Contact
Associate Professor, Faculty of Science - Computing Science
- nidhih@ualberta.ca
Overview
About
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.
Research
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.
Please see dblp, Google scholar for a complete publication list.
Courses
CMPUT 200 - Ethics of Data Science and Artificial Intelligence
This course focuses on ethics issues 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 by using case studies with an emphasis on a technical treatment including implementation work. Prerequisite: one of CMPUT 191 or 195, or one of CMPUT 174 or 274 and one of STAT 151, 161, 181, 235, 265, SCI 151, MATH 181, or CMPUT 267.
CMPUT 466 - Machine Learning Essentials
Learning is essential for many real-world tasks, including recognition, diagnosis, forecasting and data-mining. This course provides a broad overview of topics in machine learning, from foundational methods for regression, classification and dimensionality reduction to more complex modeling with neural networks. It will also provide the formal foundations for understanding when learning is possible and practical. This single course is an alternative to the more in-depth two-course sequence on machine learning with CMPUT 267 and 467. Prerequisites: CMPUT 204 or 275; any 300-level Computing Science course; MATH 125 or 127; one of MATH 115, 118, 136, 146, or 156; and one of STAT 141, 151, 161, 181, 235, 265, SCI 151, or MATH 181. Credit cannot be obtained in CMPUT 466 if credit has already been obtained for CMPUT 467.
CMPUT 566 - Machine Learning Essentials
Learning is essential for many real-world tasks, including recognition, diagnosis, forecasting and data-mining. This course provides a broad overview of topics in machine learning, from foundational methods for regression, classification and dimensionality reduction to more complex modeling with neural networks. It will also provide the formal foundations for understanding when learning is possible and practical. Credit cannot be obtained for both CMPUT 466 and 566.
MMA 609 - Responsible AI & Ethical Issues in Data Analytics
This course focuses on the ethical and legal considerations in artificial intelligence (AI) and data analytics, fields that are evolving rapidly and prompting novel ethical and regulatory concerns. It will cover subjects such as data privacy, fairness in algorithms, interpretability, and accountability. Participants will be educated on the responsible and ethical application of AI and data analytics technologies. Restricted to students registered in the MMA Program. Non-MMA students require consent of home dept and the Masters Programs Office.