Matthew J. Hoffman

Matthew J. Hoffman

Research Overview

My research interests include oceanic and lake dynamics; understanding the fate, transport, and impact of plastic pollution on freshwater and ocean systems; data assimilation; remote sensing; hyperspectral vehicle tracking; and cardiac electrical dynamics.

Personal Information

School of Mathematics and Statistics 

office:        2302 Gosnell Hall
phone:        (585) 420-6288
fax:             (585) 420-6288
address:     85 Lomb Memorial Dr.
                    Rochester NY, 14623

Comparison of 4DVAR and LETKF in Assimilating JPSS-Derived Sea-Surface Temperature in the Chesapeake Bay Operational Forecasting System

Funding source: NOAA/University of Maryland

In an effort to improve operational forecasting the Chesapeake Bay we are comparing two state-of-the-art data assimilation systems for use on the NOAA Chesapeake Bay Operational Forecast System (CBOFS) model: 4D-Var and Local Ensemble Transform Kalman Filter. We are testing both systems using simulated data and real satellite sea-surface temperature (SST) data provided by the Visible Infrared Imaging Radiometer Suite (VIIRS).

  • Lorem
  • Ipsum
  • Dolor

Intramural Forecasting of Cardiac Electrical dynamics

Funding source: NSF

In an attempt to improve understanding of the dynamics of electrical waves in the heart during arrhythmias, particularly in the interior of the tissue, we are coupling a Local Ensemble Transform Kalman filter (LETKF) based data-assimilation system with numerical models of electrical wave propogation. Simulated data is being used for initial testing, before moving onto real data obtained using optical mapping techniques. This work is done in collaboration with Elizabeth Cherry at RIT and Flavio Fenton at Georgia Tech.

  • Lorem
  • Ipsum
  • Dolor

Journal Papers

  1. Hoffman, M.J. , N.S. LaVigne, S.T. Scorse, F.H. Fenton, and E.M. Cherry, 2015. Reconstructing 3D reentrant cardiac electrical wave dynamics using data assimilation. In Review
  2. Uzkent, B., M.J. Hoffman , and A. Vodacek, 2015. Fusing Spectral and Kinematic Features in a Multi-dimensional Assignment Algorithm for Vehicle Tracking, In Review.
  3. Uzkent, B., M.J. Hoffman, A. Vodacek, and B. Chen. 2014. Feature Matching with an Adaptive Optical Sensor in a Ground Target Tracking System, Sensors Journal IEEE, 99, doi:10.1109/JSEN.2014.2346152.
  4. Urquhart, E, M.J. Hoffman, R. R. Murphy, and B.F. Zaitchik, 2013. Geospatial Interpolation of MODIS-Derived Salinity and Temperature in the Chesapeake Bay. Remote Sensing of the Environment, 135, 167-177.
  5. Greybush, S.J., E. Kalnay, M.J. Hoffman, R.J. Wilson. 2013. Identifying Martian atmospheric instabilities and their physical origins using bred vectors. Q. J. Roy. Meteor. Soc., 123 (672), 639-653, doi: 10.1002/qj.1990.
  6. Hoffman, M.J., T. Miyoshi, T. Haine, K. Ide, R. Murtugudde, and C.W. Brown. 2012. An advanced data assimilation system for the Chesapeake Bay. J. Atmos. and Oceanic Tech., 29, 1542-1557., 10.1175/JTECH-D-11-00126.1.
  7. Urquhart, E, M.J. Hoffman, B.F. Zaitchik, S. Guikema, and E.F. Geiger. 2012. Remotely Sensed Estimates of Surface Salinity in the Chesapeake Bay. Remote Sensing of the Environment. 123, 522-531, doi: 10.1016/j.rse.2012.04.008.
  8. Greybush, S. J., R. J. Wilson, R. N. Hoffman, M.J. Hoffman, T. Miyoshi, K. Ide, T. McConnochie, and E. Kalnay. 2012. Ensemble Kalman Filter Data Assimilation of Thermal Emission Spectrometer Temperature Retrievals into a Mars GCM. J. Geophys. Res., 117, E11008, doi: 10.1029/2012JE004097.
  9. Hoffman, M.J., J. Eluszkeiwicz, D. Weisenstein, G. Uymin, and J.-L. Moncet. 2012. A Critical Assessment of Mars Atmospheric Temperature Retrievals from the Thermal Emission Spectrometer Measurements. Icarus, 220 (2), 1031-1039, 10.1016/j/icarus.2012.06.039.
  10. Hoffman, M.J., S.J. Greybush, R.J. Wilson, G. Gyarmati, R.N. Hoffman, E. Kalnay, K. Ide, E. Kostelich, T. Miyoshi, I. Szunyogh. 2010. An ensemble Kalman filter data assimilation system for the Martian atmosphere: Implementation and simulation experiments. Icarus, 209, 470-481, DOI: 10.1016/j.icarus.2010.03.034.
  11. Hoffman, M.J., E. Kalnay, J.A. Carton, and S.C. Yang. 2009. Use of breeding to detect and explain instabilities in the global ocean. Geophys. Res. Lett., 36, L12608, DOI: 10.1029/2009GL037729.
  12. Gibbons, K.S., M.J. Hoffman, and W.K. Wootters. 2004. Discrete phase space based on finite fields. Phys. Rev. A, 70, 062101, DOI: 10.1103/PhysRevA.70.062101.

Peer-Reviewed Conference Papers

  1. Uzkent, B., M.J. Hoffman, and A. Vodacek, 2015. Spectral Validation of Measurements in a Vehicle Tracking DDDAS, Procedia Computer Science, 51, pp. 2493-2502, 10.1016/j.procs.2015.05.358. [Publisher Link]
  2. Uzkent, B., M.J. Hoffman, and A. Vodacek, 2015. Efficient integration of spectral features for vehicle tracking utilizing an adaptive sensor. Proc. SPIE 9407, Video Surveillance and Transportation Imaging Applications 2015, 940707 (March 4, 2015), doi:10.1117/12.2082266. [Publisher Link]
  3. Uzkent, B., M.J. Hoffman, A. Vodacek, J. P. Kerekes, and B. Chen, 2013. Feature Matching and Adaptive Prediction Models in an Object Tracking DDDAS. Procedia Computer Science, 18, 1939-1948, doi: 10.1016/j.procs.2013.05.363. [PUblisher Link]
  4. Vodacek, A., J. P. Kerekes, and M.J. Hoffman. 2012. Adaptive optical sensing in an object tracking DDDAS. Procedia Computer Science, 9, 1159-1166, 10.1016/j.procs.2012.04.125.

Conference Papers

  1. Uzkent, B., M.J. Hoffman, A. Vodacek, and B. Chen., 2015. Background image understanding and adaptive imaging for vehicle tracking Proc. SPIE 9460, Airborne Intelligence, Surveillance, Reconnaissance (ISR) Systems and Applications XII, 94600F (May 19, 2015); doi: 10.1117/12.2177494. [Publisher Link]
  2. Uzkent, B., M.J. Hoffman, E. Cherry, and N. Cahill, 2014. 3-D MRI Cardiac Segmentation using Graph Cuts. Proc. IEEE Western New York Image Processing Workshop, pp. 47-51, November 2014, doi: 10.1109/WNYIPW.2014.6999484. [Publisher Link]

Page under construction