Michel BierlaireBorn in 1967, Michel Bierlaire holds a PhD in Mathematical Sciences from the Facultés Universitaires Notre-Dame de la Paix, Namur, Belgium (University of Namur). Between 1995 and 1998, he was research associate and project manager at the Intelligent Transportation Systems Program of the Massachusetts Institute of Technology (Cambridge, Ma, USA). Between 1998 and 2006, he was a junior faculty in the Operations Research group ROSO within the Institute of Mathematics at EPFL. In 2006, he was appointed associate professor in the School of Architecture, Civil and Environmental Engineering at EPFL, where he became the director of the Transport and Mobility laboratory. Since 2009, he is the director of TraCE, the Transportation Center. From 2009 to 2017, he was the director of Doctoral Program in Civil and Environmental Engineering at EPFL. In 2012, he was appointed full professor at EPFL. Since September 2017, he is the head of the Civil Engineering Institute at EPFL. His main expertise is in the design, development and applications of models and algorithms for the design, analysis and management of transportation systems. Namely, he has been active in demand modeling (discrete choice models, estimation of origin-destination matrices), operations research (scheduling, assignment, etc.) and Dynamic Traffic Management Systems. As of August 2021, he has published 136 papers in international journals, 4 books, 41 book chapters, 193 articles in conference proceedings, 182 technical reports, and has given 195 scientific seminars. His Google Scholar h-index is 68. He is the founder, organizer and lecturer of the EPFL Advanced Continuing Education Course "Discrete Choice Analysis: Predicting Demand and Market Shares". He is the founder of hEART: the European Association for Research in Transportation. He was the founding Editor-in-Chief of the EURO Journal on Transportation and Logistics, from 2011 to 2019. He is an Associate Editor of Operations Research. He is the editor of two special issues for the journal Transportation Research Part C. He has been member of the Editorial Advisory Board (EAB) of Transportation Research Part B since 1995, of Transportation Research Part C since January 1, 2006.
Ali H. SayedAli H. Sayed est doyen de la Faculté des sciences et techniques de l’ingénieur (STI) de l'EPFL, en Suisse, où il dirige également le laboratoire de systèmes adaptatifs. Il a également été professeur émérite et président du département d'ingénierie électrique de l'UCLA. Il est reconnu comme un chercheur hautement cité et est membre de la US National Academy of Engineering. Il est également membre de l'Académie mondiale des sciences et a été président de l'IEEE Signal Processing Society en 2018 et 2019.
Le professeur Sayed est auteur et co-auteur de plus de 570 publications et de six monographies. Ses recherches portent sur plusieurs domaines, dont les théories d'adaptation et d'apprentissage, les sciences des données et des réseaux, l'inférence statistique et les systèmes multi-agents, entre autres.
Ses travaux ont été récompensés par plusieurs prix importants, notamment le prix Fourier de l'IEEE (2022), le prix de la société Norbert Wiener (2020) et le prix de l'éducation (2015) de la société de traitement des signaux de l'IEEE, le prix Papoulis (2014) de l'Association européenne de traitement des signaux, le Meritorious Service Award (2013) et le prix de la réalisation technique (2012) de la société de traitement des signaux de l'IEEE, le prix Terman (2005) de la société américaine de formation des ingénieurs, le prix de conférencier émérite (2005) de la société de traitement des signaux de l'IEEE, le prix Koweït (2003) et le prix Donald G. Fink (1996) de l'IEEE. Ses publications ont été récompensées par plusieurs prix du meilleur article de l'IEEE (2002, 2005, 2012, 2014) et de l'EURASIP (2015). Pour finir, Ali H. Sayed est aussi membre de l'IEEE, d'EURASIP et de l'American Association for the Advancement of Science (AAAS), l'éditeur de la revue Science.
