Publications

Robotics competitions at the high school level attract a large number of students across the world. However, there is little emphasis on leveraging robotics to get middle school students excited about pursuing STEM education. In this paper, we describe a new program that targets middle school students in a local, four-week setting at the Massachusetts Institute of Technology (MIT). It aims to excite students by teaching the very basics of computer vision and robotics. The students program mini car-like robots, equipped with state-of-the-art computers, to navigate autonomously in a mock race track. We describe the hardware and software infrastructure that enables the program, the details of our curriculum, and the results of a short assessment. In addition, we describe four short programs, as well as a session where we teach high school teachers how to teach similar courses at their schools to their own students. The self-assessment indicates that the students feel more confident in programming and robotics after leaving the program, which we hope will enable them to pursue STEM education and robotics initiatives at school.

Identifying and profiling threat actors are high priority tasks for a number of governmental organizations. These threat actors may operate actively, using the Internet to promote propaganda, recruit new members, or exert command and control over their networks. Alternatively, threat actors may operate passively, demonstrating operational security awareness online while using their Internet presence to gather information they need to pose an offline physical threat. This paper presents a flexible new prototype system that allows analysts to automatically detect, monitor and characterize threat actors and their networks using publicly available information. The proposed prototype system fills a need in the intelligence community for a capability to automate manual construction and analysis of online threat networks. Leveraging graph sampling approaches, we perform targeted data collection of extremist social media accounts and their networks. We design and incorporate new algorithms for role classification and radicalization detection using insights from social science literature of extremism. Additionally, we develop and implement analytics to facilitate monitoring the dynamic social networks over time. The prototype also incorporates several novel machine learning algorithms for threat actor discovery and characterization, such as classification of user posts into discourse categories, user post summaries and gender prediction.

Grayscale lithography is a widely known but underutilized microfabrication technique for creating three-dimensional (3-D) microstructures in photoresist. One of the hurdles for its widespread use is that developing the grayscale photolithography masks can be time-consuming and costly since it often requires an iterative process, especially for complex geometries. We discuss the use of PROLITH, a lithography simulation tool, to predict 3-D photoresist profiles from grayscale mask designs. Several examples of optical microsystems and microelectromechanical systems where PROLITH was used to validate the mask design prior to implementation in the microfabrication process are presented. In all examples, PROLITH was able to accurately and quantitatively predict resist profiles, which reduced both design time and the number of trial photomasks, effectively reducing the cost of component fabrication.