Devis TuiaI come from Ticino and studied in Lausanne, between UNIL and EPFL. After my PhD at UNIL in remote sensing, I was postdoc in Valencia (Spain), Boulder (CO) and EPFL, working on model adaptation and prior knowledge integration in machine learning. In 2014 I became Research Assistant Professor at University of Zurich, where I started the 'multimodal remote sensing' group. In 2017, I joined Wageningen University (NL), where I was professor of the GeoInformation Science and Remote Sensing Laboratory. Since 2020, I joined EPFL Valais, to start the ECEO lab, working at the interface between Earth observation, machine learning and environmental sciences.
Ehsan PajouheshgarExpertise
Computer Vision, Computer Graphics, Complex Systems, Machine Learning
Education Bachelor of Science | Major: Computer EngineeringMinor: Mathematics 2015 – 2020 Sharif University of Technology Computer Science | 2020 – 2025 EPFL Directed by Sabine Süsstrunk
Awards Teaching Assistance Excellence Award EPFL 2023 Best Student Paper Award From ALife 2024 Conference ALife 2024 Conference 2024 Best Paper Award From SIGGRAPH 2022 Conference ACM
Amir Roshan ZamirAmir Zamir is a Tenure-Track Assistant Professor of Computer Science at the Swiss Federal Institute of Technology in Lausanne (EPFL). Prior to EPFL, he spent time at Stanford, UC Berkeley, and UCF. His research interests are broadly in computer vision, machine learning, perception-for-robotics, and AI. He has been recognized with CVPR (2018) Best Paper Award, CVPR (2016) Best Student Paper Award, CVPR (2020) Best Paper Award Nomination, and NVIDIA Pioneering Research Award (2018), among others. His research has been covered by various press outlets, such as The New York Times or Forbes.
Pierre DillenbourgAncien instituteur primaire, Pierre Dillenbourg obtient un master en Sciences de lEducation (Université de Mons, Belgique). Dans son projet de master en 1986, il est l'un des premiers au monde à appliquer les méthodes de 'machine learning' à l'éducation, afin de développer un 'self-improving teaching system'. Ceci lui permettra de débuter une thèse de doctorat en informatique à l'Université de Lancaster (UK) dans le domaine des applications éducatives de lintelligence artificielle. Il a été Maître dEnseignement et de Recherche à lUniversité de Genève. Il rejoint l'EPFL en 2012, où Il fut le directeur du Centre de Recherche sur l'Apprentissage, la formation et ses technologies(CRAFT), puis académique du Centre pour l'Education à l'Ere Digitale (CEDE) qui met en oeuvre la stratégie MOOC de l'EPFL (plus de 2 millions d'inscriptions). Il est actuellement professeur ordinaire en technologies de formation aux sein de la faculté Informatique et Communications et dirige laboratoire d'ergonomie éducative (CHILI). Depuis 2006, il a aussi été le directeur de DUAL-T, la 'leading house' dédiée aux technologies pour les systèmes de formation professionnelle duale. Il a fondé plusieurs start-ups dans l'éducation et rejoint plusieurs conseils d'administration. En 2017, Il a créé avec des collègues le 'Swiss EdTech Collider', un incubateur qui rassemble 80 start-ups dans le domaine des technologies éducatives. En 2018, ils ont lancé LEARN, le centre EPFL pour les sciences de l'apprentissage, lequel regroupe les initiatives locales en innovation éducative. Pierre est un 'inaugural fellow of the International Society of Learning Sciences'. Il est actuellement le Vice-Président Associé pour l'Education à l'EPFL.
