van der Schaar Lab

Inspiration Exchange

Inspiration Exchange is a series of engagement sessions aiming to share ideas and discuss topics that will define the future of machine learning in healthcare. These events will target machine learning students, and will emphasize sharing of new ideas and development of new methods, approaches, and techniques.

As a lab, our purpose is to create new and powerful machine learning techniques and methods that can revolutionize healthcare. This doesn’t happen in a vacuum. At inception, we are inspired by ideas and discussions; in implementation, we need connections, trust, and partnership to make a real difference.

While you can learn about our work at major conferences in machine learning or in our papers, we think it’s a better idea to create a community and keep these conversations going. We’re also aware that many people—both in healthcare and machine learning—have questions about what we do, and how they can contribute.

For more information about Inspiration Exchange—and to sign up to join in—please have a look at the sections below, and keep checking for new updates.

Inspiration Exchange

Themed discussion sessions specifically for machine learning students (particularly masters, Ph.D., and post-docs).

We would like to:
– discuss machine learning models and techniques
– share ideas about how machine learning can revolutionize healthcare
– spark new projects and collaborations
– raise awareness about this unique and exciting area of machine learning.

Standard session format:
– presentations by van der Schaar Lab researchers
– Q&A

Eighth session on April 28, 2021!

Our next session will be held on April 28 at 16:00 BST, and will run for roughly 1 hour.

In this session (and the subsequent session), we will focus on individualized treatment effect inference, a key research area for our lab and an important topic at the intersection of machine learning and healthcare.

If you’d like to join us for future sessions, please sign up using the form below. We’ll be in touch soon with URLs and other info.

Cambridge, United Kingdom 2021-04-28 at 16:00 BST
Paris, France 2021-04-28 at 17:00 CEST
New York, USA 2021-04-28 at 11:00 EDT
San Francisco, USA 2021-04-28 at 08:00 PDT
Beijing, China 2021-04-28 at 23:00 CST
Shanghai, China 2021-04-28 at 23:00 CST

To add a custom time zone, click here.

Session 1: introduction to automated machine learning

Ahead of this session, Mihaela published a piece of content on machine learning and the future of healthcare, entitled AutoML: powering the new human-machine learning ecosystem.

Session 2: automated machine learning pipelines

This session featured presentations from 4 of the van der Schaar Lab’s researchers regarding the Lab’s key AutoML projects. In addition, three session participants presented their own proposals for building on these projects by improving the AutoML methods or applying them in new contexts or applications.

Session 3: software packages for automated machine learning

This session featured presentations from 4 of the van der Schaar Lab’s researchers regarding the lab’s AutoML software packages (AutoPrognosis and Clairvoyance), followed by a Q&A.

Session 4: recent projects in machine learning for healthcare

This session covered a range of the lab’s most recent and exciting research projects in machine learning for healthcare. A total of 9 short presentations were given by the van der Schaar Lab’s researchers, followed by a Q&A session.

Session 5: synthetic data concepts and approaches

This session focused on synthetic data generation as one of the lab’s key research areas. Following an introduction from Mihaela van der Schaar, 3 short presentations were given by the van der Schaar Lab’s researchers, followed by a Q&A session.

Session 6: synthetic data evaluation

This session focused on evaluation of synthetic data. Following an introduction from Mihaela van der Schaar, 2 short presentations were given by the van der Schaar Lab’s researchers, followed by a Q&A session.

Session 7: application-oriented projects in machine learning for healthcare

This 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.

To learn more about our team of researchers, click here. You can find our publications here.