Brian Kulis


Associate Professor
Boston University
Boston, MA

Introduction

I am an associate professor at Boston University, with appointments in the:

  • Department of Electrical and Computer Engineering
  • Department of Computer Science
  • Division of Systems Engineering
  • Faculty of Computing and Data Sciences

    • Previously, I spent four years (2019-2023) also at Amazon as an Amazon Scholar in Alexa AI. 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. 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 also focus on applications with audio and visual data. 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

  • (Senior) Area Chair: AAAI 2025
  • (Senior) Area Chair: NeurIPS 2024
  • (Senior) Area Chair: ICML 2024
  • (Senior) Area Chair: AISTATS 2024
  • (Senior) Area Chair: AAAI 2024
  • Area Chair: ICLR 2024
  • (Senior) Area Chair: NeurIPS 2023
  • Area Chair: ICML 2023
  • (Senior) Area Chair: AISTATS 2023
  • (Senior) Area Chair: AAAI 2023
  • Area Chair: ICLR 2023
  • Area Chair: NeurIPS 2022
  • Area Chair: ICML 2022
  • Area Chair: AISTATS 2022
  • (Senior) Area Chair: AAAI 2022
  • Area Chair: ICLR 2022
  • Area Chair: NeurIPS 2021
  • Area Chair: ICML 2021
  • 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, 2024. Advanced Data Structures and Algorithms
  • Spring, 2024. Deep Learning
  • Fall, 2023. Introduction to Machine Learning
  • Spring, 2023. Deep Learning
  • Fall, 2022. Introduction to Machine Learning
  • Spring, 2022. Deep Learning
  • Fall, 2021. Introduction to Machine Learning
  • Spring, 2021. Introduction to Machine Learning
  • 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

  • Sadie Allen, PhD student
  • Christopher Liao, PhD student
  • David Liu, PhD student

    • Former Group Members

  • Dallin Gordon, MS.
  • Kubra Cilingir, PhD. After BU: Amazon
  • Ali Siahkamari, PhD. After BU: JP Morgan Chase
  • Andrew Cutler, PhD. After BU: REACT Neuro
  • Xide Xia, PhD. After BU: Facebook AI
  • Natasha Frumkin, BS. After BU: PhD student, UT Austin
  • Tayler Pauls, MS. After BU: Startup, then Amazon
  • Vijay Thakkar, BS. After BU: PhD student, Georgia Tech.
  • Rachel Manzelli, BS. After BU: Marlo Inc. then Modulate AI
  • 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 (old projects, not updated).

    Contact Info

    Office: 441 PHO

    Email: bkulis [at] bu [dot] edu