Kiran Tomlinson

Kiran Tomlinson

Senior Researcher

Microsoft Research

About

I’m a Senior Researcher at Microsoft Research in the AI Interaction and Learning group, where I’m working on understanding and improving the interactions between people and their LLM-based assistants. More broadly, my research studies preferences and decision-making through algorithmic and machine learning methods.

I recently completed my PhD in Computer Science at Cornell University, where I was fortunate to be advised by Jon Kleinberg. My dissertation was on human decision-making at the individual and collective level. During my PhD, I 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.

When away from my desk, I spend my time learning to fly, 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.

[High-res headshot] [CV of Failures]

Recent news

🗣 Sep ‘25 Gave a talk on AI applicability to work at the Wharton Business & Generative AI Conference.

🔍 Aug ‘25 Given the many misunderstandings about our research on AI and jobs, we wrote a blog post clarifying what our research says.

🗞️ Jul ‘25 Our preprint on AI and jobs has gotten some media coverage, including in Newsweek, Business Insider, The Register, CNBC, and Fortune.

🛠 Jul ‘25 Our workshop What Can(’t) Transformers Do? was accepted to NeurIPS ‘25!

🗣 Feb ‘25 Gave a talk on our replicator dynamics model of candidate positioning at AAAI ‘25 in Philadelphia, PA.

All news

Collaboration Network

People are red, papers are blue.

Recent Papers

(2025). Working with AI: Measuring the Occupational Implications of Generative AI. arXiv.

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(2025). Lost in Transmission: When and Why LLMs Fail to Reason Globally. arXiv.

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(2025). When the Universe is Too Big: Bounding Consideration Probabilities for Plackett-Luce Rankings. AISTATS.

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(2025). Replicating Electoral Success. AAAI.

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