I’ve taugh several courses at Stanford University.

From 2016 to 2020 I focused on a large-scale inference course:

BIODS215: Topics in Biomedical Data Science: Large-scale inference.

This graduate-level course focused on methodology for large-scale inference from biomedical data. Topics include one-dimensional and multidimensional probability distributions; hypothesis testing and model comparison; statistical modeling; and prediction. This course will place a special emphasis on applications of these approaches to i) human genetic data; ii) hospital in-patient and health questionnaire data, which is increasingly available with the emergence of large precision initiatives like the UK Biobank and Precision Medicine Initiative; and iii) wearable and social network data.

For the past few years I’ve been focused on Artificial intelligence.

Last year, I co-taught

BIODS295: Generative AI in Healthcare

Stanford graduate course on generative artificial intelligence (AI), and its applications in the healthcare domain.

and this year

BMDS 272: Healthcare Acceleration: Artificial Intelligence

Stanford graduate course open to students from the Schools of Engineering or Medicine with lectures on AI methods relevant to healthcare, and projects focussed on solving current clinical challenges in the Stanford hospital ecosystem in partnership with Anesthesiology, ER, Hematology, Cardiology, Ophthalmology etc.