Martin SchrimpfExpertise
NeuroAI, computational neuroscience, computer vision, natural language processing, human alignment, startups [CV]
Mission
Modeling the brain: Martin's research focuses on a computational understanding of the neural mechanisms underlying natural intelligence in vision and language. To achieve this goal, he bridges Deep Learning, Neuroscience, and Cognitive Science, building artificial neural network models that match the brain’s neural representations in their internal processing and are aligned to
Olga FinkEnseignement et PhD Doctorant·es actuel·les Han Sun, Leandro Von Krannichfeldt, Keivan Faghih Niresi, Zepeng Zhang, Vinay Sharma, Chenghao Xu, Raffael Pascal Theiler, Kevin Antonio Steiner, Sergei Garmaev, Amaury Wei A dirigé les thèses EPFL de Ismail Nejjar, Mengjie Zhao Cours Data Science for infrastructure condition monitoring CIVIL-332 Le cours couvrira les étapes pertinentes de la surveillance de l'état des infrastructures basée sur les données: l'acquisition des données, en passant p
Georgios MoschidisEnseignement et PhD Doctorant·es actuel·les Abraham Gabriel Dorsaz Cours Analysis IV (for EL, GM, MX) MATH-207(c) Ce cours est une introduction à la théorie de l'analyse complexe, aux séries et transformées de Fourier, à la transformée de Laplace, avec des applications à la théorie des équations différentielles ordinaires et partielles. Differential geometry I - curves and surfaces MATH-213 Ce cours constitue une introduction à la géométrie différentielle classique, qui ét
Parmenion MavrikakisExpertise
Computational electromagnetics, integral equations, finite element analysis, metasurfaces, photonics, plasmonics and microwaves.
Current Work
Developing a C++ code for the modeling of light scattering in layered media with the surface integral equation method.
I am a PhD student in the Electrical Engineering Department (EDEE) at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Nanophotonics and Metrology Laboratory (NAM), part of the Microengineering Department (IEM) at EPFL. M
Laura GrigoriExpertise
Numerical linear algebra, deterministic and randomized algorithmsHigh performance computing for scientific applicationsHigh dimensional problems and tensor computationsCombinatorial scientific computingMathematical Software
Enseignement et PhD Doctorant·es actuel·les Israa Fakih, Zhipeng Xue, Andrea Di Antonio, Mariana Graciela Martinez Aguilar, Guifré Sánchez i Serra Cours Numerical analysis and computational mathematics MATH-456 Ce cours offre une introduction au calcul scienti
Simon Pierre Henri VincentEnseignement et PhD Cours Physique générale : électromagnétisme PHYS-201(a) Le cours traite des concepts de l'électromagnétisme et des ondes électromagnétiques.
Adriano MeleEnseignement et PhD Cours Control and Operation of Tokamaks PHYS-748
Konstantinos KarapiperisEnseignement et PhD Doctorant·es actuel·les Hrishikesh Gopakumar Menon, Thomas Henzel Cours Multiscale modelling in mechanics CIVIL-408 Ce cours présente les principes et techniques de modélisation des matériaux à différenteséchelles spatiales, depuis le niveau des atomes ou des grains jusqu'à l'échelle continue oustructurelle. L'accent est mis sur la mise à l'échelle hiérarchique (homogénéisation),tandis que les techniques concu Soil mechanics and flow through porous media CIVIL
Keivan Faghih NiresiExpertise
Computational sensing/imaging, Inverse problems, Signal processing, Graph neural networks, Domain adaptation.
Keivan is a PhD student at école Polytechnique Fédérale de Lausanne (EPFL). He joined the Intelligent Maintenance and Operations Systems (IMOS) Lab under the supervision of Prof. Olga Fink in February 2023.Prior to joining EPFL, he obtained his master's degree from the Institute of Communications Engineering, College of Electrical Engineering and Computer Science, National Tsing Hua Univer
Wenlong LiaoExpertise
deep learning, power system, renewable energy, data analysis
Lazar MilikicExpertise
LLMs (Large Language Models), NLP (Natural Language Processing), Automated Reasoning
Mission
My work focuses on developing robust methods for autoformalization and automated theorem proving using large language models. I aim to enhance the reasoning reliability of modern AI systems by reducing hallucinations, improving predictability, and integrating formal verification techniques and tools into LLM-based pipelines.
Before joining EDIC, I completed a Bachelor’s in Mathematics and Computer Science
