Brains, AI and Data! (BRAID) workshop

About this event

This workshop will explore the overlap between neuroscience, AI and data. It will look at how data and AI have influenced neuroscience, and the ideas from neuroscience that have had a considerable influence on machine learning.

Attendees will have the opportunity to hear from a range of academics, who will speak about how their cutting edge research explores the intersection between AI, data, machine learning and neuroscience. There will be the chance to network with the speakers and attendees across the neuroscience community within the University of Bristol and beyond.

The workshop is jointly hosted by the Neural Computation Research Hub (Bristol Neuroscience, Elizabeth Blackwell Institute) and The Alan Turing Institute at Bristol (Jean Golding Institute).

Call for expressions of interest to deliver a short talk - closes 10 January 2025

Agenda

09.30: Welcome - overview

09.35: Analysing brain activity and behaviour

• Emma Cahill, Lecturer, School of Physiology, Pharmacology & Neuroscience, University of Bristol

• Tom Maullin-Sapey, Lecturer in Statistics, School of Mathematics, University of Bristol

• Michael Rule, Lecturer in Computational Neuroscience and Machine Learning, University of Bristol

10.15 Panel discussion and questions - Chair, Tamara Boto, Senior Lecturer in Neuroscience, School of Physiology, Pharmacology & Neuroscience

 10.35: Disorders

• Qiang Liu, Lecturer in Data Science, School of Engineering Mathematics and Technology, University of Bristol

• Holly Fraser, Senior Research Associate in Immunopsychiatry, Bristol Medical School, University of Bristol

• Raul Santos-Rodrigues, Professor of Data Science and Intelligent Systems, School of Engineering Mathematics and Technology, University of Bristol and Turing AI Fellow

11.15: Panel discussion and questions - Chair, TBC

11.35: Coffee and discussion

12.05: Keynote- Jianfeng Feng, Professor, Department of Computer Science, University of Warwick

12.45: Free and intellectually stimulating lunch

13.40: Modelling Intelligence - part 1

• Laurence Aitchison, Lecturer, School of Engineering Mathematics and Technology, University of Bristol

• Jeffrey Bowers, Professor, School of Psychological Science, University of Bristol

• Sophie Baker, PhD student, School of Engineering Mathematics and Technology, University of Bristol

14.20: New & breaking short talks - 3 minute thesis style

 15.05: Break and discussion

 15.25: Modelling Intelligence - part 2

• Conor Houghton, Associate Professor in Computer Science, School of Engineering Mathematics and Technology, University of Bristol

• Edwin Dalmaijer, Lecturer, School of Psychological Science, University of Bristol
• Zahraa Abdallah, Senior Lecturer, School of Engineering Mathematics and Technology, University of Bristol

16.05: Interactive panel discussion - Chair, TBC

16:30: Event close

Jianfeng Feng Biography:

Professor Feng completed his under- and postgraduate degrees at Peking University in the People’s Republic of China between 1985 to 1993. From there he spent three years in Europe, first in Germany and then Italy, thanks to support from the A. von Humboldt Foundation and the Italian National Research Council respectively. His first independent research post in the UK was at Cambridge where he was Deputy Head of the Computational Neuroscience Laboratory from 1996 to 2000. From there he took up a post as Reader at Sussex University until 2005; from 2005 to 2008 he was Visiting Professor at the Department of Informatics at Sussex. In 2005 he accepted a Chair in the Centre for Scientific Computing and Computer Science, Warwick University. From 2008 he also holds a position as Dean of Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, PR China and from 2011 a Professorship in the School of Biology at Fudan.

Prof Feng is a computational biologist working in the areas of computational psychiatry, abstract and biophysical models of single neurone and neuronal networks, stochastic and nonlinear dynamics, and data mining and mathematical physics. He has been developing new mathematical, statistical and computational theories and methods to meet the challenges raised in neuroscience and mental health research. Recently, his research foci are on big data analysis and mining for neuroscience and brain diseases.