Congratulations to Dr. Sheng Zou for graduating with his Ph.D.! Sheng’s dissertation is titled “Classification with Multi-Imprecise Labels.” His research focused on developing classification approaches under the multiple instance learning framework. The goal of Sheng’s work was to model class variability using discriminative probabilistic distributions and multiple types of imprecise labels.

Read about more Sheng’s research:

S. Zou, “Classification with Multi-Imprecise Labels,” Ph.D. Thesis, Gainesville, FL, 2021.

T. Zou, N. Aljohani, K. Nagaraj, S. Zou, C. Ruben, A. Bretas, A. Zare and Janise McNair, “FDI Correction Physics-based Model: A Machine Learning Synthetic Measurement based Approach.” Under Review.

K. Nagaraj, N. Aljohani, S. Zou, T. Zou, A. Bretas, J. McNair and A. Zare, “Smart FDI Attack Design and Detection with Data Transmutation Framework for Smart Grids,” in 2021 IEEE PES General Meeting, July 25-29, 2021, Washington, DC, USA.

K. Nagaraj, N. Aljohani, S. Zou, C. Ruben,  A. Bretas, A. Zare and J. McNair, “State Estimator and Machine Learning Analysis of Residual Differences to Detect and Identify FDI and Parameter Errors in Smart Grids,” in 2020 North American Power Symposium (NAPS). In Press.

K. Nagaraj, S. Zou, C. Ruben, S. C. Dhulipala, A. Starke, A. Bretas, A. Zare and J. McNair, “Ensemble CorrDet with Adaptive Statistics for Bad Data Detection,” in IET Smart Grid.  In Press

C. Ruben, S. Dhulipala, K. Nagaraj, S. Zou, A. Starke, A. Bretas, A. Zare, and J. McNair, “Hybrid data-driven physics model-based framework for enhanced cyber-physical smart grid security,” in IET Smart Grid Special Issue: Machine Learning in Power Systems. Dec 2019

S. Zou, Y. Tseng, A. Zare, D. Rowland, B. Tillman and S. Yoon, “Peanut Maturity Classification using Hyperspectral Imagery,” in Biosystems Engineering, vol. 188, pp. 165-177, 2019.

S. Zou, P. Gader and A. Zare, “Hyperspectral Tree Crown Classification Using the Multiple Instance Adaptive Cosine Estimator,” PeerJ 7:e6405, Feb. 2019.

S. Zou, H. Sun, and A. Zare “Hyperspectral Unmixing with Endmember Variability Using Semi-supervised Partial Membership Latent Dirichlet Allocation,” in American Association of Geographers, New Orleans, LA, Apr. 2018. Abstract and Presentation Only.

S. Zou and A. Zare, “Hyperspectral Unmixing with Endmember Variability Using Partial Membership Latent Dirichlet Allocation.” in IEEE Int. Conf. Acoust, Speech and Signal Process. (ICASSP), New Orleans, LA, 2017, pp. 6200-6204.

S. Zou, H. Sun, and A. Zare, “Hyperspectral Unmixing with Endmember Variability Using Semi-supervised Partial Membership Latent Dirichlet Allocation,” in CoRR. vol abs/1703.06151. 2017.  

S. Zou, “Semi-supervised Interactive Unmixing for Hyperspectral Image Analysis,” M.Sc. Thesis, Columbia, MO, 2016

S. Zou and A. Zare, “Instance Influence Estimation for Hyperspectral Target Signature Characterization Using Extended Functions of Multiple Instances,” in Proc. SPIE 9840, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII, 2016