2 papers accepted to NIPS 2013 in Lake Tahoe.
My metric learning survey, recently published by Foundations and Trends in Machine Learning, is now up. Link here.
I am co-organizing a workshop at ICCV 2013 in Sydney on visual domain adaptation with Ruonan Li, Kate Saenko, and Fei Sha. Details here.
"MAD-Bayes: MAP-based Asymptotic Derivations from Bayes," co-authored with Tamara Broderick and Michael Jordan, was accepted to ICML 2013. See arxiv version here.
Congratulations to Ke Jiang for his accepted NIPS 2012 paper "Small-Variance Asymptotics for Exponential Family Dirichlet Process Mixture Models" (co-authored with me and Michael Jordan)!
I will serve as an area chair for ICML 2013, an area chair for ICML 2014, and the local arrangements chair for CVPR 2014.
I am an assistant professor in the CSE department at Ohio State University.
Previously, I spent three years as a postdoc at UC
Berkeley EECS (Computer Science Division), and was also affiliated
with ICSI, where I had the good fortune to work with Trevor Darrell, Stuart Russell, Michael Jordan, and Peter Bartlett. Broadly speaking, I am interested in all aspects of machine learning, with an emphasis on applications to computer vision. Most of my
research focuses on making it easier to analyze
large-scale data. A major focus is on large-scale optimization for core
problems in machine learning such as metric learning, content-based search, clustering,
and online learning. I am also interested in large-scale graphical models, Bayesian inference, and Bayesian nonparametrics.
I finished my Ph.D. in computer science in November, 2008, supervised by Inderjit Dhillon in the University of Texas at Austin computer science department.
I did my undergrad in computer science and mathematics at Cornell
University. I have also worked with John Platt and Arun Surendran at Microsoft Research on large-scale optimization, and as an undergraduate, I worked with John Hopcroft on tracking topics in networked data over time. During the Fall 2007 semester, I was a research fellow at the Institute for Pure and Applied Mathematics at U.C.L.A.
Spring, 2013. Machine Learning
Fall, 2012. Probabilistic Graphical Models
Spring, 2012. Bayesian Modeling and Inference
Click here to read more about some of my research.
Office: 599 Dreese Labs
Email: kulis [at] cse [dot] ohio-state [dot] edu