Nidhi Hegde’s current research focus is on a fundamental approach to privacy and ethics in AI.

Busting bias and preserving privacy

Nidhi Hegde’s current research focus is on a fundamental approach to privacy and ethics in AI. Her goal is to investigate how outcomes from AI and ML methods breach privacy and impact fairness and bias. She seeks to create algorithms that are private and fair by design, which involves new mathematical models and algorithms that provide desired outcomes while maintaining privacy and fairness. She likes to work on real practical problems, which often lead to fundamental questions that need to be addressed before a solution can be designed. She firmly believes that a lot of interesting theory is buried in real practical applications! This fundamental theory approach to practical problems often leads to the design of algorithms that are efficient and can be implemented in real systems. While her interests generally lie in machine learning, her recent focus is on privacy and fairness, and her recent work has included privacy in reinforcement learning, task classification and expert matching using active learning, distributed resource allocation through online learning, social information networks, and recommendations under privacy constraints.

Nidhi is a Fellow and Canada CIFAR AI Chair at Amii and an Associate Professor in the Department of Computing Science at the University of Alberta. Before joining UAlberta, she spent many years in industry research labs. Most recently, she was a Research team lead at Borealis AI (a research institute at Royal Bank of Canada), where her team worked on privacy-preserving methods for machine learning models and other applied problems for RBC. Prior to that, she spent many years in research labs in Europe working on a variety of interesting and impactful problems. She was a researcher at Bell Labs, Nokia, in France from January 2015 to March 2018, where she led a new team focussed on Maths and Algorithms for Machine Learning in Networks and Systems, in the Maths and Algorithms group of Bell Labs. She also spent a few years at the Technicolor Paris Research Lab working on social network analysis, smart grids, privacy, and recommendations. Nidhi is an associate editor of the IEEE/ACM Transactions on Networking, and an editor of the Elsevier Performance Evaluation Journal.