Imagine if your manager could know whether you actually paid attention in your last Zoom meeting. Or, imagine if you could prepare your next presentation using only your thoughts. These scenarios might soon become a reality thanks to the development of brain-computer interfaces (BCIs). To put it in the simplest terms, think of a BCI as a bridge between your brain and an external device. As of today, we mostly rely on electroencephalography (EEG) - a collection of methods for monitoring the electrical activity of the brain - to do this. But, that’s changing. By leveraging multiple sensors and complex algorithms, it’s now becoming possible to analyze brain signals and extract relevant brain patterns. Brain activity can then be recorded by a non-invasive device - no surgical intervention needed. In fact, the majority of existing and mainstream BCIs are non-invasive, such as wearable headbands and earbuds. The development of BCI technology was initially focused on helping paralyzed people control assistive devices using their thoughts.
Receiving feedback about course work not just from your teacher but from your peers, or possibly from the outside world, can be very empowering to students. In their eyes, having the ability to publish their writing on a blog suddenly transforms them into authors and publishers. Blogs make students are of careful and conscientious writing. Students are no longer writing for an audience of one. Instead, their words face an audience of their peers as well as countless others whose primary goal is not only to read, but also to provide commentary, feedback and even to critique and criticize their words. By using classroom computers for such means, students become increasingly more cautious and aware of their grammar, spelling and word-choice knowing that they are the authors of a published piece of writing that will reach a wide audience. Similarly, Wikis are commonly accessed on classroom computers due to their positive impacts.
That’s right, at Princeton. Biology is often chosen for computer science-related PhDs, but people don’t always stay in the field. Is that your case or do you still work on genomics? I no longer work on genomics. I became interested in the biology applications of computer science as I entered graduate school, so I decided to try to focus on that. But unfortunately I didn’t have the necessary training from undergrad, I had only taken the basic biology courses. So, oftentimes, I was relying on other students in my lab to help out with the domain knowledge part of analysing the machine learning results. I decided that I didn’t want to pursue genomics, but I really enjoyed the teaching that I did in graduate school, so my career path changed. Let’s start talking about learning programming. Do you have any opinion on the “learning to code” debate? A few years ago everyone seemed to think that we should all learn how to code.
Recent projects have included open source adaptations of Apple's Grand Central Dispatch, collaborative development of the OpenBSM operating system audit framework between Apple and the FreeBSD Project, and work on highly scalable multi-core TCP processing with Juniper. Slightly longer ago, I worked with several companies including Apple and nCircle to help them use and extend the TrustedBSD MAC Framework, a kernel reference monitor now used as the foundation for access control in Mac OS X, iOS, Junos, McAfee's Sidewinder firewall, nCircle's IP360 appliance, and many other products. Prior to coming to Cambridge, I worked for a series of industrial research labs in the US, including SPARTA ISSO, Trusted Information Systems - Advanced Research and Engineering, and McAfee Research. While there I led a number of network and operating system security research projects for government and industrial sponsors, including DARPA, the US Navy, Apple Computer, and others. I have received faculty research awards, faculty fellowships, or research and development grants from several companies including Google, Netapp, HP Enterprise / HP Labs, Thales E-Security, and ARM.
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