About Me

Hi, I’m Trenton (he/him)! I am a 2nd year EECS PhD Candidate at the University of Michigan with the MLD3 Lab. My primary research area is in machine learning fairness in healthcare. I earned my M.S. in Computer Science from Stanford in 2021, and my B.A. in American Studies from Stanford in 2020.

I’ve previously worked on video machine learning robustness with HazyResearch and open-domain conversational AI for the Alexa Grand Prize Socialbot Challenge with the Stanford NLP Group.

I am interested in machine learning robustness and fairness especially as they pertain to algorithmic justice and social inequity.

Email: ctrenton (at) umich (dot) edu

Current Work

I am advised by Jenna Wiens. I am working on identifying and mitigating sources of bias in clinical machine learning (ML) models. I hope to contribute to mitigating and preventing harm caused by ML models.

Recent News

  • [10/20/2022] I sat down with Rackham Graduate School to talk about my research in machine learning for healthcare, and the direction of machine learning for healthcare in general. Check out my interview here!
  • [10/01/2022] I have completed my preliminary exam, and will advance to PhD candidacy next semester!
  • [08/01/2022] Excited to announce that I will be presenting my work on disparate censorship and undertesting, a real-world source of bias with potential to cause harm in clinical machine learning. To find out what it is and what we can do about it, check out our paper.


Chi, Ethan A., Paranjape, Ashwin, See, Abigail, Chiam, Caleb, Chang, Trenton et. al. Neural Generation Meets Real People: Building a Social, Informative Open-Domain Dialogue Agent. SIGDIAL 2022. [paper] [video]

Chang, Trenton, Sjoding, Michael W., and Wiens, Jenna. Disparate Censorship & Undertesting: A Source of Label Bias in Clinical Machine Learning. MLHC 2022. [paper] [video]

Chi, Ethan A., Chiam, Caleb, Chang, Trenton et. al. Neural, Neural Everywhere: Controlled Generation Meets Scaffolded, Structured Dialogue. Alexa Prize Proceedings 2021. [paper]

Preprints/Workshop Papers

Chang, Trenton, and Fu, Daniel Y. Lost in Transmission: On the Impact of Networking Corruptions on Video Machine Learning Models. arXiv preprint arXiv:2206.05252. [paper]

Chang, Trenton, Fu, Daniel Y., Li, Sharon, and Ré, Christopher. Beyond the Pixels: Exploring the Effect of Video File Corruptions on Model Robustness. Short Paper, ECCV 2020 Workshop on Adversarial Robustness in the Real World. [paper] [video]


  • Reviewed: 2022 TS4H & ML4H workshops (NeurIPS), 2021 ML4H workshop (Best Reviewer Award)


  • [07/2022] Instructor, AI4ALL
  • [01/2021 - 03/2021] Research Mentor, Stanford ACM
  • [06/2020 - 08/2020] Instructor, Inspirit AI
  • [06/2019 - 08/2019] Residential Counselor/Teaching Assistant for Artificial Intelligence, Stanford Pre-Collegiate Studies