Danica Sutherland

Danica Sutherland is an Assistant Professor in the Department of Computer Science at the University of British Columbia (UBC), Faculty of Science. Her research focuses on artificial intelligence and machine learning, with a particular emphasis on statistical and theoretical foundations, fairness in AI, and differentially private data generation.

• Academic Rank: Assistant Professor
• University: University of British Columbia
• School: Faculty of Science
• Department: Department of Computer Science

Her work explores topics like neural network theory, kernel methods for fair representation learning, and differential privacy in image generation. She is affiliated with UBC's AMLTN (Applied Machine Learning) and CAIDA research centers.

Notable awards include the AI4MATH Best Paper Award, Outstanding Paper at ICLR 2025, and the ACM FAccT Best Paper Award. She has supervised graduate theses on topics such as infinite-width neural networks, differentially private image generation, and invariant representation learning.

At UBC, she teaches courses like Machine Learning II, Advanced Machine Learning, and Topics in Artificial Intelligence - Statistical Learning Theory. Her research leverages public data and theoretical insights to improve machine learning models while addressing privacy and fairness constraints.