Bruno Emanuel Ferreira De Sousa CorreiaThroughout my PhD and postdoctoral studies I was trained in world-renowned laboratories and institutions in the United States of America (University of Washington and The Scripps Research Institute). Very early in my scientific career I found out my fascination about protein structure and function. My PhD studies evolved in the direction of immunogen design and vaccine engineering which sparked my interest in the many needs and opportunities in vaccinology and translational research. My efforts resulted in an enlightening piece of work where for the first time, computationally designed immunogens elicited potent neutralizing antibodies. During my postdoctoral studies I joined a chemical biology laboratory at the Scripps Research Institute. In this stage I developed novel chemoproteomics methods for the identification of protein-small molecule interaction sites in complex proteomes. In March 2015, I joined the École Polytechnique Fédérale de Lausanne (EPFL) – Switzerland as a tenure track assistant professor. The focus of my research group is to develop computational tools for protein design with particular emphasis in applying these strategies to immunoengineering (e.g. vaccine and cancer immunotherapy). The activities in my laboratory focus on computational design methods development and experimental characterization of the designed proteins. Our laboratory has been awarded with 2 prestigious research grants from the European Research Council. Lastly, I have been awarded the prize for best teacher of Life sciences in 2019.
Johanni Michael BreaEnseignement et PhD Doctorant·es actuel·les Aude Maier, Lucas Louis Gruaz, Flavio Martinelli Cours Biological data science II : machine learning BIO-322 Les étudiants comprennent les concepts et méthodes de base d'apprentissage automatique. Ils savent les décrire en termes mathématiques et ils savent les appliquer aux données en utilisant un langage de haut niveau (julia/python/R). Intelligence artificielle et société HUM-286 Ce cours intègre les SHS avec les sciences de l'
Isabella Di LenardoPh.D. in Theories and Art History. Isabella di Lenardo is senior scientist in Digital Humanities and Urban History. Her research activity is focused on digital tools and methods applied to Urban History. She is an expert in ancient cartography, city representations, cadastral sources interpreted through digital modeling, extraction and analysis systems.She's also interested in network analysis questioning the production and circulation of artistic and architectural knowledge in Europe XVIth – XVIIIth centuries in particular on North-South relationships and influences. The development of collaboration projects with European institutions and the activity carried out on various initiatives supported by the European Commission have allowed her to acquire specialism in the field of science applied to heritage, developing in particular specific skills for the enhancement of Cultural Heritage through digital tools. She leads projects in collaboration with Bibliothèque national de France (Paris), Institut National d’Histoire de l’Art (Paris), Ecole nationale des chartes (Paris), Université Paris I Panthéon-Sorbonne, Centre Allemand d’Histoire de l’Art (Paris), Musée du Louvre, Archives Nationales de Paris, Bibliothèque Historique de la Ville de Paris, Réunion des Musées Nationaux. She supervised all the urban modeling simulations for the Venice Time Machine project and acted as content curator for all the exhibitions (Venice Biennale, Grand Palais-Paris, Datasquare - ArtLab-Lausanne, Museo Correr-Venice). Head of “Replica” project : digitizing 1 million photos of artworks in the Fondazione Giorgio Cini (Venice) and visual patterns extraction through a search engine for visual similarities. She has a Ph.D in Art History and also studied Archaeology and Urban Studies. She has published essays and articles about Venetian Art and Urban History and participated in many Art exhibitions in European museums. She coordinated summer schools and workshops about Digital Tools for Art History and Urban History. She attended conferences on digital methods for History in universities and patrimonial institutions.
Rémi Guillaume PetitpierreEnseignement et PhDHistoire urbaine digitale: Lausanne Time Machine 2020&2023: HUM-450, HUM-454 - Assistanat. Site du coursHumanités digitales 2023&202? : HUM-369 - Assistanat.
Amaury Pierre Jiezhi WeiExpertise
Deep Learning, Computer Vision, Physics Informed Machine Learning, Graph Neural Networks
Amaury a obtenu son Master en Robotique de l'Ecole Polytechnique Federale de Lausanne (EPFL) en 2021. Avant de rejoindre IMOS, il a travaillé pendant plusieurs années en tant que Software Engineer chez Logitech Europe S.A. dans le domaine de la Réalité Virtuelle et Augmentée. Il a développé des algorithmes de 6DoF tracking pour appareils portables, développé le firmware et conçu la stratégie de testing industr
Edoardo RemelliI am a PhD student at the Computer Vision Laboratory (EPFL) under the supervision of Prof. Pascal Fua. My research focuses on developing Geometric Deep Learning algorithms to solve problems in Computer Vision and Computational Geometry. During my studies I spent time as an intern at Facebook Reality Labs and MindMaze.
Yifan HuExpertise
Optimization, Operations Research, Reinforcement Learning
I am a Postdoc with Prof. Daniel Kuhn from EPFL and Prof. Andreas Krause from ETH Zurich. Prior to that, I got my PhD in Operations Research from University of Illinois at Urbana-Champaign, advised by Prof. Xin Chen and Prof. Niao He. My research interest lies in data-driven decision-making with problems arising from optimization, operations research, and statistics. See Research for research directions and Publication for papers.
Karen Ann J MullenersEnseignement et PhD Doctorant·es actuel·les Gaétan Raynaud, Lorenzo Ermanni, Thomas Antoine Nicolas Berger, Sahar Rezapour, Alexandros Anastasiadis, Henrike Cornelia Christ, Harshi Bavishi A dirigé les thèses EPFL de Swathi Krishna, Guillaume de Guyon-Crozier, Diego Francescangeli, Sébastien Le Fouest, Alexander Gehrke, Mrudhula Baskaran Cours Aerodynamics ME-445 Ce cours présente les bases de la dynamique des fluides pour comprendre comment l'air s'écoule autour d'ailes et de corps en deu
Loris Di NataleExpertise
(Deep) Machine Learning, (Deep) Reinforcement Learning, Building Control
