Keynote Speakers




Tentative Title

Alberto Bemporad  IMT School for Advanced Studies Lucca (Italy) Model Predictive Control: Dreams, Possibilities, and Reality
Jonas Buchli Swiss Federal Institute of Technology (Switzerland) Efficient optimal and learning control for real robots
Timm Faulwasser Karlsruhe Institute of Technology (Germany)  
Paul Goulart  University of Oxford (UK) Distributionally Robust Optimization for Chance-Constrained Systems.
Ali Mesbah  University of California, Berkley (US) Moment-Matching Scenario Tree Generation for Robust Nonlinear Model Predictive Control under Arbitrary Probabilistic Uncertainty
Joe Qin  University of South California (US) Data Analytics for Performance Troubleshooting of Feedback Controlled Manufacturing Plants
Angela Schoellig University of Toronto (Canada) Combining Model Predictive Control with Learning-Based and Adaptive Methods to Achieve Safety, Performance and Reliability in Robotics
Melanie Zeilinger Swiss Federal Institute of Technology (Switzerland) MPC for Learning-based Control with Constraints 


Alberto Bemporad

IMT School for Advanced Studies Lucca

Alberto Bemporad received his master's degree in Electrical Engineering in 1993 and his Ph.D. in Control Engineering in 1997 from the University of Florence, Italy. In 1996/97 he was with the Center for Robotics and Automation, Department of Systems Science & Mathematics, Washington University, St. Louis. In 1997-1999 he held a postdoctoral position at the Automatic Control Laboratory, ETH Zurich, Switzerland, where he collaborated as a senior researcher until 2002. In 1999-2009 he was with the Department of Information Engineering of the University of Siena, Italy, becoming an associate professor in 2005. In 2010-2011 he was with the Department of Mechanical and Structural Engineering of the University of Trento, Italy. Since 2011 he is full professor at the IMT School for Advanced Studies Lucca, Italy, where he served as the director of the institute in 2012-2015. He spent visiting periods at Stanford University, University of Michigan, and Zhejiang University. He has published more than 300 papers in the areas of model predictive control, hybrid systems, optimization, automotive control, and co-inventor of 10 patents. He is author or coauthor of various MATLAB toolboxes for model predictive control design, including the Model Predictive Control Toolbox (The Mathworks, Inc.), the Hybrid Toolbox, the MPCTool and MPCSofT toolboxes developed for the European Space Agency, and other MPC toolboxes tailored to industrial production. In 2011 he cofounded ODYS S.r.l., a company specialized in developing model predictive control systems for industrial production.

He was an Associate Editor of the IEEE Transactions on Automatic Control during 2001-2004 and Chair of the Technical Committee on Hybrid Systems of the IEEE Control Systems Society in 2002-2010. He received the IFAC High-Impact Paper Award for the 2011-14 triennial. He has been an IEEE Fellow since 2010.


Jonas Buchli

Swiss Federal Institute of Technology 

Jonas Buchli is a Research Scientist with Deepmind, London and Assistant Professor at ETH Zurich. He holds a Diploma in Electrical Engineering from ETH Zurich (2003) and a PhD from EPF Lausanne (2007), Switzerland. In his dissertation, he worked on adaptive frequency oscillators, and on the modeling and design of robotic locomotion pattern generators. At EPFL, he organized the 2006 Latsis Conference on Dynamical principles for neuroscience and intelligent biomimetic devices. From 2007 to 2010, he was a postdoctoral researcher at the Computational Learning and Motor Control Lab of the University of Southern California, USA, where he was the leader of the USC Team for the DARPA Learning Locomotion Challenge. From 2010 to 2012, he was a team leader at the Advanced Robotics Department of the Italian Institute of Technology in Genova. Jonas Buchli received a Prospective and an Advanced Researcher Fellowship from the Swiss National Science Foundation (SNSF) in 2007 and 2009 respectively. In 2012, the SNSF awarded him with a Professorship Award. Jonas Buchli was a principal investigator within the Swiss National Centre of Competence in Research (NCCR) Digital Fabrication as well as within the NCCR Robotics.

He has contributed to research in diverse fields such as dynamical systems approaches to motion generation and control, the theory of coupled oscillators, optimal planning and control of dynamic locomotion, machine learning, whole body control, whole body force and impedance control, as well as the modeling of human motor control. He was involved in the development of robotic platforms as well as software engineering projects for robotic control software.


Timm Faulwasser

Karlsruhe Institute of Technology 

Timm Faulwasser has studied Engineering Cybernetics at the University Stuttgart, with majors in systems and control and philosophy. Afterwards he joined the Institute of Automation Engineering at the Otto-von-Guericke University Magdeburg, Germany. From 2008-2012 he was a member of the International Max Planck Research School for Analysis, Design and Optimization in Chemical and Biochemical Process Engineering Magdeburg. In 2012 he obtained his PhD (with distinction) from Faculty of Electrical Engineering and Information Engineering, Otto-von-Guericke University Magdeburg, Germany. 2013-2016 he was with the Laboratoire d’Automatique, Ecole Polytechnique Fédérale de Lausanne, Switzerland. Since April 2015, he is with the Institute for Automation and Applied Informatics at the Karlsruhe Institute for Technology, where he leads the Optimization and Control Group. His main research interests are optimization-based and predictive control of nonlinear systems with applications in energy systems, mechatronics/robotics, physics, process systems engineering and climate economics.



