Brian Kulis


Assistant Professor
CSE Department
Ohio State University
Columbus, OH


What's New

  • 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, an area chair for NIPS 2014, and the local arrangements chair for CVPR 2014.

  • Introduction

    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.


    Curriculum Vitae [pdf]


    Publications by Type

    Publications Chronologically

    Google scholar profile


    Teaching

  • Spring, 2014. Survey of Artificial Intelligence II
  • Spring, 2013. Machine Learning
  • Fall, 2012. Probabilistic Graphical Models
  • Spring, 2012. Bayesian Modeling and Inference

  • Ph.D. Students

  • Ke Jiang
  • Anirban Roychowdhury
  • Jiaxin Zhang
  • Robert Finn

  • Research Details

    Click here to read more about some of my research.


    Contact Info

    Office: 599 Dreese Labs

    Email: kulis [at] cse [dot] ohio-state [dot] edu