YouTube video by LEAP
Scientific Inference with Diffusion Generative Models
Recently gave a @leapstc.bsky.social lecture at Columbia and at UCLA on a question I’m excited about: How can we design diffusion models for scientific inference—uncertainty-aware, calibrated, steerable, and heavy-tailed? www.youtube.com/watch?v=QeLZ...
17.11.2025 20:42 — 👍 6 🔁 2 💬 0 📌 0
Congratulations to Metod and the team on winning the ICLR QUESTION workshop's Best Paper Award! 🎓
02.05.2025 08:17 — 👍 2 🔁 0 💬 0 📌 0
Just gave a talk on Scientific Inference with Diffusion Models at ETH AI Center, sharing our recent work—from test-time control and distributional matching to uncertainty calibration. Great crowd, thoughtful questions, nice view. Thanks, Julia Vogt, for hosting!
22.04.2025 12:46 — 👍 5 🔁 0 💬 0 📌 1
Thrilled to share that my student Justus Will and former student Yibo Yang had their work selected as an ICLR 2025 Oral (top 2%)!
Presenting the first runtime-efficient progressive coding method using diffusion models. 👇
09.04.2025 13:35 — 👍 5 🔁 0 💬 0 📌 0
Excited to start my sabbatical at ETH Zurich! The CS department treated me to a beautiful apartment. In exchange, I brought the California sun with me. 😎
07.03.2025 11:41 — 👍 7 🔁 0 💬 1 📌 0
Happy to announce three #ICLR2025 papers:
1️⃣ Heavy-tailed diffusion models (Kushagra Pandey
+ #Nvidia collaborators)
2️⃣ Progressive Compression w/ Universally Quantized Diffusion (Yibo Yang + Justus Will)
3️⃣ AstroCompress Benchmark (with UC Berkeley Physicists)
More details soon! 🚀
24.01.2025 19:12 — 👍 14 🔁 0 💬 0 📌 0
Generative Modeling Summer School (GeMSS)
Excited about
🎓 Generative Modeling Summer School / Statlearn
🗓️ Mar 31 - Apr 4, 2025, France
ℹ️ gemss.ai
🎟️ Deadline: Jan 27, 2025
🧑🏫 Yingzhen Li, @stephanmandt.bsky.social, Aude Sportisse, Anna Korba, @glouppe.bsky.social, @jesfrellsen.bsky.social, @pamattei.bsky.social, @jmtomczak.bsky.social, TBA
10.01.2025 10:42 — 👍 24 🔁 11 💬 0 📌 1
Congratulations to newly minted PhD, Gavin Kerrigan, @gavinkerrigan.bsky.social . Not too hard to spot who Gavin is in the photo alongside his thesis committee.
05.12.2024 01:42 — 👍 11 🔁 1 💬 2 📌 0
Professor at UT Nuremberg, Germany
I’m 🇫🇷 and I work on RL and lifelong learning. Mostly posting on ML related topics.
Associate Prof @ LMU Munich
PI @ Munich Center for Machine Learning
Ellis Member
Associate Fellow @ relAI
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https://davidruegamer.github.io/ | https://www.muniq.ai/
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BNNs, UQ in DL, DL Theory (Overparam, Implicit Bias, Optim), Sparsity
Welcome to ETH AI Center! We are ethz.ch/en 's central hub leading the way towards trustworthy, accessible and inclusive #artificialintelligence
ai.ethz.ch
Coronavirus and other health-related updates. Regular updates on Outbreaks, Pathogens For more updates follow us.
https://linktr.ee/covid19_disease
Research Scientist at Yahoo! / ML OSS developer
PhD in Computer Science at UC Irvine
Research: ML, NLP, Computer Vision, Information Retrieval
Technical Chair: #CVPR2026 #ICCV2025 #WACV2026
Open Source/Science matters!
https://yoshitomo-matsubara.net
America’s Finest News Source. A @globaltetrahedron.bsky.social subsidiary.
Get the paper delivered to your door: membership.theonion.com
machine learning and artificial intelligence | University of Chicago / Google
Assistant Prof of CS at the University of Waterloo, Faculty and Canada CIFAR AI Chair at the Vector Institute. Joining NYU Courant in September 2026. Co-EiC of TMLR. My group is The Salon. Privacy, robustness, machine learning.
http://www.gautamkamath.com
Computer science, math, machine learning, (differential) privacy
Researcher at Google DeepMind
Kiwi🇳🇿 in California🇺🇸
http://stein.ke/
Professor at Penn, Amazon Scholar at AWS. Interested in machine learning, uncertainty quantification, game theory, privacy, fairness, and most of the intersections therein
Blog: https://argmin.substack.com/
Webpage: https://people.eecs.berkeley.edu/~brecht/
Research Director, Founding Faculty, Canada CIFAR AI Chair @VectorInst.
Full Prof @UofT - Statistics and Computer Sci. (x-appt) danroy.org
I study assumption-free prediction and decision making under uncertainty, with inference emerging from optimality.
Research scientist at FAIR NY ❤️ LLMs + Information Theory. Previously, PhD at UoAmsterdam, intern at DeepMind + MSRC.
Safe and robust AI/ML, computational sustainability. Former President AAAI and IMLS. Distinguished Professor Emeritus, Oregon State University. https://web.engr.oregonstate.edu/~tgd/
AI professor. Director, Foundations of Cooperative AI Lab at Carnegie Mellon. Head of Technical AI Engagement, Institute for Ethics in AI (Oxford). Author, "Moral AI - And How We Get There."
https://www.cs.cmu.edu/~conitzer/
He teaches information science at Cornell. http://mimno.infosci.cornell.edu
Research Scientist, Google DeepMind / Ex-academic / Deep learning to help people write code / ❤️s:🐱🐶☕️🍕
Recently a principal scientist at Google DeepMind. Joining Anthropic. Most (in)famous for inventing diffusion models. AI + physics + neuroscience + dynamical systems.
CS Prof at the University of Oregon, studying adversarial machine learning, data poisoning, interpretable AI, probabilistic and relational models, and more. Avid unicyclist and occasional singer-songwriter. He/him
Associate Professor (UHD) at the University of Amsterdam. Probabilistic methods, deep learning, and their applications in science in engineering.
