Lecturers

Each Lecturer will hold three lessons on a specific topic.
The Lecturers below are confirmed.


Ioannis Antonoglou
Google DeepMind, UK

Topics

General Reinforcement Learning Algorithms

Lectures



Giuseppe Di Fatta
University of Reading, UK

Topics

Data Science & Knime

Biography

Dr. Giuseppe Di Fatta is an Associate Professor of Computer Science and the Head of the Department of Computer Science at the University of Reading, UK. In 1999, he was a research fellow at the International Computer Science Institute (ICSI), Berkeley, CA, USA. From 2000 to 2004, he was with the High-Performance Computing and Networking Institute of the National Research Council, Italy. From 2004 to 2006, he was with the University of Konstanz, Germany, where he joined the initial KNIME development team until the first release of KNIME 1.0 in 2006. His research interests include data mining, machine learning, distributed and parallel computing, and data-driven multidisciplinary applications. He has published over 100 articles in peer-reviewed conferences and journals, is the founder of the IEEE ICDM Workshop on Data Mining in Networks and has chaired several other international events.

Lectures



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.

Lectures



Phillip Isola
MIT, USA

Topics

Generative Adversarial Networks

Lectures



Topics

Artificial Intelligence & Machine Learning, Robotics

Biography

Leslie Pack Kaelbling is Professor of Computer Science and Engineering at MIT. She has previously held positions at Brown University, the Artificial Intelligence Center of SRI International, and at Teleos Research.

Prof. Kaelbling has done substantial research on designing situated agents, mobile robotics, reinforcement learning, and decision-theoretic planning. In 2000, she founded the Journal of Machine Learning Research, a high-quality journal that is both freely available electronically as well as published in archival form; she currently serves as editor-in-chief.

She is an NSF Presidential Faculty Fellow, a former member of the AAAI Executive Council, the 1997 recipient of the IJCAI Computers and Thought Award, a trustee of IJCAII and a fellow of the AAAI.

She received an A. B. in Philosophy in 1983 and a Ph. D. in Computer Science in 1990, both from Stanford University.

Her goal is to make intelligent robots: she did some of the earliest work on reinforcement learning and partially observable Markov decision process (POMDP) in robotics, and is currently focused on integrating geometric, probabilistic, and logical reasoning.

Current Projects:

Robust Intelligent Robots, Task and Motion Planning for Autonomous Robots, Learning and Optimization.

RESEARCH AREAS:

Lectures



Raniero Romagnoli
Almawave, Italy

Lectures



Dolores Romero Morales
Copenhagen Business School, Denmark

Topics

Big Data

Lectures



Ruslan Salakhutdinov
Carnegie Mellon University
AI Research at Apple, USA

Topics

Deep Learning - (Lectures via Video)

Lectures



Josh Tenenbaum
MIT, USA

Topics

Computational Cognitive Science, probabilistic generative models, and probabilistic programming

Lectures



Naftali Tishby
Hebrew University, Israel

Topics

Theory of Deep Learning – Information Bottleneck

Biography

Dr. Naftali Tishby is a professor of Computer Science, and the incumbent of the Ruth and Stan Flinkman Chair for Brain Research at the Edmond and Lily Safra Center for Brain Science (ELSC) at the Hebrew University of Jerusalem. He is one of the leaders of machine learning research and computational neuroscience in Israel and his numerous ex-students serve at key academic and industrial research positions all over the world. Prof. Tishby was the founding chair of the new computer-engineering program, and a director of the Leibnitz research center in computer science, at the Hebrew university. Tishby received his PhD in theoretical physics from the Hebrew university in 1985 and was a research staff member at MIT and Bell Labs from 1985 to 1991. Prof. Tishby was also a visiting professor at Princeton NECI, University of Pennsylvania, UCSB, and IBM research.

His current research is at the interface between computer science, statistical physics, and computational neuroscience. He pioneered various applications of statistical physics and information theory in computational learning theory. More recently, he has been working on the foundations of biological information processing and deep learning and the connections between dynamics and information. He has introduced with his colleagues new theoretical frameworks for optimal adaptation and efficient information representation in biology, such as the Information Bottleneck method and the Minimum Information principle for neural coding. This year Prof. Tishby has received the prestigious IBT award in Mathematical Neuroscience.

Lectures



Joaquin Vanschoren
Eindhoven University of Technology, The Netherlands

Topics

Automatic machine learning

Biography

Joaquin Vanschoren is Assistant Professor in Machine Learning at the Eindhoven University of Technology. His research focuses on machine learning, meta-learning, and understanding and automating learning. He founded and leads OpenML.org, an open science platform for machine learning. He received several demo and open data awards, has been tutorial speaker at NeurIPS and ECMLPKDD, and invited speaker at ECDA, StatComp, AutoML@ICML, CiML@NIPS, DEEM@SIGMOD, AutoML@PRICAI, MLOSS@NIPS, and many other occasions. He was general chair at LION 2016, program chair of Discovery Science 2018, demo chair at ECMLPKDD 2013, and he co-organizes the AutoML and meta-learning workshop series at NIPS and ICML. He is also co-editor of the book ‘Automatic Machine Learning: Methods, Systems, Challenges’.

Lectures



Oriol Vinyals
Google DeepMind, UK

Topics

Deep Learning & Reinforcement Learning

Lectures





Past Lecturers

The Lecturers of the previous editions:

  • Roman Belavkin, Middlesex University London, UK
  • Yoshua Bengio, Head of the Montreal Institute for Learning Algorithms (MILA) & University of Montreal, Canada
  • Sergiy Butenko, Texas A&M University, USA
  • Marco Gori, University of Siena, Italy
  • Yi-Ke Guo, Imperial College London, UK & Founding Director of Data Science Institute
  • Peter Norvig, Director of Research, Google
  • Panos Pardalos, University of Florida, USA
  • Alex 'Sandy' Pentland, MIT & Director of MIT’s Human Dynamics Laboratory, USA
  • Marc'Aurelio Ranzato, Facebook AI Research Lab, New York, USA
  • Aleskerov Z. Fuad, National Research University Higher School of Economics, Russia