Keynote Speakers

Michael Bronstein
Imperial College London, UK

Topics

Deep Learning on Graphs and Manifolds

Biography

Michael Bronstein (PhD 2007, Technion, Israel) is a professor at Imperial College London, where he holds the Chair in Machine Learning and Pattern Recognition and Royal Society Wolfson Merit Award. He holds/has held visiting appointments at Stanford, Harvard, MIT, and TUM. Michael’s main research interest is in theoretical and computational methods for geometric data analysis. He is a Fellow of IEEE and IAPR, and ACM Distinguished Speaker. He is the recipient of four ERC grants, two Google Faculty awards, and the 2018 Facebook Computational Social Science award. Besides academic work, Michael was a co-founder and technology executive at Novafora (2005-2009) developing large-scale video analysis methods, and one of the chief technologists at Invision (2009-2012) developing low-cost 3D sensors. Following the multi-million acquisition of Invision by Intel in 2012, Michael has been one of the key developers of the Intel RealSense technology in the role of Principal Engineer. His most recent venture is Fabula AI, a startup dedicated to algorithmic detection of fake news using geometric deep learning.

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Marco Gori
University of Siena, Italy

Topics

Constraint-Based Approaches to Machine Learning

Biography

Marco Gori received the Ph.D. degree in 1990 from Università di Bologna, Italy, while working partly as a visiting student at the School of Computer Science, McGill University – Montréal. In 1992, he became an associate professor of Computer Science at Università di Firenze and, in November 1995, he joint the Università di Siena, where he is currently full professor of computer science.  His main interests are in machine learning, computer vision, and natural language processing. He was the leader of the WebCrow project supported by Google for automatic solving of crosswords, that  outperformed human competitors in an official competition within the ECAI-06 conference.  He has just published the book “Machine Learning: A Constrained-Based Approach,” where you can find his view on the field.

He has been an Associated Editor of a number of journals in his area of expertise, including The IEEE Transactions on Neural Networks and Neural Networks, and he has been the Chairman of the Italian Chapter of the IEEE Computational Intelligence Society and the President of the Italian Association for Artificial Intelligence. He is a fellow of the ECCAI (EurAI) (European Coordinating Committee for Artificial Intelligence), a fellow of the IEEE, and of IAPR.  He is in the list of top Italian scientists kept by  VIA-Academy.

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Arthur Gretton
UCL, UK

Topics

Kernel Methods to Reveal Properties and Relations in Data

Biography

Arthur Gretton is a Professor with the Gatsby Computational Neuroscience Unit, CSML, UCL, which he joined in 2010. He received degrees in physics and systems engineering from the Australian National University, and a PhD with Microsoft Research and the Signal Processing and Communications Laboratory at the University of Cambridge. He worked from 2002-2012 at the MPI for Biological Cybernetics, and from 2009-2010 at the Machine Learning Department, Carnegie Mellon University.
Arthur’s research interests in machine learning include kernel methods, statistical learning theory, nonparametric hypothesis testing, and generative modelling. He has been an associate editor at IEEE Transactions on Pattern Analysis and Machine Intelligence from 2009 to 2013, an Action Editor for JMLR since April 2013, a member of the NeurIPS Program Committee in 2008 and 2009, a Senior Area Chair for NeurIPS in 2018, an Area Chair for ICML in 2011 and 2012, and a member of the COLT Program Committee in 2013. Arthur was co-chair of AISTATS in 2016 (with Christian Robert), co-tutorials chair of ICML in 2018 (with Ruslan Salakhutdinov), co-workshop chair for ICML 2019 (with Honglak Lee), co-organiser of the Dali workshop in 2019 (with Krikamol Muandet and Shakir Mohammed), and co-organsier of MLSS 2019 in London (with Marc Deisenroth).

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Arthur Guez
Google DeepMind, Montreal, UK

Topics

General Reinforcement Learning Algorithms

Biography

Arthur Guez is currently a research scientist at DeepMind since 2014, initially in London and now based in Montreal. He focuses on reinforcement learning methods and played a key role in the development of AlphaGo and its later generalizations. He studied for his PhD at the Gatsby Computational Neuroscience Unit (UCL, London) under the supervision of Peter Dayan and David Silver. Before that, he studied for his undergraduate and MSc at McGill University in Montreal, under the supervision of Joelle Pineau.

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Kaisa Miettinen
University of Jyväskylä, Finland

Topics

Multiobjective Optimization & Decision Analytics

Biography

Kaisa Miettinen is Professor of Industrial Optimization of the University of Jyvaskyla. Her research interests include theory, methods, applications and software of nonlinear multiobjective optimization including interactive and evolutionary approaches. She heads the Research Group on Industrial Optimization and is the director of the thematic research area called Decision Analytics utilizing Causal Models and Multiobjective Optimization (DEMO, www.jyu.fi/demo).  She has authored over 170 refereed journal, proceedings and collection papers, edited 14 proceedings, collections and special issues and written a monograph Nonlinear Multiobjective Optimization. She is a member of the Finnish Academy of Science and Letters, Section of Science and the Immediate-Past President of the International Society on Multiple Criteria Decision Making (MCDM). She belongs to the editorial boards of eight international. She has previously worked at IIASA, International Institute for Applied Systems Analysis in Austria, KTH Royal Institute of Technology in Stockholm, Sweden and Helsinki School of Economics, Finland. In 2017, she received the Georg Cantor Award of the International Society on MCDM for independent inquiry in developing innovative ideas in the theory and methodology.

