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
Alireza MafiExpertise
Théorie de l'électromagnétismeAntennes et Micro-ondesCircuits Intégrés de RadiofréquenceRéseaux PhasésBioélectronique
Expertise
Théorie de l'électromagnétismeAntennes et Micro-ondesCircuits Intégrés de RadiofréquenceRéseaux PhasésBioélectronique
Je suis doctorant en Génie électrique à l'école Polytechnique Fédérale de Lausanne (EPFL). J'ai obtenu mon Master en Génie électrique (Communications Micro-ondes et Optiques) à l'Université de Technologie de Sharif, Téhéran, Iran, en 2022. J'ai également t
Alexander DittrichCurrent Work
My research interests are somewhere between neural networks with Hebbian plasticity, meta-reinforcement learning, and the control of rather unconventional soft robotic platforms like tensegrity robots.
Simon Elias BibriExpertise
• Sustainable cities• Smarter eco-cities• Sustainable smart cities• Artificial Intelligence of Things (AIoT) • Generative Artificial Intelligence of Things (GAIoT)• Foundation Models (FMs) • Quantum Generative Artificial Intelligence (QGAI)• Urban Digital Twins (UDTs)• Urban Brain (UB) • Smart Circular Metabolism• Urban planning and design• Environmental sustainability and climate resilience• Sustainable smart energy• Sustainable smart transportation• Sustainable smart waste and water management•
Adriano MeleEnseignement et PhD Cours Control and Operation of Tokamaks PHYS-748
Daryl Yee Wei LiangIncoming Assistant Professor of Electrical and Microengineering (March 2023)
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
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
Pasquale Davide SchiavoneEnseignement et PhD Cours Design technologies for integrated systems CS-472 La synthèse hardware est le processus de transformation - itérativement raffiné, détaillé et optimisé - d'une représentation faite en langage de description matérielle en un circuit. Ce cours présente les algorithmes, outils et méthodes de la compilation matérielle et de la synthèse logique.
Yujia ZhangEnseignement et PhD Doctorant·es actuel·les Yuyan Su, Shulang Shen, Jiaqi Liu Cours Advanced microfabrication practicals MICRO-373 Ce TP permet une formation approfondie sur les méthodes avancées de micro et nanofabrication dans un environnement de salle blanche pour des applications sélectionnées, d'acquérir des connaissances plus approfondies sur les processus MEMS/NEMS, de travailler dans un petit groupe avec des doctorants/ MOOC: Micro and Nanofabrication (MEMS) - Fall MICRO-
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
Wenlong LiaoExpertise
deep learning, power system, renewable energy, data analysis
Emily Caroline Bezerra AlexandreExpertise
Printed electronics, flexible sensors, sustainable electronics, additive manufacturing
Emily Bezerra obtained her BSc. degree in Science and Technology from the Federal University of Rio Grande do Norte (UFRN), Brazil, in 2019, followed by a second BSc. degree in Materials Engineering from the same institution in 2020. In 2022, she received a master´s degree in Materials Science and Engineering from the King Abdullah University of Science and Technology (KAUST), Saudi Arabia. As part of her maste
Davide TisiGraduated in Physics from the University of Modena and Reggio Emilia, Italy. He earned his PhD in SISSA under the supervision of Prof. Stefano Baroni, working on the computation of transport properties of liquids from ab initio approach and NN potential. He joined the Laboratory of Computational Science and Modelling (COSMO), led by Prof. Michele Ceriotti, in June 2022.He is currently working on the development of machine learning potential for LiPS materials for battery applications.
