Kiran Tomlinson

Kiran Tomlinson

PhD Candidate, Computer Science

Cornell University


Iā€™m a Computer Science PhD candidate at Cornell University advised by Jon Kleinberg and collaborating with Johan Ugander on problems around instant-runoff voting. More broadly, Iā€™m interested in modeling and understanding human preferences through algorithmic and machine learning methods. During my PhD, I’ve interned at Microsoft Research with Jennifer Neville on recommendations in networks and at Microsoft’s Office of Applied Research with Longqi Yang and Mengting Wan on multi-organization recommendation. In Winter and Spring 2023, I was a visiting instructor at Carleton College teaching Data Structures and Mathematics of Computer Science.

I’ll be wrapping up my PhD this summer and joining the Augmented Learning and Reasoning group at Microsoft Research!

When away from my desk, I spend my time playing guitar, building 8-bit computers, playing video games, biking, listening to music, flying quadcopters, bouldering, and playing pool. I have additional interests in spaceflight, Premier League football, and Formula 1.

Recent news

šŸŖ§ May ‘24 Presented a poster on ranking with consideration at AAMAS ‘24.

šŸ“¢ Apr ‘24 Wrote a blog post for AIhub about the moderating effect of instant runoff voting.

Mar ‘24 Accepted a Senior Researcher position at Microsoft Research, in the Augmented Learning and Reasoning group!

šŸ—£ Feb ‘24 Gave a talk on the moderating effect of instant runoff voting at AAAI ‘24.

šŸ“ Dec ‘23 Our paper on consider-then-choose ranking models was accepted as an extended abstract to AAMAS ‘24!

All news

Collaboration Network

People are red, papers are blue.

Recent Papers

(2024). Replicating Electoral Success. arXiv.

PDF Cite Code

(2024). The Moderating Effect of Instant Runoff Voting. AAAI.

PDF Cite Code Slides Video

(2024). Bounding Consideration Probabilities in Consider-Then-Choose Ranking Models. arXiv.

PDF Cite Poster

(2023). Graph-based Methods for Discrete Choice. Network Science.

PDF Cite Code DOI