Fuad Aleskerov
National Research University Higher School of Economics, Russia


Positions: a) Head, Department of Mathematics for Economics, National Research University Higher
School of Economics; b) Head, International Laboratory of Decision Choice and Analysis, National
Research University Higher School of Economics; c) Head, Laboratory of Choice Theory and Decision
Analysis, Russian Academy of Sciences Institute of Control Sciences

Education: 1969-1974, Student, Mathematics Faculty, Moscow State University; 1981, Ph.D. in
Control in Socio-Economic Systems (thesis title “Interval Choice”)

Distinctions and Awards: 1993, Doctor of Science (thesis title “Local Aggregation Models”),
Honorary Worker of Science and Technology of Russian Federation, 2011; Medal of the Order “For Merit
II” (Decree of the President of Russia on 21.12.2013)

Publications: 10 books, more than 200 articles, more than 100 in peer-reviewed journals and volumes, Copyright
certificates, patents – 5

Other Professional Activities: Member of International Economic Association (member of the Executive Council, 2011-2017); American Mathematical Society; New Economic Association, Russia Member of Editorial Board for the journals: Mathematical Social Sciences, Automation and Remote Control, Political Studies (TBF), Control Problems (in Russian), Politeia (in Russian), Economic Journal HSE (in Russian), Business-informatics (in Russian), Journal of New Economic Association (in Russian), vice-editor-in-chief, Mathematical Game Theory and its Applications, Сontrol in Large-Scale Systems (on-line journal, in Russian), International Journal of Information Technologies and Decision Making, Annals of Data Analysis, Group Decisions and Negotiation

Invited Speaker: more than 90 conferences and workshops

Other activities: 2008 – present, Head of the Board of the Congregation ‘Le Dor va Dor’ of the World Union of Progressive Judaism, Moscow, Russia


Roman Belavkin
Middlesex University London, UK


Roman Belavkin is a Reader in Informatics at the Department of Computer Science, Middlesex University,  UK.  He has MSc degree in Physics from the Moscow State University and PhD in Computer Science from the University of Nottingham, UK.   In his PhD thesis, Roman combined cognitive science and information theory to study the role of emotion in decision-making, learning and problem solving.  His main research interests are in mathematical theory of dynamics of information and optimization of learning, adaptive and evolving systems.  He used information value theory to give novel explanations of some common decision-making paradoxes.  His work on optimal transition kernels showed non-existence of optimal deterministic strategies in a broad class of problems with information constraints.

Roman’s theoretical work on optimal parameter control in algorithms has found applications to computer science and biology.  From 2009, Roman lead a collaboration between four UK universities involving mathematics, computer science and experimental biology on optimal mutation rate control, which lead to the discovery in 2014 of mutation rate control in bacteria (reported in Nature Communications http://doi.org/skb  and PLOS Biology http://doi.org/cb9s).  He also contributed to research projects on neural cell-assemblies, independent component analysis and anomaly detection, such as cyber attacks.

Yoshua Bengio
Head of the Montreal Institute for Learning Algorithms (MILA) & University of Montreal, Canada


Yoshua Bengio is Full Professor of the Department of Computer Science and Operations Research,head of the Montreal Institute for Learning Algorithms (MILA), CIFAR Program co-director of the CIFAR program on Learning in Machines and Brains,  Canada Research Chair in Statistical Learning Algorithms. His main research ambition is to understand principles of learning that yield intelligence. He supervises a large group of graduate students and post-docs. His research is widely cited (over 80000 citations found by Google Scholar in September 2017, with an H-index of 101).

Yoshua Bengio is currently action editor for the Journal of Machine Learning Research, associate editor for the Neural Computation journal, editor for Foundations and Trends in Machine Learning, and has been associate editor for the Machine Learning Journal and the IEEE Transactions on Neural Networks.

Yoshua Bengio was Program Chair for NIPS‘2008 and General Chair for NIPS‘2009 (NIPS is the flagship conference in the areas of learning algorithms and neural computation). Since 1999, he has been co-organizing the Learning Workshop with Yann Le Cun, with whom he has also created the International Conference on Representation Learning (ICLR). He has also organized or co-organized numerous other events, principally the deep learning workshops and symposiua at NIPS and ICML since 2007. Yoshua Bengio is Officer of the Order of Canada and member of the Royal Society of Canada.

