Pierre Baldi is a chancellor’s professor of computer science at University of California Irvine and the director of its Institute for Genomics and Bioinformatics.
Pierre Baldi received his Bachelor of Science and Master of Science degrees at the University of Paris, in France. He then obtained his Ph.D. degree in mathematics at the California Institute of Technology in 1986 supervised by R. M. Wilson.
From 1986 to 1988, he was a postdoctoral fellow at the University of California, San Diego. From 1988 to 1995, he held faculty and member of the technical staff positions at the California Institute of Technology and at the Jet Propulsion Laboratory, where he was given the Lew Allen Award for Research Excellence in 1993. He was CEO of a start up company called Net-ID from 1995 to 1999 and joined University of California, Irvine in 1999.
Baldi’s research interests include artificial intelligence, statistical machine learning, and data mining, and their applications to problems in the life sciences in genomics, proteomics, systems biology, computational neuroscience, and, recently, deep learning.
Baldi has over 250 publications in his field of research and four books including
- “Bioinformatics: the Machine Learning Approach” (MIT Press, 1998; 2nd Edition, 2001, ISBN 978-0262025065) a worldwide best-seller
- “Modeling the Internet and the Web. Probabilistic Methods and Algorithms“, by Pierre Baldi, Paolo Frasconi and Padhraic Smyth. Wiley editors, 2003.
- “The Shattered Self—The End of Natural Evolution“, by Pierre Baldi. MIT Press, 2001.
- “DNA Microarrays and Gene Regulation“, Pierre Baldi and G. Wesley Hatfield. Cambridge University Press, 2002.
- Extensive gene gain associated with adaptive evolution of poxviruses
Baldi is a fellow of the Association for the Advancement of Artificial Intelligence (AAAI), the AAAS, the IEEE,and the Association for Computing Machinery (ACM). He is also the recipient of the 2010 Eduardo R. Caianiello Prize for Scientific Contributions to the field of Neural Networks and a fellow of the International Society for Computational Biology (ISCB).
Deep learning algorithm solves Rubik’s Cube faster than any human.
AI solves Rubik’s Cube in one second
TopicsMachine Learning, Deep Learning, Artificial Intelligence. Keynote Speaker of the Workshop on “Biologically Plausible Learning”.
Yoshua Bengio Paris, France) is a Canadian computer scientist, most noted for his work on artificial neural networks and deep learning. He was a co-recipient of the 2018 ACM A.M. Turing Award for his work in deep learning.He is a professor at the Department of Computer Science and Operations Research at the Université de Montréal and scientific director of the Montreal Institute for Learning Algorithms (MILA).(born 1964 in
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.
TopicsMachine Learning & Artificial Intelligence
Nando de Freitas is a computer scientist who wants to understand intelligence and how brains work. His key areas of research are neural networks and deep learning, reinforcement learning, apprenticeship learning and teaching, goal and program discovery, transfer and multi-task learning, reasoning and cognition. He is a strong believer in building artificial intelligence (AI) tools to improve health care, advance science and provide decision support systems for lawyers, economists, politicians, environmentalists and others, with a goal of improving life on Earth. In his view, the price to be paid if we do not develop AI tools to extend our minds – and address our complex problems – is simply too high.
Charles A. McDowell Award for Excellence in Research, 2013
Distinguished Paper Award at IJCAI, 2013
MITACS Young Researcher Award, 2010
TopicsConstraint-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.
TopicsRobustness and fairness for machine learning and Artificial Intelligence
Marta Kwiatkowska is a computer scientists who is developing modelling and analysis methods for complex systems, such as those arising in computer networks, electronic devices and biological organisms. The distinctive aspect of her work is its focus on probabilistic and quantitative verification techniques, as well as synthesis of correct-by-construction systems from quantitative specifications. Marta’s recent contributions have centred on safety and trust for robotics and Artificial Intelligence, and specifically safety and robustness guarantees for machine learning.
Marta led the development of the PRISM model checker (www.primmodelchecker.org), the leading tool in the area and widely used for research and teaching. PRISM has been applied to study, amongst others, wireless network protocols, security protocols, molecular signalling networks, DNA computation and cardiac pacemakers.
Marta has published over 300 papers during her career and received numerous keynote invitations. She won two ERC Advanced Grants, VERIWARE and FUN2MODEL, and is a co-investigator on the EPSRC Programme Grant on Mobile Autonomy. Marta was awarded an honorary doctorate from KTH Institute of Technology and is the first female winner of the Royal Society Milner Medal. She is a Fellow of the ACM, Member of Academia Europea and Fellow of the Royal Society.