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Topics

Optimization, Complex Networks & Data Science

Biography

Panos Pardalos is a Distinguished Professor and the Paul and Heidi Brown Preeminent Professor in the Departments of Industrial and Systems Engineering at the University of Florida, and a world renowned leader in Global Optimization, Mathematical Modeling, and Data Sciences. He is a Fellow of AAAS, AIMBE, and INFORMS and was awarded the 2013 Constantin Caratheodory Prize of the International Society of Global Optimization. In addition, Dr. Pardalos has been awarded the 2013 EURO Gold Medal prize bestowed by the Association for European Operational Research Societies. This medal is the preeminent European award given to Operations Research (OR) professionals for “scientific contributions that stand the test of time.”

Dr. Pardalos has been awarded a prestigious Humboldt Research Award (2018-2019). The Humboldt Research Award is granted in recognition of a researcher’s entire achievements to date – fundamental discoveries, new theories, insights that have had significant impact on their discipline.

Dr. Pardalos is also a Member of the New York Academy of Sciences, the Lithuanian Academy of Sciences, the Royal Academy of Spain, and the National Academy of Sciences of Ukraine. He is the Founding Editor of Optimization Letters, Energy Systems, and Co-Founder of the International Journal of Global Optimization, and Computational Management Science. He has published over 500 journal papers, edited/authored over 200 books and organized over 80 conferences. He has a google h-index of 97 and has graduated 63 PhD students so far. Details can be found in www.ise.ufl.edu/pardalos

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Jan Peters
Computer Science Department, Technische Universitaet Darmstadt
Max-Planck Institute for Intelligent Systems, Germany

Topics

Intelligent Autonomous Systems, Robotics & Machine Learning

Biography

Jan Peters is a full professor (W3) for Intelligent Autonomous Systems at the Computer Science Department ofthe Technische Universitaet Darmstadt and at the same time a senior research scientist and group leader at the Max-Planck Institute for Intelligent Systems, where he heads the interdepartmental Robot Learning Group. Jan Peters has received the Dick Volz Best 2007 US PhD Thesis Runner-Up Award, the Robotics: Science & Systems – Early Career Spotlight, the INNS Young Investigator Award, and the IEEE Robotics & Automation Society’s Early Career Award as well as numerous best paper awards. In 2015, he received an ERC Starting Grant and in2019, he was appointed as an IEEE Fellow.

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Mauricio G. C. Resende
Amazon.com Research and University of Washington Seattle, Washington, USA

Topics

Combinatorial Optimization & Heuristics

Biography

Principal Research Scientist at Amazon, Seattle.

Mauricio G. C. Resende grew up in Rio de Janeiro (BR), West Lafayette (IN-US), and Amherst (MA-US). He did his undergraduate training in electrical engineering (systems engineering concentration) at the Pontifical Catholic U. of Rio de Janeiro.  He obtained an MS in operations research from Georgia Tech and a PhD in operations research from the U. of California, Berkeley.  He is most known for his work with metaheuristics, in particular GRASP and biased random-key genetic algorithms, as well as for his work with interior point methods for linear programming and network flows.  Dr. Resende has published over 200 papers on optimization and holds 15 U.S. patents.  He has edited four handbooks, including the “Handbook of Heuristics,” the “Handbook of Applied Optimization,” and the “Handbook of Optimization in Telecommunications,” and is coauthor of the book “Optimization by GRASP.” He sits on the editorial boards of several optimization journals, including Networks, Discrete Optimization, J. of Global Optimization, R.A.I.R.O., and International Transactions in Operational Research.

Prior to joining Amazon.com in 2014 as a Principal Research Scientist in the transportation area, Dr. Resende was a Lead Inventive Scientist at the Mathematical Foundations of Computing Department of AT&T Bell Labs and at the Algorithms and Optimization Research Department of AT&T Labs Research in New Jersey. Since 2016, Dr. Resende is also Affiliate Professor of Industrial and Systems Engineering at the University of Washington in Seattle. He is a Fellow of INFORMS.

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Richard E. Turner
Department of Engineering, University of Cambridge, UK

Topics

Deep Learning

Biography

Dr. Richard E. Turner is a Reader in Machine Learning at the University of Cambridge and a Visiting Researcher at Microsoft Research Cambridge. His research fuses probabilistic machine learning and deep learning to develop robust, data-efficient, flexible and automated learning systems. Richard helps lead Cambridge’s renowned Machine Learning Group, the Machine Learning and Machine Intelligence MPhil, the Centre for Doctoral Training in AI for Environmental Risk, and the Cambridge Big Data Strategic Initiative. He studied for his PhD at the Gatsby Computational Neuroscience Unit at UCL and spent his Postdoctoral Fellowship at New York University in the Laboratory for Computational Vision. He has been awarded the Cambridge Students’ Union Teaching Award for Lecturing and his work has featured on BBC Radio 5 Live’s The Naked Scientist, BBC World Service’s Click and in Wired Magazine.

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Past Keynote Speakers

The Keynote Speakers of the previous editions:

  • Jörg Bornschein, DeepMind, London, UK
  • Nello Cristianini, University of Bristol, UK
  • Peter Flach, University of Bristol, UK, and EiC of the Machine Learning Journal
  • Yi-Ke Guo, Imperial College London, UK
  • George Karypis, University of Minnesota, USA
  • Vipin Kumar, University of Minnesota, USA
  • George Michailidis, University of Florida, USA
  • Stephen Muggleton, Imperial College London, UK
  • Panos Pardalos, Center for Applied Optimization, University of Florida, USA
  • Jun Pei, Hefei University of Technology, China
  • Tomaso Poggio, MIT, USA
  • Andrey Raygorodsky, Moscow Institute of Physics and Technology, Russia
  • Ruslan Salakhutdinov, Carnegie Mellon University, USA, and AI Research at Apple
  • Vincenzo Sciacca, Almawave, Italy
  • My Thai, University of Florida, USA