Quantifying disparities in intimate partner violence: a machine learning method to correct for underreporting
npj Women’s Health · 2024
PhD Student · UC Berkeley & UCSF
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
I am starting a research internship at Alibaba DAMO Academy until fall!
New pre-print: Test-Time Hinting for Black-Box Vision-Language Models.
About
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
2019 ~ 2023
2023 ~ 2028
Summer 2022
Summer 2025
Summer 2026
Publications
npj Women’s Health · 2024
MLHC · 2023
Best Findings Paper (Honorable Mention)
AAAI / Ophthalmology · 2023
Scientific Reports · 2023