van der Schaar Lab

Inspiration Exchange: Neural differential equations

The van der Schaar Lab’s 20th Inspiration Exchange session took place on October 4, 2022. The session covered Neural differential equations and causal effect inference over time. This session was particularly engaging and interactive, with more than 100 participants joining the session live! 

To start the session, Ph.D. student Zhaozhi Qian introduced neural differential equations and motivated their use in the context of healthcare and medicine. Subsequently, Ph.D. student Samuel Holt presented Neural Laplace, a unified framework for learning diverse classes of differential equations by modeling dynamics in the Laplace domain. Additionally, Ph.D. student Nabeel Seedat presented Treatment Effect Neural Controlled Differential Equation (TE-CDE), which estimates counterfactual outcomes over time with irregularly sampled data. 

We then held our roundtable discussion, where our industry guests Dr Emma Slade (GSK) and  Dr Javier Hernández (Microsoft Research) shared their perspectives on ML and the medical impacts of the presented works. The session was then opened up to the audience for Q&A and general discussion.

Inspiration Exchange – Neural differential equations

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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.