Clément Hongler

clement.hongler@epfl.ch +41 21 693 52 87 http://csft.epfl.ch

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Nationality: CH

EPFL SB MATH CSFT
MA C1 643 (Bâtiment MA)
Station 8
1015 Lausanne

+41 21 693 52 87
+41 21 693 52 93
Office: MA C1 643
EPFL › SB › MATH › CSFT

Website: https://csft.epfl.ch/

+41 21 693 52 87
EPFL › SB › SB-SMA › SMA-ENS

Website: https://sma.epfl.ch/

+41 21 693 52 87
EPFL › VPA › VPA-AVP-DLE › AVP-DLE-EDOC › EDMA-GE

Website: https://go.epfl.ch/phd-edma

+41 21 693 52 87
EPFL › VPA › VPA-AVP-DLE › AVP-DLE-EDOC › EDFI-ENS

+41 21 693 52 87
EPFL › VPA › VPA-AVP-DLE › AVP-DLE-EDOC › EDMA-ENS

Mission

Most of our research is devoted to statistical mechanics, quantum field theory and deep learning theory.
Key projects include:
--The investigation of the connection between lattice models and conformal field theories.
--The dynamics of learning, in particular that of neural networks during training.
--Artificial life, in particular continuous cellular automata.
--Decentralized systems.

Phone

+41 79 884 69 57

Biography Teaching & PhD

2006: B.Sc. Math, EPFL
2008: M.Sc. Math, EPFL
2010: Ph.D. Math, Université de Genève
2010-2014: Ritt Assistant Professor, Columbia University
2014-2018: Tenure-Track Assistant Professor, EPFL
2019-present: Associate Professor, EPFL

Teaching & PhD

PhD Students

Ramzi Sofiane Dakhmouche

Past EPFL PhD Students

Sung Chul Park, Francesco Spadaro, Arthur Ulysse Jacot-Guillarmod, Berfin Simsek, Amire Bendjeddou, Golikov Evgenii

Courses

Probabilistic models of modern AI

MATH-415

This is a course where we will cover various topics related to recent progress in AI. Emphasis will be on understanding conceptual and mathematical principles, and on relating those to experimental data for machine learning models used in practice.