Publications

Dissertation

Deep Generative Models for High Dimensional Spatial and Temporal Data Analysis

Loughlin, Cooper. Deep Generative Models for High Dimensional Spatial and Temporal Data Analysis. Diss. Northeastern University, 2023.

---

Journal Papers

Efficient Hyperspectral Target Detection and Identification With Large Spectral Libraries

C. Loughlin, M. Pieper, D. Manolakis, R. Bostick, A. Weisner and T. Cooley, “Efficient Hyperspectral Target Detection and Identification With Large Spectral Libraries,” in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 6019-6028, 2020, doi: 10.1109/JSTARS.2020.3027155.

Performance Prediction of Hyperspectral Target Detection Algorithms via Importance Sampling

C. Loughlin, E. Truslow, D. Manolakis, A. Weisner and R. Bostick, “Performance Prediction of Hyperspectral Target Detection Algorithms via Importance Sampling,” in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 12, no. 8, pp. 3078-3091, Aug. 2019, doi: 10.1109/JSTARS.2019.2916260.

---

Conference Papers

Multivariate air quality time series analysis via a recurrent variational deep learning model

Cooper Loughlin, Dimitris Manolakis, Vinay Ingle, “Multivariate air quality time series analysis via a recurrent variational deep learning model,” Proc. SPIE 12525, Geospatial Informatics XIII , 125250G (15 June 2023); https://doi.org/10.1117/12.2663201

Spectral variability modeling with variational autoencoders for hyperspectral target analysis

Cooper Loughlin, Dimitris Manolakis, Michael Pieper, Vinay Ingle, Randall Bostick, Andrew Weisner, “Spectral variability modeling with variational autoencoders for hyperspectral target analysis,” Proc. SPIE 12519, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXIX , 125190N (13 June 2023); https://doi.org/10.1117/12.2663195