Wu Lin

Wu Lin is a Postdoctoral Research Fellow at the Vector Institute, focusing on optimization for machine learning, training dynamics for neural networks, and generative probabilistic models. Previously, as a Graduate Research Assistant at The University of British Columbia, Wu Lin conducted research on stochastic optimization and its applications in statistical machine learning and Bayesian deep learning. Contributions to the open-source Shogun machine learning toolbox included developing inference algorithms and optimization procedures. Wu Lin has also collaborated with RIKEN on data-efficient inference methods for large-scale Bayesian deep learning and served as a Machine Learning Scientist at Architech. An educational background includes a PhD in Statistical Machine Learning from The University of British Columbia and a Master's in Statistics from the University of Waterloo. Wu Lin's reviews for conferences include NIPS, ICLR, ICML, AISTATS, and JMLR.