Lecturers
Each Lecturer will hold three/four lessons on a specific topic.
Topics
Deep Learning, GenomicsBiography
Ziga Avsec is a research scientist at DeepMind where he leads the genomics initiative within DeepMind’s Science program. He obtained his Ph.D. from the Technical University of Munich, supervised by Julien Gagneur. His past research work focused on the development of sequence-based predictive models to better understand the human genome.
Lectures
Biography
Edith Elkind is a Professor of Computer Science at University of Oxford. She obtained her PhD from Princeton in 2005, and has worked in the UK, Israel, and Singapore before joining Oxford in 2013. She works in algorithmic game theory, with a focus on algorithms for collective decision making and coalition formation. Edith has published over 100 papers in leading AI conferences and journals, and has served as a program chair of WINE, AAMAS, ACM EC and COMSOC; she will serve as a program chair of IJCAI in 2023.
Lectures
Topics
Robotics, Robot Vision, Robot LearningBiography
Danica Kragic is a Professor at the School of Computer Science and Communication at the Royal Institute of Technology, KTH. She received MSc in Mechanical Engineering from the Technical University of Rijeka, Croatia in 1995 and PhD in Computer Science from KTH in 2001. She has been a visiting researcher at Columbia University, Johns Hopkins University and INRIA Rennes. She is the Director of the Centre for Autonomous Systems. Danica received the 2007 IEEE Robotics and Automation Society Early Academic Career Award. She is a member of the Royal Swedish Academy of Sciences, Royal Swedish Academy of Engineering Sciences and Young Academy of Sweden. She holds a Honorary Doctorate from the Lappeenranta University of Technology. She chaired IEEE RAS Technical Committee on Computer and Robot Vision and served as an IEEE RAS AdCom member. Her research is in the area of robotics, computer vision and machine learning. In 2012, she received an ERC Starting Grant. Her research is supported by the EU, Knut and Alice Wallenberg Foundation, Swedish Foundation for Strategic Research and Swedish Research Council. She is an IEEE Fellow.
https://en.wikipedia.org/wiki/Danica_Kragic
Lectures
Efficient representations of observed input data have been shown to significantly accelerate the performance of subsequent learning tasks in numerous domains. To obtain such representations automatically, we need to design both i) models that identify useful patterns in the input data and encode them into structured low dimensional representations, and ii) evaluation measures that accurately assess the quality of the resulting representations. We present work that addresses both these requirements. We present a short overview of representation learning techniques and different structures that can be imposed on representation spaces. We show into how these can be applied in complex robotics tasks considering physical interaction with the environment.
Topics
Computational neuroscience, Human-Robot Interaction, Cognitive Developmental RoboticsBiography
Dr. Yukie Nagai has been investigating underlying neural mechanisms for social cognitive development by means of computational approaches. She designs neural network models for robots to learn to acquire cognitive functions such as self-other cognition, estimation of others’ intention and emotion, altruism, and so on based on the theory of predictive coding. The simulator reproducing atypical perception in autism spectrum disorder (ASD), which has been developed by her group, greatly impacts the society as it enables people with and without ASD to better understand potential causes for social difficulties. She was elected to “30 women in robotics you need to know about” in 2019 and “World’s 50 Most Renowned Women in Robotics” in 2020. She serves as the principal investigator of JST CREST “Cognitive Mirroring” and CREST “Cognitive Feeling” since December 2016 and October 2021, respectively.
She is also a member of International Research Center for Neurointelligence at the University of Tokyo since 2019, and a member of Next Generation Artificial Intelligence Research Center and Forefront Physics and Mathematics Program to Drive Transformation at the University of Tokyo since 2020.
Lectures
Artificial intelligence has a great potential to uncover the underlying mechanisms of human intelligence. Neural networks inspired by the brain can simulate how humans acquire cognitive abilities and thus reveal what enables/disables cognitive development. My lecture introduces a neuroscience theory called predictive coding. We have been designing neural networks based on predictive coding and investigating to what extent the theory accounts for cognitive development. The key idea is that the brain works as a predictive machine and perceives the world and acts on it to minimize prediction errors. Our robot experiments demonstrate that the process of minimizing prediction errors leads to sensorimotor and social cognitive development and that aberrant predictive processing produces atypical development such as developmental disorders. We discuss how these findings facilitate the understanding of human intelligence and provide a new principle for cognitive development.
