Lecturers

Lecturers

Each Lecturer will hold three/four lessons on a specific topic.


Žiga Avsec

Topics

Deep Learning, Genomics

Biography

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.



Topics

Information Theory, Mathematics for Machine Learning

Biography

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



Alfredo Canziani
New York University, USA

Topics

Deep Learning, Artificial Intelligence

Biography

Alfredo Canziani is an Assistant Teaching Professor of Computer Science and a Deep Learning Research Scientist at NYU Courant Institute of Mathematical Sciences, under the supervision of professors Kyunghyun Cho and Yann LeCun. His research mainly focusses on Machine Learning for Autonomous Driving. He has been exploring deep policy networks actions uncertainty estimation and failure detection, and long term planning based on latent forward models, which nicely deal with the stochasticity and multimodality of the surrounding environment. Alfredo obtained both his Bachelor (2009) and Master (2011) degrees in Electrical Engineering cum laude at Trieste University, his MSc (2012) at Cranfield University, and his PhD (2017) at Purdue University.

Lectures



Alex Davies

Biography

Alex Davies is the founding lead of the AI for Maths initiative at DeepMind, the team which recently published their work on using AI in maths in Nature. Prior to DeepMind, Alex Davies worked at Google on Machine Intelligence and also as a guest lecturer at the University of Oxford. He obtained his Ph.D. from the University of Cambridge, supervised by Zoubin Ghahramani.

http://www.alexdavies.net

https://www.linkedin.com/in/alex-davies-13a53521/

https://www.linkedin.com/posts/alex-davies-13a53521_this-week-we-announced-our-work-collaborating-activity-6872872186252713984-JsFJ



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



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



Topics

Robotics, Robot Vision, Robot Learning

Biography

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



Topics

Computational neuroscience, Human-Robot Interaction, Cognitive Developmental Robotics

Biography

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.



Topics

Data Science, Optimization, Networks

Biography

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.

https://en.wikipedia.org/wiki/Panos_M._Pardalos

https://scholar.google.com/citations?user=4e_KEdUAAAAJ&hl=en



Topics

Machine Learning, Computer Vision

Biography

Silvio Savarese is Executive Vice President and Chief Scientist of Salesforce Research as well as an Adjunct Faculty of Computer Science at Stanford University. He earned his Ph.D. in Electrical Engineering from the California Institute of Technology in 2005 and was a Beckman Institute Fellow at the University of Illinois at Urbana-Champaign from 2005–2008. He joined Stanford in 2013 after being Assistant and then Associate Professor (with tenure) of Electrical and Computer Engineering at the University of Michigan, Ann Arbor, from 2008 to 2013. His research interests include computer vision, object recognition and scene understanding, shape representation and reconstruction, human activity recognition and visual psychophysics. He is recipient of several awards including a Best Student Paper Award at CVPR 2016, the James R. Croes Medal in 2013, a TRW Automotive Endowed Research Award in 2012, an NSF Career Award in 2011 and Google Research Award in 2010. In 2002 he was awarded the Walker von Brimer Award for outstanding research initiative.



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’.



Lenka Zdeborova

Topics

statistical mechanics; phase transitions; machine learning; statistical inference; signal processing

Biography

Lenka Zdeborová is a Professor of Physics and of Computer Science in École Polytechnique Fédérale de Lausanne where she leads the Statistical Physics of Computation Laboratory. She received a PhD in physics from University Paris-Sud and from Charles University in Prague in 2008. She spent two years in the Los Alamos National Laboratory as the Director’s Postdoctoral Fellow. Between 2010 and 2020 she was a researcher at CNRS working in the Institute of Theoretical Physics in CEA Saclay, France. In 2014, she was awarded the CNRS bronze medal, in 2016 Philippe Meyer prize in theoretical physics and an ERC Starting Grant, in 2018 the Irène Joliot-Curie prize, in 2021 the Gibbs lectureship of AMS. She is an editorial board member for Journal of Physics A, Physical Review E, Physical Review X, SIMODS, Machine Learning: Science and Technology, and Information and Inference. Lenka’s expertise is in applications of concepts from statistical physics, such as advanced mean field methods, replica method and related message-passing algorithms, to problems in machine learning, signal processing, inference and optimization. She enjoys erasing the boundaries between theoretical physics, mathematics and computer science.




 

Tutorial Speakers

Each Tutorial Speaker will hold more than five lessons on a specific topic.


Thomas Viehmann
MathInf GmbH, Germany

Topics

PyTorch

Biography

Thomas Viehmann is a PyTorch and Machine Learning trainer and consultant. In 2018 he founded the boutique R&D consultancy MathInf based in Munich, Germany. His work spans low-level optimizations to enable efficient AI to developing cutting-edge deep-learning models for clients from startups to large multinational corporations. He is a PyTorch core developer with contributions across almost all parts of PyTorch and co-author of Deep Learning with PyTorch, to appear this summer with Manning Publications. Thomas’ education in computer science included a class in Neural Networks and Pattern Recognition at the turn of the millennium. He went on to do research in pen-and-paper Calculus of Variations and Partial Differential Equations, obtaining a Ph.D. from Bonn University.

Lectures