Publications & Scientific Activities
Publications and Preprints
| 2024 | M. A. Rahman, R. J. George, M. Elleithy, D. Leibovici, Z. Li, B. Bonev, C. White, J. Berner, R. A. Yeh, J. Kossaifi, K. Azizzadenesheli, A. Anandkumar | Pretraining Codomain Attention Neural Operators for Solving Multiphysics PDEs. | NeurIPS 2024 | |
| 2024 | M. Liu-Schiaffini, J. Berner, B. Bonev, T. Kurth, K. Azizzadenesheli, A. Anandkumar | Neural Operators with Localized Integral and Differential Kernels. | ICML 2024 Oral presentation at ICLR 2024 Workshop on AI4Differential Equations In Science |
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| 2024 | H. C. Nam, J. Berner, A. Anandkumar | Solving Poisson Equations using Neural Walk-on-Spheres. | ICML 2024 |
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| 2024 | Z. Hao, C. Su, S. Liu, J. Berner, C. Ying, H. Su, A. Anandkumar, J. Song, J. Zhu | DPOT: Auto-Regressive Denoising Operator Transformer for Large-Scale PDE Pre-Training. | ICML 2024 | |
| 2023 | L. Richter, J. Berner | Improved sampling via learned diffusions. |
ICML 2023 Workshop on New Frontiers in Learning, Control, and Dynamical Systems |
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| 2023 | S. Frieder, L. Pinchetti, R. Griffiths, T. Salvatori, T. Lukasiewicz, P. Petersen, A. Chevalier, J. Berner | Mathematical Capabilities of ChatGPT. | NeurIPS 2023 Datasets and Benchmarks Track |
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| 2022 | J. Berner, L. Richter, and K. Ullrich | An optimal control perspective on diffusion-based generative modeling. | Transactions on Machine Learning Research Oral presentation at NeurIPS 2022 workshop on score-based methods |
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| 2022 | J. Berner, P. Grohs, and F. Voigtlaender | Learning ReLU networks to high uniform accuracy is intractable. |
Featured in "What’s Happening in the Mathematical Sciences" (AMS) |
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| 2022 | L. Richter, J. Berner | Robust SDE-Based Variational Formulations for Solving Linear PDEs via Deep Learning. |
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| 2021 | J. Berner, P. Grohs, G. Kutyniok, and P. Petersen | The Modern Mathematics of Deep Learning. | Mathematical Aspects of Deep Learning (Cambridge University Press) Magazine of the German Mathematical Society (shortened, in German) |
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| 2020 | J. Berner, M. Dablander, and P. Grohs | Numerically Solving Parametric Families of High-Dimensional Kolmogorov Partial Differential Equations via Deep Learning. | ||
| 2020 | C. M. Verdun, T. Fuchs, P. Harar, D. Elbrächter, D. S. Fischer, J. Berner, P. Grohs, F. J. Theis, and F. Krahmer | Group testing for SARS-CoV-2 allows for up to 10-fold efficiency increase across realistic scenarios and testing strategies. | ||
| 2019 | J. Berner, D. Elbrächter, and P. Grohs | How degenerate is the parametrization of neural networks with the ReLU activation function? | Proceedings of NeurIPS 2019 | |
| 2019 | J. Berner, D. Elbrächter, P. Grohs, and A. Jentzen | Towards a regularity theory for ReLU networks–chain rule and global error estimates. | Proceedings of SampTA 2019 | |
| 2018 | J. Berner, P. Grohs, and A. Jentzen | Analysis of the generalization error: Empirical risk minimization over deep artificial neural networks overcomes the curse of dimensionality in the numerical approximation of Black-Scholes partial differential equations. | SIAM Journal on Mathematics of Data Science Featured in "What’s Happening in the Mathematical Sciences" (AMS) |
Scientific Activities
| 07/2024 | Poster Presentation | ICML 2024 | Vienna Exhibition and Congress Center | |
| 05/2024 | Poster Presentation | ICLR 2024 | Vienna Exhibition and Congress Center | |
| 04-05/2024 | Research Visit | Machine Learning Group (Klaus-Robert Müller) | TU Berlin | |
| 04/2024 | Invited Speaker | AI Bootcamp - Physics Informed Learning and Computation | Caltech | |
| 03/2024 | Award Recipient | Excellence grant and ring of honor for Promotio sub auspiciis Praesidentis rei publicae News: Presidential Chancellery, Faculty of Mathematics, University of Vienna, Newspaper |
Presidential Chancellery of Austria | |
| 02/2024 | Oral Presentation | SIAM UQ24 | Savoia Excelsior Palace Trieste | |
| 02/2024 | Speaker | Math Machine Learning seminar MPI MIS + UCLA | Virtual-only (MPI and UCLA) | |
| 01-03/2024 | Teaching Assitant | Foundations of Machine Learning | Caltech | |
| 12/2023 | Comittee Member | Member of the AI graduate admissions committee | Caltech | |
