- About Me
Ph.D., Applied Mathematics, UCLA, September 2010 - June 2015
B.S./M.S. (Joint Program), Applied Mathematics, UCLA,
Ekaterina Merkurjev was born in St. Petersburg, Russia. She obtained her BS/MA/PhD at University of California, Los Angeles. After a UC President’s Postoctoral appointment at University of California, San Diego, she joined Michigan State University as an Assistant Professor of Mathematics and CMSE. She works on semi-supervised learning, unsupervised learning and image processing. Classification of high-dimensional data is one major application.
- Research Interests
• Optimization for Classication (clustering) using a Graphical Framework
• Machine Learning
• Text Mining Studies.
• Image Processing
Bae, E. and Merkurjev, E., "Convex Variational Methods for Multiclass Data Segmentation on Graphs", submitted.Merkurjev, E., Bertozzi, A.L., Chung, F., "A Semi-Supervised Heat Kernel Pagerank MBO Algorithm for Data Classification", submitted.Meng, G., Merkurjev, E., Koniges, A., Bertozzi, A.L., "Hyperspectral Video Analysis Using Graph Clustering Methods", in preparation.Merkurjev, E., Bertozzi, A.L., Lerman, K., Xiaoran, Y., "Modified Cheeger and Ratio Cut Methods Using the Ginzburg-Landau Functional for Classification of High-Dimensional Data", accepted in Inverse Problems.
Merkurjev, E., Bae, E., Bertozzi, A.L., and Tai, X.-C., "Global Binary Data Optimization on Graphs for Data Segmentation", Journal of Mathematical Imaging and Vision, 52(3), pp. 414-435, 2015.
SS-18: CMSE 201 Intro to Computational Modeling
Click "Teaching" link to see past courses.