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 aligning machine learning model behaviors towards high-level principles such as fairness, controllability, and robustness to gaming. I often take inspiration from health/bioinformatics and policy use cases. My work has intersected with areas including ML fairness, strategic classification, causal inference, and LLM evaluation.

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 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

• [5/28/2025] My pre-print on the steerability of LLMs with Microsoft Research & Netflix is now on arXiv! We’ve open-sourced our evaluation framework on [Github]. Learn more at our website and visualize some of our results here.

• [4/28/2025] I was a lead student contributor to the Michigan AI lab’s white paper on LLM evaluation, contributing sections on health AI and general evaluation.

• [4/9/2025] Our new paper on conditional front-door adjustment for causal effect estimation and its advantages for estimation under treatment non-adherence will appear in CHIL 2025!

Selected Publications

Chang, Trenton, Schnabel, Tobias, Swaminathan, Adith, and Wiens, Jenna. A Course Correction in Steerability Evaluation: Revealing Miscalibration and Side Effects in LLMs. arXiv preprint. [pre-print] [website] [demo]

Jabbour, Sarah*, Chang, Trenton*, Antar, Anindya Das, et. al. Evaluation Framework for AI Systems in “the Wild.” arXiv preprint. [paper]

Chang, Trenton, Warrenburg, Lindsay, Park, Sae-Hwan, Parikh, Ravi B., Makar, Maggie, and Wiens, Jenna. Who’s Gaming the System? A Causally-Motivated Approach for Detecting Strategic Adaptation. NeurIPS 2024. [paper]

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]

Service

• Reviewed: AISTATS, ICLR, ICML, MLHC, ML4H, KDD (workshop), NeurIPS, TMLR. Best Reviewer Award at Research2Clinics, 2021 NeurIPS workshop.
• Outreach Chair, ML4H Symposium Program Committee (2025)
• Michigan AI Blog Co-Coordinator (2024-present)
• Workflow Chair, ML4H Symposium Program Committee (2024)
• 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