Semi-supervised learning, unsupervised learning, image processing. Applications include classification of high-dimensional data.

Google Scholar Profile: link


[16] Merkurjev, E., A Fast Graph-Based Classification Method Applied to Unsupervised Classification of 3D Point Clouds, Pattern Recognition Letters, 136, pp. 154-160, 2020.

[15] Bertozzi, A.L., Merkurjev, E., Graph-based Optimization Approaches for Machine Learning, Uncertainty Quantification and Networks, chapter in Handbook of Numerical Analysis: Processing, Analyzing and Learning of Images, Shapes, and Forms: Part 2, vol. XX, p. 503-532, 2019. link

[14] Merkurjev, E., A Graphical Approach for Multiclass Classification and for Correcting the Labeling Errors in Mislabeled Training Data, Intelligent Data Analysis, 25(4), to be published in July 2021.

[13] Waters, A., Merkurjev, E., Asymptotics for Optimal Design Problems for the Schrodinger Equation with a Potential, Journal of Optimization, #8162845, vol. 2018, 2018. link

[12] Jacobs, M., Merkurjev, E., Esedoglu, S., Auction Dynamics: A Volume Constrained MBO Scheme, Journal of Computational Physics, 354, pp. 288-310, 2018. link

[11] Bae, E. and Merkurjev, E., Convex Variational Methods on Graphs for Multiclass Segmentation of High-Dimensional Data and Point Clouds, Journal of Mathematical Imaging and Vision, 58(3), pp. 468-493, 2017. link

[10] Merkurjev, E., Bertozzi, A.L., Chung, F., A Semi-Supervised Heat Kernel Pagerank MBO Algorithm for Data Classification, Communications in Mathematical Sciences, 16(5), pp. 1241-1265, 2018. link

[9] Meng, G., Merkurjev, E., Koniges, A., Bertozzi, A.L., Hyperspectral Image Classification Using Graph Clustering Methods, Image Processing On Line, 7, pp. 218-245, 2017. link

[8] Merkurjev, E., Bertozzi, A.L., Lerman, K., Yan, X., Modified Cheeger and Ratio Cut Methods Using the Ginzburg-Landau
Functional for Classification of High-Dimensional Data, Inverse Problems, 33(7), pp. 074003, 2017. link

[7] Merkurjev, E., Bae, E., Bertozzi, A.L., and Tai, X.-C., Global Binary Data Optimization on Graphs for Data Segmentation,
J. Math. Imag. Vis., 52(3), pp. 414-435, 2015. link

[6] Merkurjev, E., Sunu, J. and Bertozzi, A.L., Graph MBO Method for Multiclass Segmentation of Hyperspectral Stand-off Detection
Video, IEEE International Conference on Image Processing, pp. 689-693, Paris, France, October 27-30, 2014. link

[5] Merkurjev, E., Garcia-Cardona, C., Bertozzi, A.L., Flenner, A. and Percus, A., Research Announcement: Diffuse Interface Methods
for Multiclass Segmentation of High-Dimensional Data, Applied Mathematics Letters, 33, pp. 29-34, 2014. link

[4] Garcia-Cardona, C., Merkurjev, E., Bertozzi, A.L., Percus, A., Flenner, A., Multiclass Segmentation Using the Ginzburg-Landau
Functional and the MBO Scheme, IEEE Trans. Pattern Anal. Mach. Intell., 36(8), pp. 1600-1614, 2014. link

[3] Gerhart, T., Sunu, J., Lieu, L., Merkurjev, E., Chang, J.-M., Gilles, J., Bertozzi, A.L., Detection and Tracking of Gas Plumes
in LWIR Hyperspectral Video Sequence Data, SPIE Conference on Defense Security and Sensing, 87430J, Baltimore, April 29-May 3, 2013. link

[2] Merkurjev, E., Kostic, T. and Bertozzi, A.L., MBO Scheme on Graphs for Segmentation and Image Processing, SIAM J. Imag. Sci.,
6(4), pp. 1903-1930, 2013. link

[1] Peterson, G.E., Campbell, E.T., Balbas, J., Ivy, S., Merkurjev, E., Rodriguez, P., "Relative Performance of Lambert Solvers
1: 0-Revolution Methods, Adv Astronaut Sci", 136 (1), pp. 1495-1510, presented at 20th AAS/AIAA Space Flight Mechanics
Meeting, San Diego, CA, February 14-17, 2010.