Cho-Jui Hsieh (UT Austin) is a Ph.D. student at University of Texas at Austin (UT-Austin). His research interests focus on large-scale machine learning and data mining. Cho-Jui obtained his B.S. degree in 2007 and M.S degree in 2009 from the computer Science Department of National Taiwan University (advisor: Chih-Jen Lin). Currently he is a member of big data group led by Inderjit Dhillon. He is the recipient of the IBM Phd fellowship in 2013-2015, the best research paper award in KDD 2010, and best paper award in ICDM 2012.

Peder A. Olsen (IBM Research) is a research staff member in the Business Analytics and Mathematics Department at IBM T. J. Watson Research Center in New York. He received a Ph.D. in mathematics in 1996 from the University of Ann Arbor.  After graduating, he joined the speech recognition group at IBM, where he worked as a post-doc, researcher and manager from 1996-2012.  Since then his focus has been on machine learning applications in areas such as medical fraud detection and farming analytics.

Inderjit S. Dhillon (UT Austin) is a Professor of Computer Science and Mathematics at UT Austin, where he is the Director of the Center for Big Data Analytics. His main research interests are in big data, machine learning, network analysis, linear algebra and optimization. Inderjit received his B.Tech. degree from IIT Bombay, and Ph.D. from UC Berkeley. His dissertation work at Berkeley led to the fastest numerically stable algorithm for the symmetric tridiagonal eigenvalue problem, which has been adapted in all state-of-the-art numerical software libraries. Inderjit has received several prestigious awards, including the NSF Career Award in 2001, the University Research Excellence Award in 2005, the SIAM Linear Algebra Prize in 2006, the Moncrief Grand Challenge Award in 2010, the SIAM Outstanding Paper Prize in 2011, and the ICES Distinguished Research Award in 2013. Along with his students, he has received several best paper awards at leading data mining and machine learning conferences. Inderjit has published over 100 journal and conference papers, and has served on the Editorial Board of the Journal of Machine Learning Research, the IEEE Transactions of Pattern Analysis and Machine Intelligence, Foundations and Trends in Machine Learning and the SIAM Journal for Matrix Analysis and Applications. He is an IEEE Fellow, and an ACM, SIAM and AAAS member.

Pradeep Ravikumar (UT Austin) leads the Statistical Machine Learning Group at the Department of Computer Science at the University of Texas, Austin. He is also the assistant director of an upcoming Center for Big Data Analytics at UT Austin and is affiliated with the Division of Statistics and Scientific Computation, and the Institute for Computational Engineering and Sciences. Pradeep obtained his PhD from the School of Computer Science at Carnegie Mellon University in 2007 (advisor: John Lafferty), and was a postdoc at the Department of Statistics, University of California, Berkeley through 2009 (advisors: Martin Wainwright, Bin Yu). He was also Program Chair for the Sixteenth International Conference on Artificial Intelligence and Statistics (AISTATS) 2013.

Jorge Nocedal (NorthWestern) is the Walter P. Murphy Professor of Industrial Engineering at Northwestern University. He obtained his B.S. in physics from the National University of Mexico (UNAM) and a PhD. in Mathematical Sciences from Rice. His research interests are in optimization algorithms and their application in machine learning and in areas involving differential equations. He is currently the Editor in Chief for the SIAM Journal on Optimization, is a SIAM Fellow, and was awarded the 2012 George B. Dantzig Prize.