Matthew J. Hoffman

Matthew J. Hoffman

Journal Papers

  1. Wang, J., K. Bucci, P. Helm, T. Hoellein, M.J. Hoffman, R. Rooney, & C. Rochman (2022). Runoff and discharge pathways of microplastics into freshwater ecosystems: A systematic review and meta‑analysis. FACETS, 7, 1473–1492. https://doi.org/10.1139/facets‑2022‑0140. [Publisher Link]
  2. Lobyrev, F. and M. J. Hoffman (2022). A method for estimating fish density through the catches of gill nets. Fisheries Management and Ecology.   [Publisher Link]
  3. Mendez, M. J., M. J. Hoffman, E. M. Cherry, C. A. Lemmon, and S. H. Weinberg (2022). A data-assimilation approach to predict population dynamics during epithelial-mesenchymal transition.  Biophysical Journal, 121 (16). [Publisher Link]     
  4. Daily, J., A. C. Tyler, and M.J. Hoffman (2022). Modeling three-dimensional transport of and impacts of biofouling in Lake Erie and Lake Ontario. J. Great Lakes Res. 45 (5). [Publisher Link]
  5. Daily, J., V. Onink, C.E. Jongedijk, C. Laufkotter, and M.J. Hoffman (2022). Incorporating terrain specific beaching within a lagrangian transport plastics model for Lake Erie. Microplastics and Nanoplastics, 1(1), 19. [Publisher Link]
  6. Onink, V, C. Jongedijk, M.J. Hoffman, E. Van Sebille, and C. Laufkotter (2021). Global simulations of marine plastic transport show plastic trapping in coastal zones. Environmental Research Letters, 16(6), 064053. [Publisher Link]
  7. Marcotte, C. D., Fenton, F. H., Hoffman, M. J., & Cherry, E. M. (2021). Robust data assimilation with noise: Applications to cardiac dynamics. Chaos: An Interdisciplinary Journalof Nonlinear Science,31(1), 013118.https://doi.org/10.1063/5.0033539. [Publisher Link]
  8. M.J. Hoffman and E.M. Cherry (2020). Sensitivity of a data-assimilation system for reconstructing three-dimensional cardiac electrical dynamics. Phil. Trans. R. Soc. A. 378: 20190388. [Publisher Link]
  9. Rangnekar, A., N. Mokashi, E.J. Ientilucci, C. Kanan and M.J. Hoffman. 2020. AeroRIT: A New Scene for Hyperspectral Image Analysis. in IEEE Transactions on Geoscience and Remote Sensing. Early Access. [Publisher Link]
  10. Daily, J. and M.J. Hoffman. 2020. Modeling the three-dimensional transport and distribution of multiple microplastic polymer types in Lake Erie. Marine Pollution Bulletin, 154, doi:10.1016/j.marpolbul.2020.111024. [Publisher Link]
  11. Mendez, M.J., M.J. Hoffman, E.M. Cherry, C.A. Lemmon, and S.H. Weunberg. 2020. Cell Fate Forecasting: A Data-Assimilation Approach to Predict Epithelial-Mesenchymal Transition. Biophysical Journal. doi:10.1016/j.bpj.2020.02.011. [Publisher Link]
  12. van Sebille, E., Aliani, S., Law, K. L., Maximenko, N., Alsina, J. M., Bagaev, A., Bergmann, M., Chapron, B., Chubarenko, I., Cózar, A., Delandmeter, P., Egger, M., Fox‐Kemper, B., Garaba, S. P., Goddijn‐Murphy, L.,Hardesty, B. D., Hoffman, M.J., Isobe, A., Jongedijk, C. E., Kaandorp, M. L. A., Khatmullina, L., Koelmans, A. A., Kukulka, T., Laufkötter, C., Lebreton, L., Lobelle, D., Martinez‐Vicente, V., Morales Maqueda, M. A., Poulain‐Zarcos, M., Rodríguez, E., Ryan, P. G., Shanks, A. L., Shim, W. J., Suaria, G., Thiel, M., van den Bremer, T. S., & Wichmann, D. 2020. The physical oceanography of the transport of floating marine debris. Environmental Research Reviews. doi:10.1088/1748-9326/ab6d7d [Publisher Link]
  13. Hoffman, M. J., Zhang, B., Lanerolle, L. W. J., & Brown, C. W. (2020). Evaluating the benefit and cost of assim‑ilating satellite sea surface temperature into the NOAA Chesapeake Bay Operational Forecast System using4DVAR and LETKF.NOAA Technical Report,39. [Publisher Link]
  14. Mason, S.A., J. Daily, G. Aleid, R. Ricotta, M. Smith, K. Donnelly, R. Knauff, W. Edwards, and M.J. Hoffman. 2020. High levels of pelagic plastic pollution within the surface waters of Lakes Erie and Ontario. Journal of Great Lakes Research, Online 12 Jan. 2020, doi:10.1016/j.jglr.2019.12.012. [Publisher Link]
