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
A Ph.D. Student at Johns Hopkins University, I am interested in intelligent surgical systems based on explorative computer vision and deep reinforcement learning that directly improve patient outcomes. I am a member of the Advanced Robotics and Computationally Augmented Environments (ARCADE) research group and the Computational Interaction and Robotics Laboratory (CIRL).
08/2019 - present
Ph.D., Computer Science, Johns Hopkins University, Baltimore, MD, USA.
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.
08/2020 - present
Research assistant, Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
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.
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.
Best Graduate Project Award, Computer Integrated Surgical Systems and Technology course,
Johns Hopkins University, USA.
COVID-19 Dataset Award, Kaggle.
- For our county-level dataset in [M-1].
- Project: Enriching Unsupervised Feature Learning via Intermediate Subtasks.
- With Michael Peven, Shaoyan Pan, and Matthew Pittman.
My publication list is also available on Google Scholar. Asterisk (*) indicates equal contribution.
Peer-reviewed Journal Articles
A. Hundt, B. 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:
S. Ambrogio, P. Narayanan, H. Tsai, R. M. Shelby, I. Boybat, C. di Nolfo, S. Sidler,
M. Giordano, M. Bodini, N. Farinha, B. 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:
Peer-reviewed Conference Papers
X. Liu*, B. D. Killeen*, A. Sinha, M. Ishii, G. Hager, R. Taylor, M. Unberath. Neighborhood Normalization for Robust Geometric Feature Learning. To appear in The IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021.
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.
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.
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:
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.
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.
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.
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.
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
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
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.
01/2021 - present
- Max Judish, Johns Hopkins University, Baltimore, MD, USA.
08/2020 - present
- Shreya Chakraborty, Johns Hopkins University, Baltimore, MD, USA.
12/2019 - 03/2020
- Philipp Nikutta, Johns Hopkins University, Baltimore, MD, USA.
2020 - present
Graduate Student Committee Representative, Laboratory for Computational Sensing and Robotics, Baltimore, MD, USA.
Volunteer Instructor, CompileHer, Chicago, IL, USA.
- - IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- - Nature Scientific Data
- Vision as Bayesian Inference
- Reliable Software Systems
- Theory of Computation
- Parallel Programming
- Nonlinear Optimization II
- Computer Integrated Surgery II
- Computer Integrated Surgery I
- Deep Learning
- 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
- Quantum Mechanics I \& II
- Intermediate Mechanics
2020 - present
- IEEE Graduate Student Member
In my spare time, I enjoy running, climbing, cycling, and making visual art. I also write creatively:
- Creative nonfiction: benjamindkilleen.com/blog
- Science Fiction: manuscript by request.
This document is available
Last updated: March 2021