Speaker 1:  Professor Gareth Roberts FRS, University of Warwick

Speaker 2: Professor Bin Yu, University of California, Berkeley – Website]

Dates: March 16, 2016

Speaker Host: Sofia Olhede (ULD UCL)





Speaker 1: Professor Gareth Roberts FRS, University of Warwick

New challenges in Computational Statistics

The presentation will investigate some challenges and opportunities for modern Computational Statistics within the emerging super-discipline of Data Science. Within the last 30 years, Computational Statistics has been tremendously successful in transporting theoretically well-underpinned statistical paradigms through to methodology for increasingly complex and high-dimensional statistical models; and ultimately to diverse inferential problems in science and beyond. This applies both to frequentist and Bayesian approaches. However, apparently paradoxically, statistical theory tells us that with large data sets, inference ought to be increasingly straightforward, while necessary calculations are increasingly computationally expensive. On the other hand, these difficulties, together with an increased awareness of computational hardware is inspiring new directions for the subject. The lecture will conclude with a brief description of some algorithms, which draw together crucial tools from statistics, probability, numerical analysis and computing to introduce new algorithms tackle challenging emerging inference problems emerging from data science.

He completed his PhD in Applied Probability in 1988 and held academic positions at Nottingham and Cambridge before becoming Professor of Statistics at Lancaster in 1998.In 2007 he moved to Warwick to take up the position of Director of CRiSM (the Centre for Research in Statistical Methodology). He was awarded the RSS Guy Medals in Bronze and Silver in 1997 and 2008 respectively, the Rollo Davidson Prize in 1999, was elected to the Royal Society in 2013, and has been an ISI Highly Cited Researcher since 2004. His work spans Applied Probability, Computational Statistics and Bayesian Statistics and their applications.


Speaker 2: Professor Bin Yu, University of California, Berkeley

Unveiling the Mysteries in Spatial Gene Expression

Genome-wide data reveal an intricate landscape where gene activities are highly differentiated across diverse spatial areas. These gene actions and interactions play a critical role in the development and function of both normal and abnormal tissues. As a result, understanding spatial heterogeneity of gene networks is key to developing treatments for human diseases. Despite the abundance of recent spatial gene expression data, extracting meaningful information remains a challenge for local gene interaction discoveries. In response, we have developed staNMF, a method that combines a powerful unsupervised learning algorithm, nonnegative matrix factorization (NMF), with a new stability criterion that selects the size of the dictionary. Using staNMF, we generate biologically meaningful Principle Patterns (PP), which provide a novel and concise representation of Drosophila embryonic spatial expression patterns that correspond to pre-organ areas of the developing embryo. Furthermore, we show how this new representation can be used to automatically predict manual annotations, categorize gene expression patterns, and reconstruct the local gap gene network with high accuracy. Finally, we discuss on-going crispr/cas9 knock-out experiments on Drosophila to verify predicted local gene-gene interactions involving gap-genes.
Bin Yu is Chancellor’ s Professor in the Departments of Statistics and of Electrical Engineering & Computer Science at the University of California at Berkeley. She obtained her B.S. degree in Mathematics from Peking University in 1984, her M.A. and Ph.D. degress in Statistics from the University of California at Berkeley in 1987 and 1990, respectively. She held faculty positions at the Univ of Wisconsin-Madison and Yale University and was a Member of Technical Staff at Bell Labs, Lucent. She was Chair of Department of Statistics at UC Berkeley and is a founding co-director of the Microsoft Lab on Statistics and Information Technology at Peking University, China, and Chair of the Scientific Advisory Committee of the Statistical Science Center at Peking University. She is Member of the U.S. National Academy of Sciences and Fellow of the American Academy of Arts and Sciences. She was a Guggenheim Fellow in 2006, an Invited Speaker at ICIAM in 2011, and the Tukey Memorial Lecturer of the Bernoulli Society in 2012. She was President of IMS (Institute of Mathematical Statistics) in 2013-2014, and will be the Rietz Lecturer of IMS in 2016. She is a Fellow of IMS, ASA, AAAS and IEEE.