The Rajpurkar Lab has started up in the Department of Biomedical Informatics at Harvard Medical School.
Greetings! We are a new lab at Harvard DBMI led by 🔗 Dr. Pranav Rajpurkar. We work on building reliable AI technologies for medical decision making. We have developed deep learning algorithms that can read medical images at the level of experts, we have built large-scale open medical datasets, and we have demonstrated the positive and negative effects of AI on medical decision making.
What will it take to make medical AI part of routine clinical care? We believe that algorithm development for limited labeled data settings, real-world dataset curation at scale, and the design of effective clinical deployment setups are critical directions. I welcome you to join us in our mission.
We welcome undergraduate, graduate, and post-doctoral students and research volunteers with a background in any one of artificial intelligence, software engineering, or medicine. By working with our lab, you will build solutions to important problems in medicine. You will learn and sharpen cutting-edge AI through your research, your peers, and reading groups. You will work together in small teams with other scientists, engineers, and clinicians, and receive mentorship to author a research paper.
We expect that students will be able to commit at least 20 hours per week to research and be
able to commit at least 6 months of research with the lab.
If you are interested in applying, please email your CV and brief description of interest to
specifying whether you are Harvard undergrad or graduate student, volunteer researcher, visiting scholar, or postdoc in the subject line.
We cultivate collaborations with academic institutions, hospitals and industry. We are currently
interested in collaborating to deploy medical imaging AI models. We are also partnering with hospitals
and industry to create large multi-institutional imaging datasets. If you
are interested in a possible collaboration with the Rajpurkar lab, please email
with the subject line Possible Research Collaboration.