  15. Floyd, C., M.J. Hoffman, and E. Fokoue. 2019. Shot Shot-by-shot stochastic modeling of individual tennis points. Journal of Quantitative Analysis in Sports, Online 12 Oct. 2019, doi:10.1515/jqas-2018-0036. [Publisher Link]
  16. Greybush, S.J., E. Kalnay, R.J. Wilson, R.N. Hoffman, T. Nehrkorn, M. Leidner, J. Eluszkiewicz, H.E. Gillespie, M. Wespetal, Y. Zhao, M.J. Hoffman, P. Dudas, T. McConnochie, A. Kleinbohl, D. Kass, D. McCleese, and T. Miyoshi. 2019. The Ensemble Mars Atmosphere Reanalysis System (EMARS) Version 1.0. Geosci Data J., 00, 1-14, https://doi.org/10.1002/gdj3.77. [Publisher Link]
  17. Parthasarathy, A., A.C. Tyler, M.J. Hoffman, M.A. Savka, and A.O. Hudson. 2019. Is Plastic Pollution in Aquatic and Terrestrial Environments a Driver for the Transmission of Pathogens and the Evolution of Antibiotic Resistance? Environ. Sci. Technol. doi: 10.1021/acs.est.8b07287. [Publisher Link]
  18. Bachmann, C.M., R.S. Eon, C.S. Lapszynski, G.P. Badura, A. Vodacek, M.J. Hoffman, D. McKeown, R.L. Kremens, M. Richardson, T. Bauch, and M. Foote. 2019. A Low-Rate Video Approach to Hyperspectral Imaging of Dynamic Scenes. J. Imaging, 5(1), 6, doi: 10.3390/jimaging5010006. [Publisher Link]
  19. Uzkent, B., Rangnekar, A., and M.J. Hoffman. 2018. Tracking in Aerial Hyperspectral Videos using Deep Kernalized Correlation Filters. IEEE Transactions on Geoscience and Remote Sensing. 57 (1), 449-461, doi: 10.1109/TGRS.2018.2856370. [Publisher Link].
  20. Lobyrev, F. and M.J. Hoffman. 2018. A morphological and geometric method for estimating the selectivity of gill nets. Reviews in Fish Biology and Fisheries, 28, doi: 10.1007/s11160-018-9534-1. [Publisher Link]
  21. LaVigne, N.S., N. Holt, M.J. Hoffman, E.M. Cherry. 2017. Effects of model error on cardiac electrical wave state reconstruction using data assimilation. Chaos, 27, 093911.Inventory and transport of plastic debris in the Laurentian Great Lakes.
  22. M.J. Hoffman and E. Hittinger. 2017. Inventory and transport of plastic debris in the Laurentian Great Lakes. Marine Pollution Bulletin. 115, 1-2, 273-281.[Publisher Link][Preprint]
  23. Uzkent, B., M.J. Hoffman, and A. Vodacek. 2016. Integrating Hyperspectral Liklihoods in a Multi-dimensional Assignment Algorithm for Aerial Vehicle Tracking. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 99, 1-9, doi: 10.1109/JSTARS.2016.2560220.
  24. Hoffman, M.J. , N.S. LaVigne, S.T. Scorse, F.H. Fenton, and E.M. Cherry, 2016. Reconstructing 3D reentrant cardiac electrical wave dynamics using data assimilation, Chaos, 26, 013107, doi: 10.1063/1.4940238
  25. 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.
  26. 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.
  27. 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.
  28. 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, doi: 10.1175/JTECH-D-11-00126.1.
  29. 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.
  30. 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.
  31. 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, doi: 10.1016/j/icarus.2012.06.039.
  32. 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.
  33. 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.
  34. 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.  Rangnekar, A., Kanan, C. and   Hoffman M. J. (2022).  Semantic Segmentation with Active Semi-Supervised Representation Learning. Accepted at the 2022 British Machine Vision Conference.
  2. Mulhollan, Z., Gamarra, M., Vodacek, A. and Hoffman, M. J. (2022). Essential Properties of a Multimodal Hypersonic Object Detection and Tracking System. DDDAS2022 Conference, October 2022.
  3. Rangnekar, A., Ientilucci, E., Kanan, C., and Hoffman, M.J. (2022). SpecAL: Towards Active Learning for Semantic Segmentation of Hyperspectral Imagery  
