Stephen Flynn, professor of political science; co-director, Kostas Research Institute for Homeland Security
Flynn is a pioneer in advocating an approach to security based on resilience: because we can never completely eliminate the threat of natural and man-made disasters, we need to focus on mitigating the consequences.
For example, he has led post-9/11 efforts to safeguard the flow of international commerce while deterring terrorists by advocating for stepped-up inspections of cargo containers overseas—before they are shipped to U.S. ports—and promoting new requirements and technologies to improve monitoring of legitimate container movements.
Building that kind of resilience into our infrastructure requires collaboration by experts across a multitude of disciplines, such as engineering, economics, management, public policy, psychology, and computer science.
Flynn and his team are cultivating that expertise to identify the critical points in our systems, analyze their vulnerabilities, and decide how best to minimize the damage—and impact—of a blow, whether from a bomb or a hurricane.
Carey Rappaport, professor of electrical and computer engineering
Rappaport and his team are developing technology for airport scanning that will make your trip through the security checkpoint faster—and that could make your flight safer.
Existing millimeter wave scanners—the kind you’d walk through at any airport from Boston to Beijing—are good at distinguishing between skin and a metal knife. But human bodies have contours and curves, and existing scanning technology is not so good at detecting objects, such as weapons and explosive devices, small enough to fit into those contours.
In those cases, the scanner can yield an ambiguous picture, which then requires a manual pat-down of the passenger, or it can miss an object completely.
Rappaport’s system uses multiple sensors and detectors rather than the current scanner’s single detector. Coupled with highly advanced processing algorithms, his technology has the capability to detect more kinds of materials, including explosives, more quickly and accurately.
William Robertson, assistant professor of computer and information science and electrical and computer engineering
Among researchers focused on cleaning up the world’s black market of Internet insecurity, Robertson is a leader—in large part because he has learned to think like a cybercriminal.
This highly sophisticated set of hackers, members of a global Internet mafia, sit quietly behind backlit screens, coding their way into our sensitive data. From credit card numbers to computing power, nothing is off limits. So Robertson spends his days studying their malicious software: how it is constructed, how it works, and how it behaves. With that knowledge, he can create more robust security tools and safer systems.
For instance, Robertson uses machine-learning techniques to develop security programs that recognize the normal behavior of users and other programs. Then, when a threat presents itself and demonstrates anomalous behavior, the security program can automatically intervene to stop it. Robertson’s goal is not to catch the criminals, but rather to fatally cripple their ability to operate.
Dave Kaeli, professor of electrical and computer engineering
Kaeli is designing crowd analytics platforms to automatically identify suspicious behavior in vulnerable gathering places, such as transportation hubs and concert halls—and he is the first in this field to solve the data processing issues by employing graphics processing units.
Continuous detection of individual threats in crowded locales requires comparisons of billions of data points, which even state-of-the-art surveillance systems cannot handle. But Kaeli’s GPUs can. Data from cameras, infrared devices, motion detectors, microphones, and even heat sensors, can be quickly processed in parallel using this technology.
Kaeli’s team has developed novel algorithms to define normal behavior in terms of these data variables. If something out of the ordinary is happening, the computer will recognize it. In turn, reasoning schema being developed by members of his team will enable the computer to decide whether the anomalous data indicate a significant—and potentially high-impact—event is about to take place.