I'm an Associate Professor in the Department of Computing Science and a PI at Amii.
Before joining the University of Alberta in February 2020, I spent many years in industry research working on many interesting problems. Most recently I was a research team lead at Borealis AI (a research institute for the Royal Bank of Canada), where my team worked on privacy-preserving methods for machine learning models and other applications for the bank. Prior to that I spent many years in research labs such as Bell Labs, Technicolor, and Orange.
Please see here for more details.
My research interests are in probabilistic modelling and algorithmic design of machine learning for networked and multi-agent systems, and inference under bias and privacy constraints.
My current focus is on privacy, and fairness and bias in machine learning.
This course focuses on ethics issues in Artificial Intelligence ethics in Artificial Intelligence (AI) and Data Science (DS). The main themes are privacy, fairness/bias, and explainability in DS. The objectives are to learn how to identify and measure these aspects in outputs of algorithms, and how to build algorithms that correct for these issues. The course will follow a case-studies based approach, where we will examine these aspects by considering real-world case studies for each of these ethics issues. The concepts will be introduced through a humanities perspective before a technical treatment including implementation work. Prerequisite: CMPUT 174 or 274. Co-requisite: CMPUT 191.
This course introduces the fundamental statistical, mathematical, and computational concepts in analyzing data. The goal for this introductory course is to provide a solid foundation in the mathematics of machine learning, in preparation for more advanced machine learning concepts. The course focuses on univariate models, to simplify some of the mathematics and emphasize some of the underlying concepts in machine learning, including: how should one think about data, how can data be summarized, how models can be estimated from data, what sound estimation principles look like, how generalization is achieved, and how to evaluate the performance of learned models. Prerequisites: CMPUT 174 or 274; one of MATH 100, 114, 117, 134, 144, or 154. Corequisites: CMPUT 175 or 275; CMPUT 272; MATH 125 or 127; one of STAT 141, 151, 235, or 265, or SCI 151.