Benjamin D. Killeen
Ph.D. Student, Johns Hopkins University
Department of Computer Science
3400 N Charles St
Baltimore, MD 21218, USA
Benjamin D. Killeen - 0000-0003-2511-7929 - benjamindkilleen - @bdkilleen
Summary
A Ph.D. Student at Johns Hopkins University, I am a member of the Advanced Robotics and Computationally Augmented Environments (ARCADE) research group and the Computational Interaction and Robotics Laboratory (CIRL). My research interests include computer vision, reinforcement learning, and domain generalization, with a focus on applications in robotic manipulation, medical imaging, and clinician-centered surgical robotics.
Education
08/2019 - present
Ph.D., Computer Science, Johns Hopkins University, Baltimore, MD, USA.
- With Mathias Unberath and Gregory D. Hager.
09/2015 - 06/2019
B.A., Computer Science with Honors (Physics Minor), University of Chicago, Chicago, IL, USA.
- Thesis: Starting from Scratch: Deep Learning for Novel Scientific Image Analysis
- With Gordon Kindlmann.
Research Experience
08/2020 - present
Research assistant, Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
- With Mathias Unberath, Gregory D. Hager.
08/2019 - 06/2020
Research Assistant, Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD, USA.
- With Gregory D. Hager, Mathias Unberath, and Russel Taylor.
- Recipient: LCSR Fellowship for Outstanding Incoming Ph.D. Students.
03/2018 - 08/2019
Research assistant, Department of Computer Science, University of Chicago, Chicago, IL, USA.
- With Gordon Kindlmann.
Professional Experience
06/2020 - 07/2020
Computer Vision / AI Intern, Applied Research, Intuitive Surgical Inc., Sunnyvale, CA, USA.
- With Omid Mohareri.
06/2018 - 08/2018
Software Development Intern, Cognitive Computing, Epic Systems, Verona, WI, USA.
06/2017 - 08/2017
Research Intern, IBM Research - Almaden, San Jose, CA, USA.
- With Geoffrey Burr.
Selected Honors
05/2020
Best Graduate Project Award, Computer Integrated Surgical Systems and Technology course,
Johns Hopkins University, USA.
04/2020
COVID-19 Dataset Award, Kaggle.
- For our county-level dataset in [M-1].
12/2019
Intuitive Surgical Best Project Award,
Deep Learning course, Johns Hopkins University, USA.
- Project: Enriching Unsupervised Feature Learning via Intermediate Subtasks.
- With Michael Peven, Shaoyan Pan, and Matthew Pittman.
Publications
My publication list is also available on Google Scholar. *Asterisk indicates equal contribution.
Peer-reviewed Journal Articles
J-2
A. Hundt, B. D. Killeen, H. Kwon, C. Paxton, GD Hager. “Good Robot!”: Efficient
Reinforcement Learning for Multi-Step Visual Tasks with Sim to Real Transfer. IEEE Robotics and
Automation Letters, vol. 5, no. 4, pp. 6724–6731, Oct. 2020. doi:
10.1109/LRA.2020.3015448.
J-1
S. Ambrogio, P. Narayanan, H. Tsai, R. M. Shelby, I. Boybat, C. di Nolfo, S. Sidler,
M. Giordano, M. Bodini, N. Farinha, B. D. Killeen, C. Cheng, Y. Jaoudi,
G. W. Burr. Equivalent-accuracy accelerated neural-network training using analogue
memory. Nature, vol. 558, no. 7708, p. 60, Jun. 2018. doi:
10.1038/s41586-018-0180-5.
Peer-reviewed Conference Papers
C-2
C. Gao, X. Liu, W. Gu, B. D. Killeen, M. Armand, R. Taylor, M. Unberath. Generalizing
Spatial Transformers to Projective Geometry with Applications to 2D/3D
Registrationc. MICCAI, 2020, arxiv:2003.10987.
C-1
X. Liu, Y. Zhang, B. Killeen, M. Ishii, G. Hager, R. Taylor, M. Unberath. Extremely Dense Point
Correspondences using a Learned Feature Descriptor. Proceedings of the IEEE/CVF Conference on
Computer Vision and Pattern Recognition, pp. 4847-4856, 2020.
Preprints
M-3
X. Liu*, B. D. Killeen*, A. Sinha, M. Ishii, G. Hager, R. Taylor, M. Unberath. Neighborhood Normalization for Robust Geometric Feature Learning. Submitted to The IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021.
