About me
I am a third year Ph.D. student in the Allen School of Computer Science and Engineering at the University of Washington, advised by Yulia Tsvetkov. I do research in Natural Language Processing and Cognitive Modeling, with a growing emphasis on personalization and proactive learning (question-asking).
I'm particularly interested in using computational methods to model cognitive processes, including how humans reason, communicate uncertainty, and make decisions in complex domains like healthcare. My long-term goal is to build socially and cognitively aligned AI systems that support safer, more personalized, and equitable care.
Research interests: Proactive Learning, Social Reasoning, AI for Health, Safety & Reliability, and more!
Before grad school, I received my B.S. and M.S.E. at Johns Hopkins with majors in Computer Science, Cognitive Science (linguistics focus), and Applied Mathematics (statistics focus). I worked as a research assistant at the Center for Language and Speech Processing advised by Philipp Koehn and Kenton Murray.
Please contact me at stelli [at] cs.washington.edu if you are interested in my work!
Click here to view my CV (updated July 25)
I'm thinking about...
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Proactive Reasoning
How to identify and proactively seek information using LLMs to improve model safety & reliability with statistical guarantee. How to make LLMs ask good questions? How do we model "intuition" in expert domains like medicine?
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Socially-Intelligent Personalization
Modeling how different social groups express health concerns and interpret medical advice. Aiming to personalize AI systems for more equitable, culturally-aware health communication.
News
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2026-03
Guest lecture at UBC NLP: "Proactive Question Asking for Reliable and Personalized LLMs." [Slides].
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2026-02
Check out our new paper "Cold-Start Personalization via Training-Free Priors from Structured World Models" that learns priors from population preferences for interactive personalization.
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2026-01
Our paper "🪩PrefDisco: Benchmarking Proactive Personalized Reasoning" got accepted to ICLR 2026🇧🇷!
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2025-11
Check out our new paper "Cognitive Foundations for Reasoning and Their Manifestation in LLMs" that extracts and analyze patterns in LLM and human reasoning.
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2025-11
Guest lecture at UT Austin Computational Discourse and NLG class on PrefPalette [Slides].
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2025-08
"PrefPalette: Personalized Preference Modeling with Latent Attributes" won a Spotlight at COLM 2025🏆!
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2025-06
Presenting Spurious Rewards at Cohere Labs [YouTube] [Slides].
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2025-06
Prompt engineering can elicit similar behaviors in models as RLVR does. We show that "Spurious Prompt" can boost Qwen2.5-Math MATH-500 performance by 20% as well‼️ Check out our new blogpost "Spurious Rewards and Spurious Prompts."
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2025-05
Doing RLVR on incorrect and even random rewards can boost Qwen2.5-Math MATH-500 performance by 20%🤯 We explore how and why this happens in our new paper "Spurious Rewards: Rethinking Training Signals in RLVR" (blogpost).
