This video series presents the people behind the center, from our Board Members to our Scientific Committee. Each team member gives an insight into the role of intelligent systems in their field and how they will impact our future.
We work on the theoretical challenges and practical applications of socially-aware systems, i.e., machines that can not only perceive human behavior, but reason with social intelligence in the context of transportation problems and smart spaces.
We envision a future where intelligent machines are ubiquitous, where self-driving cars, delivery robots, and self-moving Segways are facts of everyday life. Beyond embodied agents, we will also see our living spaces – our homes, buildings, and cities – become equipped with ambient intelligence which can sense and respond to human behavior. However, to realize this future, intelligent machines need to develop social intelligence and the ability to make safe and consistent decisions in unconstrained crowded social scenes. Self-driving vehicles must learn social etiquette in order to navigate cities like Paris or Naples. Social robots need to comply with social conventions and obey (unwritten) common-sense rules to effectively operate in crowded terminals. For instance, they need to respect personal space, yield right-of-way, and “read” the behavior of others to predict future actions.
Our research is centered around understanding and predicting human social behavior with multi-modal visual data. Our work spans multiple aspects of socially-aware systems: from 1- collecting multi-modal data at scale, 2- Extracting coarse-to-fine grained behaviours in real-time, 3- designing deep learning methods that can learn to predict human social behavior in a fully data-driven way, to 4- integrating the developed methods in real-world systems such as a vehicle or a socially-aware robot that navigates crowded social scenes.
Alexandre is leading the Visual Intelligence for Transportation laboratory (VITA) in ENAC. Before joining EPFL in 2017, he spent multiple years at Stanford University (CS) as a Post-doc and Research Scientist. His research lies at the intersection of Computer Vision, Machine Learning, and Robotics applied to autonomous vehicles, and the built environment (digital twins). He envisions a new type of Artificial Intelligence (AI), namely socially aware AI, i.e., intelligent systems equipped with perception and social intelligence.
He won the CVPR Open Source Award (2012) for his work on Retina-inspired image descriptors, and the ICDSC Challenge Prize (2009) for his sparsity-driven algorithm that has tracked more than 100 million pedestrians to date. His research has been covered internationally by BBC, ABC, PBS, Euronews, Wall Street Journal, and other national news outlets around the world. Alexandre has also co-founded multiple startups such as Visiosafe, and won several startup competitions. He was elected as one of the Top 20 Swiss Venture leaders in 2010.
+41 21 693 26 08
His research interests are at the intersection between robotics, computational neuroscience, nonlinear dynamical systems, and applied machine learning. He is interested in using numerical simulations and robots to get a better understanding of animal locomotion and movement control, and in using inspiration from biology to design novel types of robots and locomotion controllers (see for instance Ijspeert et al, Science, Vol. 315. no. 5817, pp. 1416 – 1420, 2007 and Ijspeert, Science Vol. 346, no. 6206, 2014). He is regularly invited to give talks on these topics (e.g. TED talk given at TED Global Geneva, Dec 8 2015). With his colleagues, he has received paper awards at ICRA2002, CLAWAR2005, IEEE Humanoids 2007, IEEE ROMAN 2014, and CLAWAR 2015.
He is member of the Board of Reviewing Editors of Science magazine, and associate editor for Soft Robotics and for the International Journal of Humanoid Robotics. He has acted as an associate editor for the IEEE Transactions on Robotics (2009-2013) and as a guest editor for the Proceedings of IEEE, IEEE Transactions on Biomedical Engineering, Autonomous Robots, IEEE Robotics and Automation Magazine, and Biological Cybernetics. He has been the organizer of 6 international conferences (BioADIT2004, SAB2004, AMAM2005, BioADIT2006, LATSIS2006, SSRR2016), and a program committee member of over 50 conferences. Please visit the BioRob Home and BioRob publication pages or have a look at his CV for more information about his research and publications (See also Ijspeert’s Google Scholar Profile).
Auke Ijspeert is a full professor at the EPFL (the Swiss Federal Institute of Technology at Lausanne), and head of the Biorobotics Laboratory (BioRob). He has a B.Sc./M.Sc. in physics from the EPFL (1995), and a PhD in artificial intelligence from the University of Edinburgh (1999). He carried out postdocs at IDSIA and EPFL, and at the University of Southern California (USC). He then became a research assistant professor at USC, and an external collaborator at ATR (Advanced Telecommunications Research institute) in Japan. In 2002, he came back to the EPFL as an SNF assistant professor. He was promoted to associate professor in October 2009 and to full professor in April 2016. His primary affiliation is with the Institute of Bioengineering, and secondary affiliation with the Institute of Mechanical Engineering.
