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 “Influencing Trust in Cybersecurity by Hacking the Human Factor”.
Dr. Ben D. Sawyer, Scientist at MIT’s AgeLab, is known for investigating successes and failures of attention. He specializes in in human-machine systems, with expertise in automotive UX, consumer and warfighter electronics, and cybersecurity. Through simulation and biosignal analysis he traces the unfolding of human error, and writes about how engineering design can help. His models and algorithms power trustworthy machines that work to keep their human partners attentive and informed. His design tips and recommendations are used by Fortune 500 companies. His work has been covered by Forbes, Reuters, Fast Company, and The BBC, and more.
Dr. Sawyer 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. He has received The Human Factors Prize, for Cybersecurity research, The K.U. Smith Award for Best HF/E Paper, for groundbreaking consumer electronics work investigating driving distraction and Google Glass, and a UCF College of Sciences Outstanding Dissertation Award for work investigating the applied psychophysics of warfighter multitasking. 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.
Ben volunteers helping people to learn to repair their own electronics. In his leisure time he enjoys adventure travel with his wife, swimming, sailing the Charles, and building things. 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.