题目：Data Assimilation: High-Dimensional State Estimation for Space Science 时间：2017年6月12日（星期一）11：00AM 地点：玉泉校区、行政楼117会议室 报告人：Dr. Mark D. Butala NASA Jet Propulsion Laboratory, California Institute of Technology, US A |
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专家介绍：
Dr. Mark D. Butala received an Honors Bachelor of Electrical Engineering degree with Distinction from the University of Delaware in 2002, graduating summa cum laude. At the University of Illinois at Urbana-Champaign, he received the M.S. and Ph.D. degrees in Electrical and Computer Engineering in 2004 and 2010, respectively. In 2010, he joined the technical staff at the NASA Jet Propulsion Laboratory, California Institute of Technology. His contributions have been recognized with NASA Group Achievement Awards “for outstanding development of real-time techniques to detect ionospheric perturbations due to tsunami using the Global Positioning System”, “for outstanding achievement in the operation and successful execution of the Curiosity rover’s mission of exploration to the surface of Gale Crater on Mars”, and “for outstanding and innovative technical leadership to improve deep space navigation by using the GLONASS constellation of navigation satellites”. His research interests include the theory of remotely sensed image formation, Monte Carlo and statistical signal processing theory and practice, and the application of rigorous Bayesian methodology to big data problems in space science.
报告内容：
The accelerating volume of information generated from ever less expensive sensing and processing technology is transforming all domains concerned with empirical determination. The space science community, with its multibillion-dollar and ever expanding fleet of spacecraft dedicated to observing the Sun, near-Earth space, and intermediate regions, is now positioned to take advantage of the “big data” revolution. In this seminar, I describe how data assimilation, the systematic Bayesian inference of a high-dimensional state given a huge data volume and physical constraints on system dynamics, yields a deeper scientific characterization for the potentially destructive space weather phenomena facing our increasingly technological society. Attention is given both to the underlying Bayesian foundations and computational concerns.
