Concept
Optimisation convexe
Personnes associées (197)
Bernard Dacorogna
Après avoir obtenu sa licence ès sciences mathématiques à l'Université de Genève, puis une maîtrise (Master of Science) à l'Université d'Aberdeen (G.B.), il reçoit son doctorat (Ph.D.) de l'Université Heriot-Watt (G.B.) en 1980. Il rejoint l'EPFL en 1981, après une année à Brown University (E.U.). Il est nommé professeur ordinaire au département de mathématiques de l'EPFL en 2003. Son enseignement porte sur des cours d'analyse. Ses recherches, qui ont abouti à la publication de plusieurs livres et d'une centaine d'articles, sont dans le domaine des équations aux dérivées partielles et du calcul des variations.
Yingzhao Lian
Yingzhao graduated with a MSc in Mechanical Enginerring from EPFL in 2018. He specialized in optimization and learning theory. He jointly conducted his master’s thesis with the Automatic Control Lab, EPFL and ABB Corporate Research, Baden, where he investigated the problem of data-driven model based optimal control. In August 2018, he joined the Automatic Control Laboratory at EPFL as a PhD student under the supervision of Professor Colin Jones.
Tony Alan Wood
Tony A. Wood received a B.Sc. degree in Information Technology and Electrical Engineering in 2010 and an M.Sc. degree in Robotics, Systems and Control in 2013, both from ETH Zurich, Switzerland. In 2018 he completed a Ph.D. at the Automatic Control Laboratory of ETH Zurich. From 2018 to 2021, he was a Research Fellow in the Department of Electrical and Electronic Engineering at the University of Melbourne, Australia. He is currently a Scientist in the Systems Control and Multiagent Optimization Research Group (Sycamore) at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. His research interests lie in the fields of optimisation and automatic control with a particular focus on multi-agent systems. The specific topics he investigates include system identification, task allocation, path planning, model predictive control, delay compensation, and formal specification satisfaction for uncertain systems encountered in applications such as energy systems, smart buildings, biology, and robotics.