Anne Gelb
Department of Mathematics
Dartmouth College
29 N. Main Street
Hanover, NH 03755

Phone: (603) 646-2419
FAX: (603) 646-1312
Office: 207 Kemeny Hall
Anne.E.Gelb@Dartmouth.edu

 

 

 

Anne Gelb

Education

Employment

  • 2016-Current: John G. Kemeny Parents Professor, Department of Mathematics, Dartmouth College
  • 2007-2016: Professor, Department of Mathematics and Statistics, Arizona State University
  • 2001-2007: Associate Professor, Department of Mathematics and Statistics, Arizona State University
  • 1998-2001: Assistant Professor, Department of Mathematics and Statistics, Arizona State University
  • 1996-1998: Postdoctoral Fellow, Applied Mathematics, California Institute of Technology

Recent Events

  • I am the PI on a Multidisciplinary University Research Initiative (MURI) project sponsored by the Department of Defense (DoD) through the Office of Naval Research (ONR). Our team consists of mathematicians and engineers from Dartmouth College, Arizona State University, and MIT, and collaborates with scientists working at the Cold Regions Research and Engineering Laboratory (CRREL). More information on the Sea Ice Modeling and Data Assimilation (SIMDA) project may be found  here.

Research Interests

  • I am a numerical analyst focusing on developing highly accurate, robust, and efficient methods for computational partial differential equations, data assimilation, signal and image restoration, classification, and change detection for real and complex signals from temporal sequences of collected data. There are a wide variety of applications for my work, including forecasting, speech recognition, medical monitoring, credit card fraud detection, automated target recognition, and video surveillance. A common assumption made in these applications is that the magnitude of the underlying signal or image is sparse in some domain. While detecting such changes from direct data (e.g. images already formed) has been well studied, my focus is on applications where the temporal sequence of data are acquired indirectly, such as the k-space data in magnetic resonance imaging (MRI) and the phase history data in synthetic aperture radar (SAR). In particular, I develop algorithms that retain critical information for identification, such as edges, that is stored in the indirect data. They are also designed to maintain phase information for coherent imaging applications. More recently I am extending these techniques to a Bayesian setting so that the uncertainty of the solutions may also be quantified, with specific applications to sensing, modeling, and data assimilation for sea ice prediction. My research is funded in part by the Air Force Office of Scientific Research (AFOSR), the Office of Naval Research (ONR), the National Science Foundation (NSF), and the Department of Energy (DOE), and I regularly collaborate with scientists at the Wright-Patterson Air Force Research Lab (AFRL), Oak Ridge National Laboratory (ORNL), and the Cold Regions Research and Engineering Laboratory (CRREL).  

Recent PhD Student Dissertations

Some Recently Taught Courses