He has worked on problems such as learning from non-iid samples, learning from biased samples, learning from data with missing features and automatic kernel selection for kernelized algorithms such as SVM.
Afshin is a research scientist at Google Research NY, where he specializes in designing and applying machine learning algorithms. He received his BS in Electrical Engineering and Computer Science from UC Berkeley, his PhD in Computer Science from the Courant Institute at NYU with advisor Mehryar Mohri and was a post-doc at UC Berkeley in Peter Bartlett's group.
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