We are very happy to announce that Shijing Guo passed her PhD viva today, on the topic of “Systemic Analysis and Modelling of Diagnostic Errors in Medicine”.
Dr. Guo has also been offered a position at IBM Research in Beijing, China.
Many congratulations and all the very best to her from the group!
We are very happy to announce that Son Tran passed his PhD viva earlier this year in January, on the topic of “Representation Decomposition for Knowledge Extraction and Sharing using Restricted Boltzmann Machines”.
Dr. Tran has also been offered a research position in machine learning at Commonwealth Scientific and Industrial Research Orgainisation (CSIRO), Australia.
Many congratulations and all the very best to him from the group!
This is an announcement being made on behalf of the Open Research Forum Organising Committee.
The Open Research Forum is an initiative to bring together PhD and early career researchers from various disciplines for a day-long workshop at the LSE.
The goal is to create an informal and inclusive dialogue around current issues in the wider information systems field and this year’s topic is “Data science meets social sciences: Understanding origins, means and consequences of social computing”. Registration is now open and whether as guests or participants, it would be great to have PhD and EC researchers from City University represented!
The call for submissions attached – the deadline is 31st of March.
Additional information, registration and contact details can be found at: https://orflse.wordpress.com/
The call for submissions in DOCX format is available here: orf_call_for_submissions.docx .
In the next seminar of the City Research Centre for Machine Learning, we will have a talk by Dr. Greg Wayne who is a researcher at Google DeepMind.
Venue: B104 (University Building)
Date & Time: Mar 18, 2016 (12:00-13:00)
Title: Differentiable Neural Computers for Memory-Based Control
Abstract: I will describe a neural network control circuit that interfaces to a large, external memory buffer, which it can learn to read from and write to. I will show that this system, called a Differentiable Neural Computer, excels at learning to represent and compute transformations of data structures. It can also learn strictly from task-related reinforcement signals to compute beneficial courses of action.
Speaker Bio: Greg Wayne received his B.S. in Symbolic Systems from Stanford University, his M.S. in Applied Mathematics from City University of New York, and his Ph.D. in Neuroscience from Columbia University, working in the theoretical neuroscience laboratory of Larry Abbott. Since 2014 he has been at Google DeepMind in London, pursuing research primarily on artificial neural network memory systems and motor control.
Google DeepMind is a research centre whose mission is “to solve intelligence and use it to make the world a better place”.