First Summer School: Machine Learning and Vision for Industrial Applications

Bringing together pioneering international scientists in machine learning and computer vision.

Sist oppdatert: Feb 13, 2020

Registration opens: March 01, 2020.

Early bird registration: March 01-May 31, 2020.

Summer School days: July 28-31, 2020.

Machine learning and computer vision have the potential to significantly improve the automation and autonomy of many industrial applications (e.g. offshore, automotive, telecommunication, gaming, and multimedia) by enhancing the operational performance, decreasing cost related to manual operations, increasing benefits, minimizing losses, optimizing productivity and improving safety and security.

The best-intuitive industrial application for an autonomous and automated system is process monitoring, where camera systems can permanently inspect for integrity of process operations. Examples include offshore drilling process and high-yield in metal production without human intervention for safe and high-performance and low downtime industrial operations.

However, many natural industrial environments still present major challenges for full automation and autonomy because of changing environment conditions, e.g. challenging illuminations, difficult weather conditions and the highly dynamic environment, reducing visibility and safe operation without human intervention.

While recent scientific advances in sensing, learning and computing aim to reduce the gap between scientific findings and their practical industrial deployment, the transfer of scientific approaches into practical and robust industrial solutions is not straightforward.

The goal of this Summer School MALVIC is to bring together pioneering international scientists in machine learning and computer vision with both academia and practitioners from the industrial fields on a unique setting for the discussion and demonstration of practical, hands-on machine learning and vision research and development. Offshore industrial applications and industrial process scenarios are examples for the summer school target.

The organizers and participants are passionate about the future of machine learning and vision fundamentals and applications. The Summer School is the 1st event in Norway. Our community of machine learning and computer vision professionals and enthusiasts hail from both industry and academia under the core topics of pattern analysis and machine intelligence

The Summer School will take place at NORCE’s facility in Kristiansand within the Norwegian Southern Riviera.

Invited speakers

Guy Theraulaz

Prof. Guy Theraulaz is a world-leading expert in the study of collective intelligence and collective behaviors in animal and human groups. He is also a leading researcher in the field of swarm intelligence and computational biology, primarily studying social insects but also distributed algorithms, e.g. for collective robotics, directly inspired by nature. He was awarded the PhD. in Neurosciences and Ethology from the University of Marseille in France in 1991 and is working at CNRS since 1992 at different positions. He is holding the research director position of CRCA since 2004. His researches focus on the understanding of a broad spectrum of collective behaviors in animal societies and human groups by quantifying and then modeling the individual level behaviors and interactions, thereby elucidating the mechanisms generating the emergent, group-level properties. He is one of the main characters of the development of quantitative social ethology and collective intelligence in France. He received the Bronze Medal of the CNRS 1996 for Neurosciences on Behavioral and Cognitive Sciences and the CNRS scientific excellence award for the years 2010-2013. He has more than 102 scientific publications include one contribution into Nature, 2 in Science and 4 in PNAS. Among his 6 co-authored books, two books are considered as key references “Swarm Intelligence: From Natural to Artificial Systems” (Oxford University Press, 1999) and “Self-organization in biological systems” (Princeton University Press, 2001).

Jürgen Schmidhuber

Prof. Jürgen Schmidhuber is a computer scientist most noted for his work in the field of artificial intelligence, deep learning and artificial neural networks. He is a co-director of the Dalle Molle Institute for Artificial Intelligence Research in Manno, in the district of Lugano, in Ticino in southern Switzerland. He is sometimes called the "father of (modern) AI" or, one time, the "father of deep learning." Since age 15 or so, the main goal of professor Jürgen Schmidhuber has been to build a self-improving Artificial Intelligence (AI) smarter than himself, then retire. His lab's Deep Learning Neural Networks (such as LSTM) based on ideas published in the "Annus Mirabilis" 1990-1991 have revolutionised machine learning and AI. By 2017, they were on 3 billion devices, and used billions of times per day through the users of the world's most valuable public companies, e.g., for greatly improved (CTC-based) speech recognition on over 2 billion Android phones (since mid-2015), greatly improved machine translation through Google Translate (since Nov 2016) and Facebook (over 4 billion LSTM-based translations per day as of 2017), Apple's Siri and Quicktype on almost 1 billion iPhones (since 2016), the answers of Amazon's Alexa (since 2016), and numerous other applications. In 2011, his team was the first to win official computer vision contests through deep neural nets, with superhuman performance. In 2012, they had the first deep NN to win a medical imaging contest (on cancer detection). This attracted enormous interest from industry.

