Yike Guo

Yike Guo
Imperial College
London, United Kingdom

Special session on Big Data

Will talk about: Small brain and big data

Bio sketch:

Yike Guo is a Professor of Computing Science in the Department of Computing at Imperial College London. He leads the Discovery Science Group in the department, as well as being the founding Directo of the Data Science Institute at Imperial College. 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 healthcare intelligence company, 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.
Talk abstract:

Brain research is largely data driven.  At Imperial Data Science Institute, we focus on applying modern statistics and machine learning methods for studying  drain diseases prediction and monitoring. In this talks, I will cover few of our work in this area including applying deep learning methods  for epilepsy prediction, and using a novel regularization method to automatically detect significant voxel activation for fMRI analysis. Also, I will present some of our new work in the area of neuroconnectivity research by applying big data analysis methods.