agents are sufficiently well informed. Particularly, we show that such an improvement can be achieved via a simple greedy algorithm. We quantify the improvement using simulations.

  • Observational Learning in Dynamic Waitlist Mechanisms
    • with Moran Koren and Itai Ashlagi, working paper
    • Abstract
      Many scarce resources are allocated through waitlists without monetary transfers. We consider a model, in which multiple objects with heterogeneous qualities are dynamically offered to strategic agents through a waitlist in a first-come-first-serve manner. Agents, upon receiving an offer, accept or reject it based on both a private signal about the quality of the object as well as decisions of agents ahead of them on the list. This model combines observational learning and dynamic incentives, two features that have been studied separately. We characterize the equilibrium and quantify the inefficiency that arises due to herding and selectivity. We find that objects with intermediate expected quality are discarded while objects with a lower expected quality may be accepted. These findings help in understanding the reasons for the high discard rate of organ allocation despite the large shortage of organ supply.

Education

  • Ph.D., Operations Research, Stanford University MS&E. 2017 - 2022 (Expected)
  • M.S., Operations Research, Stanford University MS&E. 2019.
  • B.S., Operations Research, Columbia University IEOR. 2013 - 2017

Teaching

  • MS&E 220: Probabilistic Analysis (Summer 2019, Fall 2019)
  • MS&E 230: Incentives and Algorithms (Spring 2020)
  • MS&E 260: Intro to Operations Management (Fall 2020)
  • IEOR 4150: Intro to Probability and Statistics (Fall 2016)