van der Schaar Lab

Inspiration Exchange: application-oriented projects in machine learning for healthcare

The van der Schaar Lab’s seventh Inspiration Exchange engagement session took place virtually on March 30, 2021.

The session featured 4 presentations (given by current and former lab members) on a range of application-oriented projects in machine learning for healthcare. Topics ranged from treatment effect estimation to multi-omics data integration, organ transplantation, and clinical trials.

A Q&A/open discussion took place in the latter half of the session, with participants asking researchers about their projects, and sharing their thoughts.

Introduction – 0:00
Welcome from Mihaela – 2:17
Presentation 1 [Nonparametric estimation of heterogenous treatment effects // Alicia Curth] – 4:48
Presentation 2 [A variational information bottleneck approach to multi-omics data integration // Changhee Lee] – 13:26
Presentation 3 [Learning matching representations for individualized organ transplantation allocation // Ahmed Alaa] – 20:40
Presentation 4 [SDF-Bayes: cautious optimism in safe dose-finding clinical trials with drug combinations and heterogenous patient groups // Cong Shen] – 24:32
Q&A session – 36:50
Closing words from Mihaela – 53:27
Intro to next sessions – 54:23

Sign up for our upcoming sessions here.

Titles, authors and abstracts for all projects featured in this session are given below.

Nonparametric Estimation of Heterogeneous Treatment Effects:
From Theory to Learning Algorithms

Alicia Curth, Mihaela van der Schaar

Abstract

A Variational Information Bottleneck Approach to Multi-Omics Data Integration

Changhee Lee, Mihaela van der Schaar

Abstract

Learning Matching Representations for Individualized Organ Transplantation Allocation

Can Xu, Ahmed Alaa, Ioana Bica, Brent D. Ershoff, Maxime Cannesson, Mihaela van der Schaar

Abstract

SDF-Bayes: Cautious Optimism in Safe Dose-Finding Clinical Trials with Drug Combinations and Heterogeneous Patient Groups

Hyun-Suk Lee, Cong Shen, William Zame, Jang-Won Lee, Mihaela van der Schaar

Abstract

Nick Maxfield

From 2020 to 2022, Nick oversaw the van der Schaar Lab’s communications, including media relations, content creation, and maintenance of the lab’s online presence.

Mihaela van der Schaar

Mihaela van der Schaar is the John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge and a Fellow at The Alan Turing Institute in London.

Mihaela has received numerous awards, including the Oon Prize on Preventative Medicine from the University of Cambridge (2018), a National Science Foundation CAREER Award (2004), 3 IBM Faculty Awards, the IBM Exploratory Stream Analytics Innovation Award, the Philips Make a Difference Award and several best paper awards, including the IEEE Darlington Award.

In 2019, she was identified by National Endowment for Science, Technology and the Arts as the most-cited female AI researcher in the UK. She was also elected as a 2019 “Star in Computer Networking and Communications” by N²Women. Her research expertise span signal and image processing, communication networks, network science, multimedia, game theory, distributed systems, machine learning and AI.

Mihaela’s research focus is on machine learning, AI and operations research for healthcare and medicine.