Sergiy Butenko
Texas A&M University, USA


Dr. Butenko’s research concentrates mainly on global and discrete optimization and their applications. In particular, he is interested in theoretical and computational aspects of continuous global optimization approaches for solving discrete optimization problems on graphs. Applications of interest include network-based data mining, analysis of biological and social networks, wireless ad hoc and sensor networks, energy, and sports analytics.


Marco Gori
University of Siena, Italy


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.

Yi-Ke Guo
Imperial College London, UK & Founding Director of Data Science Institute


Yike Guo is a Professor of Computing Science in the Department of Computing at Imperial College London. He is the founding Director of the Data Science Institute at Imperial College, as well as leading the Discovery Science Group in the department. Professor Guo also holds the position of CTO of the tranSMART Foundation, a global open source community using and developing data sharing and analytics technology for translational medicine.

Professor Guo received a first-class honours degree in Computing Science from Tsinghua University, China, in 1985 and received his PhD in Computational Logic from Imperial College in 1993 under the supervision of Professor John Darlington. He founded InforSense, a software company for life science and health care data analysis, and served as CEO for several years before the company’s merger with IDBS, a global advanced R&D software provider, in 2009.

He has been working on technology and platforms for scientific data analysis since the mid-1990s, where his research focuses on knowledge discovery, data mining and large-scale data management. He has contributed to numerous major research projects including: the UK EPSRC platform project, Discovery Net; the Wellcome Trust-funded Biological Atlas of Insulin Resistance (BAIR); and the European Commission U-BIOPRED project. He is currently the Principal Investigator of the European Innovative Medicines Initiative (IMI) eTRIKS project, a €23M project that is building a cloud-based informatics platform, in which tranSMART is a core component for clinico-genomic medical research, and co-Investigator of Digital City Exchange, a £5.9M research programme exploring ways to digitally link utilities and services within smart cities.

Professor Guo has published over 200 articles, papers and reports. Projects he has contributed to have been internationally recognised, including winning the “Most Innovative Data Intensive Application Award” at the Supercomputing 2002 conference for Discovery Net, and the Bio-IT World “Best Practices Award” for U-BIOPRED in 2014. He is a Senior Member of the IEEE and is a Fellow of the British Computer Society.


Yann LeCun
Director of AI Research, Facebook
New York University, USA


Yann LeCun is Director of AI Research at Facebook, and Silver Professor of Dara Science, Computer Science, Neural Science, and Electrical Engineering at New York University, affiliated with the NYU Center for Data Science, the Courant Institute of Mathematical Science, the Center for Neural Science, and the Electrical and Computer Engineering Department.

He received the Electrical Engineer Diploma from Ecole Superieure d’Ingenieurs en Electrotechnique et Electronique (ESIEE), Paris in 1983, and a PhD in Computer Science from Universite Pierre et Marie Curie (Paris) in 1987. After a postdoc at the University of Toronto, he joined AT&T Bell Laboratories in Holmdel, NJ in 1988. He became head of the Image Processing Research Department at AT&T Labs-Research in 1996, and joined NYU as a professor in 2003, after a brief period as a Fellow of the NEC Research Institute in Princeton. From 2012 to 2014 he directed NYU’s initiative in data science and became the founding director of the NYU Center for Data Science. He was named Director of AI Research at Facebook in late 2013 and retains a part-time position on the NYU faculty.

His current interests include AI, machine learning, computer perception, mobile robotics, and computational neuroscience. He has published over 180 technical papers and book chapters on these topics as well as on neural networks, handwriting recognition, image processing and compression, and on dedicated circuits and architectures for computer perception. The character recognition technology he developed at Bell Labs is used by several banks around the world to read checks and was reading between 10 and 20% of all the checks in the US in the early 2000s. His image compression technology, called DjVu, is used by hundreds of web sites and publishers and millions of users to access scanned documents on the Web. Since the late 80’s he has been working on deep learning methods, particularly the convolutional network model, which is the basis of many products and services deployed by companies such as Facebook, Google, Microsoft, Baidu, IBM, NEC, AT&T and others for image and video understanding, document recognition, human-computer interaction, and speech recognition.

