معرفی
Prof. Dr. Aurelien Lucchi is an Assistant Professor in the Department of Mathematics and Computer Science at the University of Basel, Faculty of Science. His research group focuses on the intersection of optimization and machine learning, particularly in advancing theoretical understanding and algorithmic design for deep learning systems.
His research interests include:
- Stochastic and non-convex optimization
- Deep learning theory and generalization
- Kernel methods and spectral analysis
- Transformer architectures and training dynamics
- Batch Normalization and initialization effects
- Modeling optimization via stochastic differential equations (SDEs)
The recent publications (2023–2025) highlight a strong focus on theoretical machine learning, especially in characterizing optimization landscapes, generalization in kernel methods, and the role of noise and adaptive methods in training. There is a clear trend toward using advanced mathematical tools—such as random matrix theory, SDEs, and curvature analysis—to explain phenomena in deep learning. His group actively publishes in top venues including NeurIPS, ICML, ICLR, and AISTATS.
Scientific awards and recognitions include:
- SNF Consolidator Grant (1.7M CHF)
Prof. Lucchi leads an active research group with postdoctoral fellows and ongoing projects, including work on quantum machine learning and noise-adaptive optimization. He has secured competitive research funding and mentors early-career researchers. His group has received recent paper acceptances at ICLR 2025, AISTATS 2025 (oral), and NeurIPS 2024, indicating strong momentum in theoretical and algorithmic machine learning. He previously held a scientific research position at ETH Zurich (2014–2021) and earned his PhD from EPFL.
The group is currently involved in two major ongoing projects:
- Designing and Training Hybrid Hierarchical Quantum Neural Networks with Quantum Advantage
- Noise-Adaptive Optimization Methods and their Robustness Properties
