Prof. Courtney Paquette recipient of the CAIMS-SCMAI/PIMS Early Career Award (2025)

The 2025 CAIMS/PIMS Early Career Award is presented to Prof. Courtney Paquette in recognition of her fundamental contribution to the analysis of stochastic optimization algorithms and their applications to machine learning. Prof. Paquette's major contributions include developing the first framework to analyze the exact dynamics of multi-pass stochastic gradient descent in high-dimensions, designing the first linearly convergent sub-gradient methods for nonconvex optimization with applications to the phase retrieval problem, and providing deep theoretical insights into the average-case complexity of optimization algorithms.