I am an associate professor in the College of Information and Computer Sciences at the University of Massachusetts Amherst where I direct the Robust and Efficient Machine Learning (REML) Lab. I was previously a fellow of both the Pacific Institute for the Mathematical Sciences and the Killam Trusts at the University of British Columbia where I was based in the Laboratory for Computational Intelligence in the Department of Computer Science. I completed my PhD in machine learning in the Department of Computer Science at the University of Toronto.
From a methodological perspective, my current research focuses on the development of machine learning models and algorithms that both robust and efficient. The REML Lab studies multiple aspects of robustness including robustness to uncertainty and missing data, as well as multiple aspects of efficiency including data efficiency, computational scalability, and communication efficiency. The Lab’s foundations are in probabilistic machine learning. Recent research topics include modeling sparse and irregularly sampled time series, real-time active learning, hierarchical zero-shot learning, and Bayesian deep learning.
The Lab’s research is informed by multiple real-world applications domains and machine learning deployment contexts including clinical and mobile health, embedded systems, and the Internet of Things. See the REML Lab web page for details on lab members, current and completed projects and publications.