Artificial intelligence has a great potential to uncover the underlying mechanisms of human intelligence. Neural networks inspired by the brain can simulate how humans acquire cognitive abilities and thus reveal what enables/disables cognitive development. My lecture introduces a neuroscience theory called predictive coding. We have been designing neural networks based on predictive coding and investigating to what extent the theory accounts for cognitive development. The key idea is that the brain works as a predictive machine and perceives the world and acts on it to minimize prediction errors. Our robot experiments demonstrate that the process of minimizing prediction errors leads to sensorimotor and social cognitive development and that aberrant predictive processing produces atypical development such as developmental disorders. We discuss how these findings facilitate the understanding of human intelligence and provide a new principle for cognitive development.
Topics
Global Optimization, Mathematical Modeling, Energy Systems, Financial applications, and Data SciencesBiography
Panos Pardalos was born in Drosato (Mezilo) Argitheas in 1954 and graduated from Athens University (Department of Mathematics). He received his PhD (Computer and Information Sciences) from the University of Minnesota. He is a Distinguished Emeritus Professor in the Department of Industrial and Systems Engineering at the University of Florida, and an affiliated faculty of Biomedical Engineering and Computer Science & Information & Engineering departments.
Panos Pardalos is a world-renowned leader in Global Optimization, Mathematical Modeling, Energy Systems, Financial applications, and Data Sciences. He is a Fellow of AAAS, AAIA, AIMBE, EUROPT, and INFORMS and was awarded the 2013 Constantin Caratheodory Prize of the International Society of Global Optimization. In addition, Panos 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.”
Panos 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.
Panos Pardalos is also a Member of several Academies of Sciences, and he holds several honorary PhD degrees and affiliations. He is the Founding Editor of Optimization Letters, Energy Systems, and Co-Founder of the International Journal of Global Optimization, Computational Management Science, and Springer Nature Operations Research Forum. He has published over 600 journal papers, and edited/authored over 200 books. He is one of the most cited authors and has graduated 71 PhD students so far. Details can be found in www.ise.ufl.edu/pardalos
Panos Pardalos has lectured and given invited keynote addresses worldwide in countries including Austria, Australia, Azerbaijan, Belgium, Brazil, Canada, Chile, China, Czech Republic, Denmark, Egypt, England, France, Finland, Germany, Greece, Holland, Hong Kong, Hungary, Iceland, Ireland, Italy, Japan, Lithuania, Mexico, Mongolia, Montenegro, New Zealand, Norway, Peru, Portugal, Russia, South Korea, Singapore, Serbia, South Africa, Spain, Sweden, Switzerland, Taiwan, Turkey, Ukraine, United Arab Emirates, and the USA.
Lectures
into an optimization problem, and we will introduce techniques that can be utilized to search for solutions to the global optimization problem that arises when the most common reformulation is performed.
Topics
Automatic machine learningBiography
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
Automated machine learning is the science of learning how to build machine learning models in a data-driven, efficient, and objective way. It replaces manual (and often frustrating) trial-and-error with automated, principled processes. It also democratizes machine learning, allowing many more people to build high-quality machine learning systems.
In the first lecture, we will explore the state of the art in automated machine learning. We will cover the best techniques for neural architecture search, as well as learning complete machine learning pipelines. We explain how to design model search spaces, and how to efficiently search for the best models within this space. We’ll also cover useful tips and tricks to speed up the search for good models, as well as pitfalls and best practices.
In the second lecture, we’ll cover techniques to continually learn how to build better machine learning models. Just as human experts get ever better at building better models, automated machine learning systems should also get better every time they run. We’ll cover research on the intersection of automated machine learning, meta-learning, and continual learning that enables us to learn and capture which models work well, and transfer that knowledge to build better machine learning models, faster.
Tutorial Speakers
Each Tutorial Speaker will hold more than five lessons on a specific topic.
Topics
Information Theory, Mathematics for Machine LearningBiography
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.
Lectures
Topics
Statistical physics for optimization & learning; Machine Learning; Statistical Mechanics; Disordered SystemsBiography
Bruno Loureiro is currently a research scientist at the “Information, Learning and Physics” (IdePHICS) laboratory at EPFL, working on the crossroads between Machine Learning and Statistical Physics. Before moving to EPFL, he was a postdoctoral researcher at the Institut de Physique Théorique (IPhT) in Paris, and received his PhD from the University of Cambridge. He is interested in Bayesian inference, theoretical machine learning and high-dimensional statistics more broadly. His research aims at understanding how data structure, optimisation algorithms and architecture design come together in successful learning.