I am the co-chair for the annual BU Data Science Day in January, 2018.
New paper (with Xide Xia): "W-Net: A Deep Model for Fully Unsupervised Image Segmentation" posted here.
Anirban has defended his PhD thesis! Congratulations, Anirban!
Video of Rachel Manzelli (recipient of a Lutchen fellowship) talking about her work in deep learning for music here.
New paper (with Ben Usman and Kate Saenko): "Stable Distribution Alignment using the Dual of the Adversarial Distance" posted here.
New paper (with Trevor Campbell and Jon How): "Dynamic Clustering Algorithms via Small-Varaince Asymptotics of Markov Chain Mixture Models" posted here.
Ke has defended his PhD thesis! Congratulations, Ke!
New paper (with Ke Jiang and Suvrit Sra) published at AISTATS 2017: "Combinatorial Topic Models using Small-Variance Asymptotics."
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: ICML 2018
Senior Program Committee: 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, 2018. Advanced Data Structures and Algorithms
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
Robert Finn, PhD student (now tenure track at St. Peters University)
Andrew Cutler, PhD student
Kubra Cilingir, PhD student
Ali Siahkamari, PhD student
Xide Xia, PhD student
Rachel Manzelli, Undergraduate
Vijay Thakkar, Undergraduate
Former Group Members
Anirban Roychowdhury, PhD, 2017. After graduation: Research Scientist at Facebook.
Ke Jiang, PhD, 2017. After graduation: Data Scientist at Microsoft.
Xiangyang Xhou, MS, 2016. After graduation: Google.
Lizzy Burl, BS, 2015. After graduation: Google.
Jiaxin Zhang, MS, 2014. After graduation: Google.
Ye Liu, MS, 2014. After graduation: PhD student, University of Michigan.
Siddharth Singh, MS, 2013. After graduation: IBM, then Amazon.
Click here to read more about some of my research.
Office: 441 PHO
Email: bkulis [at] bu [dot] edu