Luc PatinyExpertise
Cheminformatics, Data management, Data processing, Javascript developmentLast updated on April 16, 2023.
Teaching & PhD Courses Advanced general chemistry II CH-120 Fundamental knowledge on the reactivity of organic compounds and their identification by mass spectrometry, IR spectroscopy and nuclear magnetic resonance. Basic and advanced NMR - Level 1 A (EPFL) CH-601(x) Basic theoretical and experimental aspects of NMR will be taught. Students will be familarized w
Robert WestRobert West is a tenure-track assistant professor of computer science at EPFL, where he heads the Data Science Lab. In his research, he develops and applies techniques in machine learning, computational social science, natural language processing, social network analysis, and data mining. Bob also collaborates closely with the Wikimedia Foundation, in his role as a Wikimedia Research Fellow. Bob’s work has won several awards, including best/outstanding paper awards at ICWSM’21, ICWSM’19, and WWW’13, a best-paper runner-up award at WWW’16, a Google Faculty Research Award, a Facebook Research Award, a Hewlett-Packard Graduate Fellowship, and a Facebook Graduate Fellowship. He is actively involved in the research community, e.g., as an Associate Editor of ICWSM and EPJ Data Science and as a co-founder of the Wiki Workshop (held at WWW and ICWSM) and the Applied Machine Learning Days. Bob received his PhD in Computer Science from Stanford University, his MSc from McGill University, Canada, and his undergraduate degree from Technische Universität München, Germany.[Last updated: 25 Aug 2021]
Florence Graezer BideauSenior Lecturer and Senior Scientist at the College of Humanities and at the School of Architecture, EPFLVisiting Professor at the Department of Architecture and Design, Politecnico di Torino PhD in History and Civilization (EHESS, Paris) Director of the Minor in Area and Cultural Studies (MACS) between 2012 and 2016Member of the Research group Heritage, culture and the cityAssociated researcher at the China Room Research Group and South China-Torino Collaboration Lab, Politecnico di Torino Associate member of the Laboratoire d’anthropologie culturelle et sociale (LACS), UNIL Member of the EDAR committee (Doctoral Program Architecture and Sciences of the City) at the School of Architecture, Civil and Environment Engineering in EPFL Florence Graezer Bideau trained as an anthropologist and a sinologist, and received her PhD in History and Civilization in 2005. Before joining the Centre for Area and Cultural Studies (CACS) at EPFL in 2010, she was a lecturer in anthropology at the University of Lausanne, where she taught courses in cultural theory and fieldwork methodology. She is Senior Lecturer and Senior Scientist at the College of Humanities where she teaches area studies, anthropology of China, critical heritage studies and urban studies. She has been acting as Director of the Minor in Area and Cultural Studies between 2012 and 2016 and she is currently a member of the EDAR committee (Doctoral Program Architecture and Sciences of the City) at the School of Architecture, Civil and Environment Engineering in EPFL. Since 2015, Florence has also been Visiting Professor at the Department of Architecture and Design, Politecnico di Torino, Italy. Her fields of expertise include anthropology of China, urban sociology, modes of sociability and governmentality. Florence’s research is on the relation between culture and power (making of cultural policy in China; emergence of maker movement (makerspaces) and politics of innovation in China), heritage issues (process of heritagization and multiculturalism in Malaysia and Singapore; implementation of the UNESCO Convention for Safeguarding Intangible Cultural Heritage in Switzerland; historic urban landscape in heritage policy of Beijing, Rome and Mexico City), and the making of the city (informal resistances toward the violence of urbanism in Caracas, Chennai and Guangzhou; uses of public spaces in Chinese new towns).
Roland John TormeyI am a sociologist and learning scientist researching and teaching on engineering education. I'm particularly focused on the roles of emotion and self-regulation in engineering teaching and learning. My profile is accessible here.
Wulfram GerstnerWulfram Gerstner is Director of the Laboratory of Computational Neuroscience LCN at the EPFL. His research in computational neuroscience concentrates on models of spiking neurons and spike-timing dependent plasticity, on the problem of neuronal coding in single neurons and populations, as well as on the link between biologically plausible learning rules and behavioral manifestations of learning. He teaches courses for Physicists, Computer Scientists, Mathematicians, and Life Scientists at the EPFL. After studies of Physics in Tübingen and at the Ludwig-Maximilians-University Munich (Master 1989), Wulfram Gerstner spent a year as a visiting researcher in Berkeley. He received his PhD in theoretical physics from the Technical University Munich in 1993 with a thesis on associative memory and dynamics in networks of spiking neurons. After short postdoctoral stays at Brandeis University and the Technical University of Munich, he joined the EPFL in 1996 as assistant professor. Promoted to Associate Professor with tenure in February 2001, he is since August 2006 a full professor with double appointment in the School of Computer and Communication Sciences and the School of Life Sciences. Wulfram Gerstner has been invited speaker at numerous international conferences and workshops. He has served on the editorial board of the Journal of Neuroscience, Network: Computation in Neural Systems', Journal of Computational Neuroscience', and `Science'.
Johanni Michael BreaTeaching & PhD PhD Students Aude Maier, Lucas Louis Gruaz, Flavio Martinelli Courses Artificial intelligence and society HUM-286 This course integrates social sciences and humanities with engineering sciences, and introduces students to the social, technical and political issues surrounding artificial intelligence. Biological data science II : machine learning BIO-322 Students understand basic concepts and methods of machine learning. They can describe them in mathematical
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