  4. Rangnekar, A., Mulhollan, Z., Vodacek, A., Hoffman, M.J., Sappa, A. D., Blasch, E., Yu, J., Zhang, L., Du, S., Chang, H., Lu, K., Zhang, Z., Gao, F., Yu, Y., Shuang, F., Wang, L., Ling, Q., Shyam, P., Yoon, K.‑J., & Kim, K.‑S. (2022). Semi‑supervised hyperspectral object detection challenge results ‑ PBVS 2022. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, June 2022.  [Publisher Link]
  5. Rangnekar, A., Wong, C., Rochman, C., & Hoffman, M.J. (2022). On fine‑grained micro‑plastics classification - FGVC 2022. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, June 2022.  [Publisher Link]
  6. Rangnekar, A., Yao, Y., Hoffman, M.J., & Divakaran, A. (2021). Fine‑Tuning for One‑Look Regression Vehicle Count‑ ing in Low‑Shot Aerial Datasets. In A. Del Bimbo, R. Cucchiara, S. Sclaroff, G. M. Farinella, T. Mei, M. Bertini, H. J. Escalante, & R. Vezzani (Eds.), Pattern Recognition. ICPR International Workshops and Challenges (pp. 5– 18). Springer International Publishing. [Publisher Link]
  7. Mulhollan, Z., Rangnekar, A., Vodacek, A., & Hoffman, M. J. (2020). Occlusion Detection for Dynamic Adaptation. In F. Darema, E. Blasch, S. Ravela, & A. Aved (Eds.), Dynamic Data Driven Application Systems (pp. 337– 344). Springer International Publishing. https://doi.org/10.1007/978‑3‑030‑61725‑7_39
  8. Rangnekar A., Ientilucci E., Kanan C., Hoffman M.J. (2020) Uncertainty Estimation for Semantic Segmentation of Hyperspectral Imagery. In: Darema F., Blasch E., Ravela S., Aved A. (eds) Dynamic Data Driven Application Systems. DDDAS 2020. Lecture Notes in Computer Science, vol 12312. Springer, Cham. https://doi.org/10.1007/978-3-030-61725-7_20
  9. Mulhollan, Z., A. Rangneka, T.   Bauch, M.J. Hoffman, and A. Vodacek, 2020. Calibrated Vehicle Paint Signatures for Simulating Hyperspectral Imagery. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, June 2020.  [Publisher Link]
  10. Li, H.,  L. Pan, E.J . Lee, Z. Li, M .J. Hoffman, A. Vodacek, and S. S. Bhattacharyya , 2019. Hyperspectral Video Processing on Resource-Constrained Platforms, 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), Amsterdam, Netherlands, 2019, pp. 1-5, doi: 10.1109/WHISPERS.2019.8921138. 
  11. Rangnekar, A., & Hoffman, M. J. (2019). Learning representations to predict landslide occurrences and detect illegal mining across multiple domains. Proceedings of the 36th International Conference on Machine Learning. Retrieved December 17, 2020, from https://www.climatechange.ai/papers/icml2019/43.html
  12. Uzkent, B., A. Rangnekar, and M.J. Hoffman, 2017. Aerial Vehicle Tracking by Adaptive Fusion of Hyperspectral Likelihood Maps. CVPR Workshop: Perception Beyond the Visible Spectrum, July 2017.
  13. Uzkent, B., M.J. Hoffman, and A. Vodacek, 2016. Real-time Vehicle Tracking in Aerial Video using Hyperspectral Features, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshop: Moving Cameras Meet Video Surveillance, June 2016. [Publisher Link]
  14. 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]
  15. 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]
  16. 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]
  17. 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.

Commentrary/Popular Press

  1. Hoffman, M.J. and A.C. Tyler. 2018. Tons of plastic trash enter the Great Lakes every year – where does it go? The Conversation.

Conference Papers

  1. H. Li, L. Pan, Z. Li, M. J. Hoffman, A. Vodacek and S. S. Bhattacharyya, 2018.  Design methods for hyperspectral video processing on resource-constrained platforms. Proceedings of the Hyperspectral Imaging & Applications Conference, pp. 2, October 2018. 
  2. 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]
  3. 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]
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