Austin O'Brien
Austin O'Brien
Associate Professor/Coordinator for PhD Computer Science & MS Computer Science
Department
The Beacom College of Computer & Cyber Sciences
Education
Ph.D., Computational Statistics - South Dakota State University
M.S., Computer Science - South Dakota State University
B.S., Computer Science - South Dakota State University
Biography
Austin O’Brien is an Associate Professor of Computer Science and Artificial Intelligence and Program Coordinator for the MS in Computer Science. He teaches courses at the undergraduate and graduate levels in the areas of design and analysis of computer algorithms, algorithms and optimization, data structures, machine learning fundamentals, machine learning for cybersecurity, and reinforcement learning.
Contact
Office Location: East Hall
Phone: (605) 256-5838
Email
Website
O'Brien teaches courses at the undergraduate and graduate levels. His focus is in the following areas of design and analysis of computer algorithms, algorithms and optimization, data structures, computer science II, machine learning fundamentals, machine learning for cybersecurity, and reinforcement learning.
He also takes an interest in teaching outside of standard college courses, providing instruction on artificial intelligence in cybersecurity for CAE faculty, and serving as an instructor for a variety of youth camps, including GenCyber Coed Camp, CybHER Jr. High Girls Camp, and Antigua Youth STEM Camp.
Many of O'Brien's research endeavors involve working with students. He recently served as a research mentor for the AI Sweden Exchange Summer Research in 2022. Currently, he serves as an advisor for DSU Honors College Thesis, Graduate Student Research Initiative, and the undergraduate Student Research Initiative.
- O'Brien is the 2020 recipient of the Ernest M. Teargarden Award for Excellence in Teaching.
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Nelson, T. O’Brien, A. Noteboom, C. (2023). Machine Learning Applications in Malware Classification:
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A Meta-analysis Literature Review. International Journal on Cybernetics & Informatics, Vol. 12, No. 1, February 2023.
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Ofori, M., El-Gayar, O., O'Brien, A. and Noteboom, C. (2022). A deep learning model compression and ensemble approach for weed detection. Hawaii International Conference on System Sciences.
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Chaudhary, S., O’Brien, A., Xu, S. (2020) Automated Post-Breach Penetration Testing through Reinforcement Learning. 2020 IEEE Conference on Communications and Network Security, Avignon, France, pp. 1-2
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Feb. 2023. “Artificial Intelligence: Cyber, Agriculture, and Beyond,” East River Electric Energize Forum. Sioux Falls, SD.
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Aug. 2022. “The Current State of Artificial Intelligence and Where We’re Going,” Danebod Folk Meeting. Tyler, MN.
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Nov. 2019. “Alert Prioritization and Strengthening: Towards an Industry Standard Priority Scoring System for IDS Analysts Using Open-Source Tools and Models of Machine Learning.” CLEAR Cyber
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Leaders Conference. Sioux Falls, SD. Research Paper Presentation.
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Jul. 2019. “AI & Technology – Future of Workforce,” Brookings Area Workforce Conference. Brookings, SD.