Workshop on industrial applications of numerical analysis and machine learning

Benjamin Peherstorfer is Associate Professor at Courant Institute of Mathematical Sciences. Until 2016, he was a Postdoctoral Associate in the Aerospace Computational Design Laboratory (ACDL) at the Massachusetts Institute of Technology (MIT), working with Professor Karen Willcox. He received B.S., M.S., and Ph.D. degrees from the Technical University of Munich (Germany) in

2008, 2010, and 2013, respectively. His Ph.D. thesis was recognized with the Heinz-Schwaertzel prize, which is jointly awarded by three German universities to an outstanding Ph.D. thesis in computer science. Benjamin was selected for a Department of Energy (DoE) Early Career Award in the Applied Mathematics Program in 2018 and for an Air Force Young Investigator Program (YIP) award in Computational Mathematics in 2020. In 2021, Benjamin received a National Science Foundation (NSF) CAREER award in Computational Mathematics. His research focuses on computational methods for data- and compute-intensive science and engineering applications, including scientific machine learning, mathematics of data science, model reduction, and computational statistics.

Elena Celledoni is a professor at the Department of Mathematical Sciences at the Norwegian University of Science and Technology, Trondheim, Norway. She has a Ph.D in computational mathematics from the University of Padua, Italy. She held post doc positions at the University of Cambridge, UK, at the Mathematical Sciences Research Institute, Berkeley, CA.

Her research field is numerical analysis structure preserving algorithms for differential equations and geometric numerical integration. More recently she has also been working on structure preservation in neural networks, and geometric methods for shape analysis.

She is the vice President of the European Consortium of Mathematics in Industry and the secretary of the Society of Foundations of Computational Mathematics. She has served as the co-chair for research at the Department of Mathematical Sciences at NTNU.

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Marcelo Pereyra's research advances the statistical foundations of quantitative and scientific imaging. Over the past 15 years, he has made important contributions to Bayesian imaging sciences and developed significant connections between the statistical, variational and machine learning approaches to imaging. He is particularly interested in robust uncertainty quantification in imaging inverse problems, automatic calibration and verification of statistical image models, scalable Bayesian computation algorithms derived from stochastic diffusion processes, and applications of imaging with high social or environmental value.

Nicolas Boullé is an Assistant Professor in Applied Mathematics at Imperial College London. He obtained a PhD in numerical analysis at the University of Oxford in 2022 and was a postdoc at the University of Cambridge from 2022-2024. His research focuses on the intersection between numerical analysis and deep learning, with a specific emphasis on scientific machine learning and learning physical models from data, particularly in the context of partial differential equations learning. He was awarded a Leslie Fox Prize in 2021 and a SIAM Best Paper Prize in Linear Algebra in 2024 for his work on operator learning .

Andreas Rosskopf studied Applied Mathematics with a focus on Numerical Simulation in Erlangen, Germany. Since 2012 he's with Fraunhofer IISB in Erlangen; in 2018 he founded the working group "AI-augmented Simulation “ combing AI and numerical approaches for the simulation and optimization of power electronic devices and systems. Since 2023 he's head of the "Modeling and Artificial Intelligence" department of the Fraunhofer IISB designing digital solutions in the field of power electronics, Technology Computer-Aided Design and lithography.

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Marta D'Elia is the Director of AI and ModSim at Atomic Machines and an Adjunct Professor at the Institute for Computational and Mathematical Engineering at Stanford University. She previously worked at Pasteur Labs, Meta, and Sandia National Laboratories as a Principal Scientist and Tech Lead. She holds a PhD in Applied Mathematics and master's and bachelor's degrees in Mathematical Engineering. Her work deals with development and analysis of machine-learning models and optimal design and control for manufacturing applications, especially at the micro scale. She is an expert in multiscale modeling and simulation, optimal control, and scientific machine learning. She is an Associate Editor of SIAM and Nature journals, a member of the SIAM industry committee, and the Vice Chair of the SIAM Northern California section.

Tiago Botari is a computational physicist working as a Senior Machine Learning Researcher at ASML, developing physics-informed AI for fast image processing in metrology and inspection within the semiconductor industry. He earned his PhD in Physics from the University of Campinas (UNICAMP) and conducted postdoctoral research at UC Berkeley, TU Berlin, and the University of São Paulo. His interdisciplinary work combines physics, computer science, and engineering to tackle complex industrial challenges.

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The conference will be held at the Congress Centre of Amsterdam Science Park, next to Centrum Wiskunde & Informatica (CWI).

Address: Science Park 125, 1098 XG Amsterdam.

Google Maps Congress Centre, Science Park 125

Please be aware that hotel prices in Amsterdam can be quite steep. We strongly recommend all participants to secure their hotel reservations as early as possible!

Hotel Recommendations:

• Generator Hostel
• MEININGER Hotel (Amsterdam Amstel)
• Hotel Casa
• The Manor Amsterdam
• The Lancaster Hotel Amsterdam

From these hotels, the venue can be reached in 15-30 minutes with public transport. In all public transportation, you can check in and out with a Mastercard or Visa contactless credit card and also with Apple Pay and Google Wallet.