Panel Q and A

Speaker Details

I am a member of Machine Learning and Optimization, and Algorithms and Modeling Research Group at Microsoft Research, Bangalore, India. My research interests are in machine learning, statistical learning theory, and optimization algorithms in general. I am also interested in applications of machine learning to privacy, computer vision, text mining and natural language processing.

Earlier, I completed my PhD at the University of Texas at Austin under Prof. Inderjit S. Dhillon.

Chih-Jen Lin is currently a distinguished professor at the Department of Computer Science, National Taiwan University. He obtained his B.S. degree from National Taiwan University in 1993 and Ph.D. degree from University of Michigan in 1998. His major research areas include machine learning, data mining, and numerical optimization. He is best known for his work on support vector machines (SVM) for data classification. His software LIBSVM is one of the most widely used and cited SVM packages. For his research work he has received many awards, including the ACM KDD 2010 and ACM RecSys 2013 best paper awards. He is an IEEE fellow, an AAAI fellow, and an ACM distinguished scientist for his contribution to machine learning algorithms and software design. More information about him can be found at http://www.csie.ntu.edu.tw/~cjlin.

Suvrit Sra is a Research Scientist at the Max Planck Institute for Intelligent Systems (formerly Biological Cybernetics) in Tübingen, Germany. He obtained his M.S. and Ph.D. in Computer Science from the University of Texas at Austin in 2007, and a B.E. (Hons.) in Computer Science from BITS, Pilani (India) in 1999. His main research focus is on large-scale optimization (convex, nonconvex, deterministic, stochastic, etc.): most notably for applications in machine learning, scientific computing, and computational statistics. He takes avid interest in various flavors of analysis, especially convex, harmonic, and matrix.

His research has won awards at several international venues; the most recent being the “SIAM Outstanding Paper Prize (2011)” for his work on metric nearness. He regularly organizes the Neural Information Processing Systems (NIPS) workshops on “Optimization for Machine Learning”.

Stefanie Jegelka is a postdoctoral researcher at UC Berkeley, supervised by Michael I. Jordan and Trevor Darrell. She received a Ph.D. in Computer Science from ETH Zurich in 2012, in collaboration with the Max Planck Institute for Intelligent Systems. She completed her studies for a Diploma in Bioinformatics with distinction at the University of Tuebingen (Germany) and the University of Texas at Austin. She was a fellow of the German National Academic Foundation (Studienstiftung) and its scientific college for life sciences, and has received a Google Anita Borg Europe Fellowship and an ICML Best Paper Award. She has also been a research visitor at Georgetown University Medical Center and Microsoft Research and has held tutorials and workshops on submodularity in machine learning.

Date:
Speakers:
Prateek Jain, Chin-Jen Lin, Aditya Gopalan, Suvrit Sra, and Stefanie Jegelka
Affiliation:
Microsoft, National Taiwan University, IISc, Max Planck Institute for Intelligent Systems, UC Berkeley