Ke has defended his thesis! Congratulations, Ke!
New paper (with Ke Jiang and Suvrit Sra) published at AISTATS 2017: "Combinatorial Topic Models using Small-Variance Asymptotics."
New paper (with Anirban Roychowdhury and Srini Parthasarathy) accepted to ICML 2016: "Robust Monte Carlo Sampling using Riemannian Nose-Poincare Hamiltonian Dynamics."
New paper (with Ke Jiang and Suvrit Sra) posted on arxiv here called "Combinatorial Topic Models using Small-Variance Asymptotics."
New paper (with Robert Finn) posted on arxiv here called "A Sufficient Statistics Construction of Bayesian Nonparametric Exponential Family Conjugate Models" that provides general results on conjugacy for stochastic processes, even for continuous likelihoods.
I moved to Boston University.
I received the NSF CAREER Award.
I am an assistant professor in the Department of Electrical and Computer Engineering and the Department of Computer Science at Boston University. I am also a core member of the Division of Systems Engineering. From 2012-2015 I was an assistant professor in the Department of Computer Science and Engineering and the Department of Statistics at Ohio State University.
Before that, 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, Bayesian nonparametrics, and deep learning.
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.
Area chair: AAAI 2018
Area chair: NIPS 2017
Area chair: ICML 2017
Area chair: AISTATS 2017
Area chair: ICML 2016
Area chair: AISTATS 2016
Area chair: ICML 2015
Area chair: AISTATS 2015
Area chair: NIPS 2014
Area chair: ICML 2014
Local arrangements chair: CVPR 2014
Area chair: ICML 2013
Spring, 2017. Deep Learning
Spring, 2017. Advanced Data Structures and Algorithms
Fall, 2016. Advanced Data Structures and Algorithms
Spring, 2015. Bayesian Modeling and Inference
Fall, 2014. Survey of Artificial Intelligence II
Spring, 2014. Survey of Artificial Intelligence II
Spring, 2013. Machine Learning
Fall, 2012. Probabilistic Graphical Models
Spring, 2012. Bayesian Modeling and Inference
Current Ph.D. Students
Former Group Members
Ke Jiang, PhD, 2017. After graduation: Data Scientist at Microsoft.
Siddharth Singh, MS, 2013. After graduation: IBM, then Amazon.
Ye Liu, MS, 2014. After graduation: PhD student, University of Michigan.
Jiaxin Zhang, MS, 2014. After graduation: Google.
Lizzy Burl, BS, 2015. After graduation: Google.
Xiangyang Xhou, MS, 2016. After graduation: Google.
Click here to read more about some of my research.
Office: 441 PHO
Email: bkulis [at] bu [dot] edu