Recent content on Irene Y. Chen - UC Berkeley and UCSF
I spent the last week at SAIL 2025, an intimate conference in Puerto Rico of clinicians, patient advocates, academics, hospital executives, and many others. The sunsets were beautiful, and the conversations were candid. Because the conference operates under Chatham House Rule, I’ve compiled some de-identified quotes – both from the stage and in smaller discussions – that capture…
NeurIPS submissions are done, grades are entered, and thus my second semester as professor has ended. In contrast to the academic job market which has many online guides, there are fewer resources for being a professor. Basically none focus on the transition to junior faculty. In an effort to fill that gap, here are my reflections and learnings from my first 100 days as an assistant professor.…
Irene Chen is an assistant professor at UC Berkeley and UCSF. She studies machine learning systems for healthcare to be more robust, impactful, and equitable. Her work has been published in machine learning conferences (NeurIPS, AAAI) and medical journals (Nature Medicine, Lancet Digital Health), and has been covered by media outlets including MIT Tech Review, NPR/WGBH, and Stat News. She has been…
Over 300 clinicians and machine learning researchers met in Palo Alto for three days of tutorials, keynotes, and papers.
Didn’t go? Went and forgot everything? Here’s your Machine Learning for Healthcare 2017 recap in 30 bullet points.
Calculating log-partition functions for discrete probabiity distributions has never been so easy.
I work to build trustworthy and reliable AI. My research program spans clinical decision-making, fairness and inequality in machine learning, and rigorous evaluation of increasingly advanced AI systems. I am an Assistant Professor at UC Berkeley and UCSF in CPH, EECS, and BAIR. My research has been recognized with best paper and best poster awards, honors from Google and Apple, and Rising Star…
Research Talks MIT J-Clinic AI for Healthcare Equity Talk MIT J-Clinic AI for Healthcare Equity Teaser Harvard CRCS Rising Stars Talk Harvard DBMI Open Insights MIT News, "The Heart of the Matter" NeurIPS 2018 Interviews and Panels Visible Women Podcast with Caroline Criado Perez TWiML AI Podcast with Sam Charington UT-Austin Fairness in Machine Learning Panel AI Talk with Karen Hao Boston Local…
One of my goals as an advisor for postdocs, PhD students, and undergraduate researchers in the lab is to train the next generation of researchers. Here I describe my expectations for a healthy and productive advisor-advisee relationship. Student Development: The goal of the PhD is for students to develop their own research directions and the skill set necessary for independent work. Towards that…
Selected Papers (Show all) Aggregated Individual Reporting for Post-Deployment Evaluation. Jessica Dai, Inioluwa Deborah Raji, Benjamin Recht, and Irene Y. Chen. ICML 2026. Falsifying Sparse Autoencoder Reasoning Features in Language Models. George Ma, Zhongyuan Liang, Irene Y. Chen, Somayeh Sojoudi. ICML 2026. Access to care improves EHR reliability and clinical risk prediction model performance.…
Best Findings Paper, ML4H 2024 Best Poster, CERSI Scientific Symposium 2024 Google Research Scholar 2024 Apple Machine Learning Research Grant 2024 Rising Star in AI - Harvard CRCS 2021 Rising Star in EECS - University of California Berkeley 2021 Rising Star in ML - University of Maryland 2021 Neurips Top 400 Reviewer 2019 Seth J. Teller Award for Excellence, Inclusion, and Diversity 2018 PD Soros…
Email: iychen@berkeley.edu Twitter: @irenetrampoline Github: irenetrampoline Google Scholar
We are actively recruiting research assistants/engineers, visiting students, and postdoctoral fellows. We are especially interested in hearing from individuals from nontraditional or underrepresented backgrounds. If you are interested in joining or learning more about the lab, please read position-level descriptions below and email Irene (iychen at berkeley dot edu). Please include in your email:…
I like to share what I’m reading to keep me motivated. Below are books I’ve read. Books I’ve especially loved are marked with an asterik. 2026 On the Calculation of Volume I, Solvej Balle* On the Calculation of Volume II, Solvej Balle* On the Calculation of Volume III, Solvej Balle* Yesteryear, Caro Claire Burke Strangers, Belle Burden* The Wedding People, Alison Espach On the…
Jul 2026: We investigate how uncertainty drives social bias change in new research accepted to COLM 2026. Congratulations Stan and Sanae! Apr 2026: Two papers accepted to ICML 2026! See our work on leveraging public feedback for AI evals and debunking SAE reasoning features. Congrats Jessica and Zhongyuan! Dec 2025: New papers! Two at ML4H on patient safety and one on AI scribe evaluation. Also…
Advice for aspiring and current ML researchers Enormous list of resources for all things research-related – by Shaily Bhatt (@shaily99) Applications for computer science PhD – by Jean Yang (@jeanqasaur) Applying to Ph.D. Programs in Computer Science – by Mor Harchol-Balter Emailing professors – by Dan Roy (@roydanroy) Interviewing for PhD programs – by Nils Gehlenborg…
AI and Society Organizer 2025- AHLI Board Member 2022- CHIL 2025 General Chair NeurIPS 2024 Tutorial Chair CHIL 2024 Program Chair NeurIPS 2023 Comms Chair Toward Algorithmic Justice in Precision Medicine 2023 Advisory Committee FAccT 2023 Doctoral Colloquium Chair ML4H Symposium 2022 General Chair CHIL Tutorials Chair 2020-21 CHIL 2020 Track Chair Fair ML for Health Workshop at NeurIPS 2019…
Thank you for your interest in working with us! Please read my advising statement for more information about what it’s like working with our lab. Prospective PhD students: The Chen Lab is looking for 1-2 PhD students starting Fall 2027. Apply through the Computational Precision Health or EECS (AI-H) admissions portals. If you are a US citizen or permanent resident, you may be eligible for a…