The G-Research Lecture series features the world’s leading experts in science and technology speaking on a range of topics in which they are recognised thought-leaders. The topic of the lectures explores major fields of current research at a level that will engage researchers in the field and industry experts, while being accessible and engaging for those without specialist knowledge. It is an opportunity for G-Research to support our partners and friends in the academic community by providing access to world-leading thinkers and ideas.
Mike will be explores the rapid growth in the size and scope of datasets in science and technology, which has created a need for novel foundational perspectives on data analysis that blend the inferential and computational sciences.
The G-Research Lecture series returned on June 20th with an exciting lecture by Data Journalist and Information Designer, David McCandless. Missed the lecture? Watch it on YouTube.
We are proud to announce that G-Research will be collaborating with The Alan Turing Institute to launch a lecture series on the subject of data science, its theories and applications.
Researchers from the Institute will deliver six technical talks on their research and ideas – ranging from machine learning and statistical methods to new ways to make sense of data. These will be available to the public on the the G-Research YouTube channel.
The talks will be delivered to G-Research employees, our partners in academia and Turing researchers.
Watch them below:
The first speaker was Professor Stephen Roberts, Turing Faculty Fellow and Professor of Machine Learning at the University of Oxford.
Speaking on the 4th July on the topic of ‘The Bayesian Crowd’, Professor Roberts spoke about how in applications such as crowdsourcing, a large amount of data needs to be combined in an intelligent manner, and looks at how Bayesian models can help do this.
A lecture given by Research Director for the Alan Turing Institute and former Laboratory Director of Microsoft Research (Cambridge), Prof. Andrew Blake:
The visible world is ambiguous, so estimating physical properties by machine vision relies on probabilistic methods. Prior distributions over shape can help significantly to make finding and tracking objects more robust. Learned distributions for colour and texture make the estimators even more discriminative. These ideas fit into a philosophy of vision as inference: exploring hypotheses for the contents of a scene that explain an image as fully as possible.
“Just the Essential Information Please”– Professor Jared Tanner (TBD)
Professor of the Mathematics of Information at the University of Oxford and The Alan Turing Institute’s University Liaison Director, Professor Jared Tanner will show both how to apply compressed sensing and matrix completion, as well as the elegant geometric interpretations.
“The Automatic Statistician”– Professor Zoubin Ghahramani (18/04/18)
Talk given by Professor of Information Engineering at the University of Cambridge, leader of the Cambridge Machine Learning Group, and the Cambridge Liaison Director of the Alan Turing Institute; Zoubin Ghahramani. The lecture will regard the use of Bayesian model selection strategies that automatically select and use models to generate human readable reports.
Those wishing to attend any of the lectures should contact firstname.lastname@example.org.
We work in partnership with several institutions, publications and specialist events, some of which are listed below:
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