Hi, I’m Trenton! I am a 4th year EECS PhD Candidate at the University of Michigan AI lab, advised by Jenna Wiens. During Summer 2024, I was on internship at Microsoft Research Redmond with the Augmented Reasoning & Learning Group mentored by Adith Swaminathan and Tobias Schnabel.

I work on machine learning fairness inspired by healthcare use-cases. My work focuses on modeling, understanding, and mitigating biases in 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. I was also a contributor to the Google BIG-bench language model benchmark.

Contact. If you’re another researcher in the machine learning, causal inference, or healthcare space–please reach out if you want to chat about research!

For current Michigan undergraduate and Masters’ students interested in working with me, please email me to set up a one-off coffee chat if you’d like to discuss some research ideas.

Email: ctrenton (at) umich (dot) edu

Recent News

• [10/30/2024] My study on racial biases in laboratory testing is now published in PLOS Global Public Health. Read more from Michigan Engineering News here.

• [10/24/2024] Honored to represent the Michigan AI lab in the department-wide research honors competition! Thank you to my peers and my advisor Jenna for their continued support on my research.

• [10/12/2024] I will be presenting preliminary results from my internship project at Microsoft Research on designing an evaluation framework for the steerability of large language models at the NeurIPS SafeGenAI workshop! Pre-print coming soon.

• [09/25/2024] My paper, “Who’s Gaming the System? A Causally-Motivated Approach for Detecting Strategic Adaptation” is accepted to NeurIPS 2024. We present a method for using causal inference as a method for fraud detection, inspired by upcoding in Medicare.

Publications

Chang, Trenton, Nuppnau, Mark, He, Ying, Kocher, Keith E., Valley, Thomas S., Sjoding, Michael W., Wiens, Jenna. Racial differences in laboratory testing as a potential mechanism for bias in AI: A matched cohort analysis in emergency department visits. PLOS Global Public Health. [paper]

Chang, Trenton, Wiens, Jenna. From Biased Selective Labels to Pseudo-Labels: An Expectation-Maximization Framework for Learning from Biased Decisions. ICML 2024. [paper] [code]

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]

Srivastava, Aarohi, …, Chang, Trenton, …, et. al. Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models. TMLR 2022. [paper]

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]

Service

• Reviewed: AISTATS, MLHC, ML4H, KDD (workshop), NeurIPS. Best Reviewer Award at Research2Clinics, 2021 NeurIPS workshop.
• Michigan AI Blog Co-Coordinator (2024-present)
• Workflow Chair, ML4H Symposium Program Committee (2024-present)
• University Relations Chair, Computer Science & Engineering Graduate Student Organization, University of Michigan (2023-2024)
• Panelist, Summer Research Opportunity Program, University of Michigan (2023)
• AI Lab Graduate Admissions Committee Volunteer, Division of Computer Science & Engineering, University of Michigan (2022 & 2024)

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