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

Igor Babuschkin
DeepMind - Google, London, UK

Pierre Baldi
University of California Irvine, USA

Michael Bronstein
Twitter & Imperial College London, UK

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


Constraint-Based Approaches to Machine Learning


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.

Diederik P. Kingma


Adam algorithm, generative models, variational (Bayesian) inference & stochastic optimization


  • Current (2018 – …): Senior Research Scientist at Google Brain (San Francisco); generative models, identifiability, among other topics.
  • 2015-2018: Research Scientist at OpenAI (San Francisco). Part of the founding team of OpenAI and lead of the Algorithms team, focused on basic research.
  • 2013-2017: Ph.D. (cum laude) at University of Amsterdam. Thesis: Variational Inference and Deep Learning: A New Synthesis.


  • 2019: The Gerrit van Dijk prijs from the Royal Holland Society of Sciences and Humanities, for my work in machine learning.
  • 2019: The ELLIS PhD Award for “outstanding research achievements during the dissertation phase of outstanding students working in the field of artificial intelligence and machine learning”.
  • 2017: PhD with ‘cum laude’, highest distinction in the Netherlands, and first time it was awarded at the CS department in 30 years.
  • 2015: Google’s first European Doctoral Fellowship in Deep Learning


Risto Miikkulainen


Risto Miikkulainen is a Finnish-American computer scientist and professor at the University of Texas at Austin. In 2016, he was named Fellow of the Institute of Electrical and Electronics Engineers (IEEE) “for contributions to techniques and applications for neural and evolutionary computation”.

Risto Miikkulainen is  AVP of Evolutionary Intelligence at Cognizant Technology Solutions. His current research focuses on methods and applications of neuroevolution, as well as neural network models of natural language processing and vision; he is an author of over 400 articles in these research areas.

Honors and Awards

  • IEEE CIS Evolutionary Computation Pioneer Award, 2020
  • Gabor Award, the International Neural Network Society, 2017
  • Outstanding Paper of the Decade Award, International Society for Artificial Life, 2017.
  • IEEE Fellow, 2016
  • IEEE Computational Intelligence Society Distinguished Lecturer, 2015-2017.
  • Deployed Application Award, AAAI/IAAI-2013, AAAI/IAAI-2018
  • Best Paper Awards at GECCO-2002, 2003, 2005, 2007, 2008, 2014, 2015, 2017
  • Best Paper Awards at CIG-2005, 2006, 2009, 2011
  • BotPrize Award (Turing test for game bots), 2012
  • Honorable mention, Ziskind-Somerfield Research Award, Society of Biological Psychiatry, 2012
  • Winner, Annual Competition of Pseudo-Boolean SAT Solvers at SAT-2010 and SAT-2011
  • Bronze Medal, Human Competitive Results Competition, GECCO-2005, GECCO-2017

His research focuses on biologically-inspired computation such as neural networks and evolutionary computation. On one hand, the goal is to understand biological information processing, and on the other, to develop intelligent artificial systems that learn and adapt by observing and interacting with the environment. The three main focus areas are: (1) Neuroevolution, i.e. evolving complex deep learning architectures and recurrent neural networks for sequential decision tasks such as those in robotics, games, and artificial life; (2) Cognitive Science, i.e. models of natural language processing, memory, and learning that, in particular, shed light on disorders such as schizophrenia and aphasia; and (3) Computational Neuroscience, i.e. development, structure, and function of the visual cortex, episodic memory, and language processing.

See the UTCS Neural Networks Research Group website for research projects, publications, demos, and software. A few highlights: TexasExes interview/skit on artificial evolution; O’Reilly Radar Podcast on evolutionary computation; Digital Nibbles interview on BotPrize (i.e. Turing test for game bots); a 2-min soundbite on neuroevolution; the NERO machine learning game; an interactive demo of schizophrenic language model; the Computational Maps in the Visual Cortex book.

