Hi! I'm Divya Ramesh.

I am an empirical computer scientist, whose work contributes to human-centered methodologies for AI governance. Specifically, I explore the principles of algorithmic and platform observability as a strategy for enabling AI governance.

My research mobilizes applied machine learning skills, human-computer interaction methods, and STS sensibilities to introduce Analytical Divergence, a sociotechnical, risk-sensitive approach to designing and developing AI systems. My dissertation demonstrates how Analytical Divergence can 1) help develop holistic understandings of risks of AI systems to society, and 2) reimagine models of accountability to achieve effective AI governance.

The building blocks of Analytical Divergence have appeared in premier HCI and AI venues such as ACM CHI, FAccT, DIS, TOCHI and AAMAS. This work has received a Best Paper nomination, a Pragnesh Jay Modi Best Student Paper award, contributed to public conversations via popular media outlets such as CNBC TV18 and Techcrunch Perceptron, and shaped the course of policy outcomes for Google in 2023. For this work, I was recognized as a Quad Fellow by the governments of Australia, India, Japan and the US for 2023-24, and as a Barbour Scholar by the University of Michigan for 2024-25. This work has received generous support from the Schmidt Futures Foundation, Google, and the Rackham Graduate School.

I am on the job market for 2024-25! I am exploring roles within academic departments, think tanks, and/or corporate teams, where interdisciplinary thinking is valued as a means to bridge technology design and public policy.

I expect to graduate with a PhD in Computer Science and Engineering and a certificate in Science, Technology, and Public Policy from the University of Michigan, Ann Arbor in 2025.

Before starting my PhD, I led the R&D efforts in Computer Vision at CloudSight Inc. At Cloudsight, I architected the company's first human-AI interaction pipeline for analyzing visual content in real-time, powering many visual recognition apps including the award-winning accessibility app, TapTapSee.

Please feel free to reach out to me by email if you would like to hear more about my work! My pronouns are she/her.


The Building Blocks of Analytical Divergence

Divya Ramesh, Caitlin Henning, Nel Escher, Haiyi Zhu, Min Kyung Lee, Nikola Banovic. "Ludification as a Lens for Algorithmic Management: A Case Study of Gig-Workers’ Experiences of Ambiguity in Instacart Work." In Designing Interactive Systems Conference (DIS ’23), July 10–14, 2023, Pittsburgh, PA, USA. [Preprint]
Divya Ramesh, Vaishnav Kameswaran, Ding Wang, Nithya Sambasivan. "How Platform-User Power Relations Shape Algorithmic Accountability: A Case Study of Instant Loan Platforms and Financially Stressed Users in India." In Proceedings of 2022 ACM Conference on Fairness, Accountability and Transparency (FAccT 2022). Seoul, South Korea. [Preprint] [Techcrunch Perceptron]
Divya Ramesh, Anthony Liu, Jean Song, Andres Echeverria, Nicholas Waytowich, Walter Lasecki."Yesterday's Reward is Today's Punishment: Contrast Effects in Human Feedback to Reinforcement Learning Agents." In Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2020). Auckland, New Zealand. [Preprint] [Talk] [Slides]

Pragnesh Jay Modi Best Student Paper Award

My other work advancing the principles of algorithmic and platform observability can be found on my Google Scholar page.