David M. Knigge
@davidmknigge
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PhD candidate w/ @egavves at @uva_amsterdam geometric deep learning and dynamics. @ic4ai POP-AART Lab.
Amsterdam
Joined July 2022
🌀 Equivariant Neural Fields (ENFs) for continuous PDE solving! We use ENFs as representation for solving PDEs in continuous space/time on different geometries while respecting their symmetries! (such as this internally heated ball of fluid) More details 👇
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Cool news: our extended Riemannian Gaussian VFM paper is out! 🔮 We define and study a variational objective for probability flows 🌀 on manifolds with closed-form geodesics. @FEijkelboom @a_ppln @CongLiu202212 @wellingmax @jwvdm @erikjbekkers 🔥 📜 https://t.co/PE6I6YcoTn
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We've raised $100M from Kleiner Perkins, Index Ventures, Lightspeed, and NVIDIA. Today we're introducing Sonic-3 - the state-of-the-art model for realtime conversation. What makes Sonic-3 great: - Breakthrough naturalness - laughter and full emotional range - Lightning fast -
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🌍 From earthquake prediction to robot navigation - what connects them? Eikonal equations! We developed E-NES: a neural network that leverages geometric symmetries to solve entire families of velocity fields through group transformations. Grid-free and scalable! 🧵👇
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🚀🚀🚀Our paper "Probabilistic Interactive 3D Segmentation with Hierarchical Neural Processes" has been accepted to ICML 2025! A small but exciting step toward interpretable and interpretable Human-AI Interaction. Find more details in our project page: https://t.co/7k3xndOiIe
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So excited for @newtheoryai's first open hire! Their approach—building world models via symmetry/geometry, rooted in the foundational principles of nature & intelligence—is, I believe, the *only* way forward for AI to truly grasp our world. Foundational work! 👇
Excited to share our first open position at @newtheoryai. Come join the early team as a Senior AI Researcher. We’re building new foundational architectures to understand the world grounded in symmetry, geometry, and mathematical structures ubiquitous in nature. 📍 San Francisco
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✨ Job opportunity!! ✨ Interested in working for a startup building the next gen of scalable AI using Geometric Deep Learning? Check out @newtheoryai's opening for Sr AI Researcher! Interning here currently, loving every second of it. The team and their ideas are awesome🥰🚀
Excited to share our first open position at @newtheoryai. Come join the early team as a Senior AI Researcher. We’re building new foundational architectures to understand the world grounded in symmetry, geometry, and mathematical structures ubiquitous in nature. 📍 San Francisco
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We are happy to introduce Cosmos-Reason1, our reasoning model specifically trained for Physical AI. Really proud of being part of this immense effort 💪
DeepSeek R1 demonstrates AI mastering math through reinforcement learning. We introduce Cosmos-Reason1, which learns with Physical AI rewards and excels in physical AI tasks.
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🔥 CuspAI is on fire! We're on a mission to revolutionize the world with AI-designed materials that can change the game. 🤖 We’re thrilled to welcome our new board members: @ylecun , @KPatBerkeley and @verityharding 🚀 Check out the news: https://t.co/kTt0O0gzgd 1/
fortune.com
The company is building AI models to help find new materials that can help combat climate change.
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Super happy to share our work was accepted as an Oral at the Delta Workshop @ ICLR 2025! 🎉 Can’t wait to talk about it in Singapore 😎 Congrats to the amazing team @FEijkelboom @a_ppln @erikjbekkers 🔥
Variational Flow Matching goes Riemannian! 🔮 In this preliminary work, we derive a variational objective for probability flows 🌀 on manifolds with closed-form geodesics. My dream team: @FEijkelboom @a_ppln @erikjbekkers 💥 📜 https://t.co/ZhHNpKP6FY 🧵1/5
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🤹 Excited to share Erwin: A Tree-based Hierarchical Transformer for Large-scale Physical Systems joint work with @wellingmax and @jwvdm Core components of Erwin: - hierarchical organization of data via ball trees - localized attention for linear complexity - hardware-efficient
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We are looking for an intern to join MLR at Apple ASAP. You will be working with me, @YuyangW95 and other scientists in the team on domain-agnostic generative modeling at scale. If you are interested please reach out to mbautistamartin at apple by the end of this week.
