Anne Gelb

Department of Mathematics
Dartmouth College
27 N. Main Street
Hanover, NH

Phone: (603) 646-2419
FAX: (603) 646-1312
Office: 207 Kemeny Hall




Anne Gelb



  • 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 new 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 high order methods for 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 speech recognition, medical monitoring, credit card fraud detection, automated target recognition, and video surveillance. A common assumption made in these applications is that 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 such as magnetic resonance imaging (MRI), ultrasound, and synthetic aperture radar (SAR), where the temporal sequence of data are acquired indirectly. In particular, I develop algorithms that retain critical information for identification, such as edges, that is stored in the indirect data. I am currently investigating how to use these techniques in a Bayesian setting so that the uncertainty of the solutions may also be quantified, and am interested in applying these techniques for purposes of sensing, modeling, and data assimilation for sea ice prediction. My research is funded in part by the Air Force Office of Scientific Research, the Office of Naval Research, the National Science Foundation, and the National Institutes of Health, and she regularly collaborates with scientists at the Wright-Patterson Air Force Research Lab and the Cold Regions Research and Engineering Laboratory (CRREL).  

Recent PhD Student Dissertations

Recently Taught Courses