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.

Kaihua (William) Hou portrait

News

Recent updates

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

  1. Quantifying disparities in intimate partner violence: a machine learning method to correct for underreporting

    npj Women’s Health · 2024

  2. Coarse race data conceals disparities in clinical risk score performance

    MLHC · 2023

    Best Findings Paper (Honorable Mention)

  3. Predicting Visual Field Worsening with Longitudinal OCT Data Using a Gated Transformer Network

    AAAI / Ophthalmology · 2023

  4. A Deep Learning Model Incorporating Spatial and Temporal Information Detects Visual Field Worsening

    Scientific Reports · 2023