Brian Kulis

Ph.D. Student in Computer Science

University of Texas at Austin

Austin, TX

I am a fifth-year doctoral student at UT Austin. I finished my undergraduate education in May, 2003 with a B.A. in Computer Science and Mathematics from Cornell University.

During the Fall 2007 semester, I was a research fellow at the Institute for Pure and Applied Mathematics at U.C.L.A.

Research Interests

Machine Learning
Data Mining / Data Analysis
Numerical Optimization
Applications to Computer Vision and Other Domains

My advisor is Inderjit Dhillon. A major focus of my research is on large-scale optimization for problems in machine learning; some specific interests include convex analysis, kernel methods, spectral methods, semi-definite programming, and numerical linear algebra. Furthermore, I am interested in large-scale applications of machine learning to practical problems in computer vision and bioinformatics. I have also worked with John Platt and Arun Surendran at Microsoft Research on large-scale optimization. As an undergraduate, I worked with John Hopcroft on tracking topics in networked data over time.

Click here to read more about my research.

Here is my CV.

Papers in Reverse Chronological Order (or by Publication Type)

P. Jain, B. Kulis, K. Grauman. Fast Image Search for Learned Metrics. Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2008. (CVPR 2008 Best Student Paper Award)

B. Kulis, S. Basu, I. Dhillon, R. Mooney. Semi-supervised Graph Clustering: A Kernel Approach. Machine Learning, to appear, 2008.

P. Jain, B. Kulis, K. Grauman. Fast Similarity Search for Learned Metrics. UTCS Technical Report #TR-07-48, September, 2007.

B. Kulis, S. Sra, S. Jegelka, I. Dhillon. Scalable Semidefinite Programming using Convex Perturbations. UTCS Technical Report #TR-07-47, September, 2007.

B. Kulis, M. Sustik, I. Dhillon. Low-Rank Kernel Learning with Bregman Matrix Divergences. Submitted for publication, 2007.

J. Davis, B. Kulis, P. Jain, S. Sra, I. Dhillon. Information-theoretic Metric Learning. Proc. 24th International Conference on Machine Learning, 2007. (ICML 2007 Best Student Paper Award)

P. Jain, B. Kulis, I. Dhillon. Online Linear Regression using Burg Entropy. UTCS Technical Report #TR-07-08, February, 2007.

I. Dhillon, Y. Guan, B. Kulis. Weighted Graph Cuts without Eigenvectors: A Multilevel Approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 29, No. 11, pp. 1944-1957, 2007.

B. Kulis, A. Surendran, J. Platt. Fast Low-rank Semidefinite Programming for Embedding and Clustering. Proc. 11th Intl. AISTATS Conference, 2007.

J. Davis, B. Kulis, S. Sra, I. Dhillon. Information-theoretic Metric Learning. NIPS 2006 Workshop on Learning to Compare Examples, 2006.

B. Kulis, M. Sustik, I. Dhillon. Learning Low-Rank Kernel Matrices. Proc. 23rd International Conference on Machine Learning, 2006.

I. Dhillon, Y. Guan, B. Kulis. A Fast Kernel-based Multilevel Algorithm for Graph Clustering. Proc. 11th ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining, 2005.

B. Kulis, S. Basu, I. Dhillon, R. Mooney. Semi-supervised Graph Clustering: A Kernel Approach. Proc. 22nd International Conference on Machine Learning, 2005. (ICML 2005 Best Student Paper Award)

I. Dhillon, Y. Guan, B. Kulis. A Unified View of Kernel k-means, Spectral Clustering and Graph Cuts. UTCS Technical Report #TR-04-25, July, 2004.

I. Dhillon, Y. Guan, B. Kulis. Kernel k-means, Spectral Clustering, and Normalized Cuts. Proc. 10th ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining, 2004.

J. Hopcroft, O. Khan, B. Kulis, B. Selman. Tracking Evolving Communities in Large Linked Networks. Proc. of the Natl. Academy of Sciences, Vol. 101, pp. 5249-5253, April, 2004.

J. Hopcroft, O. Khan, B. Kulis, B. Selman. Natural Communities in Large Linked Networks. Proc. 9th ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining, 2003.

Contact Info

Office: Taylor 137

Phone: 512-471-9741