Professor Kento Morita
Graduate School of Engineering, Mie University, Japan
Title: "Medical Image Analysis for Computer-aided Diagnosis"
Abstract: Medical and healthcare issues such as medical disparity, aged society, and medical expenses become a big problem in the world. ICT technologies are expected to solve some of these problems. Recently, artificial intelligence related technologies are attracting a lot of attention. Medical doctors have a big interest in such technologies to reduce workload and improve the quality of diagnosis and treatment. Therefore, there are a very large number of research themes in the medical engineering field. In this talk, I will present some collaborative research on medical image analysis based computer-aided diagnosis systems.
Dr. Varuna De Silva
Loughborough University London, United Kingdom
He is a Senior Lecturer in Machine Intelligence at Loughborough University. He obtained his Ph.D. in video coding and communications from Center for Vision Speech and Signal Processing at University of Surrey in 2011. He has worked in 3 major European Union Funded Projects during 2010-2013. Between 2013 and 2016, Varuna worked as a senior research engineer in computer vision at Apical Ltd (Now part of ARM UK). He was appointed lecturer at Loughborough University in 2016. He currently supervises 7 Ph.D.s and 2 Post doctoral research fellows in the area of Artificial Intelligence (Multimodal machine learning and multi-agent reinforcement learning) with a strong focus on engineering applications in driverless vehicle technology and team sport analytics.
Title: "An Engineering Perspective on Artificial Intelligence: Theory, Applications and the Future"
Abstract: The past two decades have demonstrated a massive growth in the development of the application of Machine Learning techniques to large scale high dimensional data. This talk will look at the developments in the domain of Artificial intelligence, in terms of Deep learning and reinforcement learning learning. Specifically, the talk will look from a high-level how the theoretical foundations from Information theory, computational learning theory and game theory, combined with large scale function approximators and massive amounts of computational resources have contributed to the most recent developments in AI. The state-of-the-art neural networks and their applications as deep generative models will be introduced along with developments in deep reinforcement learning and multi-agent reinforcement learning and their Engineering applications. Through real world engineering applications in driverless vehicle technology, sports analytics, financial and epidemiological modelling the talk will discuss the limitations of the current state-of-the-art machine learning techniques, the role of advanced mathematical modelling and opportunities that lie ahead.
Professor Genci Capi
Department of Mechanical Engineering, Hosei University, Japan
Genci Capi received the B.E. degree from Polytechnic University of Tirana, in 1993 and the Ph.D. degree from Yamagata University, in 2002. He was a Researcher at the Department of Computational Neurobiology, ATR Institute from 2002 to 2004. In 2004, he joined the Department of System Management, Fukuoka Institute of Technology, as an Assistant Professor, and in 2006, he was promoted to Associate Professor. In 2010, he joined as a Professor at the Department of Electrical and Electronic Systems Engineering, University of Toyama, Toyama, Japan. He is currently a Professor at the Department of Mechanical Engineering, Hosei University, Tokyo, Japan.
His research interests include intelligent robots, BMI, multi robot systems, humanoid robots, learning and evolution.
Title: "Machine learning for socially assistive intelligent robots operating in human environments"
Abstract: The research on intelligent robots will produce robots that are able to operate in everyday life environments, to adapt their policy as environment changes, and to cooperate with other team members and humans. Operating in human environments, the robots must process in real time a large number of sensory data such as vision, laser, microphone, in order to determine the best action. Learning and evolution have been proved to give good results generating a good mapping of various sensory data to robot action. In this talk, I will overview the existing efforts including our attempts at creating intelligent robots operating in everyday life environments. I will focus on remotely operating surveillance robot, robot navigation in urban environments, and assistive humanoid robot. I will show experimental results that demonstrate the effectiveness of proposed algorithms.