Ruzena Bajcsy received the Master’s and Ph.D. degrees in electrical engineering from Slovak Technical University, Bratislava, Slovak Republic, in 1957 and 1967, respectively, and the Ph.D. in computer science from Stanford University, Stanford, CA, in 1972. From 1972 until 2001 she was a professor of Computer and Information Science department at the University of Pennsylvania, Philadelphia. Here she established in 1978 an interdisciplinary robotics laboratory, named GRASP lab .GRASP stands for General Robotics and Active Perception. As the director of the GRASP lab she fostered interdisciplinary research activities and attracted faculty from Electrical and mechanical Engineering as well as from Psychology/Cognitive Science and of course Computer Science. For 28 years while at UPENN she worked on Robotics research, including Computer Vision, tactile perception and in general problem of system identification. In addition she also worked on medical imaging, and developed with her students digital anatomy atlas coupled with elastic matching algorithms that enabled to automatically identify anatomic structures of the brain, first in X-ray tomography, later MRI, and positron image tomography. This technology is now a standard in medical practice. In 2001 she accepted the position to become the founding director of the Center for Information Technology Research in the Interest of Science (CITRIS) and Professorship of Electrical Engineering and Computer Sciences at the University of California, Berkeley. CITRIS is a multi-campus organization comprised of 4 campuses: UC Berkeley, UC Davis, UC Santa Cruz and UC Merced. As part of her activities in CITRIS, she together with the University California Center for Humanities, played a founding role in establishing program of Digital Humanities. Prior to joining Berkeley, she headed the Computer and Information Science and Engineering Directorate at the National Science Foundation between 1999 and 2001. Dr. Bajcsy is a member of the National Academy of Engineering and the National Academy of Science Institute of Medicine as well as a Fellow of the Association for Computing Machinery (ACM) and the American Association for Artificial Intelligence. In 2001, she received the ACM/Association for the Advancement of Artificial Intelligence Allen Newell Award, and was named as one of the 50 most important women in science in the November 2002 issue of Discover Magazine. She is the recipient of the Benjamin Franklin Medal for Computer and Cognitive Sciences (2009) and the IEEE Robotics and Automation Award (2013) for her contributions in the field of robotics and automation. In 2016, she received the National Academy of Engineering Simon Ramo Founders Award. Here current research is in the use of robotic technology, namely measuring and extracting noninvasively kinematic and dynamic parameters of individual in order to assess the physical movement capabilities or limitations. If there are limitations, her students have designed assistive devices that can compensate for the lack of kinematic agility and /or physical strength. As part of this activity she is also modeling the driver physical and some cognitive aspects to facilitate the interaction of the human driver and the autonomous car changing lanes.
Natural language researchers and data scientists apply a multitude of techniques for analysis and extraction of knowledge from potentially massive data. For example, natural language processing of unstructured data enables the discovery of possible cyber threats. Extraction of goals and intentions may lead to predictions of when an event may occur in cyberspace. This talk describes IHMC research on predictive analytics applied to formal and informal textual data and includes a cross-field examination of problems, measures, and evaluation paradigms designed to support solutions to data-science problems that span a wide range of fields.