Talks

2026

• “Dynestyx: A probabilistic programming library for dynamical systems”, Mathematical and Computational Foundations of Digital Twins, Institut d’Études Scientifiques de Cargèse (IESC), November 2026 (slides).
• “Gaussian Processes for Operator Learning: from Surrogates to Machine Precision”, SIAM Conference on Uncertainty Quantification (UQ26).
• “Machine Learning for partial differential equations: solvers and operator learning”, Youssef Marzouk’s group, MIT, February 2026.
• Talk and tutorial on operator learning, Institute of Mathematical Statistics and Actuarial Science, University of Bern, hosted by Prof. David Ginsbourger, January 2026 (slides, tutorial).
• “Machine Learning for partial differential equations: solvers and operator learning”, PLATON - INRIA and Polytechnique, hosted by Pietro Congedo and Olivier Le Maître.

2025

• “Solving Roughly Forced Nonlinear PDEs via Misspecified Kernel Methods and Neural Networks”, Early Career Pioneers in Uncertainty Quantification and AI for Science, Isaac Newton Institute, University of Cambridge, August 2025.
• “Operator learning using kernel methods”, Mathematical foundations of digital twins, Centre International de Rencontres Mathématiques, Marseille, August 2025 (slides).
• “Solving Roughly Forced Nonlinear PDEs via Misspecified Kernel Methods and Neural Networks”, ICOSAHOM 2025, Montreal, July 2025.
• “Solving Roughly Forced Nonlinear PDEs via Misspecified Kernel Methods and Neural Networks”, Workshop on Kernel Methods in Uncertainty Quantification and Experimental Design, Institute for Mathematical and Statistical Innovation (IMSI), April 3, 2025 (slides).
• “Solving Roughly Forced Nonlinear PDEs via Misspecified Kernel Methods and Neural Networks”, SIAM Conference on Computational Science and Engineering, March 7, 2025.
• Poster: “Solving Roughly Forced Nonlinear PDEs via Misspecified Kernel Methods and Neural Networks”, Workshop on Dynamical Systems and Machine Learning, Tokyo, February 17, 2025.
• “Solving Roughly Forced Nonlinear PDEs via Misspecified Kernel Methods and Neural Networks”, Workshop on Differential Equations for Data Science, Kyoto, February 11, 2025.

2024

• “Kernel methods are competitive for operator learning”, SIAM Mathematics of Data Science, October 24, 2024.
• “Kernel methods and PINNs for rough partial differential equations”, Digital Twins for inverse problems in Earth sciences (CIRM), July 22, 2024 (slides).
• “Kernel methods and PINNs for rough partial differential equations”, SciCADE, National University of Singapore, July 15, 2024.
• Research overview, Franca Hoffmann group meeting, May 31, 2024.
• “Kernel methods for rough partial differential equations”, SIAM Conference on Mathematical Aspects of Materials Science, May 20, 2024.
• “Kernel methods for rough partial differential equations”, Southern California Applied Mathematics Symposium, UC San Diego, April 27, 2024.
• “Kernel methods for rough partial differential equations”, SIAM Uncertainty Quantification 2024, March 1, 2024 (slides).
• “One shot learning of SDEs with Gaussian processes”, International Conference: Differential Equations for Data Science, February 22, 2024 (slides).

2023

• “Kernel methods for operator learning”, One World Mathematics of Machine Learning seminar (online), October 18, 2023 (watch).
• “Kernel Methods are Competitive for Operator Learning”, 2nd IACM Mechanistic Machine Learning and Digital Engineering for Computational Science Engineering and Technology, University of Texas at El Paso, September 23, 2023.
• “Kernel Methods are Competitive for Operator Learning”, 10th International Congress on Industrial and Applied Mathematics, August 22, 2023 (slides).
• “Kernel Methods are Competitive for Operator Learning”, Argonne National Lab LANS Seminar, August 2, 2023 (slides).
• “One Shot Learning of Stochastic Differential Equations with Gaussian Processes”, SIAM Conference on Applications of Dynamical Systems, May 18, 2023 (slides).
• “Kernel methods are competitive for Operator Learning”, DataSig Rough Path Interest Group (online), May 11, 2023.
• “Benchmarking Operator Learning with Simple and interpretable Kernel Methods”, Workshop on Establishing Benchmarks for Data-Driven Modeling of Physical Systems, April 6, 2023 (slides).
• “One shot learning of stochastic differential equations with Gaussian Processes”, SIAM Computational Science and Engineering, Amsterdam, March 6, 2023.

2022 and earlier

• Poster: “One shot learning of stochastic differential equations with Gaussian Processes”, Third Symposium on Dynamical Systems and Machine Learning, Fields Institute, Toronto, 2022 (poster).
• “One shot learning of stochastic differential equations with Gaussian processes and computational graph completion”, ICCOPT 2022, Lehigh University (slides).
• “Kernel Flows Demystified: Application to Regression”, Second Symposium on Dynamical Systems and Machine Learning, Fields Institute, Toronto, 2020 (watch).