Pradeep Ravikumar

Pradeep Ravikumar is an assistant professor who leads the Statistical Machine Learning group in the Department of Computer Science. He is also affiliated with the ICES Computational Visualization Center and UT’s Division of Statistics and Scientific Computation.

He received his B.Tech. in computer science and engineering from the Indian Institute of Technology, Bombay, and his Ph.D. in machine learning from the School of Computer Science at Carnegie Mellon University (CMU). He was then a postdoctoral scholar in the Department of Statistics at the University of California, Berkeley.

His thesis received honorable mention in the 2008 Association for Computing Machinery’s SIG Knowledge Discovery and Data Doctoral Dissertation Award and the CMU School of Computer Science Distinguished Dissertation award. He was also selected as a 2007 Siebel Scholar, and an Indian National Talent Search Scholar. He is the author of over 30 publications with a total of around 2,000 citations. He has served as the area chair for numerous premier conferences in statistical machine learning, such as the International Conference in Machine Learning, the International Conference on Artificial Intelligence and Statistics, and the Neural Information Processing Systems Conference, and is a member of the editorial board of the “Machine Learning Journal.”

Ravikumar’s main area of research is in statistical machine learning. The core problem here combines the statistical imperative of inferring reliable conclusions from limited observations or data with the computational imperative of doing so with limited computation. Of particular interest are modern settings where the dimensionality of data is high, and simultaneously achieving these twin objectives is difficult. His recent research has been on the foundations of such statistical machine learning, with particular emphasis on graphical models, high-dimensional statistical inference, and optimization.


Email: [129, 125, 116, 253, 202, 132, 209, 257, 81, 110, 134, 199, 218, 147, 198, 263, 114, 126, 65, 254, 201, 148]
Phone: 512-232-9136
Office: GDC 4.808