Associate Professor
Department of Electrical and Computer Engineering
Department of Computer Science
Division of Systems Engineering
Boston University
Boston, MA
What's New
New paper accepted to NeurIPS 2020: "Learning to Approximate a Bregman Divergence" 3 papers accepted to Interspeech 2020 (links coming soon) New paper accepted to ECCV 2020: "Joint Bilateral Learning for Real-time Universal Photorealistic Style Transfer" (with Xide Xia, Meng Zhang, Tianfan Xue, Zheng Sun, Hui Fang, and Jiawen Chen). Arxiv link here New paper accepted to ICML 2020: "Deep Divergence Learning" (with Kubra Cilingir and Rachel Manzelli). Arxiv link here New paper accepted to ICML 2020: "Piecewise Linear Regression via a Difference of Convex Functions" (with Ali Siahkamari, Aditya Gangrade, and Venkatesh Saligrama) I am spending the academic year 2019-20 at Amazon, working on machine learning for Alexa. New paper on arXiv (with Ali Siahkamari, David Castanon, and Venkatesh Saligrama): "Learning Bregman Divergences," posted here.
Introduction
I am an associate professor in the Department of Electrical and Computer Engineering and the Department of Computer Science at Boston University, as well sa a core member of the Division of Systems Engineering. I also work at Amazon as an Amazon Scholar in Alexa. From 2015-2018 I was the Peter J. Levine Career Development Assistant Professor in ECE and CS at Boston University, and 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.
Curriculum Vitae [pdf]
Recent Service
Area Chair: AISTATS 2021 (Senior) Area Chair: AAAI 2021 Area Chair: ICLR 2021 Area Chair: NeurIPS 2020 Area Chair: ICML 2020 Area Chair: AISTATS 2020 (Senior) Area Chair: AAAI 2020 Area chair: NeurIPS 2019 Area chair: ICML 2019 Area chair: AISTATS 2019 Area chair: NIPS 2018 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
Publications by Type
Publications Chronologically
Google scholar profile
Teaching
Fall, 2020. Deep Learning Spring, 2019. Introduction to Machine Learning Fall, 2018. Deep Learning 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
Current Students
Andrew Cutler, PhD student Kubra Cilingir, PhD student Ali Siahkamari, PhD student Xide Xia, PhD student
Former Group Members
Natasha Frumkin, BS. After BU: PhD student, UT Austin Tayler Pauls, MS. After BU: Stealth Startup Vijay Thakkar, BS. After BU: Graduate student, Georgia Tech. Rachel Manzelli, BS. After BU: Marlo Inc. Siva Sankarapandian, MS. After BU: Proscia Inc. Robert Finn, PhD. After OSU: Assistant Professor, St. Peters University. Anirban Roychowdhury, PhD. After OSU: Research Scientist at Facebook. Ke Jiang, PhD. After OSU: Data Scientist at Microsoft. Xiangyang Xhou, MS. After OSU: Google. Lizzy Burl, BS. After OSU: Google. Jiaxin Zhang, MS. After OSU: Google. Ye Liu, MS. After OSU: PhD student, University of Michigan. Siddharth Singh, MS. After OSU: IBM, then Amazon.
Research Details
Click here to read more about some of my research.
Contact Info
Office: 441 PHO
Email: bkulis [at] bu [dot] edu