Given an optimization problem and a feasible solution to it, the corresponding
inverse optimization problem is to find a minimal adjustment of the cost vector
under some norm such that the given solution becomes optimum. Inverse
optimization problems have been applied in diverse areas, ranging from geophysical sciences, traffic networks, communication networks, facility location problems, finance, electricity markets, and medical decision-making. It has been studied in various optimization frameworks including linear programming, combinatorial optimization, conic, integer and mixed-integer programming, variational inequalities, and countably infinite linear problems and robust optimization.
In this talk, we mainly concentrate on inverse combinatorial optimization
problems (ICOP). We will introduce some classes of ICOP as well as general
methods to solve them. Some open problems are proposed. We also discuss
some generalized inverse optimization problems.
We introduce inverse optimization problems on spanning trees and mainly concentrate
on the inverse max+sum spanning tree problems (IMMST) in which the original
problem aims to minimize the sum of a maximum weight and a sum cost of a spanning tree
Keywords: inverse combinatorial optimization, spanning tree problems.
TopicsMotor Control & Learning, Robotics, Machine Learning, Biomimetic Systems.
Jan Peters (born on August 14, 1976 in Hamburg, Germany) is a German computer scientist. He is Professor of Intelligent Autonomous Systems at Department of Computer Science of the Technische Universität Darmstadt and Head of the Robot Learning Group at the Max Planck Institute for Intelligent Systems.
Peters is renowned for his research in machine learning and robotics.
Jan Peters graduated from the University of Hagen in 2000 with a diplom in computer science and from Technical University of Munich in 2001 with a diplom in electrical engineering. Fron 2000 to 2001, he spent two semesters as visiting student at the National University of Singapore. He then studied at the University of Southern California where he earned a Master of Science degree in Computer Science and a Master of Science degree in Aerospace and Mechanical Engineering. He received his Ph.D. in Computer Science from the University of Southern California in 2007. Since 2011 he has been Head of the Intelligent Autonomous Systems Institute at the Technische Universität Darmstadt.
In 2008, Nicholas Roy, Russ Tedrake, Jun Morimoto and Jan Peters founded the IEEE Robotics and Automation Society’s Technical Committee on Robot Learning.
For his contributions, he has received the Robotics & Automation Early Career Award, the highest ranked early career award of the Institute of Electrical and Electronics Engineers, and the Young Investigator Award of the International Neural Network Society. In addition, he received an ERC Starting Grant in 2014 as well as numerous best paper awards. He was appointed Fellow of the Institute of Electrical and Electronics Engineers(IEEE) in 2019 “for contributions to robot learning of dexterous motor skills”.
Raniero Romagnoli is currently CTO of Almawave, that he joined in 2011, with the responsibility of defining and implementing the company’s technology strategy, with a special focus on R&D labs, helping Almawave create and evolve its products and solutions, that are based on proprietary Natural Language Processing technology to leverage speech and text information and communications in order to govern processes and improve both self and assisted engagement with users. Before joining Almawave Raniero worked for 2 years in RSA and before that Raniero worked for Hewlett Packard, for almost 10 years, in different technology and divisions, covering roles both in Product Management and R&D in intelligent support systems area. Raniero has a broad experience in the artificial intelligence field, starting from his research activities in the late ’90s on Machine Learning and Neural Networks for image processing, than in the security space, and since he joined Almawave in the field of speech and text analysis.
Past Keynote Speakers
The Keynote Speakers of the previous editions:
- Jörg Bornschein, DeepMind, London, UK
- Michael Bronstein, Imperial College London, UK
- Nello Cristianini, University of Bristol, UK
- Peter Flach, University of Bristol, UK, and EiC of the Machine Learning Journal
- Marco Gori, University of Siena, Italy
- Arthur Gretton, UCL, UK
- Arthur Guez, Google DeepMind, Montreal, UK
- 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
- Kaisa Miettinen, University of Jyväskylä, Finland
- Stephen Muggleton, Imperial College London, UK
- Panos Pardalos, University of Florida, USA
- Jan Peters, Computer Science Department, Technische Universitaet Darmstadt, and Max-Planck Institute for Intelligent Systems, Germany
- Tomaso Poggio, MIT, USA
- Andrey Raygorodsky, Moscow Institute of Physics and Technology, Russia
- Mauricio G. C. Resende, Amazon.com Research and University of Washington Seattle, Washington, USA
- Ruslan Salakhutdinov, Carnegie Mellon University, USA, and AI Research at Apple
- Vincenzo Sciacca, Almawave, Italy
- My Thai, University of Florida, USA
- Richard E. Turner, Department of Engineering, University of Cambridge, UK