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Jeremy Straub

Assistant Professor of Computer Science and Director, NDSU Institute for Cyber Security Education and Research, North Dakota State University-Main Campus

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About Jeremy

Straub's research focuses on technology development and technology policy. Overarching themes in his writings include artificial intelligence, cybersecurity, 3D printing and aerospace. Straub serves as a member of the North Dakota K-20W initiative, an all-of-government initiative which seeks to ensure that students have access to computer and cyber science education throughout all levels of education (from kindergarten to the doctoral level).

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In the News

Opinion: "Warren’s Ransomware Bill Victimizes Targets To Collect Data," Jeremy Straub (with Zahid Anwar), The Hill, October 13, 2021.
Opinion: "The U.S. Has Lots to Lose and Little to Gain from Banning TikTok and WeChat," Jeremy Straub, Fast Company, August 31, 2020.
Opinion: "3D-Printed Firearms Could be Deadlier for Shooters Than for Targets," Jeremy Straub, Slate, January 8, 2019.
Opinion: "Your Next Pilot Could be Drone Software," Jeremy Straub, CNN, April 20, 2018.
Opinion: "Does Regulating Artificial Intelligence Save Humanity or Stifle Innovation?," Jeremy Straub, Business Standard, October 23, 2017.
Opinion: "Artificial Intelligence Cyberattacks are Coming--But What Does That Mean?," Jeremy Straub, GCN, August 28, 2017.


"Defining, Evaluating, Preparing or and Responding to a Cyber Pearl Harbor" Technology in Society 65 (2021).

Discusses what a "Pearl Harbor-level" cybersecurity incident would be and how nations can prepare for and respond to it.

"Cyber-Mitigation: Cybersecurity Emergency Management" Journal of Emergency Management 18, no. 6 (2020): 463-473.

Discusses how to prepare for cybersecurity to be part of the emergency management framework with a particular focus on mitigation activities.

"Expert System Gradient Descent Style Training: Development of a Defensible Artificial Intelligence Technique" Knowledge-Based Systems 228 (2021).

Proposes a new form of artificial intelligence which avoids bias and non-causal associations through the combination of two existing techniques, neural networks and rule-fact expert systems.

"In Search of Technology Readiness Level (TRL) 10" Aerospace Science and Technology 46 (2015): 312-320.

Describes how the TRL scale can be augmented with another level to be ready for recurrent space activities.

"Analysis of the Changing Demographics of Computing Doctoral Degree Recipients at U.S. Universities and the Implications of Change" ACM Inroads 12, no. 1 (2021): 26-36.

Discusses changes in PhD degree recipients in the United States over time and analyzes what the implications of these changes are.