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 interested in intelligent surgical systems based on simulated training environments and deep learning systems 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).

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, Minor in Physics, 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

04/2021 Best Presentation, Reliable Software Systems course, Johns Hopkins University, USA.

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. 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. 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-4 J. D. Opfermann*, B. D. Killeen*, C. Bailey, M. Khan, A. Uneri, K. Suzuki, M. Armand, F. Hui, A. Krieger, M. Unberath. Feasibility of a Cannula-mounted Piezo Robot for Image-guided Vertebral Augmentation: Toward a Low Cost, Semi-autonomous Approach. To appear: IEEE International Conference on Bioinformatics and Bioengineering, 2021.

C-3 X. Liu*, B. D. Killeen*, A. Sinha, M. Ishii, G. Hager, R. Taylor, M. Unberath. Neighborhood Normalization for Robust Geometric Feature Learning. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021.

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.

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

08/2019 - present Computer Integrated Surgery, Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.

• With Russ Taylor and Emad Boctor.

03/2019 - 06/2019 Machine Learning and Large Scale Data Analysis, Department of Computer Science, University of Chicago, Chicago, IL, USA.

• With 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

06/2021 - present Mentor, Polygence.

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

• We have covered topics such as differential calculus, neural networks, computer vision, natural language processing, and functional programming.

Supervision

I have supervised a number of talented masters and undergraduate students on research projects and theses:

05/2021 - present

• Nethra Venkatayogi, The University of Texas at Austin, Austin, TX, USA.

01/2021 - 08/2021

• Max Judish, Brown University, Providence, RI, USA.

08/2020 - 09/2021

• Shreya Chakraborty, Johns Hopkins University, Baltimore, MD, USA.

12/2019 - 03/2020

• Philipp Nikutta, Technical University of Munich, Munich, Germany. (Now at Argo AI.)

Service

06/2021 - present Family Member, Thread, Baltimore, MD, USA.

• Thread builds long-term relationships with high schoolers in Baltimore City.

09/2020 - present Graduate Student Committee Representative, Laboratory for Computational Sensing and Robotics, Baltimore, MD, USA.

• Head of Student Resources

2019 Volunteer Instructor, CompileHer, Chicago, IL, USA.

Peer Review

2021

• - IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

2020

• - Nature Scientific Data

Selected Coursework

Graduate

GPA: 3.82

• 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

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
• Quantum Mechanics I \& II
• Intermediate Mechanics
• Electronics
• Wizards

*Graduate level.

Memberships

2020 - present

• IEEE Graduate Student Member

Extracurricular

In my spare time, I enjoy climbing, running, and making visual art. I also write creatively:

• Creative nonfiction: benjamindkilleen.medium.com
• Science fiction.

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: September 2021