van der Schaar Lab

Inspiration Exchange: Causal Deep Learning

The van der Schaar Lab’s seventeenth Inspiration Exchange session took place on April 14, 2022, and was attended by students and professionals from the AI and machine learning community. This was our first Inspiration Exchange session following a newly introduced format.

This was an especially interactive session, featuring a roundtable discussion with industry guests, lively Q&A and discussions. The session topic was “Causal Deep Learning.”

To start the session, Prof. Mihaela van der Schaar shared a vision for combining causality and deep learning methods, which was further developed by Ph.D. student Zhaozhi Qian in an extended tutorial, where he presented the “Rung 1.5” framework for causality. Current and former lab members, Ioana Bica and Dr. Alexis Bellot, then presented two ways of adopting causality principles in generalisable imitation learning and causally aware imputation.

We then held our first roundtable discussion, where our industry guests Silvia Chiappa (DeepMind), Kim Branson (GSK), and Lindsay Edwards (Relation Therapeutics) shared their perspectives on practical challenges that can be addressed through causal deep learning.

The session was then opened up to the audience for Q&A and general discussion.

Inspiration Exchange – Causal Deep Learning

See our newly published research pillar here for more on this topic:

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Nabeel Seedat

Before joining the van der Schaar Lab, Nabeel received a merit scholarship for a master’s degree at Cornell University, researching Bayesian deep learning and uncertainty estimation for high stakes applications. In addition, he holds a master’s degree from the University of the Witwatersrand (South Africa), where he was awarded a National Research Foundation grant for his work applying signal processing and machine learning to Parkinson’s disease diagnostics in low-resource settings.

Professionally, Nabeel has worked as a machine learning engineer in the United States and South Africa. The computer vision and natural language processing models he worked on are currently deployed and serving millions of customers on a daily basis.

Nabeel is keenly aware that taking methods from the lab to the bedside “requires a unique focus beyond just high-performance predictive models; it requires the development of a toolkit of methods for transfer learning across domains and locations, learning on smaller datasets, understanding model biases and quantifying model reliability and uncertainty are fundamentally needed to bridge this divide.”

Nabeel’s research is supported by funding from the Cystic Fibrosis Trust.