DataBites Seminar - A path through Machine Learning, Artificial Intelligence, and using it beyond cat videos or credit scoring

Research Centre: Centre for Machine Learning

Abstract:

In this talk meant to be very interactive, I will try not to bore the audience to death by discussing, on a sample size of 1: how I navigated from computer science to math to statistics to "data science" to machine learning (ML) and artificial intelligence (AI); what metrics I used to guide decisions that eventually led me to become one of the earliest researchers at DeepMind, working on both fundamental research on Bayesian Deep Learning and on applications to healthcare; and why after four incredible years there I decided last year to move on and try to empower "good" international actors such as Amnesty International with the ML/AI toolbox that is too often reserved to powerful tech behemoths.

Bio:

Julien Cornebiseis a researcher in machine learning and artificial intelligence. After an undergrad spent coding his nights away and a PhD in Computational Statistics / Applied Probability in France for which he received the 2010 Savage Award from the International Society for Bayesian Analysis, Julien travelled through two postdocs in local universities that had the patience and the kindness to host him - SAMSI/Duke University + UBC Vancouver, and University College London. After a follow-up stint through independent mathematical consulting on pharmaceutical applications, focusing on understanding needs and constraints, solving and most importantly explaining the solutions, he became in 2012 an early employee in a then small and unknown startup, DeepMind Technologies Limited, which was later acquired by a somewhat less small technology company, Google. Over four years he led some fundamental research lines used in early demos and fundraising, then helped build and lead the Health applied research team. He left in 2016, and has since been working with Amnesty International.

Slides from this presentation can be found here