LOGML 2025 is shaping up to be incredible! 🔥
If you're into geometry & ML, this is your chance to dive deep, meet top researchers, and work on projects that matter—all in London with an amazing community.
Trust me, it's as fun as it is inspiring.
Apply now! ⏳👇
11.03.2025 15:26 — 👍 1 🔁 0 💬 0 📌 0
💡Excited by geometry & ML like our organizing committee?💡
Mentor at LOGML 2025 and help shape projects that can become top publications. It’s academic, social, and seriously fun—Plus, it’s in London 🇬🇧 (July 7-11) and we’ve got you covered. Apply now 👇👇👇
22.01.2025 13:08 — 👍 2 🔁 0 💬 0 📌 0
Special shout-out to my amazing co-authors Tolga Birdal & Stefanos Zafeiriou for making this UV-free vision a reality! 🙌🔥
#NeurIPS #CVML #CVPR #AI #Graphics
29.11.2024 19:01 — 👍 2 🔁 0 💬 0 📌 0
Attention-enhanced Heat Diffusion operators
Quick hits:
✨Avoid seams, distortions, non-uniform resolution or wasted UV space!
✨Textures are represented and generated directly onto the objects' surface by UV3-TeD.
✨UV3-TeD is a denoising diffusion model (DDPM) based on our attention-enhanced heat diffusion.
29.11.2024 19:01 — 👍 1 🔁 0 💬 1 📌 0
🎙️ Did you turn up the volume of the video??? Yes, it's exactly what you’re thinking—an AI-generated podcast of the paper!
Want the full podcast? Want to play with the code? Head to our project website for all that (and more). 🖥️🎶👇
uv3ted.github.io
29.11.2024 18:56 — 👍 0 🔁 0 💬 1 📌 0
🚨 In just 2 weeks, we’ll present "UV-free Texture Generation with Denoising and Geodesic Heat Diffusions" at #NeurIPS2024! No more UV map struggles—just point cloud textures & heat diffusion magic. 🔥Curious? Keep reading. Oh, and definitely turn up the audio 🎧👇
29.11.2024 18:56 — 👍 6 🔁 0 💬 1 📌 0
PhD student at MIT💻 I love Computer Graphics🎨 Previously at SNU, ETH Zurich, Nvidia, Inria and École polytechnique🔙 I run, cycle and 🍻 during my free time.
Postdoc @ Technical University of Munich | Intern @ Qualcomm AI Research | PhD @ University of Twente | Neural operators for cardiovascular flow
🧑🎓 PhD stud. @ Sapienza, Rome
🥷stealth-mode CEO
🔬prev visiting @ Cambridge | RS intern @ Amazon Search | RS intern @ Alexa.
🆓 time 🎭improv theater, 🤿scuba diving, ⛰️hiking
Postdoctoral Fellow at Technion — Geometric Deep Learning in some of its various forms — PhD at Imperial College London — Previously Twitter, Fabula AI and Politecnico di Milano「 心 」🌟
Assistant Professor at Harvard | Faculty @Harvard @KempnerInst AI | Faculty @broadinstitute @harvard_data | Cofounder @ProjectTDC | @AI_for_Science
Dad · Geometry ∩ Topology ∩ Machine Learning
Professor at University of Fribourg 🇨🇭
🏠 https://bastian.rieck.me/
🏫 https://aidos.group
☕️ https://ko-fi.com/pseudomanifold
machine learning researcher @ Apple machine learning research
Associate Professor, UiT the Arctic University of Norway, Graph Machine Learning, Time Aeries Analysis, Complex Networks
Pre-training lead at World Labs. Former research scientist at Google. Ph.D UWCSE.
📍 San Francisco 🔗 keunhong.com
PhD student, Computer Graphics Lab, TU Braunschweig.
Radiance Fields and Point Rendering.
Webpage: https://fhahlbohm.github.io/
Research Scientist at BIGAI, 3D Vision, prev @UCLA, @MPI_IS, @Amazon, https://yixchen.github.io
PhD Student @ Verizon Connect and University of Florence
Working on computer vision for transportation 🚘
📍Florence, Italy
Math Assoc. Prof. (on leave, Aix-Marseille, France)
Interest: Prob / Stat / ANT. See: https://sites.google.com/view/sebastien-darses/research?authuser=0
Teaching Project (non-profit): https://highcolle.com/
I am a Professor of Computer Science at EPFL in Switzerland. My main research interests are in Computer Vision, Machine Learning, Computer Assisted Engineering, and Biomedical imaging.
Professor@UCL and Chief Research Scientist @ Niantic
PhD student at TU Munich
Working on 3D reconstruction, optimization theory and such things
website: linusnie.github.io/
github: github.com/Linusnie
scholar: scholar.google.com/citations?user=HWAA
Statistician, Data scientist and Researcher. I enjoy working with data. Husband, father to two kids and one dog. I work for Adobe Research. Interested in History and Languages.
LOGML (London Geometry and Machine Learning) aims to bring together mathematicians and computer scientists to collaborate on a variety of problems at the intersection of geometry and machine learning.
Chapman-Schmidt Fellow at I-X, Imperial College London
PhD student at Imperial College London.
Visiting Researcher at Harvard University
Multimodal foundation models for precision medicine
