Teaching

Massachusetts Institute of Technology

• Machine Learning for Healthcare, Spring 2017
  6.S897, HST.S53

  Course description: Explores machine learning methods for clinical and healthcare applications. Covers concepts of algorithmic fairness, interpretability, and causality. Discusses application of time-series analysis, graphical models, deep learning and transfer learning methods to solving problems in healthcare. Considers how newly emerging machine learning techniques will shape healthcare policy and personalized medicine.

New York University

• Fall 2016: Inference and Representation (DS-GA-1005 and CSCI-GA.2569)
• Spring 2016: Introduction to Machine Learning (CSCI-UA.0480-007)
• Fall 2015: Inference and Representation (DS-GA 1005)
• Fall 2014: Inference and Representation (DS-GA-1005 and CSCI-GA.2569)
• Spring 2014: Machine Learning and Computational Statistics (DS-GA-1003 and CSCI-GA.2567)
• Fall 2013: Introduction to Machine Learning (CSCI-UA.0480-002)
• Spring 2013: Probabilistic Graphical Models (CSCI-GA.3033-006)
• Fall 2012: Introduction to Machine Learning (CSCI-UA.0480-002)
• Spring 2012: Probabilistic Graphical Models (CSCI-GA.3033-006)