Perspectives on Computational Sensing: Physics, Data, and Uncertainty

ECE Seminar: Perspectives on Computational Sensing: Physics, Data, and Uncertainty

Starts at: March 31, 2016 4:30 PM

Ends at: 6:00 PM

Location: Scaife Hall 125

Speaker: Dr. Eric L. Miller

Affiliation: Professor and Chair Electrical and Computer Engineering Tufts University

Refreshments provided: Yes

Link to Abstract

Link to Video (1)



Whether seeking safer and more effective means of imaging the human body, developing the next generation of metal alloys for use in cars, airplanes, buildings and bridges, or searching for new sources of energy in the earth’s subsurface, the extraction of information from data collected by sensors of all sorts and varieties is central to a broad range of problems. The era of Big Data has seen remarkable achievements in information processing for problems involving social and economic data, video, and imagery. Still, Data Science has largely bypassed fields where complex physical phenomenology separates raw data from the information needed to solve a problem. Such Computational Sensing problems come with their own, interesting set of challenges. Here we consider three: (a) difficulties associated with modeling the physics of the sensing modality; (b) complications arising from the nature of the data, which can be Big, Small, but all too often insufficient; and (c) and systematic uncertainty associated with the sensor system itself or the medium under investigation. This talk will focus on a discussion of these issues, how they intersect, and methods for addressing them in the context of a number of application areas including hyperspectral diffuse optical tomography, subsurface contaminant source zone characterization, satellite remote sensing, and X-ray CT for airport security scanning


Eric L. Miller received the BS, MS, and Ph.D. degrees all in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology, Cambridge, MA. His research interests include physics-based tomographic image formation and object characterization, inverse problems, as well as statistical signal and imaging processing. He is currently Professor and Chair of the Department of Electrical and Computer Engineering at Tufts University.