PI: Haewon Jeong

Assistant Professor, UCSB ECE Affiliated Faculty, UCSB CS

Co-director of REAL AI 🦾

📧 first name at ucsb dot edu

💼 Harold Frank Hall 3161

🎓 Ph.D @ Carnegie Mellon University

🎓 B.Eng @ KAIST

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We’re hiring two postdoc positions at REAL AI. See more information here.

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Check out our REAL AI Bootcamp launching this September! All highschool students are welcome 😊

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Our Lab News (Sep ‘25) We’re launching REAL AI Bootcamp in collaboration with UCSB’ School of Scientific Thought program. (Aug ‘25) Congratulations Rasta for passing the Major Area Exam at UCSB’s CS department 🥳 (Jul ‘25) Sid presented a poster on “CosmoFlow: Scale-Aware Representation Learning for Cosmology with Flow Matching” at ICML 2025 Workshop on ML4Astro. (Jul ‘25) Rasta’s work on “FAIM: Fair Imputation with Adversarial Training for Mitigating Bias in Missing Data” got accepted at ICML 2025 workshop on DataWorld: Unifying Data Curation Frameworks Across Domains. (Jun ‘25) Arjun gave a talk on our paper “Gone With the Bits: Revealing Racial Bias in Low-Rate Neural Compression for Facial Images” at FAccT 2025. In parallel, Tian and Rasta, two other PhD students who led the project, presented a poster at “Learn to Compress & Compress to Learn” workshop at ISIT 2025. (Jun ‘25) Congratulations Iain Weissburg for graduation👨🏻‍🎓🎉 He has participated in three papers while he was working at our lab. Incredible achievement and exciting next step at Nvidia! (May ‘25) Iain and Sathvika presented our work on “LLMs are biased teachers: Evaluating llm bias in personalized education” at NAACL 2025. (Apr ‘25) Youngseok presented our work on “Model Collapse in the Self-Consuming Chain of Diffusion Finetuning: A Novel Perspective from Quantitative Trait Modeling” at the DATA-FM@ICLR’25 workshop. (Apr ‘25) Our collaborator Richard (JP Morgan AI Research) presented our work on "Can We Catch the Two Birds of Fairness and Privacy?” at the Financial AI @ ICLR 2025 workshop. (Apr ‘25) PI Jeong presented a paper on “Correlated privacy mechanisms for differentially private distributed mean estimation” at SatML ‘25. (Feb ‘25) PI Jeong gave a public talk at Pacific Views Lecture Series on “Ethical AI: Serving Humanity or Falling Short?”

Research

Our lab’s mission is to do science for machine learning and machine learning for science. We advance our fundamental understanding of machine learning algorithms and we apply data-driven approaches to solve challenging scientific problems. Specifically, our lab’s research centers around three research themes: reliable computing for ML, responsible AI, and ML for science. See below for more information about each theme.

Keywords: Machine Learning, Responsible AI, Algorithmic Fairness, Generative Models, Information Theory, Differential Privacy, Distributed Computing, Fault-tolerant Computing, Quantum Computing, AI for Science, AstroAI

Responsible AI