Steve Maybank Professor, Birkbeck, The University of London, FRSS, FIEEE
The Fisher-Rao metric is a Riemannian metric defined on any manifold that forms the parameter space for a family of probability distributions. The metric is specified by quadratic forms defined on the tangent spaces of the manifold. If a parameterisation of the manifold is chosen then each quadratic form is given by a symmetric positive definite matrix. Lengths, areas, volumes and hyper-volumes calculated using the Fisher-Rao metric are invariant under reparameterisation. This invariance is essential in practice because the parameterisation can be changed arbitrarily while keeping the data unchanged. The Fisher-Rao metric is obtained as a limit of the expected value of the log likelihood ratio for two nearby probability distributions.
The inverse of the Fisher-Rao matrix is the Cramer-Rao lower bound on the covariance of an unbiased estimate of a parameter. The Fisher-Rao metric is used to divide the parameter space for the Hough transform method for detecting structures in data. Each division or accumulator is invariant under reparametrerisation, and the number of accumulators is proportional to the volume of the parameter space. Accurate approximations to the Fisher Rao metric are obtained for lines, catadioptric images of lines, circles, ellipses and the cross ratio. It is shown that the Fisher-Rao metric can be used to compare the amount of information in point features with the amount of information in edge element features.
Steve Maybank received the BA degree in mathematics from King's College Cambridge in 1976 and the PhD degree in computer science from Birkbeck College, University of London in 1988. He was a research scientist at GEC from 1980 to 1995, first at MCCS, Frimley, and then, from 1989, at the GEC Marconi Hirst Research Centre in London. In 1995, he became a lecturer in the Department of Computer Science at the University of Reading and in 2004, he became a professor in the Department of Computer Science and Information Systems at Birkbeck College, University of London. Steve's research interests include camera calibration, visual surveillance, tracking, filtering, applications of projective geometry to computer vision and applications of probability, statistics and information theory to computer vision. He is the author or co-author of more than 200 scientific publications and one book. He is a Fellow of the IEEE, a Fellow of the Royal Statistical Society and a Member of the Academia Europaea. He received the Koenderink Prize in 2008.
Jiawei Han Abel Bliss Professor, University of Illinois at Urbana-Champaign, FACM, FIEEE
The real-world big data are largely unstructured, interconnected, and dynamic, in the form of natural language text. It is highly desirable to transform such massive unstructured data into structured knowledge. Many researchers rely on labor-intensive labeling and curation to extract knowledge from such data, which may not be scalable, especially considering that a lot of text corpora are highly dynamic and domain specific. We believe that massive text data itself may disclose a large body of hidden patterns, structures, and knowledge. With domain-independent and domain-dependent knowledge bases, we propose to explore the power of massive data itself for turning unstructured data into structured knowledge. By organizing massive text documents into multidimensional text cubes, we show structured knowledge can be extracted and used effectively.
In this talk, we introduce a set of methods developed recently in our group for such an exploration, including mining quality phrases, entity recognition and typing, multi-faceted taxonomy construction, and construction and exploration of multi-dimensional text cubes. We show that data-driven approach could be a promising direction at transforming massive text data into structured knowledge.
Jiawei Han is Abel Bliss Professor in the Department of Computer Science, University of Illinois at Urbana-Champaign. He has been researching into data mining, information network analysis, database systems, and data warehousing, with over 900 journal and conference publications. He has chaired or served on many program committees of international conferences in most data mining and database conferences. He also served as the founding Editor-In-Chief of ACM Transactions on Knowledge Discovery from Data and the Director of Information Network Academic Research Center supported by U.S. Army Research Lab, and is the co-Director of KnowEnG, an NIH funded Center of Excellence in Big Data Computing (2014-2019) He is Fellow of ACM, Fellow of IEEE, and received 2004 ACM SIGKDD Innovations Award, 2005 IEEE Computer Society Technical Achievement Award, 2009 M. Wallace McDowell Award from IEEE Computer Society, and 2018 Japan’s Funai Achievement Award. His co-authored book "Data Mining: Concepts and Techniques" has been adopted as a textbook popularly worldwide.
Jian Pei Professor, Simon Fraser University, FACM, FIEEE
Data science embraces interdisciplinary methodologies and tools, such as those in statistics, artificial intelligence/machine learning, data management, algorithms, and computation. The art of practicing data science to empower innovative applications, however, remains an art due to many factors beyond technology, such as sophistication of application scenarios, business demands, and the central role of human being in the loop. In this talk, through two stories I will share with the audience some experience and lessons I learned from my practice of data science research and development. In the first story, using network embedding as an example, I will demonstrate that the nature of data science practice is to connect challenges in vertical applications with general scientific principles and tools. In the second story, I will illustrate the core value of using local patterns to build domain-oriented, end-to-end data science solutions that can help people gain new interpretable domain knowledge.
Jian Pei’s professional interest is to facilitate efficient, fair, and sustainable usage of data for social, commercial and ecological good. Through inventing, implementing and deploying a series of data mining principles and methods, he produced visible values to academia and industry. His algorithms have been adopted by industry, open source toolkits and textbooks. His publications have been cited over 87,000 times. He is also an active and productive volunteer for professional community services, such as chairing ACM SIGKDD, running many premier academic conferences in his areas, and being editor-in-chief or associate editor for the flagship journals in his fields. His academic accomplishments have been acknowledged by the ACM Fellowship, IEEE Fellowship, ACM SIGKDD Innovation Award, ACM SIGKDD Service Award, influential paper awards, best paper awards, and several other prestigious awards. He was lucky to obtain his Ph.D. degree under Professor Jiawei Han’s supervision at Simon Fraser University. Currently he is a full professor at Simon Fraser University and also a consultant for several industry partners. He held executive positions at two Fortune 500 companies in his recent no-pay leave from academia.
Jianping ShiExecutive R&D Director
Visual recognition technology is very important for autonomous driving especially in direction of mass production. In this talk, we will introduce the algorimic progress for SenseTime in autonoumous driving, as well as our platform foundation for AI technology. Based on this, we illustrate how we make use of these technology into mass production product for autonomous driving.
Jianping Shi is an Executive R&D Director at SenseTime. She got her Ph.D. degree in Computer Science and Engineering Department in the Chinese University of Hong Kong in 2015. Currently, she lead the autonomous driving R&D team in SenseTime and built long term strategic collaboration relationship with Honda. They are developing fundamental algorithms and practical system for autonomous driving including perception, localization, mapping, decision and planning, control, etc. Jianping has published over 40 papers on top conference and jornals. She lead team to win several competitions including Microsoft COCO 2018, 2017, ImageNet Scene Parsing 2016, LSUN challenge 2017, etc. She has received a number of hornorship including MIT TR35, Microsoft research asia fellowship, Hong Kong PhD fellowship.