LeCun has been on the editorial board of IJCV, IEEE PAMI, and IEEE Trans. Neural Networks, was program chair of CVPR’06, and is chair of ICLR 2013 and 2014. He is on the science advisory board of Institute for Pure and Applied Mathematics, and has advised many large and small companies about machine learning technology, including several startups he co-founded. He is the lead faculty at NYU for the Moore-Sloan Data Science Environment, a $36M initiative in collaboration with UC Berkeley and University of Washington to develop data-driven methods in the sciences. He is the recipient of the 2014 IEEE Neural Network Pioneer Award.

Peter Norvig
Director of Research, Google


Peter Norvig is a Director of Research at Google Inc. Previously he was head of Google’s core search algorithms group, and of NASA Ames’s Computational Sciences Division, making him NASA’s senior computer scientist. He received the NASA Exceptional Achievement Award in 2001. He has taught at the University of Southern California and the University of California at Berkeley, from which he received a Ph.D. in 1986 and the distinguished alumni award in 2006. He was co-teacher of an Artifical Intelligence class that signed up 160,000 students, helping to kick off the current round of massive open online classes. His publications include the books Artificial Intelligence: A Modern Approach (the leading textbook in the field), Paradigms of AI Programming: Case Studies in Common LispVerbmobil: A Translation System for Face-to-Face Dialog, and Intelligent Help Systems for UNIX. He is also the author of the Gettysburg Powerpoint Presentation and the world’s longest palindromic sentence. He is a fellow of the AAAIACMCalifornia Academy of Science and American Academy of Arts & Sciences.


Alex 'Sandy' Pentland
MIT & Director of MIT’s Human Dynamics Laboratory, USA


Professor Alex “Sandy” Pentland directs the MIT Connection Science and Human Dynamics labs and previously helped create and direct the MIT Media Lab and the Media Lab Asia in India. He is one of the most-cited scientists in the world, and Forbes recently declared him one of the “7 most powerful data scientists in the world” along with Google founders and the Chief Technical Officer of the United States. He has received numerous awards and prizes such as the McKinsey Award from Harvard Business Review, the 40th Anniversary of the Internet from DARPA, and the Brandeis Award for work in privacy.

He is a founding member of advisory boards for Google, AT&T, Nissan, and the UN Secretary General, a serial entrepreneur who has co-founded more than a dozen companies including social enterprises such as the Data Transparency Lab, the Harvard-ODI-MIT DataPop Alliance and the Institute for Data Driven Design. He is a member of the U.S. National Academy of Engineering and leader within the World Economic Forum.

Over the years Sandy has advised more than 60 PhD students. Almost half are now tenured faculty at leading institutions, with another one-quarter leading industry research groups and a final quarter founders of their own companies. Together Sandy and his students have pioneered computational social scienceorganizational engineeringwearable computing (Google Glass), image understanding, and modern biometrics. His most recent books are `Social Physics,’ published by Penguin Press, and ‘Honest Signals‘, published by MIT Press.

Interesting experiences include dining with British Royalty and the President of India, staging fashion shows in Paris, Tokyo, and New York, and developing a method for counting beavers from space.

Mauricio Resende
Amazon, USA


Mauricio’s research interests include combinatorial optimization, engineering of algorithms, networks and graphs, interior point methods, massive data sets, mathematical programming, metaheuristics, network design, operations research modeling, and parallel computing.
He is on the editorial boards of Investigação Operacional – The Journal of the Portuguese Association of Operational Research, Pesquisa Operacional – The Journal of the Brazilian Operational Research Society, Combinatorial Algorithms Test Sets (CATS): The ACM/EATCS Platform for Experimental Research, Computational Optimization and Applications, Journal of Combinatorial Optimization, Investigación Operativa, Journal of Global Optimization, Journal of Heuristics, and Networks. He is a Permanent Member of the Center for Discrete Mathematics and Theoretical Computer Science (DIMACS) at Rutgers University and External Member of the Computational Optimization Research Center ( CORC) at Columbia University.