Congratulations to Dr. Xiaoxiao Du for graduating with her Ph.D. this past week! Her dissertation is titled “Multiple Instance Choquet Integral for Multi-resolution Sensor Fusion.” Her research focused on developing trained sensor and classifier fusion methods that can learn from ambiguously and imprecisely labeled training data. The goal of her work is to optimize fusion while minimizing the effort needed to precisely label data.

Read about more her research:

X. Du, “Multiple Instance Choquet Integral For Multi-Resolution Sensor Fusion,” Ph.D. Thesis, Columbia, MO, 2017.

X. Du, A. Seethepalli, H. Sun, A. Zare and J. T. Cobb, “Environmentally-Adaptive Target Recognition for SAS Imagery ” in Proc. SPIE 10182, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXII, 2017.

J. T. Cobb, X. Du, A. Zare, and M. Emigh, “Multiple-instance Learning-based Sonar Image Classification ” in Proc. SPIE 10182, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXII, 2017.

X. Du, A. Zare, J. Keller, and D. Anderson, “Multiple Instance Choquet Integral for Classifier Fusion,” in IEEE Congr. Evol. Computation (CEC), Vancouver, BC, 2016, pp. 1054-1061.

X. Du, A. Zare, and J. T. Cobb, “Possibilistic context identification for SAS imagery,” in Proc. SPIE 9454, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XX, 2015.

X. Du, A. Zare, P. Gader, and D. Dranishnikov, “Spatial and Spectral Unmixing Using the Beta Compositional Model,” IEEE J. Sel. Topics. Appl. Earth Observ., vol. 7, pp. 1994-2003, June, 2014.

X. Du, “Accounting for Spectral Variability in Hyperspectral Unmixing Using Beta Endmember Distribution,” Master Thesis, Columbia, MO, 2013.