Yike Guo is a Professor of Computing Science in the Department of Computing at Imperial College London. He is the founding Director of the Data Science Institute at Imperial College, as well as leading the Discovery Science Group in the department. Professor Guo also holds the position of CTO of the tranSMART Foundation, a global open source community using and developing data sharing and analytics technology for translational medicine.
Professor Guo received a first-class honours degree in Computing Science from Tsinghua University, China, in 1985 and received his PhD in Computational Logic from Imperial College in 1993 under the supervision of Professor John Darlington. He founded InforSense, a software company for life science and health care data analysis, and served as CEO for several years before the company's merger with IDBS, a global advanced R&D software provider, in 2009.
He has been working on technology and platforms for scientific data analysis since the mid-1990s, where his research focuses on knowledge discovery, data mining and large-scale data management. He has contributed to numerous major research projects including: the UK EPSRC platform project, Discovery Net; the Wellcome Trust-funded Biological Atlas of Insulin Resistance (BAIR); and the European Commission U-BIOPRED project. He is currently the Principal Investigator of the European Innovative Medicines Initiative (IMI) eTRIKS project, a €23M project that is building a cloud-based informatics platform, in which tranSMART is a core component for clinico-genomic medical research, and co-Investigator of Digital City Exchange, a £5.9M research programme exploring ways to digitally link utilities and services within smart cities.
Professor Guo has published over 200 articles, papers and reports. Projects he has contributed to have been internationally recognised, including winning the “Most Innovative Data Intensive Application Award” at the Supercomputing 2002 conference for Discovery Net, and the Bio-IT World "Best Practices Award" for U-BIOPRED in 2014. He is a Senior Member of the IEEE and is a Fellow of the British Computer Society.
Jeremy Wyatt is Professor of Robotics and Artificial Intelligence at the University of Birmingham. He conducts research in autonomy, intelligent robotics, machine learning, robot manipulation, robot vision, robot task planning, robot motion planning, and decision making under uncertainty.
He has worked at the University of Birmingham for 19 years, and previously obtained his PhD in Artificial Intelligence from the University of Edinburgh. Professor Wyatt has published more than 90 papers, edited three books, and his career research funding exceeds £10m. He has coordinated two major international research projects in robotics: CogX (35 researchers, 6 universities) on robot planning and learning in unstructured worlds, and PacMan (25 researchers) on robot manipulation. He has won three best paper awards, and gathered in excess of 2000 citations of his work. He works with a variety of industry partners on early stage technology transfer. His work on robot manipulation has been featured in the media worldwide; he regularly gives public lectures, and is interviewed in the national and international media, on robotics and artificial intelligence.
Talk title: Robots in Our World
To make transfer to applications in everyday domains robots require the ability to cope with novelty, incomplete information and uncertainty. In this talk I will describe a line of work carried out over ten years that provides methods to tackle this. In particular I will focus on two problems: object search and manipulation. Both require the ability to reason and learn in open or novel worlds. The results are demonstrated in a variety of robot systems: in particular the Dora and Boris robots. Dora is one of the first mobile robots able to plan in open worlds, using the notion of assumptions. Dora also uniquely attempts to explain and then verify explanations in the face of failure. Boris is a robot system for manipulation that learns to grasp novel objects from a very small number of example grasps.