We are delighted to announce details of our new annual lecture series – The David Blackwell Lecture. Professor David Blackwell (1919 – 2010) was a distinguished American statistician and mathematician who made significant contributions to game theory, probability theory, information theory, and Bayesian statistics. He is one of the eponyms of the Rao–Blackwell theorem and became the first African-American elected member of the US National Academy of Sciences.
As a Department, we wanted to mark his ground-breaking work and are delighted to introduce an annual lecture named after him to be held in October each year.
Date: Thursday 28th October 2021, 3.30pm (BST)
Speaker: John Jumper, Senior Research Scientist, DeepMind
Title: Highly accurate protein structure prediction with AlphaFold
Abstract: Predicting a protein’s structure from its primary sequence has been a grand challenge in biology for the past 50 years, holding the promise to bridge the gap between the pace of genomics discovery and resulting structural characterization. In this talk, we will describe work at DeepMind to develop AlphaFold, a new deep learning-based system for structure prediction that achieves high accuracy across a wide range of targets. We demonstrated our system in the 14th biennial Critical Assessment of Protein Structure Prediction (CASP14) across a wide range of difficult targets, where the assessors judged our predictions to be at an accuracy “competitive with experiment” for approximately 2/3rds of proteins. The talk will cover both the underlying machine learning ideas and the implications for biological research.
Bio: John Jumper received his PhD in Chemistry from the University of Chicago, where he developed machine learning methods to simulate protein dynamics. Prior to that, he worked at D.E. Shaw Research on molecular dynamics simulations of protein dynamics and supercooled liquids. He also holds an MPhil in Physics from the University of Cambridge and a B.S. in Physics and Mathematics from Vanderbilt University. At DeepMind, John is leading the development of new methods to apply machine learning to protein biology.
Please register your place below to receive the joining instructions: