Publications & Scientific Activities
Publications and Preprints
2024  M. LiuSchiaffini, 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 

2024  H. C. Nam, J. Berner, A. Anandkumar  Solving Poisson Equations using Neural WalkonSpheres.  ICML 2024 

2024  Z. Hao, C. Su, S. Liu, J. Berner, C. Ying, H. Su, A. Anandkumar, J. Song, J. Zhu  DPOT: AutoRegressive Denoising Operator Transformer for LargeScale PDE PreTraining.  ICML 2024  
2023  L. Richter, J. Berner, G.H. Liu  Improved sampling via learned diffusions. 
ICML 2023 Workshop on New Frontiers in Learning, Control, and Dynamical Systems 

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 

2022  J. Berner, L. Richter, and K. Ullrich  An optimal control perspective on diffusionbased generative modeling.  Transactions on Machine Learning Research Oral presentation at NeurIPS 2022 workshop on scorebased methods 

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) 

2022  L. Richter, J. Berner  Robust SDEBased Variational Formulations for Solving Linear PDEs via Deep Learning. 


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) 

2020  J. Berner, M. Dablander, and P. Grohs  Numerically Solving Parametric Families of HighDimensional 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 SARSCoV2 allows for up to 10fold 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 BlackScholes partial differential equations.  SIAM Journal on Mathematics of Data Science Featured in "What’s Happening in the Mathematical Sciences" (AMS) 
Scientific Activities
05/2023  Poster Presentation  ICLR 2024  Vienna Exhibition and Congress Center  
0405/2024  Research Visit  Machine Learning Group (KlausRobert 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  Virtualonly (MPI and UCLA)  
0103/2024  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  Virtualonly (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  
0912/2022  Intern  Remote extension of the internship at Fair Labs  Magnit @ Meta  
0508/2022  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  Virtualonly 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  
0712/2021  Invited Participant  Mathematics of deep learning  Isaac Newton Institute for Mathematical Sciences  
20192020  Reviewer  Reviews for AAP, ISIT, SampTA  
12/2020  Poster Presentation  NeurIPS 2020  Virtualonly Conference  
09/2020  Participant  Summer School  Vienna School of Mathematics  
0510/2020  Intern  AI Research (semisupervised & 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: VDSPESI 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  CoOrganizer  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 