Benjamin D. Killeen

Ph.D. Student, Johns Hopkins University

Department of Computer Science
3400 N Charles St
Baltimore, MD 21218, USA

killeen@jhu.edu

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.

MIT License.

Last updated: November 2020