Panos Pardalos
University of Florida, USA


Optimization, Networks & Data Science


Panos M. Pardalos serves as distinguished professor of industrial and systems engineering at the University of Florida. Additionally, he is the Paul and Heidi Brown Preeminent Professor of industrial and systems engineering. He is also an affiliated faculty member of the computer and information science Department, the Hellenic Studies Center, and the biomedical engineering program. He is also the director of the Center for Applied Optimization. Pardalos is a world leading expert in global and combinatorial optimization. His recent research interests include network design problems, optimization in telecommunications, e-commerce, data mining, biomedical applications, and massive computing.

José C. Principe


Jose C. Principe is a Distinguished Professor of Electrical and Computer Engineering at the University of
Florida where he teaches advanced signal processing, machine learning and artificial neural networks
(ANNs). He is Eckis Professor and the Founder and Director of the University of Florida Computational
NeuroEngineering Laboratory (CNEL) www.cnel.ufl.edu. The CNEL Lab innovated signal and pattern
recognition principles based on information theoretic criteria, as well as filtering in functional spaces. His
secondary area of interest has focused in applications to computational neuroscience, Brain Machine
Interfaces and brain dynamics.
Dr. Principe is a Fellow of the IEEE, AIMBE, and IAMBE. He received the Gabor Award, from the INNS, the
Career Achievement Award from the IEEE EMBS and the Neural Network Pioneer Award, of the IEEE CIS.
He has more than 33 patents awarded over 800 publications in the areas of adaptive signal processing,
control of nonlinear dynamical systems, machine learning and neural networks, information theoretic
learning, with applications to neurotechnology and brain computer interfaces. He directed 93 Ph.D.
dissertations and 65 Master theses. He wrote in 2000 an interactive electronic book entitled “Neural and
Adaptive Systems” published by John Wiley and Sons and more recently co-authored several books on
“Brain Machine Interface Engineering” Morgan and Claypool, “Information Theoretic Learning”, Springer,
“Kernel Adaptive Filtering”, Wiley and “System Parameter Adaption: Information Theoretic Criteria and
Algorithms”, Elsevier. He has received four Honorary Doctor Degrees, from Finland, Italy, Brazil and
Colombia, and routinely serves in international scientific advisory boards of Universities and Companies.
He has received extensive funding from NSF, NIH and DOD (ONR, DARPA, AFOSR).

Mihaela van der Schaar


Machine Learning for Medicine, Data Science and decisions, Artificial Intelligence


Professor van der Schaar is John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge and a Turing Fellow at The Alan Turing Institute in London, where she leads the effort on data science and machine learning for personalised medicine. She is an IEEE Fellow (2009). She has received the Oon Prize on Preventative Medicine from the University of Cambridge (2018).  She has also been the recipient of an NSF Career Award, 3 IBM Faculty Awards, the IBM Exploratory Stream Analytics Innovation Award, the Philips Make a Difference Award and several best paper awards, including the IEEE Darlington Award. She holds 35 granted USA patents.

The current emphasis of her research is on machine learning with applications to medicine, finance and education. She has also worked on data science, network science, game theory, signal processing, communications, and multimedia.


Five papers accepted at NeurIPS 2019

Tutorial Speakers

Chip Huyen
Stanford University, USA


Adam Paszke
University of Warsaw, Poland


Past Lecturers

The Lecturers of the previous editions:

  • Ioannis Antonoglou, Google DeepMind, UK
  • 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
  • Giuseppe Di Fatta, University of Reading, UK
  • Marco Gori, University of Siena, Italy
  • Yi-Ke Guo, Imperial College London, UK & Founding Director of Data Science Institute
  • Phillip Isola, MIT, USA
  • Leslie Kaelbling, MIT - Computer Science & Artificial Intelligence Lab, USA
  • Ilias S. Kotsireas, Wilfrid Laurier University, Canada
  • 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
  • Dolores Romero Morales, Copenhagen Business School, Denmark
  • Ruslan Salakhutdinov, Carnegie Mellon University, and AI Research at Apple, USA
  • Josh Tenenbaum, MIT, USA
  • Naftali Tishby, Hebrew University, Israel
  • Joaquin Vanschoren, Eindhoven University of Technology, The Netherlands
  • Oriol Vinyals, Google DeepMind, UK
  • Aleskerov Z. Fuad, National Research University Higher School of Economics, Russia