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.

09/2015 - 06/2019 B.A., Computer Science with Honors (Physics Minor), University of Chicago, Chicago, IL, USA.

Research Experience

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.

03/2018 - 08/2019 Research assistant, Department of Computer Science, University of Chicago, Chicago, IL, USA.

Professional Experience

06/2020 - 07/2020 Computer Vision / AI Intern, Applied Research, Intuitive Surgical Inc., Sunnyvale, CA, USA.

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.

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.

12/2019 Intuitive Surgical Best Project Award, Deep Learning course, Johns Hopkins University, USA.

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-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

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.”

Grading

01/2019 - 08/2019 Department of Computer Science, University of Chicago, Chicago, IL, USA

Tutoring

06/2020 - present Topics in Computer Science, Machine Learning, Baltimore, MD, USA.

Supervision

08/2019 - present 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.

Selected Coursework

Graduate

Undergraduate

GPA: 3.81

*Graduate level.

Memberships

2020 - present

Interests

Metadata

This document is available

Created based on markdown-cv by Eliseo Papa with styles based on David Whipp.

MIT License.


Last updated: November 2020