Hacking the Human Factor: The Prevalence Paradox in Cybersecurity is the winner of the 2017 Human Factors Prize. Request a prepress copy here.
I am PI on a 2018 AFOSR YIP grant for my proposal “Influencing Trust in Cybersecurity by Hacking the Human Factor”.
Dr. Ben D. Sawyer is a Postdoctoral Associate at MIT’s AgeLab. He studies successes and failures of attention in human-machine systems, investigates the foundations of human error in cognition, and writes about how design can tip the balance. He holds a PhD in Applied Experimental and Human Factors Psychology advised by Peter Hancock, as well as an MS in Industrial Engineering advised by Waldemar Karwowski. Dr Sawyer’s work has been covered by Forbes, Reuters, Fast Company, and The BBC, among others.
Dr. Sawyer is the recipient of paper awards including The Human Factors Prize, The K.U. Smith Award for Best HF/E Paper, for work with driving distraction and Google Glass, and a College of Sciences Outstanding Dissertation Award for work investigating the applied psychophysics of warfighters. A two-time Repperger Research Fellow with the Air Force Research Laboratory (AFRL), he performed research with the 711th Human Performance Wing in both their Applied Neuroscience and Battlefield Acoustics (BATMAN group) divisions. Dr. Sawyer has been the recipient of design and research design awards for both Engineering and Psychology initiatives, including an APA Division 19 Student Research Design Award, the Intelligence Community Academic Excellence Scholarship, and the NHTSA Enhanced Safety of Vehicles Design Award.
In addition to scientific pursuits, Ben is a member of the entrepreneurial business community. He enjoys history, philosophy, science fiction and fantasy, virtual reality, adventure travel, swimming, sailing the Charles, and hackathons. He does not enjoy writing about himself in the third person, and will now stop.
If you would like to get in touch, please do so at the email in my CV, available at the top of the page.
If you would like to give me advice, I provide this anonymous feedback form.