Research Experience

Algorithms that allocate scarce resources should serve the people on the other end of the decision, not just optimize a number.

I study online decision-making under uncertainty in settings where algorithmic choices shape access, fairness, opportunity, and long-term social value. My work focuses on socially responsible and behaviorally aware algorithms for operations, with applications in hiring, education, nonprofit operations, platform design, and other human-centric systems.

Methodologically, I develop models, policies, and insights, drawing on online algorithms, stochastic optimization, mechanism and information design, learning, and game theory to inform the design of efficient, adaptive, and human-centered operational systems.

💡 Research Vision: AI and the Future of Education

As AI increasingly absorbs content delivery and personalized instruction, what should schools shift their focus to? Some of what students need most remains deeply human: belonging and teamwork, aspiration and motivation, the freedom to discover their own interests, and the cultivation of independent, analytical thinking.

To me, this is also an operations problem: if AI can absorb more of the routine work, schools can better allocate teachers’ and mentors’ limited time to the students and moments where human judgment and connection matter most.

Published/Accepted Papers:

• Online Job Selection: Reward Rate vs. Remaining Value (formerly Real-Time Personalized Order Holding)
  Introduce an online resource allocation framework and index-based policies with theoretical guarantees to optimize the trade-off between keeping an ongoing job to harvest its remaining value vs. preempting it to serve a new request in limited capacity systems.
  with Will Ma (Columbia GSB) and Linwei Xin (Chicago Booth)
  Forthcoming in Management Science
  • Featured in Chicago Booth Review:
    At the E-Commerce Warehouse, a Distribution Dilemma
    How E-commerce Platforms Should Deal with Your Multiple Orders
• Markovian Search with Ex-Ante Constraints: Theory and Applications to Socially Aware Algorithmic Hiring
  Design socially-responsible sequential search framework for algorithmic hiring and resource allocation. Develop dual-adjusted index policies and primal-dual algorithms to satisfy ex-ante constraints efficiently.
  with Vahideh Manshadi (Yale SOM) and Rad Niazadeh (Chicago Booth)
  Forthcoming in Management Science, Appeared in EAAMO 2024
  • Featured in Chicago Booth Review:
    Algorithms and AI Can Make Hiring More Diverse
  • Third Place, IBM Service Science Best Student Paper Award — INFORMS Annual Meeting 2023

Working Papers:

• Fundraising for Education: Optimizing Crowdfunding Platforms (Job Market Paper)
  Design dynamic assortment policies for crowdfunding platforms to prevent “project starvation” and maximize the value of fully funded campaigns.
  with Rad Niazadeh (Chicago Booth)
  PDF available upon request.

• Optimal Bayesian Online Allocation of Reusable Resources
  Generalize classical online resource allocation theory to the reusable setting by developing a time-invariant admission policy that guarantees ex-post feasibility and matches the information-theoretic tight bounds of non-reusable models.
  with Rad Niazadeh (Chicago Booth), Pranav Nuti (Chicago Booth)

• Stationary Online Contention Resolution Schemes
  Create the S-OCRS framework to unify the design of online selection algorithms, achieving optimal selectability and resolving open problems across various feasibility environments, like matchings and matroids.
  with Rad Niazadeh (Chicago Booth), Pranav Nuti (Chicago Booth)
  Appeared in EC 2026

Workshop Notes:

• Magicians Don’t Move: An Easy Peasy OCRS
  Prepared for Easy Peasy workshop at EC 2026, based on Stationary Online Contention Resolution Schemes. with Rad Niazadeh (Chicago Booth), Pranav Nuti (Chicago Booth)

• A Match Made by Entropy
  Prepared for Easy Peasy workshop at EC 2026, based on Stationary Online Contention Resolution Schemes. with Rad Niazadeh (Chicago Booth), Pranav Nuti (Chicago Booth)