Anushka Murthy

Anushka Murthy

PhD Student at Stanford University

About Me

I am a PhD student at Stanford where I’m very fortunate to be advised by Ramesh Johari and Irene Lo, and to work closely with Gabriel Weintraub. I grew up in Michigan and completed my B.S. in Applied Math and CS at Columbia University. Up until the beginning of grad school I was primarily interested in probability theory and theoretical CS, but now I am mostly interested in using techniques from these areas to study problems at the intersection of market design and data science. At Stanford, I am affiliated with the Operations Research group in the MS&E department, the Institute for Human-Centered AI, the Center for Computational Market Design, and the Society and Algorithms Lab. I’m also thankful to be supported by a Stanford Human-Centered AI and Data Science Scholarship, an NSF Graduate Research Fellowship, and an EDGE Doctoral Fellowship.

Research Interests

Most of my research studies the data-driven interventions that online platforms use to guide search, learning, and decision-making. Examples of such interventions include the practice of running A/B experiments, and the deployment of recommender systems and AI agents. A central theme of my work is that when these interventions are deployed in market settings rather than on individuals in isolation, capacity constraints create congestion, which can generate unintended system-level effects. My research develops models and methods to understand these effects and to design interventions that account for market interactions and produce socially desirable outcomes. I am particularly interested in applications of these ideas to hiring and school admissions markets.

I also have a secondary interest in contract theory. My earlier work in this area focused on dynamic contracting for environmental conservation programs, and more recently I have become interested in using contract-theoretic frameworks to study human–AI and multi-agent negotiation, particularly in supply chains and contract labor markets.

News

  • [November 2026]: Giving two talks on two papers:
    • Organizing and speaking in a Revenue Management and Pricing session at INFORMS on “AI for Markets and Public Systems.” I’ll talk about our new paper, “Can Labor Markets Function in the Age of AI? The Evaluation Bottleneck in Hiring.”
    • Giving a parallel presentation at CODE@MIT 2026 on our other new paper, “Experiment Design in Two-Sided Markets.”
  • [July 2026]: Our paper, “When Does Interference Matter? Decision-Making in Platform Experiments,” was accepted for presentation at EC 2026 in Rome! Also looking forward to stopping by MATCH-UP 2026 in Paris the previous week.
  • [November 2025]: Giving a parallel presentation at CODE@MIT 2025 on our paper, “When Does Interference Matter? Decision-Making in Platform Experiments.”
  • [October 2025]: Giving an invited talk at INFORMS on our paper, “When Does Interference Matter? Decision-Making in Platform Experiments.” The talk will be in the Revenue Management session on “Experimentation in Operations: Design, Estimation and Decision.” I’m also organizing an Applied Probability Society session on Experimentation and Interference with Andy Zheng and Wenqian Xing.
  • [June 2025]: Starting my first ever internship! I’m joining Amazon’s Middle Mile Product and Technology team in Bellevue as an Applied Scientist intern. Looking forward to working on ML and experimentation methods for the freight marketplace!
  • [May 2025]: Giving three talks on our paper, “When Does Interference Matter? Decision-Making in Platform Experiments.”