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 researchers interested in healthcare, 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 researchers in academia or industry interested in healthcare.

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:
– brief tutorial on chosen topic
– presentation of cutting-edge solutions by lab researchers
– roundtable discussions with industry leaders
– Q&A

Next session on 22 November 2022!

Our next session will be held on 22 November 2022, and will run for roughly 1 hour.

The focus of our upcoming session is AutoPrognosis 2.0, our lab’s newest framework and software package, which utilizes the power of AutoML in a flexible and interpretable way

We will begin the session by motivating the clinical need for tools like AutoPrognosis and giving a short introduction to our tool’s capabilities.

Finally, we will welcome guests from industry and medicine to join our discussion.

We are delighted to be joined for this session by our guests Dr Aditya Nori (Microsoft), Prof Eoin McKinney (Cambridge), and Dr Thomas Callender (UCL).

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

Cambridge, United Kingdom Tues, 25 October 2022 at 16:00 BST
Paris, France Tues, 25 October 2022 at 17:00 CEST
New York, USA Tues, 25 October 2022 at 11:00 EDT
San Francisco, USA Tues, 25 October 2022 at 08:00 PDT
Beijing, China Tues, 25 October 2022 at 23:00 CST

More time zones can be found 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.

Session 8: individualized treatment effect inference (key concepts and challenges)

This was the first of 2 planned sessions focusing on individualized treatment effect inference (ITE inference).

The former half of the session featured 2 presentations introducing individualized treatment effect inference. This was followed by a participative discussion in which members of the lab and session participants discussed several key topics related to ITE inference, ranging from the evaluation of models to working with different types of data, to potentially relaxing some of the typical assumptions related to ITE inference.

Session 9: individualized treatment effect inference (time series)

This was the second in a pair of sessions focusing on individualized treatment effect inference (ITE inference). Building on our discussions the previous month, this session examined a range of ITE inference problems and approaches specifically within the time series setting.

The former half of the session featured 4 short presentations, followed by a participative discussion in which members of the lab and members of the audience discussed several key topics relevant to ITE inference in the time series setting, including counterfactuals, assumptions, missing data, and interpretability.

Session 10: quantitative epistemology

This session offered an introduction to quantitative epistemology—the van der Schaar Lab’s transformational new research area that aims to use AI and machine learning to understand and empower human learning and decision-making.

The former half of the session featured a high-level introduction to quantitative epistemology by Mihaela van der Schaar, followed by more in-depth explanations of specific frameworks and approaches by Ph.D. student Alihan Hüyük. The latter half of the session focused on a participative discussion in which Mihaela and Alihan answered questions from audience members about quantitative epistemology.

Session 11: time series in healthcare

The focus of the session was time series—an absolutely vital topic (and key research pillar for the van der Schaar Lab) that touches all aspects of machine learning for healthcare.

The first half of the session featured short presentations given by Mihaela van der Schaar, former Ph.D. student Changhee Lee, and research engineer Evgeny Saveliev. The latter half of the session featured a Q&A, in which Mihaela and members of the lab answered questions from audience members about time series in healthcare.

Session 12: what’s next for individualized treatment effects?

This session revisited the crucial topic of individualized treatment effect (ITE) inference, with a particular focus on looking to the future and introducing and discussing some brand-new approaches.

The first half of the session featured short presentations given by Ph.D. students Alicia Curth and Zhaozhi Qian. The latter half of the session featured a Q&A, in which Mihaela, Alicia, and Zhaozhi answered questions from audience members about their research and individualized treatment effects in general.

Session 13: how can we make ML models as robust and useful as possible?

This session raised a critically important question: how can we make ML models as robust and useful as possible?

Looking beyond predictions and analytics, there are a range of requirements and attributes that affect the degree of robustness and utility of a machine learning model—both in and beyond healthcare.

This session focused on three of these in particular: synthetic data, uncertainty estimation, and data imputation. Our lab members presented new and compelling work on each of these, while also conducting polls of the audience and asking attendees for their opinions and personal experiences.

Session 14: ML to transform organ transplantation

This was a particularly application-oriented session on ML tools for organ transplantation (one of the lab’s key research impact areas).

To start the session, clinician Alexander Gimson framed the importance and complexity of organ transplantation as a domain, and gave a quick overview of some of his collaborative projects with the lab to date.

Ph.D. student Jeroen Berrevoets then introduced OrganITE and OrganSync, two new ML-powered approaches to organ transplantation problems such as transplant benefit prediction, matchmaking, and queueing. Research engineer Bogdan Cebere presented a demonstrator for OrganBoard, a clinician-oriented survival prediction and matchmaking tool for organ transplantation. Finally, postdoc Fergus Imrie and PhD. student Yuchao Qin presented iTransplant, a machine learning project aiming to understand and improve decision-making around accepting and rejecting organs.

The session wrapped up with a short roundtable with a panel of clinical collaborators (all of whom are transplantation experts) and a brief audience Q&A.

Session 15: next frontiers in interpretability

This was an especially interactive session, featuring an audience poll and a lively Q&A and discussion.

To start the session, Mihaela van der Schaar shared a framework for understanding and categorizing the various different types of machine learning interpretability (in and beyond the healthcare setting).

Ph.D. students Jonathan Crabbé and Zhaozhi Qian then presented two fundamentally different ways of approaching interpretability (and related projects), and discussed their respective strengths. The session was then opened up to the audience for Q&A and general discussion.

Session 16: discovery using machine learning

This was an especially interactive session, featuring an audience poll and a lively Q&A and discussion. The session topic was “Discovery Using Machine Learning.”

To start the session, Mihaela 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.

Session 17: Causal Deep Learning

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, Mihaela 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.

Session 18: Causal Deep Learning Part 2

The session was a part 2 discussion on “Causal deep learning.”

To start the session, Ph.D. student Jeroen Berrevoets presented an extended tutorial, showcasing the “Rung 1.5” approach for imputation when estimating treatment effects. Subsequently, Ph.D. student Boris van Breugel introduced a method to adapt causality principles in generating fair synthetic data.

We then held our roundtable discussion, where our industry guests Amit Sharma (Microsoft Research), Cheng Zhang (Microsoft Research), and Jim Weatherall (AstraZeneca) 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.

Session 19: ICML 2022 Preview

The session previewed the van der Schaar Lab’s accepted publications at the upcoming ICML 2022 conference and featured a roundtable discussion with industry guests, lively Q&A, and discussions. 

To start the session, there was a medley of presentations highlighting our 7 accepted papers, highlighting our lab’s continuing and impactful research. Our research encompasses a diverse array of topics, including discovering diverse classes of differential equations from data, machine learning interpretability, synthetic data, data-centric AI, AutoML, individualised treatment effects, and, last but not least, augmenting human skills using machine learning.

We then held our roundtable discussion, where our industry guests Dr Ari Ercole (University of Cambridge) and Dr Razvan Pascanu (DeepMind) 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

Session 20: Neural differential equations

The session covered neural differential equations

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.

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

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