Alexander S. Broad

Doctoral Candidate,
Northwestern University

I am a graduate student in the Computer Science department at Northwestern University. I work in the Assistive & Rehabilitation Robotics Laboratory with Professor Brenna Argall. Previously, I received a B.A. in Applied Mathematics and P-N-P (Philosophy-Neuroscience-Psychology) from Washington University in St. Louis. I also graduated with a M.S. in Computer Science from the same university, during which time I researched Machine Learning and Robotics with Professor Bill Smart and Dr. Tom Erez. Before returning to graduate school, I worked as an Associate Staff member at MIT Lincoln Laboratory where I applied Machine Learning techniques to large datasets in support of systems for US intelligence analysts.

You can find my curriculum vitæ here.

Publications

Learning Models for Shared Control of Human-Machine Systems with Unknown Dynamics
Broad, A., Murphey, T., Argall, B.
Robotics: Science and Systems (RSS). 2017.
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Real-Time Natural Language Corrections for Assistive Robotic Manipulators
Broad, A., Arkin, J., Ratliff, N., Howard, T., Argall, B.
International Journal of Robotics Research (IJRR). 2017.
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Geometry-Based Region Proposals for Real-Time Robot Detection of Tabletop Objects
Broad, A., Argall, B.
arXiv CORR abs/1703.04665. 2017.
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An Empirical Analysis of Methods for Learning Robot Kinematics from Demonstration
Broad, A., Gopinath, D., Murphey, T., Argall, B.
Midwest Robotics Workshop (MWRW). 2017.
Trust Adaptation Leads to Lower Control Effort in Shared Control of Crane Automation
Broad, A., Derry, M., Schutlz, J., Murphey, T., Argall, B.
Robotics and Automation Letters (RA-L). 2016. Also presented at the Conference on Automation Science and Engineering (CASE). 2016.
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Towards Real-Time Natural Language Corrections for Assistive Robots
Broad, A., Arkin, J., Ratliff, N., Howard, T., Argall, B.
RSS Workshop on Model Learning for Human-Robot Communication. Oral. 2016.
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Geometry-Based Region Proposals for Accelerated Image-Based Detection of 3D Objects
Broad, A., Argall, B.
RSS Workshop on Deep Learning. Oral. 2016.
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Inverted Trust Improves Shared Control of Complex Dynamic Systems
Broad, A., Derry, M., Schutlz, J., Murphey, T., Argall, B.
RSS Workshop on Social Trust in Autonomous Robots. Oral. 2016.
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Path Planning under Interface-Based Constraints for Assistive Robotics
Broad, A., Argall, B.
International Conference on Automated Planning and Scheduling (ICAPS). 2016.
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Probabilistic Models for Real-Time Natural Language Corrections to Assistive Robotic Manipulators
Arkin, J., Broad, A., Howard, T., Argall, B.
Midwest Robotics Workshop (MWRW). 2016.
Assistive Robotic Manipulation through Shared Autonomy and a Body-Machine Interface
Jain, S., Farshchiansadegh, A., Broad, A., Abdollahi, F., Mussa-Ivaldi, F., Argall, B.
IEEE International Conference on Rehabilitation Robotics. 2015.
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Feature-Rich Plagiarism Detection Using Structured Prediction
Broad, A., King, D., Asarina, A.
Tech Report. 2014.
Generating Muscle Driven Arm Movements Using Reinforcement Learning
Broad, A.
Master’s Thesis, Washington University in St. Louis. 2011.
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Projects

My research has included work in areas as diverse as Reinforcement Learning, Optimal Control, Natural Language Processing, Computer Vision, Structured Prediction, Feature Learning, 3D Object Detection and Robotic Path Planning. In addition to my listed publications, some examples of my other work is listed below.

Teaching Experience

Northwestern's Introduction to Robotics Laboratory (T.A. - Fall, 2015)
Wash. U's Programming Systems and Languages (T.A. - Fall 2010)
Wash. U's Logic and Discrete Mathematics (T.A. - Spring 2010)
Mentor for undergraduate student through Northwestern's URG program.

Selected Honors

Todd M. and Ruth Warren Fellowship. Northwestern University. (2014 - 2019)

Highly selective fellowship program for graduate studies in the Department of Electrical Engineering and Computer Science.


Walter P. Murphy Fellowship. Northwestern University. (2014 - 2015)

Fellowship program for the first year of doctoral studies.


Irene & Eric Simon Brain Research Fellowship. New York University. (2008)

Fellowship program "for bright, motivated undergraduate or first year graduate or medical students."