| 12/2023 | Poster Presentation | NeurIPS 2023 | New Orleans Convention Center | |
| 10/2023 | Invited Panelist | ARIT 2023 | Millennium Biltmore Hotel | |
| 09/2023 | Fellowship Recipient | Wally Baer and Jeri Weiss Postdoctoral Fellowship in IST | Caltech | |
| 06/2023 | PhD Defense | Committee: Christoph Reisinger (University of Oxford), Siddhartha Mishra (ETH Zurich), Philipp Grohs (University of Vienna, Supervisor) | University of Vienna | |
| 05/2023 | Poster Presentation | ICLR 2023 | Kigali Convention Centre | |
| 2023 | Reviewer | Reviews for ICLR '24, NeurIPS '23, ICML '23, Nature Mach. Intell., AISTATS '24, SN PDEA, SIMODS, ACHA, Journal of Complexity, ICML '23 Workshop Frontiers4LCD, NeurIPS '23 Datasets and Benchmarks Track | ||
| 03/2023 | Speaker | Learning on Graphs and Geometry (LoGG) reading group | Virtual-only (MIT and Valence Discovery) | |
| 2022 | Reviewer | Reviews for ICLR, NeurIPS, ICML (outstanding reviewer), JUQ, SISC | ||
| 12/2022 | Oral Presentation | New Orleans Convention Center | ||
| 11/2022 | Speaker | PhD Colloquium | Vienna School of Mathematics | |
| 10/2022 | Speaker | Research Group Meeting (Anima Anandkumar) | Caltech | |
| 09/2022 | Participant | Summer School | Vienna School of Mathematics | |
| 09/2022 | Speaker | Research Group Meeting (Christoph Lampert) | ISTA | |
| 07/2022 | Paper Presentation | ICML 2022 | Baltimore Convention Center | |
| 09-12/2022 | Intern | Remote extension of the internship at Fair Labs | Magnit @ Meta | |
| 05-08/2022 | Intern | AI Research (generative modeling in the context of neural compression) with Karen Ullrich and Matthew Muckley | Meta (Fair Labs) | |
| 04/2022 | Participant | ICLR 2022 | Virtual-only Conference | |
| 03/2022 | Invited Participant | LMS Invited Lectures on the Mathematics of Deep Learning | Isaac Newton Institute | |
| 2021 | Reviewer | Reviews for NeurIPS, ICLR (highlighted reviewer), ACHA, SISC | ||
| 08/2021 | Invited Guest | The Mathematics of Deep Learning | ACIT Science Podcast | |
| 08/2021 | Participant | Mathematics of Machine Learning | Center for Interdisciplinary Research, Bielefeld University | |
| 07-12/2021 | Invited Participant | Mathematics of deep learning | Isaac Newton Institute for Mathematical Sciences | |
| 2019-2020 | Reviewer | Reviews for AAP, ISIT, SampTA | ||
| 12/2020 | Poster Presentation | NeurIPS 2020 | Virtual-only Conference | |
| 09/2020 | Participant | Summer School | Vienna School of Mathematics | |
| 05-10/2020 | Intern | AI Research (semi-supervised & active learning for autonomous driving) with Elmar Haussmann and Christoph Angerer | NVIDIA | |
| 03/2020 | Speaker | VSM Student Retreat | Vienna School of Mathematics | |
| 02/2020 | Invited Tutor | Machine Learning in Physics: VDSP-ESI Winter School 2020 | Erwin Schrödinger International Institute for Mathematics and Physics | |
| 12/2019 | Poster Presentation | NeurIPS 2019 | Vancouver Convention Center | |
| 11/2019 | Participant | WeAreDevelopers Congress 2019 | Hofburg Vienna | |
| 11/2019 | Speaker | Oberwolfach Graduate Seminar: Mathematics of Deep Learning | Mathematical Research and Conference Center in Będlewo | |
| 10/2019 | Speaker | Mathematical and Computational Aspects of Machine Learning | Centro di Ricerca Matematica Ennio De Giorgi at Scuola Normale Superiore | |
| 09/2019 | Participant | Summer School | Vienna School of Mathematics | |
| 09/2019 | Invited Participant and Speaker | Innovative Approaches to the Numerical Approximation of PDEs | Oberwolfach Research Institute for Mathematics | |
| 07/2019 | Speaker | SampTA 2019 | Université de Bordeaux | |
| 06/2019 | Speaker | RTG Summer Lectures | University of Chicago | |
| 06/2019 | Speaker | Research Group Meeting (Joan Bruna and Afonso Bandeira) | NYU Center for Data Science | |
| 03/2019 | Participant | Winter School on Quantum Computation | VDS Mathematics | |
| 02/2019 | Speaker | GAMM Annual Meeting 2019 | TU Vienna | |
| Since 02/2019 | Co-Organizer | Deep Learning Seminar | University of Vienna, OFAI, ARI |
Theses
| 2023 | Mathematical Analysis of Deep Learning with Applications to Kolmogorov Equations. | PhD Thesis | University of Vienna | |
| 2018 | Solving stochastic differential equations and Kolmogorov equations by means of deep learning and Multilevel Monte Carlo simulation. | Master's Thesis | University of Vienna | |
| 2016 | Diskrete Kosinustransformation in der Bildverarbeitung (in German). | Bachelor's Thesis | University of Vienna |
"Mathematicians are like Frenchmen: whatever you say to them they translate into their own language and forthwith it is something entirely different." - Goethe