Adams Wei Yu


Hometown: Wuzhou, China
Carnegie Mellon University

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Research:  Adams' research lies between large scale optimization and deep learning, two important and closely related directions in modern artificial intelligence and machine learning. He is fascinated in inventing efficient optimization algorithms for general machine learning tasks in a distributed manner, providing theoretical convergence guarantee, and building novel deep neural network models to speed up several applications, such as natural language processing. 

Bio: Adams Wei Yu is a PhD candidate in Machine Learning Department, School of Computer Science at Carnegie Mellon University, advised by Jaime Carbonell and Alex Smola. His research lies in large scale optimization, deep learning, statistical machine learning and their applications. He has interned with the Google Brain team and MSR machine learning group. He received his B.S. in Math from Beihang University and his M.Phil in CS from the University of Hong Kong.