Benjamin Alexander Kellenberger2020–: Postdoctoral Researcher, EPFL, Sion, Switzerland2020: Postdoctoral researcher, Wageningen University, Netherlands2017–2020: PhD (cont'd); Wageningen University, Netherlands2016–2017: PhD; University of Zurich, Switzerland2009–2014: BSc. and MSc. in geography (remote sensing and GIS) and computer science; University of Zurich, Switzerland
Jérôme ChenalExpertise
UrbanismePlanification urbaineGestion urbaineProcessus d
Situation actuelle
Directeur du Centre "Excellence in Africa "Directeur de la CEATProfesseur affilié UM6P
Associations prof. et activités académiques Membre du Conseil d'administration de l'Université Senghor, Alexandrie (2025-2029)Directeur du Board of Directors de l'African Cities LabEditeur de l'African Cites JournalPrésident de la Biennale Africaine d'Architecture et d'urbanisme (BAAU)Médias Jérôme Chenal, la passion des villes et de l'A
Andreas ZüttelEnseignement et PhD Doctorant·es actuel·les Boyu Ouyang, Wai Kwan Liu, Guillaume Bérenger Rod, Aleksandar Mihaylov Mikov, Rui Li, Akhil Varma Penmathsa A dirigé les thèses EPFL de Robin Tobias Andreas Mutschler, Kun Zhao, Loris Giovanni Lombardo, Mo Li, Jie Zhang, Youngdon Ko, Cedric David Koolen, Thi Ha My Pham, Zohreh Akbari, Yasemen Kuddusi Cours Thermodynamics of energy conversion and storage ChE-414 Intro à la conversion d'énergie. Accent mis sur la thermodynamique des moteurs système
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.
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
Guillaume Romain ObozinskiGuillaume Obozinski est Chef Data Scientist Adjoint au Swiss Data Science Center. Il a reçu le diplôme de doctorat en Statistiques de l'Université de Californie à Berkeley en 2009. Post-doc puis chercheur dans les équipes Willow et Sierra à l'INRIA / Ecole Normale Supérieure à Paris jusqu'en 2012, il a été chercheur à l'Ecole des Ponts ParisTech à Paris jusqu'en 2018. Ses domaines d'expertise sont principalement en apprentissage automatique, en statistique et en optimisation. Il a travaillé au fil du temps sur les méthodes d'apprentissage parcimonieux, les techniques d'optimisation pour l'apprentissage, les modèles graphiques probabilistes, l'apprentissage relationnel et les représentations vectorielles sémantiques. Il est intéressé par l'application de ces techniques à tous les domaines des data sciences, depuis la biologie computationnelle à l'analyse automatique d'images.
Mathew Magimai DossEnseignement et PhD Doctorant·es actuel·les Karl El Hajal, Tilak Purohit A co-dirigé les thèses EPFL de Ramya Rasipuram, Marzieh Razavi, Hannah Muckenhirn, Sandrine Tornay, Subrahmanya Pavankumar Dubagunta, Enno Hermann, Julian David Fritsch, Neha Tarigopula, Mostaani Zohreh, Eklavya Sarkar Cours Automatic speech processing EE-554 L'objectif de ce cours est de présenter les principaux formalismes, modèles et algorithmes permettant la réalisation d'applications mettant en oeuvre des techniq
Alexander MathisAlexander studied pure mathematics with a minor in logic and theory of science at the Ludwig Maximilians University in Munich. For his PhD also at LMU, he worked on optimal coding approaches to elucidate the properties of grid cells. As a postdoctoral fellow with Prof. Venkatesh N. Murthy at Harvard University and Prof. Matthias Bethge at Tuebingen AI, he decided to study olfactory behaviors such as odor-guided navigation, social behaviors and the cocktail party problem in mice. During this time, he increasingly got interested sensorimotor behaviors beyond olfaction and started working on proprioception, motor adaption, as well as computer vision tools for measuring animal behavior.
In his group, he is interested in elucidating how the brain gives rise to adaptive behavior. One of the major goals is to synthesize large datasets into computationally useful information. For those purposes, he develops algorithms and systems to analyze animal behavior (e.g. DeepLabCut), neural data, as well as creates experimentally testable computational models.
Rüdiger UrbankeRüdiger L. Urbanke obtained his Dipl. Ing. degree from the Vienna University of Technology, Austria in 1990 and the M.Sc. and PhD degrees in Electrical Engineering from Washington University in St. Louis, MO, in 1992 and 1995, respectively. He held a position at the Mathematics of Communications Department at Bell Labs from 1995 till 1999 before becoming a faculty member at the School of Computer & Communication Sciences (I&C) of EPFL. He is a member of the Information Processing Group. He is principally interested in the analysis and design of iterative coding schemes, which allow reliable transmission close to theoretical limits at low complexities. Such schemes are part of most modern communications standards, including wireless transmission, optical communication and hard disk storage. More broadly, his research focuses on the analysis of graphical models and the application of methods from statistical physics to problems in communications. From 2000-2004 he was an Associate Editor of the IEEE Transactions on Information Theory and he is currently on the board of the series "Foundations and Trends in Communications and Information Theory." In 2017 he was President of the Information Theory Society. From 2009 till 2012 he was the head of the I&C doctoral school, in 2013 he served as Dean a. i. of I&C, and since 2016 he is the Associated Dean for teaching of I&C. He is a co-author of the book "Modern Coding Theory" published by Cambridge University Press. Awards: 2021 IEEE Information Theory Society Paper Award 2016 STOC Best Paper Award 2014 La Polysphere Teaching Award 2014 IEEE Hamming Medal 2013 IEEE Information Theory Society Paper Award 2011 MASCO Best Paper Award 2011 IEEE Koji Kobayashi Award 2009 La Polysphere Teaching Award 2002 IEEE Information Theory Society Paper Award Fulbright Scholarship My students have won the following awards: M. Mondelli, 2021 IEEE Information Theory Paper Award M. Mondelli, EPFL Doctorate Award 2018 M. Mondelli, Patrick Denantes Award, 2017 M. Mondelli, IEEE IT Society Student Paper Award at ISIT, 2015 M. Mondelli, Dan David Prize Scholarship, 2015 H. Hassani, Inaugural Thomas Cover Dissertation Award, 2014 S. Kudekar, 2013 & 2021 IEEE Information Theory Paper Award A. Karbasi, Patrick Denantes Award, 2013 V. Venkatesan, Best Paper Award at MASCOTS, 2011 A. Karbasi, Best Student Paper Award at ICASSP, 2011 (with R. Parhizkar) A. Karbasi, Best Student Paper Award at ACM SIGMETRICS, 2010 (with S. Oh) S. Korada, ABB Dissertation Award, 2010 S. Korada, IEEE IT Society Student Paper Award at ISIT, 2009 (with E. Sasoglu) S. Korada, IEEE IT Society Student Paper Award at ISIT, 2008