René Vidal

Prof. René Vidal is the Herschel Seder Professor of Biomedical Engineering and the Inaugural Director of the Mathematical Institute for Data Science at The Johns Hopkins University. He has secondary appointments in Computer Science, Electrical and Computer Engineering, and Mechanical Engineering. He is also a faculty member in the Center for Imaging Science (CIS), the Institute for Computational Medicine (ICM) and the Laboratory for Computational Sensing and Robotics (LCSR). He is also Chief Scientist at NORCE Norwegian Research Centre AS Vidal's research focuses on the development of theory and algorithms for the analysis of complex high-dimensional datasets such as images, videos, time-series and biomedical data. His current major research focus is understanding the mathematical foundations of deep learning and its applications in computer vision and biomedical data science. His lab has pioneered the development of methods for dimensionality reduction and clustering, such as Generalized Principal Component Analysis and Sparse Subspace Clustering, and their applications to face recognition, object recognition, motion segmentation and action recognition. His lab creates new technologies for a variety of biomedical applications, including detection, classification and tracking of blood cells in holographic images, classification of embryonic cardio-myocytes in optical images, and assessment of surgical skill in surgical videos

Thomas Bäck

Prof. Thomas Bäck is head of the Natural Computing Research Group and Director of Education at the Leiden Institute of Advanced Computer Science (LIACS). He is also Chief Scientist at NORCE Norwegian Research Centre AS He received his PhD in Computer Science from Dortmund University, Germany, in 1994. He has been Associate Professor of Computer Science at Leiden University since 1996 and full Professor for Natural Computing since 2002.

Thomas Bäck has more than 250 publications on data science and nonlinear global optimization and decision support, is the author of a book on evolutionary algorithms, entitled Evolutionary Algorithms in Theory and Practice, and co-editor of the Handbook of Evolutionary Computation.

He is editorial board member and associate editor of a number of journals on evolutionary and natural computation (Journal of Natural Computing, Theoretical Computer Science C, Evolutionary Computation), co-editor of the Natural Computation Book Series (Springer), and has served as program chair for all major conferences in evolutionary computation.

He received the best dissertation award from the Gesellschaft für Informatik (GI) in 1995 and is an elected fellow of the International Society for Genetic and Evolutionary Computation for his contributions to the field. In 2015, he received the prestigious IEEE Evolutionary Computation Pioneer Award for his contributions in synthesizing evolutionary algorithms. Thomas’ research interests are also in applications of data science and optimization to the life sciences, and in industrial applications in areas such as industry 4.0, process optimization, and product development. In his research projects, he collaborates with companies such as BMW, Honda Research, Tata Steel, and many others.

Horst Bischof

Prof. Horst Bischof received his M.S. and Ph.D. degree in computer science from the Vienna University of Technology in 1990 and 1993. In 1998 he got his Habilitation (venia docendi) for applied computer science. Currently he is Vice Rector for Research at Graz University of Technology and Professor at the Institute for Computer Graphics and Vision at the Graz University of Technology, Austria. H. Bischof is a member of the scientific board of Joanneum Research. His research interests include object recognition, visual learning, on-line and life-long learning, motion and tracking, visual surveillance and biometrics and medical computer vision where he has published more than 750 peer reviewed scientific papers.

Horst Bischof was General Chair of CVPR 2015 and ECCV2018. He was program co-chair of ECCV2006 and ECCV 2020. He is multiple times Area chair of all major vision conferences. He was Associate Editor for IEEE Trans. on Pattern Analysis and Machine Intelligence, Pattern Recognition, Computer and Informatics and the Journal of Universal Computer Science.

Horst Bischof is member of the European academy of sciences and has received several awards >20 among them the Most Influential Paper over the Decade Award from MVA 2019, the Jan Konderink award at ECCV 2018 and the 29th Pattern Recognition award in 2002, the main price of the German Association for Pattern Recognition DAGM in 2007 and 2012, the Best scientific paper award at the BMCV 2007, the BMVC best demo award 2012 and the Best scientific paper awards at the ICPR 2008, ICPR2010, PCV 2010, AAPR2010 and ACCV 2012.

Marius Leordeanu

Prof. Marius Leordeanu is an Associate Professor at the University "Politehnica" of Bucharest (UPB) and Senior Researcher at the Institute of Mathematics "Simion Stoilow" of the Romanian Academy (IMAR). He received the PhD in 2009 from the Robotics Institute of Carnegie Mellon University and Bachelor's in Computer Science and Mathematics in 2003, from Hunter College of the City University of New Yor. He is interested in the nature of intelligence, life and consciousness. In particular his research focuses on computer vision, machine learning and robotics.

At UPB, he introduced in 2014 the courses of Computer Vision and Robotics and at IMAR I started the Computer Vision Reading Group, with weekly meetings since 2016.

For his work on unsupervised learning for graph matching he received, in 2014, the “Grigore Moisil” Prize - the most prestigious award in Mathematics given by the Romanian Academy. Currently, he leads research projects on various computer vision and deep learning problems, e.g. unsupervised learning, video to language translation, object tracking, semantic segmentation, vision for drones and self-driving cars, vision for the wood industry. Together with his students, they aim to understand vision at its deepest levels, from learning about concepts in an unsupervised fashion to understanding its relationship to natural language.

Daimler Speaker (Tentative)

NVIDIA Speaker (Tentative)

Workshop Organizers

Ahmed Nabil Belbachir, NORCE Norwegian Research Centre, Norway

René Vidal, Johns Hopkins University, USA

Stephen John Sayfritz, EYDE Cluster, Norway

Marianne Engvoll, GC NODE

Nadia Saad Noori, NORCE NorwegianResearch Centre, Norway


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