1/n 🚨New preprint! Our work “Coordinate In and Value Out: Training Flow Transformers in Ambient Space” https://t.co/3VwBEKFJ9F presents a domain-agnostic and end2end flow-matching generative model that effectively handles various modalities like images and point clouds.
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We’re looking for an intern at Apple MLR 🍎 ASAP. Join us if interested in building universal diffusion/flow-matching model at scale!
We are looking for an intern to join MLR at Apple ASAP. You will be working with me, @YuyangW95 and other scientists in the team on domain-agnostic generative modeling at scale. If you are interested please reach out to mbautistamartin at apple by the end of this week.
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Join Us for an Exciting Hackathon on Equivariant Neural Fields (ENFs) & Geometry-Grounded Representations! ✨ The Deep Thinking Hour, ELLIS unit Amsterdam & the New Theory joint forces, and host a hackathon focused on ENFs and geometry-grounded representations
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Variational Flow Matching goes Riemannian! 🔮 In this preliminary work, we derive a variational objective for probability flows 🌀 on manifolds with closed-form geodesics. My dream team: @FEijkelboom @a_ppln @erikjbekkers 💥 📜 https://t.co/ZhHNpKP6FY 🧵1/5
arxiv.org
We present Riemannian Gaussian Variational Flow Matching (RG-VFM), a geometric extension of Variational Flow Matching (VFM) for generative modeling on manifolds. In Euclidean space, predicting...
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🚀New paper at #ICLR2025!🚀 We introduce Equivariant Neural Fields (ENFs): a new class of Conditional Neural Fields (CNFs) that explicitly ground continuous representations in geometry across image & 3D shapes reconstrution, PDE forecasting & more 🧵Read on for details! 👇
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AI/ML Internships in Drug Discovery🚨 Our team is hiring PhD research interns for summer 2025 in the US. Come work with us on cutting-edge drug discovery projects! We have 3 openings: 1. Hyperbolic NNs for sequence modeling: https://t.co/RCGiyPSefS 2. Causal Inference and
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Our team is actively recruiting at all levels. Come join us in this exciting journey! 🚀
Our team is actively recruiting at various seniority levels. We’re looking for candidates with deep expertise in video generative models, LLMs, VLMs, large-scale model training, or data processing. Join us in shaping the next generation of Cosmos models for Physical AI!
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🔥 3/4 #ICLR2025 Grounding Continuous Representations in Geometry: Equivariant Neural Fields (ENF), for geometry-informed continuous signal representations. Learn more: https://t.co/TWOQ1f7Kjt with D. Wessels, @davidmknigge @samuelepapa_ @RValperga @SharvVadgama @erikjbekkers
openreview.net
Conditional Neural Fields (CNFs) are increasingly being leveraged as continuous signal representations, by associating each data-sample with a latent variable that conditions a shared backbone...
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Very happy to share my first project at NVIDIA! 🚀 We present Meshtron, a pure Transformer model that consistently reasons over 500k+ tokens to generate 3D meshes of outstanding quality. Webpage: https://t.co/xr4FGiZbBu w/ @zekunhao19951 @TsungYiLinCV & @liu_mingyu 🧵
research.nvidia.com
NVIDIA's world class researchers and interns work in areas such as AI, deep learning, parallel computing, and more. Explore what's new, learn about our vision of future exascale computing systems.
Revolutionize 3D mesh generation with artist-like precision and detail. Meshtron from #NVIDIAResearch creates intricate meshes with up to 64,000 faces at 1024-level resolution. Transform your 3D creations easily and with fine detail. 🎨 ✨ ➡️ https://t.co/1Vm0mXipUp
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