Aude BillardExpertise
Machine LearningRoboticsMechatronicsDynamical SystemsComputational Neuroscience
Enseignement et PhD Doctorant·es actuel·les Hanli Zhang, Maximilian Schmitz, Luyin Hu, Baiyu Peng, Erfan Etesami, Sthithpragya Gupta, Kuanqi Cai A dirigé les thèses EPFL de Eric Sauser, Sylvain Calinon, Micha Hersch, Florent Günter, Biljana Petreska von Ritter-Zahony, Basilio Noris, Elena Gribovskaya, Seyed Mohammad Khansari Zadeh, Seungsu Kim, Ashwini Shukla, Klas Jonas Alfred Kronander, Luka Lukic, Miao Li, Ravin L
Petr MotlicekEnseignement et PhD Doctorant·es actuel·les Shashi Kumar A co-dirigé les thèses EPFL de Subhadeep Dey, Weipeng He, Juan Pablo Zuluaga Gomez Cours Digital Speech and Audio Coding EE-719 The goal of this course is to introduce the engineering students state-of-the-art speech and audio coding techniques with an emphasis on the integration of knowledge about sound production and auditory perception through signal processing techniques.
Viktor KuncakViktor Kunčak joined EPFL in 2007, after receiving a PhD degree from MIT. Since then has been leading the Laboratory for Automated Reasoning and Analysis and supervised at least 12 completed PhD theses. His works on languages, algorithms and systems for verification and automated reasoning. He served as an initiator and one of the coordinators of a European network (COST action) in the area of automated reasoning, verification, and synthesis. In 2012 he received a 5-year single-investigator European Research Council (ERC) grant of 1.5M EUR. His invited talks include those at Lambda Days, Scala Days, NFM, LOPSTR, SYNT, ICALP, CSL, RV, VMCAI, and SMT. A paper on test generation he co-authored received an ACM SIGSOFT distinguished paper award at ICSE. A PLDI paper he co-authored was published in the Communications of the ACM as a Research Highlight article. His Google Scholar profile reports an over-approximate H-index of 38. He was an associate editor of ACM Transactions on Programming Languages and Systems (TOPLAS) and served as a co-chair of conferences on Computer-Aided Verification (CAV), Formal Methods in Computer Aided Design (FMCAD), Workshop on Synthesis (SYNT), and Verification, Model Checking, and Abstract Interpretation (VMCAI). At EPFL he teaches courses on functional and parallel programming, compilers, and verification. He has co-taught the MOOC "Parallel Programming" that was visited by over 100'000 learners and completed by thousands of students from all over the world.
Valentin Alexandre Guy GabeffRechercheWildAI WildAI is an interdisciplinary project that aims to bring together behavioral neurosciences, ecology, environmental computational sciences, computer vision and machine learning to scale up conservation research. From unmanned aerial vehicles (UAV) and camera trap (CT) acquired images, we seek to model the wildlife habitats in the Swiss National Park to complement ecological knowledge of how wildlife interacts with its environment and how perturbations in the environment can in turn affect w
Martin JaggiMartin Jaggi is a Tenure Track Assistant Professor at EPFL, heading the Machine Learning and Optimization Laboratory. Before that, he was a post-doctoral researcher at ETH Zurich, at the Simons Institute in Berkeley, and at École Polytechnique in Paris. He has earned his PhD in Machine Learning and Optimization from ETH Zurich in 2011, and a MSc in Mathematics also from ETH Zurich.
Mengjie ZhaoMengjie Zhao holds degrees in Computational Mechanics (MSc) with honors track and in Engineering Science (BSc) from the Technical University of Munich (TUM).
From the early years of her studies, Mengjie was fascinated by the modeling of multiphysics and multiscale systems. As a student research assistant at TUM and research intern at International Centre for Numerical Methods in Engineering (CIMNE), she gained a solid understanding of both the theoretical and algorithmic fundamentals as well as a wide range of applications. Through the BGCE project with the Elitenetzwerk Bayern (ENB), which dealt with the mesh sensitivity prediction with a deep neural network, she realized that leveraging data could bring physical modeling far beyond the current computational limits. Later, in her master's thesis in cooperation with Siemens, she turned to the reduced-order modeling with enforced physical invariants, which showed better accuracy and generality.
In her Ph.D., she would like to step towards a further combination of deductive research (modeling and simulation) and inductive (data-driven) research by embedding physics into machine learning.