Paul Goulart 

University of Oxford

Paul Goulart joined the University of Oxford in 2014 as an Associate Professor in Engineering Science and a Tutorial Fellow in Engineering Science. He received his SB and MSc degrees in Aeronautics and Astronautics from the Massachusetts Institute of Technology (MIT). Following his undergraduate studies he was a software developer in the flight operations centre for the Chandra X-Ray Observatory at the Harvard-Smithsonian Centre for Astrophysics, and later an engineer in the Autonomous Systems research group at the Charles Stark Draper Laboratory.

In 2003 he was selected as a Gates Scholar at the University of Cambridge, where he received a PhD in Control Engineering in 2007. From 2007 to 2011 he was a Lecturer in control systems in the Department of Aeronautics at Imperial College London, and from 2011 to 2014 a Senior Researcher in the Automatic Control Laboratory at ETH Zurich. He is currently a member of the Control Group in the department of Engineering Science.

His research interests are mainly in robust and high speed optimisation and control, with a wide range of application areas including fluid flows, economics and traffic networks.



Ali Mesbah 

University of California, Berkley 

Ali Mesbah is Assistant Professor of Chemical and Biomolecular Engineering at the University of California at Berkeley. Before joining UC Berkeley, he was a senior postdoctoral associate at MIT. He holds a Ph.D. degree in systems and control from Delft University of Technology. He is a senior member of the IEEE Control Systems Society and AIChE. He is on the IEEE Control Systems Society conference editorial board as well as the editorial board of IEEE Transactions on Radiation and Plasma Medical Sciences. He is recipient of the AIChE's 35 Under 35 Award in 2017, the IEEE Control Systems Outstanding Paper Award in 2017, and the AIChE CAST W. David Smith, Jr. Graduation Publication Award in 2015. His research interests are in the areas of optimization-based systems analysis, fault diagnosis, and predictive control of uncertain systems.



Joe Qin 

University of South California   

 Dr. S. Joe Qin obtained his B.S. and M.S. degrees in Automatic Control from Tsinghua University in Beijing, China, in 1984 and 1987, respectively, and his Ph.D. degree in Chemical Engineering from University of Maryland at College Park in 1992. He is the Professor at the Viterbi School of Engineering of the University of Southern California. 

Dr. Qin is a Fellow of IEEE and Fellow of the International Federation of Automatic Control (IFAC). He is a recipient of the National Science Foundation CAREER Award, the 2011 Northrop Grumman Best Teaching award at Viterbi School of Engineering, the DuPont Young Professor Award, Halliburton/Brown & Root Young Faculty Excellence Award, NSF-China Outstanding Young Investigator Award, Chang Jiang Professor of Tsinghua University, National “Thousand Talent” Professor of China, and recipient of the IFAC Best Paper Prize for a model predictive control survey paper published in Control Engineering Practice. He is currently a Subject Editor for Journal of Process Control and a Member of the Editorial Board for Journal of Chemometrics. He has published over 140 papers in SCI journals or book chapters, with over 10,000 Web of Science citations and an associated h-index of 49. He has given over 40 invited plenary or keynote speeches and over 100 invited technical seminars worldwide. Dr. Qin’s research interests include process data analytics, machine learning, process monitoring and fault diagnosis, model predictive control, system identification, building energy optimization, multi-step batch process control, and control performance monitoring.



Angela Schoellig

University of Toronto

Angela Schoellig is an assistant professor at the University of Toronto Institute for Aerospace Studies, an associate director of the Centre for Aerial Robotics Research and Education at the University of Toronto, and an instructor of Udacity’s flying-car nanodegree program. She conducts research at the interface of robotics, controls, and machine learning. Her goal is to enhance the performance, safety, and autonomy of robots by enabling them to learn from past experiments and from each other. She is a recipient of a Sloan Research Fellowship (US/Canada-wide award, one of two in robotics); a Canadian Ministry of Research, Innovation & Science Early Researcher Award; and a Connaught New Researcher Award. Her team won the 2018 GM/SAE AutoDrive Challenge, a North-America-wide self-driving competition. She is one of MIT Technology Review’s Innovators Under 35 (2017), one of Robohub’s “25 women in robotics you need to know about (2013),” winner of MIT’s 2015 Enabling Society Tech Competition, a 2015 finalist in Dubai’s $1 million “Drones for Good” competition, and the youngest member of the 2014 Science Leadership Program, which promotes outstanding scientists in Canada. Her PhD was awarded the ETH Medal and the 2013 Dimitris N. Chorafas Foundation Award (one of 35 worldwide). She holds both an M.Sc. in Engineering Science and Mechanics from the Georgia Institute of Technology (with Prof. Magnus Egerstedt) and a Masters degree in Engineering Cybernetics from the University of Stuttgart, Germany (with Prof. Frank Allgöwer). More information can be found at:



Melanie Zeilinger

Swiss Federal Institute of Technology 

Melanie N. Zeilinger is an Assistant Professor at the Department of Mechanical and Process Engineering at ETH Zurich, Switzerland. She received the Diploma degree in engineering cybernetics from the University of Stuttgart, Germany, in 2006, and the Ph.D. degree with honors in electrical engineering from ETH Zurich, Switzerland, in 2011. She was a Marie Curie fellow and Postdoctoral Researcher with the Max Planck Institute for Intelligent Systems, Tübingen, Germany until 2015 and with the Department of Electrical Engineering and Computer Sciences at the University of California at Berkeley, CA, USA, from 2012 to 2014. From 2011 to 2012 she was a Postdoctoral Fellow with the Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland. Her research interests include distributed control and optimization, as well as safe learning-based control, with applications to mechatronic systems, energy distribution networks and human-in-the-loop control.





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