Lecturers
Each Lecturer will hold two/three lessons on a specific topic. The Lecturers below are confirmed.
Topics
AI, Neuroscience, 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
Abstract TBA
Topics
Computational NeuroEngineering, Signal Processing, Machine Learning, NeurotechnologyBiography
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. Dr. Principe is a Fellow of the IEEE, AAAS, NAI, AIMBE, and IAMBE. Dr. Principe received the Gabor Award, from the INNS, the Career Achievement Award from the IEEE EMBS, the Neural Network Pioneer Award, of the IEEE CIS and the Shannon-Nyquist Technical Achievement Award from the IEEE SP society. He has more than 44 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 109 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).
Lectures
I-Requisites for a Cognitive Architecture
- Processing in space
- Processing in time with memory
- Top down and bottom up processing
- Extraction of information from data with generative models
- Attention
II- Putting it all together
- Fovea Vision
- Attention Based Object Recognition
- Recognition in high clutter environments
III- Closing the loop through the world
- Theory of consciousness
- Deep Reinforcement Learning
- Internal Working memory and object affordances
- Perception-Action Cycle with Reinforcement Learning
Topics
Theoretical Neuroscience, Machine LearningBiography
Professor of Theoretical Neuroscience and Machine Learning,
Director, Gatsby Computational Neuroscience Unit
Maneesh Sahani is Professor of Theoretical Neuroscience and Machine Learning at the Gatsby Computational Neuroscience Unit at University College London (UCL). Graduating with a B.S. in physics from Caltech, he stayed to earn his Ph.D. in the Computation and Neural Systems program, supervised by Richard Andersen and John Hopfield. After periods of postdoctoral work at the Gatsby Unit and the University of California, San Francisco, he returned to the faculty at Gatsby in 2004 and was elected to a personal chair at UCL in 2013. His work spans the interface of the fields of machine learning and neuroscience, with particular emphasis on the types of computation achieved within the sensory and motor cortical systems. He has helped to pioneer analytic methods which seek to characterize and visualize the dynamical computational processes that underlie the measured joint activity of populations of neurons. He has also worked on the link between the statistics of the environment and neural computation, machine-learning based signal processing, and neural implementations of Bayesian and approximate inference.
Lectures
Topics
Machine Learning, Computational NeuroscienceBiography
I am a principal scientist and research director at Google DeepMind interested in vision, language and learning. I lead organizations interested a basic and applied research focused on machine learning, computer vision and basic science research. Some accomplishments included the invention of TensorFlow, the deployment of many production systems, and several large collaborations with Waymo.
To learn more about my work, please see a list of my former interns as well as my publications and tutorials.
Lectures
Lectures
Topics
Neuroscience, Axon GuidanceBiography
Group Leader, Assistant Professor, Cellular Molecular Biology, Faculty of Medicine, University of Crete, Greece
I grew up in Greece and studied Biology at the University of Crete, in Heraklion, where I also pursued a MSc in Molecular Biology, and a PhD in Developmental Neuroscience, at the University of Crete and the Institute of Molecular Biology & Biotechnology (IMBB). During my graduate studies I focused my research on the development of the cerebral cortex, and became interested in neuronal migration and axon guidance. I continued my postdoctoral research at MIT, in the laboratory of Frank Gertler, where I worked on neuronal cell biology, uncovering novel mechanisms of local translation regulation during neuronal development.
Lectures
Tutorial Speakers
(TBA)