Prof. Auke Ijspeert
His research interests focus on methods to design, control, model, and optimize distributed intelligent systems, including multi-robot systems, sensor and actuator networks, and intelligent vehicles. He is also interested in the understanding and control of mixed societies consisting of natural and artificial components. His research policy relies on iteratively closing the loop between theory and physical experiments using modeling and computational techniques. His research output ranges from fundamental, methodological aspects to more applied contributions, often associated with application areas of interest in his school, especially in environmental and civil engineering.
Alcherio Martinoli received his Diploma in Electrical Engineering from the Swiss Federal Institute of Technology in Zurich (ETHZ), and a Ph.D. in Computer Science from the Swiss Federal Institute of Technology in Lausanne (EPFL). He is currently an Associate Professor at the School of Architecture, Civil, and Environmental Engineering (ENAC) and the head of the Distributed Intelligent Systems and Algorithms Laboratory. Before joining EPFL he carried out research activities at the Institute of Biomedical Engineering of the ETHZ, at the Institute of Industrial Automation of the Spanish Research Council in Madrid, Spain, and at the California Institute of Technology, Pasadena, U.S.A. Additional information can be found on his full CV.
Prof. Alcherio Martinoli
Prof. David Atienza leads the Embedded Systems Laboratory (ESL) at EPFL. He is an expert on the next-generation embedded systems for the Internet of Things (IoT), working for the last 15 years on smart wearables for humans and a wider range of other intelligent objects, including cars, smart buildings and Industry 4.0. He focuses on how these objects can interact to create new intelligent systems composed of autonomous objects. ESL has worked with more than 40 companies worldwide in this area. His expertise also covers 2D/3D thermal modeling and management for multiprocessor system-on-chip, electronic design automation, and low-power hardware and software co-design for embedded machine learning. He has published more than 350 publications and has received several awards in these areas. He is an IEEE Fellow, an ELLIS Fellow, and a Distinguished Member of ACM.
Prof. David Atienza is an associate professor of EE and director of the Embedded Systems Laboratory (ESL) at EPFL, Switzerland. He received his MSc and PhD degrees in computer science and engineering from UCM, Spain, and IMEC, Belgium, in 2001 and 2005, respectively. His research interests include system-level design methodologies for multi-processor system-on-chip (MPSoC) servers and edge AI architectures.
Dr. Atienza has co-authored more than 350 papers, one book, and 12 patents in these previous areas. He has also received several recognitions and award, among them, the ICCAD 10-Year Retrospective Most Influential Paper Award in 2020, Design Automation Conference (DAC) Under-40 Innovators Award in 2018, the IEEE TCCPS Mid-Career Award in 2018, an ERC Consolidator Grant in 2016, the IEEE CEDA Early Career Award in 2013, the ACM SIGDA Outstanding New Faculty Award in 2012, and a Faculty Award from Sun Labs at Oracle in 2011. He has also earned two best paper awards at the VLSI-SoC 2009 and CST-HPCS 2012 conference, and five best paper award nominations at the DAC 2013, DATE 2013, WEHA-HPCS 2010, ICCAD 2006, and DAC 2004 conferences. He serves or has served as associate editor of IEEE Trans. on Computers (TC), IEEE Design & Test of Computers (D&T), IEEE Trans. on CAD (T-CAD), IEEE Transactions on Sustainable Computing (T-SUSC), and Elsevier Integration. He was the Technical Program Chair of DATE 2015 and General Chair of DATE 2017. He served as President of IEEE CEDA in the period 2018-2019 and was GOLD member of the Board of Governors of IEEE CASS from 2010 to 2012. He is a Distinguished Member of ACM and an IEEE Fellow.
+41 21 693 11 31
Martin Jaggi is a Tenure Track Assistant Professor at EPFL, heading the Machine Learning and Optimization Laboratory.
His research focuses on distributed and decentralized machine learning, optimization, deep learning, and text understanding. Algorithms developed by his team have found adoption in industry, such in Google’s TensorFlow, by Facebook for PyTorch, and by IBM and Nvidia’s in their cloud machine learning offerings, as well as in natural language processing frameworks. Before joining EPFL, Martin 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. He is also the founder of the Zurich Machine Learning and Data Science Meetup and a co-founder of EPFL’s Applied Machine Learning Days.