Contact Us


David’s email: dsontag {@ | at}


How can I join the group as a PhD student?

As of fall 2018, David is accepting PhD students into the lab. Graduate admissions are handled at the department level at MIT, so please first apply to EECS. Students interested in healthcare should also consider applying to MEMP, part of the Harvard-MIT HST program. David also occasionally takes students from CSBi, IDSS, and ORC.

For advice on CS PhD admissions, Jean Yang and Philip Guo have written useful guides.

How can I join the group as a UROP/SuperUROP or MEng student?

When looking for undergraduate or MEng students to join the lab, we look for students who have taken at least one machine learning class (e.g. 6.036 or a more advanced class) or have had significant machine learning experience beforehand.

When contacting David, it is also helpful to have read over lab research projects beforehand.

I am an outside collaborator. How can I work with you?

The lab has had several successful collaborations with clinicians, insurance providers, and other parties. Please email David with a detailed description of the project, the data available, and the current challenges.

How can I learn more about machine learning and healthcare in general?

Clinical machine learning is a growing and important field, and we are excited that more people are interested in the topic. In addition to a general machine learning class, you may find useful the course materials for David’s Spring 2017 class 6.S897/HST.S53: Machine Learning for Healthcare.

Once you’ve developed a foundational base, research papers are an exciting way to learn more about the field. In addition to our recent publications, consider reading papers from relevant machine learning for healthcare conferences and workshops (e.g. MLHC and ML4H Workshop at NIPS) and general machine learning conferences (e.g. NIPS, ICML, AISTATS).