van der Schaar Lab

Inspiration Exchange: Discovery Using Machine Learning

The van der Schaar Lab’s sixteenth Inspiration Exchange session took place virtually on March 4, 2022, and was attended by students and professionals from the AI and machine learning community.

This was an especially interactive session, with a lively Q&A and discussion. The session topic was “Discovery using machine learning.”

To start the session, Mihaela van der Schaar shared her reasons for believing that machine learning can enable new discoveries (both in and beyond the healthcare setting) and why this is a new frontier for machine learning.

Ph.D. students Zhaozhi Qian and Krzysztof Kacprzyk then presented how machine learning can discover governing equations from data, highlighting a recent approach from our lab, and discussed the limitations of previous approaches. The session was then opened up to the audience for Q&A and general discussion.

Inspiration Exchange – discovery using ML

Introduction – 0:00
Welcome message from Mihaela van der Schaar – 2:20
Presentation by Zhaozhi Qian [Discovery of Governing Equations Using Machine Learning] – 4:22
Presentation by Krzysztof Kacprzyk [D-CODE] – 26:51
Audience Q&A – 40:44
Intro to next sessions and refreshed format – 57:04

A full list of the lab’s publications can be found here.

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.