Keynote Speakers for MODELS 2018
Silvija Seres is an independent advisor, investor and board member, now
serving as a Board Director of Nordea Bank AB, Norwegian Broadcasting
Corporation, Oslo Stock Exchange and DNV-GL. She has previously worked as a
leader at Microsoft and Fast Search & Transfer. She invests actively in
technology startups through TechnoRocks, focusing on energy, media and
Silvija holds a Ph.D. in Mathematical Sciences from Oxford University, MSc
in Information Technology from University of Oslo and an M.B.A. From INSEAD.
She has worked as a Professor in Saudi Arabia at Dar al Hekma Univeristy and
at DEC SRC in Silicon Valley.
Silvija is married, and has four children. She lives in Oslo, Norway.
James R. Cordy is Professor and past Director of the School of Computing at Queen's University in Kingston, Ontario, Canada, and Director of the NSERC CREATE Graduate Specialization in Ultra-Large Scale Software Systems. From 1995 to 2000 he was Vice President and Chief Research Scientist at Legasys Corporation, a software technology company specializing in legacy software system analysis and renovation. As leader of the TXL source transformation project with hundreds of academic and industrial users worldwide, he has been involved in computer software analysis and transformation systems for more than 30 years.
Dr. Cordy has published more than 200 refereed contributions in software engineering, programming languages and artificial intelligence, an in the past five years has been invited as keynote speaker at the IEEE International Conference on Software Maintenance and Evolution, the IEEE International Conference on Software Product Lines, the International Workshop on Automotive Software Architecture, and the International Workshop on Open and Original Problems in Software Language Engineering. He serves widely as member and chair of conferences, workshops and evaluation committees in software engineering, and has received multiple Best Reviewer awards.
Dr. Cordy is a Senior Member of the IEEE, a Distinguished Scientist of the Association for Computing Machinery and an IBM Visiting Scientist and Faculty Fellow. He was recognized as IBM Centre for Advanced Studies Faculty Fellow of the Year in both 2009 and 2013. In 2008 he received the Queen’s University Award for Excellence in Graduate Supervision, and in 2016 the Queen’s University Prize for Excellence in Research.
In recent years Dr. Cordy’s research group has concentrated on similarity in software systems, with particular application to model-driven systems in Simulink using the SIMONE near-miss model clone detector. Most recently, he has been studying profiles of language feature use in transformation languages such as TXL and ATL, with an eye to informing the future of model transformation systems.
Martijn Wisse is Professor of Biorobotics and Director of the Robotics Institute at Delft University of Technology, The Netherlands. He has co-founded the TU Delft Robotics Institute, the robotics ecosystem “RoboValley”, and the two successful spin-off companies Lacquey (now FTNON Lacquey) and Fizyr. He served as the coordinator of the EU FP7 project “Factory-in-a-Day” and currently coordinates the EU H2020 project “ROSIN” which focuses on the open-source robot software framework ROS-Industrial.
The research of prof. Wisse has led from passive dynamic two-legged walking robots via biologically inspired underactuated robot hands and arms to the use of ROS-Industrial and deep learning for intelligent industrial robot systems. A recent highlight was winning the 2016 edition of the Amazon Picking Challenge which required the combination of 3D vision, object recognition, motion planning, and robust localization. Currently, prof. Wisse is working in the field of Active Inference, where he aims to combine information-theoretic models of the neural operations of the biological brain with practical implementations in embodied robots. The ultimate aim is to give the robots an automated way to infer models of themselves and their environment, and simultaneously to learn how to control themselves, in order to maximize their success at predicting their future sensor inputs.