The traditional lecture and laboratory approach used in teaching science and engineering has dominated education at high schools and universities for centuries. Although classroom demonstrations are sometimes used to provide instructive and motivating examples of taught concepts, in large classes they are difficult to see and without direct “hands on” involvement of the students have limited effect. Our initiative to address this shortcoming is MICA (Measurement, Instrumentation, Control and Analysis) an educational approach designed for subjects in Science, Technology, Engineering and Mathematics (STEM). Students interact with an experimental workstation (MICA workstation) to conduct experiments, analyze data, undertake parameter estimation, and fit mathematical models, while learning the theory and relevant subject history under the guidance of a virtual tutor (MICA avatar). As students interact with the MICA workstations their skill level, rate of learning and progress is quantified. Based on these data, deep learning techniques and mathematical modelling are then used to generate an individualized model of a student’s state of knowledge which is augmented every time the student interacts with a MICA workstation. This ‘state of knowledge’ model is then used by the MICA tutor to personalize (and eventually optimize) the teaching pace as well as the way in which subject material is delivered.
Principal Investigator Henrik Schmidt
Nan-Wei Gong and figur8 are spurring the growth of wearable technology within the sports medicine and digital health sectors, where they aim to commercialize digitized 3-D body movement technologies.
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In this talk, I will present an overview of my research in the past decade on large scale optimization for machine learning and collective behavior in networked,natural, engineering, and social systems. These collective phenomena include social aggregation phenomena as well as emergence of consensus, swarming, and synchronization in complex network of interacting dynamic systems such as mobile robots and sensors. A common underlying theme in this line of study is to understand how a desired global behavior can emerge from purely local interactions. The evolution of these ideas into social systems has lead to development of a new theory of collective decision making among people and organizations. Examples include participation decisions in uprisings, social cascades, investment decisions in public goods, and decision making in large organizations. I will investigate distributed strategies for information aggregation, social learning and detection problems in networked systems where heterogeneous agents with different observations (with varying quality and precision) coordinate to learn a true state (e.g., finding aggregate statistics or detecting faults and failure modes in spatially distributed wireless sensor networks, or deciding suitability of a political candidate, quality of a product, and forming opinions on social issues of the day in social networks) using a stream of private observations and interaction with neighboring agents. I will end the talk with a a new vision for research and graduate education at the interface of information and decision systems, data science and social sciences.