Date: 29 November 2016
Time: 11:00am – 12:30pm
Watch the live stream here: bit.ly/TuringLive
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Professor Jon Crowcroft (CAMBRIDGE)
Jon Crowcroft has been the Marconi Professor of Communications Systems in the Computer Laboratory since October 2001. He has worked in the area of Internet support for multimedia communications for over
30 years. Three main topics of interest have been scalable multicast routing, practical approaches to traffic management, and the design of deployable end-to-end protocols. Current active research areas are Opportunistic Communications, Social Networks, and techniques and algorithms to scale infrastructure-free mobile systems. He leans towards a “build and learn” paradigm for research.
He graduated in Physics from Trinity College, University of Cambridge in 1979, gained an MSc in Computing in 1981 and PhD in 1993, both from UCL. He is a Fellow the Royal Society, a Fellow of the ACM, a Fellow of the British Computer Society, a Fellow of the IET and the Royal Academy of Engineering and a Fellow of the IEEE.
He likes teaching, and has published a few books based on learning materials.
Computing Systems at scale are the basis for much of the excitement over Data Science, but there are many challenges to continue to address ever larger amounts of data, but also to provide tools and techniques implemented in robust software, that are usable by statisticians and machine learning experts without themselves having to become experts in cloud computing.
This vision of distributed computing only really works for “embarrassingly parallel” scenarios. The challenge for the research community is to build systems to support more complex models and algorithms that do not so easily partition into independent chunks; and to give answers in near real-time on a given size data centre efficiently.
Users want to integrate different tools (for example, R on Spark); don’t want to have to program for fault tolerance, yet as their tasks & data grow, will have to manage this; meanwhile, data science workloads don’t resemble traditional computer science batch or single-user interactive models. These systems put novel requirements on data centre networking operating systems, storage systems, databases, and programming languages and runtimes. As a communications systems researcher for 30 years, I am also interested in specific areas that involve networks, whether as technologies (the Internet, Transportation etc), or as observed phenomena (Social Media), or in abstract (graphs).
Professor Brad Love (UCL)
Brad Love is Professor of Cognitive and Decision Sciences at UCL. He works at the intersection of Neuroscience, Experimental Psychology, and Machine Learning. Current interests include using brain imaging data to select between competing models of cognitive function, and combining big data and psychological theory to understand consumer behaviour.
One interest is understanding consumer behaviour using large datasets, such as loyalty card data. Topics include how people explore product options and construe product categories.
I am interested in relating deep learning networks to brain function.
Additionally, I would like to pursue a machine learning collaboration in which lessons from neural computation could be used to improve the performance of these models on a variety of tasks, including object recognition.