Hello! I am a first-year CS Ph.D. student at Princeton University, advised by Prof. Olga Russakovsky. During my undergraduate studies at UC San Diego, I was fortunate to be advised by Prof. Rose Yu and Prof. Yian Ma on spatio-temporal causal discovery, and Prof. Manmohan Chandraker on 3D scene generation with Large Language Models.
I'm broadly interested in visual reasoning and generalizability of multimodal models.
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Princeton UniversityDepartment of Computer Science
Ph.D. StudentSep. 2025 - Present -
UC San DiegoB.S. in Mathematics & Computer ScienceSep. 2021 - Jun. 2025
Selected Publications (view all )
In-Context Learning Can Help Vision-Language Models Overcome Training Prior
Kun Wang, Xindi Wu, Sanghyuk Chun, Olga Russakovsky, Esin Tureci
Coming Soon 2026
Vision-language models often fail on images that violate their training priors—a deficit usually read as missing visual capability—yet we show that controlled visual in-context learning lets them overcome these priors, recovering large gains (up to +30.3%) on prior-conflicting examples while leaving real accuracy unchanged and revealing grounding abilities that standard evaluations overlook.
In-Context Learning Can Help Vision-Language Models Overcome Training Prior
Kun Wang, Xindi Wu, Sanghyuk Chun, Olga Russakovsky, Esin Tureci
Coming Soon 2026
Vision-language models often fail on images that violate their training priors—a deficit usually read as missing visual capability—yet we show that controlled visual in-context learning lets them overcome these priors, recovering large gains (up to +30.3%) on prior-conflicting examples while leaving real accuracy unchanged and revealing grounding abilities that standard evaluations overlook.
Discovering Latent Causal Graphs from Spatio-Temporal Data
Kun Wang*, Sumanth Varambally*, Duncan Watson-Parris, Yian Ma, Rose Yu (* equal contribution)
International Conference on Machine Learning (ICML) 2025 | Oral Presentation at NeurIPS 2024 Causal Representation Learning Workshop
This paper presents a novel approach to discovering latent causal structures from spatio-temporal data, addressing the challenge of identifying causal relationships in complex dynamical systems.
Discovering Latent Causal Graphs from Spatio-Temporal Data
Kun Wang*, Sumanth Varambally*, Duncan Watson-Parris, Yian Ma, Rose Yu (* equal contribution)
International Conference on Machine Learning (ICML) 2025 | Oral Presentation at NeurIPS 2024 Causal Representation Learning Workshop
This paper presents a novel approach to discovering latent causal structures from spatio-temporal data, addressing the challenge of identifying causal relationships in complex dynamical systems.