Berk Ustun (@berkustun) header
Berk Ustun (@berkustun) profile picture

Assistant Prof @UCSD. I work on interpretability, fairness, reliability, and personalization in ML. Previously @GoogleAI @Harvard @MIT @UCBerkeley🇨🇭🇹🇷

San Diego, CA
Joined March 2009
Photo by Berk Ustun (@berkustun)Photo by Berk Ustun (@berkustun)Photo by Berk Ustun (@berkustun)Photo by Berk Ustun (@berkustun)Photo by Berk Ustun (@berkustun)Photo by Berk Ustun (@berkustun)Photo by Berk Ustun (@berkustun)Photo by Berk Ustun (@berkustun)Photo by Berk Ustun (@berkustun)
Denied a loan by an ML model? You should be able to change something to get approved! In a new paper w @AlexanderSpangh & @yxxxliu, we call this concept "recourse" & we develop tools to measure it for linear classifiers. PDF bit.ly/2xh0idm CODE bit.ly/2xiEvls
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So excited for this research! I've always been annoyed at the supremacy of verified rewards, and now we can finally show it. In this work, we develop ways of measuring: how much of a reward signal is verifiable, how much can we put into words, and how much is shared but tacit, or impossible to articulate?
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4/5 of the papers in my ICLR review pile read like pure AI slop. Anyone else seeing this?
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Berk Ustun retweeted
Update: scE2G is now online at @NatureGenetics! 📄 nature.com/articles/s41588-0… Since the preprint, we added a new benchmark on a fully held-out CRISPR dataset. scE2G continues to show state-of-the-art performance, reinforcing that it generalizes well 1/
Introducing scE2G: a new model to link enhancers to target genes using single-cell data. Excited that scE2G will enable building enhancer maps in hundreds of cell types in the human body! Wonderful collaboration with @robin_andersson @613weilin @mayayayas and others 👇
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An encyclopedia of enhancer-gene regulatory interactions — online today! nature.com/articles/s41586-0… Now with an improved model, expanded maps across 1400+ biosamples, larger validation CRISPR datasets, and guidance on applying the model 1/
An encyclopedia of enhancer-gene regulatory interactions in the human genome teapawt.net/argschwind/status/1724… Very proud—4 years in the making, huge team effort. This is a paper where the author list cannot convey everyone's incredible contributions. My holiday highlights below: 1/
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Please RT! Great opportunity for early career researchers! Apply for Rising Stars in #DataScience @DSI_UChicago and join the info session on July 23 Applications due Aug 3: datascience.uchicago.edu/res…
✨ We’re excited to announce the Rising Stars in Data Science 2026 workshop! ❓ Want to learn more? Join us for an info session on July 23rd! Registration info is available at buff.ly/mOMCBjj
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Neurips 2025 was such a blast! We snuck a grand piano into the CreativeAI Track to demo Aria, our pretrained chat-style music model:
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A 'pragmatic intepretability' turn sounds a lot like our argument/framework for evaluating explanation methods--Time to replace task-agnostic fortunetelling w/concrete decision problem specs + theoretic & empirical evidence of expected performance boost arxiv.org/abs/2506.22740
The GDM mechanistic interpretability team has pivoted to a new approach: pragmatic interpretability Our post details how we now do research, why now is the time to pivot, why we expect this way to have more impact and why we think other interp researchers should follow suit
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GPT-5 on Sudoku-Bench 🧩 Since releasing Sudoku-Bench in May 2025, when no LLM could solve a classic 9x9 puzzle, we've been evaluating the latest generation of models. GPT-5 now leads our leaderboard with 33% puzzles solved--approximately 2x the previous leader--and is the first LLM we've tested to solve a 9x9 Sudoku variant. However, with 67% of the much harder puzzles remaining unsolved, Sudoku-Bench continues to present significant challenges for AI reasoning. Modern Sudoku variants require models to first understand novel rulesets through meta-reasoning, then maintain global consistency across long reasoning chains. Our experiments with GRPO fine-tuning on Qwen2.5-7b and "Thought Cloning" (training on expert human reasoning from Cracking the Cryptic) show that current approaches still struggle with the spatial reasoning and creative "break-in" points that human solvers use naturally. We believe new approaches are required to solve our benchmark. These results highlight persistent gaps between computational problem-solving and human-like reasoning, particularly in tasks requiring integrated mathematical logic, spatial awareness, and creative insight. Read more about our update here: 🔗 Blogpost → pub.sakana.ai/sudoku-gpt5/
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✨ Very overdue update: I'll be starting as an Assistant Professor in CS at University of Minnesota, Twin Cities, Fall 2026. I will be recruiting PhD students!! Please help me spread the word! [Thread] 1/n
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About a week away from the deadline to submit to the ✨ Workshop on Algorithmic Collective Action (ACA) ✨ acaworkshop.github.io/ at NeurIPS 2025!
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I often wonder whether the prospective grad students who contact me understand what they are signing up for. I hope this does the trick
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PhD in Computer Science, University of California San Diego 🎓 My research focused on uncertainty and safety in AI systems, including 🤷‍♀️letting models say "I don't know" under uncertainty 🔎understanding and reducing hallucinations 🔁 methods for answering "how much will providing data X improve performance on Y?" at inference time Many thanks to my advisor @berkustun, to my incredible research collaborators, and to my wonderful friends, husband and family. Getting a PhD while becoming a first-time parent is definitely a recipe for growth!
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Our new ICML 2025 oral paper proposes a new unified theory of both Double Descent and Grokking, revealing that both of these deep learning phenomena can be understood as being caused by prime numbers in the network parameters 🤯🤯 🧵[1/8]
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Explainable AI has long frustrated me by lacking a clear theory of what explanations should do. Improve use of a model for what? How? Given a task what's max effect explanation can have? It's complicated bc most methods are functions of features & prediction but not true state 1/
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Explanations don't help us detect algorithmic discrimination. Even when users are trained. Even when we control their beliefs. Even under ideal conditions... 👇
Right to explanation laws assume explanations help people detect algorithmic discrimination. But is there any evidence for that? In our latest work w/ David Danks @berkustun, we show explanations fail to help people, even under optimal conditions. PDF shorturl.at/yaRua
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We’ll be presenting @FAccTConference on 06.24 at 10:45 AM during the Evaluating Explainable AI session! Come chat with us. We would love to discuss implications for AI policy, better auditing methods, and next steps for algorithmic fairness research. #AIFairness #xAI
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Right to explanation laws assume explanations help people detect algorithmic discrimination. But is there any evidence for that? In our latest work w/ David Danks @berkustun, we show explanations fail to help people, even under optimal conditions. PDF shorturl.at/yaRua
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Berk Ustun retweeted
💡LLMs don’t have to be black boxes. We introduce CB-LLMs -- the first LLMs with built-in interpretability for transparent, controllable, and safer AI. 🚀Our #ICLR2025 paper: lilywenglab.github.io/CB-LLM… #TrustworthyAI #ExplainableAI #AI #MachineLearning #NLP #LLM #AIResearch
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When RAG systems hallucinate, is the LLM misusing available information or is the retrieved context insufficient? In our #ICLR2025 paper, we introduce "sufficient context" to disentangle these failure modes. Work w J. Zhang, C.S. Ferng, @DaChengJuan1, @ankurtaly @CyrusRashtchian
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Denied a loan, an interview, or an insurance claim by machine learning models? You may be entitled to a list of reasons. In our latest w @anniewernerfelt @berkustun @kdphd, we show how existing explanation frameworks can fail and present an alternative tailored for recourse 🧵
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