Test-Time Hinting for Black-Box Vision-Language Models
arXiv preprint · 2026
PhD Student · UC Berkeley & UCSF
Building trustworthy AI for Healthcare
I am Kaihua (William) Hou, a PhD student of Computational Precision Health at UC Berkeley & UCSF. My work focuses on machine learning methods that are reliable, equitable, and useful in real clinical settings.
News
Recent updates
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Spring 2026
New pre-print! Test-Time Hinting for Black-Box Vision-Language Models
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Spring 2026
I am starting a research internship at Alibaba DAMO Academy until fall!
About
Research with practical impact
I am fortunate to be advised by Ahmed Alaa and Geoff Tison at UC Berkeley & UCSF. Previously, I have recieved my B.S. in Computer Science at Johns Hopkins University, where I was advised by Jithin Yohannan and Mathias Unberath. During my undergraduate studies, I have also had the pleasure to work with Emma Pierson and John Guttag as a research assistant at Massachusetts Institute of Technology.
Focus: robust and equitable machine learning in healthcare, with a strong emphasis on model reliability, representation quality, and transparent evaluation.
Roadmap
Path so far
Johns Hopkins University
2019 ~ 2023
UC Berkeley & UCSF
2023 ~ 2028
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
Massachusetts Institute of Technology
Summer 2022
Amazon
Summer 2025
Alibaba
Summer 2026
Publications
Selected work
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ReasonEdit: Editing Vision-Language Models using Human Reasoning
ICML · 2026
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Quantifying disparities in intimate partner violence: a machine learning method to correct for underreporting
npj Women’s Health · 2024
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Coarse race data conceals disparities in clinical risk score performance
MLHC · 2023
Best Findings Paper (Honorable Mention)
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Predicting Visual Field Worsening with Longitudinal OCT Data Using a Gated Transformer Network
AAAI / Ophthalmology · 2023
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A Deep Learning Model Incorporating Spatial and Temporal Information Detects Visual Field Worsening
Scientific Reports · 2023