Duke AI Health Launches Distinguished Lecture Series

January 22, 2020

The Duke AI Health Distinguished Lecture Series inaugurated its 2020 cycle of offerings with a guest lecture from Benjamin Marlin, PhD, an associate professor in the College of Information and Computer Sciences at University of Massachusetts Amherst and co-director of the Machine Learning for Data Science Lab. Dr. Marlin's lecture, titled " Active Learning Methods for Model Personalization in Mobile Health," addressed the use of labeled data to enable active learning methods in the context of mobile health, as well as steps needed to support just-in-time adaptive interventions. A recorded version of the lecture, which took place on January 17th, 2020, can be viewed here (Duke University log-in required).

The second lecture in the series, "Data-Powered Patient-Centered Care," was delivered by Noemie Elhadad, PhD, Associate Professor and co-Interim Director, Department of Biomedical Informatics, Columbia University. Dr. Elhadad's lecture addressed ways to overcome the difficulty of delivering actionable, trustworthy data to clinicians at the point of care, in particular through efforts to create and deploy "tools to support clinicians in their decision-making workflow, as well as facilitating the patient-provider partnership in shared-decision making." A recording of the January 31, 2020 lecture is available here (Duke log-in required).

One additional AI Health Distinguished Lectures is currently scheduled for 2020:

  • Friday, February 21, 2020, 12:00 noon to 1:00 PM: Kamalika Chaudhuri, PhD (Associate Professor, Department of Computer Science and Engineering, University of California, San Diego) presents "Challenges in Reliable Machine Learning." Dr. Chaudhuri's talk will focus on the challenges created by selection bias and overfitting, as well as ways of detecting and correcting these issues.

A video recording of Dr. Chaudhuri's presentation will be posted shortly after the Friday session. All lectures will take place on Duke University campus at Bryan Research 103.