Research interests
His research interests lie at the interface between statistical physics, computer science, machine learning and information theory. I’m interested in fundamental aspects as well as in the development of statistical physics algorithms for optimization and learning problems.
Basic topics:
- Machine Learning & Deep Learning
- Statistical physics, stochastic processes
- Inverse problems
- Distributed algorithms for optimisation, constraint satisfaction problems and statistical inference
- Information Theory
His past studies include combinatorial optimization, probabilistic and message-passing algorithms, statistical physics of complex systems (disordered systems), out-of-equilibrium dynamics, analysis of algorithms and interdisciplinary applications of statistical physics (learning algorithms, inverse problems in systems biology, source coding, game theoretical models).