I was invited to give a lecture and practical sessions at the Workshop on Machine Learning and Automatic Differentiation in JAX for Scientific Computing hosted by the University of Strasbourg in June 2026. For this, I presented a lecture on hybridization (i.e., how to marry neural networks and numerical solvers) as well as a practical session on using the Exponax solver suite in JAX for solving…
I had the honor to be invited to the RISE ML seminar series and speak about my current research. You can find the recording here and the slides here . In it, I build a bigger narrative arc around the results from APEBench and my recent NeurIPS 2025 paper on Neural Emulator Superiority . Below is the abstract of the talk: The potential for computational speedups and tackling unsolved problems has…
🎉 I am happy to announce NeurIPS2024 paper: APEBench . 🧵 Check out the project page 👉 Code 🤖 Or install via pip (requires JAX): pip install apebench In this Bluesky thread , I summarize APEBench’s main contributions. Check out the Twitter/X thread with more background and acknowledgements .
As part of our master course in Advanced Deep Learning for Physics (IN2298) , I gave a lecture on autodiff and adjoint methods. You can find the lecture slides here . The lecture was recorded and is available on YouTube: In it, I cover: A functional (JAX/Julia-inspired) viewpoint on autodiff in terms of Jvp/Pushforward and vJp/Pullback Identifying hierarchy levels in autodiff (scalar-mode,…