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.
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
Next session date and topic to be announced soon!
Our next session will be held on July 12, and will run for roughly 1 hour.
In this session, we will turn our focus to quantitative epistemology, a new and transformative area of study pioneered by our lab, the aim of which is to use machine learning to understand, support, and improve human decision-making.
To learn more about quantitative epistemology, click here.
We’re currently deciding when to hold the next Inspiration Exchange session, and what topic would be best for us to discuss.
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 2021-07-12 at 16:00 BST
Paris, France 2021-07-12 at 17:00 CEST
New York, USA 2021-07-12 at 11:00 EDT
San Francisco, USA 2021-07-12 at 08:00 PDT
Beijing, China 2021-07-12 at 23:00 CST
Shanghai, China 2021-07-12 at 23:00 CST
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.