About Me
Hi, my name is Davide and I am a PhD student in Mathematics at the University of Münster - Cluster of Excellence Mathematics (Germany).
Research
University of Muenster - Cluster of Excellence Mathematics
PhD in Applied Mathematics
July 2023 - Current
University profile
The PhD focuses on exploring novel techniques in Machine Learning and their mathematical understanding. Supervisor: Arnulf Jentzen.
Published Papers
- Julian Kranz, Davide Gallon, Steffen Dereich, Arnulf Jentzen. SAD Neural Networks: Divergent Gradient Flows and Asymptotic Optimality via o-minimal Structures. Advances in Neural Information Processing Systems (NeurIPS 2025). [link] (2025).
- Davide Gallon, Philippe von Wurstemberger, Patrick Cheridito, Arnulf Jentzen. Physics-informed diffusion models in spectral space. Accepted at ICML 2026. [ArXiv] (2026).
Preprints
- Davide Gallon, Arnulf Jentzen, Philippe von Wurstemberger. An overview of diffusion models for generative artificial intelligence. [ArXiv] (2024).
- Davide Gallon, Arnulf Jentzen, Felix Lindner. Blow up phenomena for gradient descent optimization methods in the training of artificial neural networks. [ArXiv] (2022).
News
(July 2026) My paper Physics-informed diffusion models in spectral space was accepted at the International Conference on Machine Learning (ICML 2026). I will attend the conference in South Korea.
(April 2026 - September 2026) I am the teaching assistant for the course Numerical Methods for PDEs II at the University of Münster.
(December 2025) I attended the Conference on Neural Information Processing Systems (NeurIPS 2025) in San Diego with the paper SAD Neural Networks: Divergent Gradient Flows and Asymptotic Optimality via o-minimal Structures by Julian Kranz, Davide Gallon, Steffen Dereich, and Arnulf Jentzen.
(March 2025 - March 2026) I was a visiting PhD student at ETH Zurich, under the supervision of Patrick Cheridito. Thank you for this opportunity!
(September 2024) I attended the Mediterranean Machine Learning Summer School in Milan.
(August 2024) I attended the Deep Learning Theory Summer School at Princeton.
(April 2024 - September 2024) I was the teaching assistant for the course Numerical Optimization at the University of Münster.
Experience
Inspire the next
My primary responsibility was to design and implement innovative solutions to complex data-related problems, working closely with a international team of researchers and engineers. One of the major projects I have worked on is the development of a machine learning model to predict transformer’s performance. The resulting model was integrated into the company’s application.
Education
University of Padua
Master degree in Mathematical Engineering
October 2019 - March 2022
Graduated with 110/110 cum laude. I have dedicated my studies to numerical methods for differential equations, dynamic simulations, neural networks for deep learning, numerical methods for big data, stochastic equations.
University of Padua
Bachelor degree in Mathematics
October 2016 - September 2019
The Bachelor in Mathematics program provided me a comprehensive education in theoretical and applied mathematics.
A Little More About Me
I have a deep interest in staying informed about the latest AI technological advancements.