## John Wright (Columbia)Mar 9, 2-3PM, 540 Cory. ## Title and Abstract
Our analysis highlights the key roles of symmetry and negative curvature in the behavior of efficient methods. We sketch connections to broader families of “benign” nonconvex problems in data representation and imaging, in which efficient methods obtain global optima independent of initialization. We describe experiments in computer vision and scanning tunneling microscopy, where nonconvex optimization supports new data analysis and data acquisition strategies. Joint work with Yuqian Zhang, Yenson Lau, Han-Wen Kuo, Dar Gilboa, Sky Cheung, Abhay Pasupathy. ## BioJohn Wright is an Associate Professor in the Electrical Engineering Department at Columbia University, and a member of Columbia’s Data Science Institute. He received his PhD in Electrical Engineering from the University of Illinois at Urbana-Champaign in 2009, and was with Microsoft Research from 2009-2011. His research is in the area of high-dimensional signal and data analysis, optimization, and computer vision. His work has received a number of awards and honors, including the 2009 Lemelson-Illinois Prize for Innovation for his work on face recognition, the 2009 UIUC Martin Award for Excellence in Graduate Research, and a 2008-2010 Microsoft Research Fellowship, and the Best Paper Award from the Conference on Learning Theory (COLT) in 2012, the 2015 PAMI TC Young Researcher Award |