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Jonathan K. Millen
Rockport, Massachusetts

Jonathan K. Millen retired from his position as senior principal computer scientist in the cybersecurity division of The MITRE Corporation in 2012.

Jon obtained his PhD in mathematics from Rensselaer Polytechnic Institute in 1969; it came a year before RPI established a doctoral degree program in computer science. His initial programming experience was earlier, on Harvard’s UNIVAC II, writing binary machine language instructions on a form that was sent to punched-card machine typists. Working at The MITRE Corporation, in 1970, he was a member of a team that developed a COBOL program to produce Grade 2 Braille output for the MIT Sensory Aids Evaluation and Development Center. He has also used a Symbolics Lisp machine, acquired for MITRE artificial intelligence (AI) projects.

His work in computer security has involved various kinds of formal, symbolic analysis. It began with methods of information flow analysis to detect covert channels in operating system security kernels. These systems were being developed to satisfy government evaluation criteria for high-confidence multilevel security, as specified in the so-called Orange Book published by the National Computer Security Center in 1985. Jon was one of the authors of the subsequent Red Book for network security. During this period, he also developed a symbolic analysis tool to find security vulnerabilities in cryptographic key distribution protocols.

In 1988, Jon founded the Computer Security Foundations Workshop for the IEEE Computer Society. This workshop became the CSF Conference in 2008. He served terms as chair of the IEEE Technical Committee on Security and Privacy, and as general chair of its annual symposium. He cofounded the Journal of Computer Security, in 1991, and was co-editor-in-chief until retirement.

From 1997 to 2004, Jon worked at the SRI International Computer Science Laboratory as a senior computer scientist. It was there that he developed an improved protocol analyzer: the constraint solver. After returning to MITRE in 2004, he joined a team working on analysis of trusted platform modules. He was an editorial board member of ACM Transactions on Information and System Security from 1997 to 2006, and received an ACM SIGSAC Outstanding Innovation award in 2009.

Jon has been a reviewer for Computing Reviews since 1993.


 Fifty years of P vs. NP and the possibility of the impossible
Fortnow L., Fortnow L. Communications of the ACM 65(1): 76-85, 2021.  Type: Article

The P versus NP problem is one of the most fundamental and well-known unresolved questions in computer science. In comparison with the 2009 Communications article by the same author [1], the current survey is less about progress...


General video game artificial intelligence
Pérez Liébana D., Lucas S., Gaina R., Togelius J., Khalifa A., Liu J., Morgan & Claypool, San Rafael, CA, 2020. 192 pp.  Type: Book (978-1-681736-46-4)

It’s a jungle out there: exploding asteroids, fire-breathing dragons, and maniacal cultists, among other lethal threats. What and how can you teach newborn babies, somehow fitted with a hero’s body and magical weapo...


 The effects of mixing machine learning and human judgment
Vaccaro M., Waldo J. Communications of the ACM 62(11): 104-110, 2019.  Type: Article

Automated risk assessment systems are often used in situations that require human judgment. One motivation for doing this is to remove human bias. Even when the automated system has been shown to be more accurate than human assessments...


 A survey of methods for explaining black box models
Guidotti R., Monreale A., Ruggieri S., Turini F., Giannotti F., Pedreschi D. ACM Computing Surveys 51(5): 1-42, 2019.  Type: Article

Computerized decision support systems have significant social consequences, and yet they are capable of mistakes or bias. Can an autonomous driving system be trusted, for example, when its visual scene recognition was implemented as a ...


Minimally sufficient conditions for the evolution of social learning and the emergence of non-genetic evolutionary systems
Gonzalez M., Watson R., Bullock S. Artificial Life 23(4): 493-517, 2017.  Type: Article

Social learning is defined here as the imitation of behaviors exhibited by other members of a population. In a population of humans, we might be talking about memes, or more broadly, culture. The authors mention animal examples such as...


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