Volkan CevherVolkan Cevher received the B.Sc. (valedictorian) in electrical engineering from Bilkent University in Ankara, Turkey, in 1999 and the Ph.D. in electrical and computer engineering from the Georgia Institute of Technology in Atlanta, GA in 2005. He was a Research Scientist with the University of Maryland, College Park from 2006-2007 and also with Rice University in Houston, TX, from 2008-2009. Currently, he is an Associate Professor at the Swiss Federal Institute of Technology Lausanne and a Faculty Fellow in the Electrical and Computer Engineering Department at Rice University. His research interests include machine learning, signal processing theory, optimization theory and methods, and information theory. Dr. Cevher is an ELLIS fellow and was the recipient of the Google Faculty Research award in 2018, the IEEE Signal Processing Society Best Paper Award in 2016, a Best Paper Award at CAMSAP in 2015, a Best Paper Award at SPARS in 2009, and an ERC CG in 2016 as well as an ERC StG in 2011.
Michael Christoph GastparMichael Gastpar is a (full) Professor at EPFL. From 2003 to 2011, he was a professor at the University of California at Berkeley, earning his tenure in 2008. He received his Dipl. El.-Ing. degree from ETH Zürich, Switzerland, in 1997 and his MS degree from the University of Illinois at Urbana-Champaign, IL, USA, in 1999. He defended his doctoral thesis at EPFL on Santa Claus day, 2002. He was also a (full) Professor at Delft University of Technology, The Netherlands. His research interests are in network information theory and related coding and signal processing techniques, with applications to sensor networks and neuroscience. He is a Fellow of the IEEE. He is the co-recipient of the 2013 Communications Society & Information Theory Society Joint Paper Award. He was an Information Theory Society Distinguished Lecturer (2009-2011). He won an ERC Starting Grant in 2010, an Okawa Foundation Research Grant in 2008, an NSF CAREER award in 2004, and the 2002 EPFL Best Thesis Award. He has served as an Associate Editor for Shannon Theory for the IEEE Transactions on Information Theory (2008-11), and as Technical Program Committee Co-Chair for the 2010 International Symposium on Information Theory, Austin, TX.
Rachid GuerraouiRachid Guerraoui has been affiliated with Ecole des Mines of Paris, the Commissariat à l'Energie Atomique of Saclay, Hewlett Packard Laboratories and the Massachusetts Institute of Technology. He has worked in a variety of aspects of distributed computing, including distributed algorithms and distributed programming languages. He is most well known for his work on (e-)Transactions, epidemic information dissemination and indulgent algorithms.
He co-authored a book on Transactional Systems (Hermes) and a book on reliable distributed programming (Springer). He was appointed program chair of ECOOP 1999, ACM Middleware 2001, IEEE SRDS 2002, DISC 2004 and ACM PODC 2010.
His publications are available at http://lpdwww.epfl.ch/rachid/papers/generalPublis.html Jean-Philippe ThiranJean-Philippe Thiran was born in Namur, Belgium, in August 1970. He received the Electrical Engineering degree and the PhD degree from the Université catholique de Louvain (UCL), Louvain-la-Neuve, Belgium, in 1993 and 1997, respectively. From 1993 to 1997, he was the co-ordinator of the medical image analysis group of the Communications and Remote Sensing Laboratory at UCL, mainly working on medical image analysis. Dr Jean-Philippe Thiran joined the Signal Processing Institute (ITS) of the Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland, in February 1998 as a senior lecturer. He was promoted to Assistant Professor in 2004, to Associate Professor in 2011 and is now a Full Professor since 2020. He also holds a 20% position at the Department of Radiology of the University of Lausanne (UNIL) and of the Lausanne University Hospital (CHUV) as Associate Professor ad personam. Dr Thiran's current scientific interests include
Computational medical imaging: acquisition, reconstruction and analysis of imaging data, with emphasis on regularized linear inverse problems (compressed sensing, convex optimization). Applications to medical imaging: diffusion MRI, ultrasound imaging, inverse planning in radiotherapy, etc.Computer vision & machine learning: image and video analysis, with application to facial expression recognition, eye tracking, lip reading, industrial inspection, medical image analysis, etc.
