Hi, I’m Trenton (he/him)! I am a 3rd year EECS PhD Candidate at the University of Michigan with the MLD3 Lab advised by Jenna Wiens. My primary research area is machine learning fairness and causal inference inspired by healthcare use-cases. My work focuses on the following:

• Algorithms for mitigating the impact of biases on the performance and fairness of AI models
• Identifying potential sources of inequity in the data used to train AI models

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.

Email: ctrenton (at) umich (dot) edu

Recent News

• [10/23/2023] I delivered a course lecture on Fairness in Machine Learning as a Causal Question for “Causality and Machine Learning” (EECS 598).
• [10/08/2023] I had the pleasure of giving a talk about my work for the Ann Arbor Machine Learning Meetup, an informal community gathering for those interested in healthcare.
• [08/30/2023] This semester, I will be serving as the Graduate Student Instructor for the inaugural offering of Maggie Makar’s Causality and Machine Learning course (EECS 598-009)!

Publications

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]

Departmental Service

• Reviewed: ML4H, AISTATS, MLHC, KDD (workshop), NeurIPS (workshop). Best Reviewer Award at 2021 Research2Clinics workshop (NeurIPS).
• University Relations Chair, Computer Science & Engineering Graduate Student Organization, University of Michigan (2023-present)
• Panelist, Summer Research Opportunity Program, University of Michigan (2023)
• AI Lab Graduate Admissions Committee Volunteer, Division of Computer Science & Engineering, University of Michigan (2022)

Teaching & Mentoring

Undergraduate/graduate level

• [08/2023 - 12/2023] Graduate student instructor, Causality and Machine Learning, EECS 598-009, University of Michigan
• [01/2021 - 03/2021] Research Mentor, Stanford ACM

K-12 level

• [07/2023 - 08/2023] Workshop organizer, Xplore Engineering & Discover Engineering, Division of Computer Science & Engineering, University of Michigan
• [07/2022] Instructor, AI4ALL
• [06/2020 - 08/2020] Instructor, Inspirit AI
• [06/2019 - 08/2019] Residential Counselor/Teaching Assistant for Artificial Intelligence, Stanford Pre-Collegiate Studies