M-2
J. Y. Wu*, B. D. Killeen*, P. Nikutta, M. Thies, A. Zapaishchykova, S. Chakraborty,
M. Unberath. Changes in Reproductive Rate of SARS-CoV-2 Due to Non-pharmaceutical Interventions in
1,417 U.S. Counties. medRxiv preprint, Jun. 2020, doi:
10.1101/2020.05.31.20118687.
M-1
B. D. Killeen*, J. Y. Wu*, K. Shah, A. Zapaishchykova, P. Nikutta, A. Tamhane, S. Chakraborty,
J. Wei, T. Gao, M. Thies, M. Unberath. A County-level Dataset for Informing the United States’
Response to COVID-19. arXiv preprint, 2020, arXiv:2004.00756.
Patents
P-1
G. W. Burr and B. D. Killeen. 2020. Efficient Processing of Convolutional Neural Network
Layers Using Analog-memory-based Hardware. 20200117986, filed March 25, 2019, and issued April
16, 2020, uspto.report/patent/app/20200117986.
Selected Press
2020
Dziarkach, Andrei. “Details with Andrei Dziarkach.” Voice of America. November 21, 2020
Accessed November 26, 2020. golosameriki.com/a/detali/5671254.html.
BBC. “Dog Training Technique Helps Robot Learn and Other News.” BBC News. October 30, 2020. Accessed October 31, 2020. bbc.com/news/av/technology-54645279.
Rosso, Cami. “New AI Trains Robots like Dogs.” Psychology Today. October 28, 2020. Accessed October 28, 2020. psychologytoday.com/us/blog/the-future-brain/202010/new-ai-trains-robots-dogs.
Heater, Brian. “Teaching Robots through Positive Reinforcement.” TechCrunch. October 26, 2020. Accessed October 28, 2020. techcrunch.com/2020/10/26/teaching-robots-through-positive-reinforcement/.
Rosen, Jill. “Dog Training Methods Help JHU Teach Robots to Learn New Tricks.” The Johns Hopkins University Hub. The Johns Hopkins University, October 26, 2020. hub.jhu.edu/2020/10/26/positive-reinforcementfor-robots.
Teaching
Assistant Teaching
03/2019 - 06/2019
Machine Learning and Large Scale Data Analysis, Department of Computer Science, University of Chicago, Chicago, IL, USA
- With Prof. Yali Amit.
- Wrote supplementary course material and held weekly lab sessions.
Selected review: “Ben was incredibly patient during office hours and always responsive to student questions. In addition, he often presented demos during office hours or showed easier ways to handle the homework assignments; both were very helpful.”
- More reviews available at benjamindkilleen.com/teaching/2019-spring-lsda
Grading
01/2019 - 08/2019
Department of Computer Science, University of Chicago, Chicago, IL, USA
- - Scientific Visualization
- - Introduction to Computer Science I
- - Introduction to Computer Science II
Tutoring
06/2020 - present
Topics in Computer Science, Machine Learning, Baltimore, MD, USA.
- I tutor young people (middle- and high-school age) who are interested in CS and ML.
- More info at benjamindkilleen.com/teaching/2020-tutoring.
Supervision
01/2020 - present
Max Judish, Johns Hopkins University, Baltimore, MD, USA.
08/2020 - 12/2020
Shreya Chakraborty, Johns Hopkins University, Baltimore, MD, USA.
12/2019 - 03/2020
Philipp Nikutta, Johns Hopkins University, Baltimore, MD, USA.
Service
2020 - present
Graduate Student Committee Representative, Laboratory for Computational Sensing and Robotics, Baltimore, MD, USA.
2019
Volunteer Instructor, CompileHer, Chicago, IL, USA.
Peer Review
2021
- - Conference on Computer Vision and Pattern Recognition
2020
- - Nature Scientific Data
Selected Coursework
Graduate
- Theory of Computation
- Parallel Programming
- Nonlinear Optimization II
- Computer Integrated Surgery II
- Computer Integrated Surgery I
- Deep Learning
Undergraduate
GPA: 3.81
- Unsupervised Learning*
- Computer Vision
- Machine Learning and Large Scale Data Analysis
- Operating Systems
- Honors Combinatorics
- Honors Algorithms
- Honors Discrete Mathematics
- Scientific Visualization
- Programming Languages
- Networks and Distributed Systems
*Graduate level.
Memberships
2020 - present
- IEEE Graduate Student Member
Extracurricular
- Creative nonfiction: benjamindkilleen.com/blog
- Science Fiction: manuscript available by request.
Metadata
This document is available
- - online: benjamindkilleen.com/markdown-cv.
- - as a PDF: benjamindkilleen.com/files/cv.pdf.
Created based on markdown-cv by Eliseo Papa with styles based on David Whipp.
Last updated: November 2020