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EKATERINA RAPINCHUK (MERKURJEV)

Curriculum Vitae

My vitae (pdf version) can be found at the following link: vitae. I include it below.

Employment

* Tenure-Track Assistant Professor, Michigan State University (August 2018 - present)
* Assistant Professor, Michigan State University (August 2016 - July 2018)
* UC President's Postdoctoral Fellow, University of California, San Diego (Fall 2015 - July 2016)

Education

* PhD in Mathematics, UCLA (September 2010- June 2015), GPA: 4.00
* BS/MS (joint degree) in Mathematics, UCLA (September 2006- June 2010), GPA: 3.954

Research Interests

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

Google Scholar Profile: link

Publications

[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.

Grants and Awards

* AWM-NSF Travel Grant (2017)
* AMS Simons Travel Grant (2017)
* UC President's Postdoctoral Fellowship (2015-2017)
* 2015 Pacific Journal of Mathematics Dissertation Prize
* Dissertation Year Fellowship (2014-2015)
* NSF Graduate Fellowship (2011-2014)
* Eugene-Cota Robles Fellowship (2010-2011)
* NSF Research and Training Grant (RTG) Fellowship in Applied Mathematics (2010-2011)
* Sherwood Award (for excellence in undergraduate studies) (2010)
* Departmental Scholar at UCLA (2009-2010)
* Basil Gordon Prize ($1000) for Putnam exam (2008)

Expertise

* Solid background in applied and computational mathematics, optimization, scientific computing, parallel computing,
differential equations, numerical analysis/linear algebra.

* Programming skills: C++, Matlab, OpenMP, Maple, Mathematica

Invited Talks/Conference Presentations/Posters

*  SIAM Conference on Computational Science and Engineering, Fort Worth, Texas, US, March 1-5, 2021
*  Theory and Algorithms in Graph-Based Learning Workshop, University of Minnesota, September 14-18, 2020, via Zoom
*  SIAM Mathematics of Data Science Conference (MDS) in Cincinnati, May 5-7, 2020, via Zoom
* Association for Women in Mathematics, MSU Chapter Seminar, October 23, 2018
* Arjun Krishnan Seminar, Michigan State University, June 18, 2018
* SIAM Conference on Imaging Science, Bologna, Italy, June 5-8, 2018
* Numerical Analysis and Approximation Theory meets Data Science, BIRS, Canada, April 22-27, 2018
* Colloquium, Michigan State University, April 17, 2018
* Top-SUM (Topical Seminar for Undergraduate Mathematicians), MSU, February 16, 2018
* Inverse Problems in Machine Learning, Caltech, February 9-11, 2018
* SIAM Conference on Analysis of Partial Differential Equations, December 9-12, 2017
* CMSE Department Seminar, Michigan State University, October 27, 2017
* Invited Speaker, John H. Barrett Memorial Lectures, University of Tennessee, May 1-3, 2017
* Applied Mathematics Seminar, Michigan State University, April 26, 2017
* Applied Mathematics Seminar, University of Michigan, Ann Arbor, February 17, 2017
* SIAM Conference on Computational Science and Engineering, Atlanta, Feb. 27 - March 3, 2017
* Applied Mathematics Colloquium, University of California, Los Angeles, February 8, 2017
* Applied Mathematics Seminar, Michigan State University, November 10, 2016
 * Midwest Optimization Meeting, Michigan State University, October 22, 2016
* Applied Mathematics Seminar, University of California, Los Angeles, CA, April 29, 2016
* Department of Mathematics Seminar, Michigan State University, January 14, 2016
* Joint Mathematics Meeting, Seattle, WA, January 6-9, 2015
* Department of Mathematics Seminar, Syracuse University, December 14, 2015
* WiMSoCal Symposium, Pomona College, CA, November 7, 2015
* Computational Sciences Seminar, San Diego State University, October 30, 2015
* MURI meeting, ISI Institute, Marina del Rey, CA, September 25, 2015
* 13th U.S. National Congress on Computational Mechanics, San Diego, July 27-30, 2015
* ENS Cachan Seminar, Paris, France, July 8, 2015
* Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland, July 3, 2015
* AWM Research Symposium, College Park, MD, April 11-12, 2015
* IEEE International Conference on Image Processing, Paris, October 27-30, 2014
* Keck Meeting, California NanoSystems Institute, Los Angeles, CA, August 18, 2014
* Algorithms for Threat Detection Workshop, Boulder, CO, March 10-12, 2014
* Fall Western Sectional Meeting (#1095), UCR, Riverside, CA, Nov. 2-3, 2013
* ONR Math Data Science Program Review Meeting, Durham, NC, Sept. 16-19, 2013
* Level Set Seminar, Institute for Pure and Applied Mathematics, CA, August 27, 2013
* Algorithms for Threat Detection Workshop, San Diego, CA, Nov. 26-29, 2012

Teaching Experience

* Fall 2020: Instructor for Introduction to Computational Modeling (CMSE 201), MSU
* Spring 2020: Instructor for Calculus I (Math 132), MSU
* Fall 2019: Instructor for Calculus I (Math 132), MSU
* Fall 2018: Instructor for Capstone Seminar: Machine Learning (Math 496), MSU
* Fall 2018: Instructor for Introduction to Computational Modeling (CMSE 201), MSU
* Spring 2018: Instructor for Introduction to Computational Modeling (CMSE 201), MSU
* Fall 2017: Instructor for Linear Algebra (Math 309), MSU
* Fall 2016: Instructor for Calculus I (Math 132), MSU
* Winter 2016: Instructor for Linear Algebra (Math 20F), UCSD
* Summer 2014: Instructor for 2014 UCLA Math GRE Workshop, UCLA
* Summer 2014: Mentor for RIPS program, Institute for Pure and Applied Mathematics
* Summer 2012: Mentor for Applied Mathematics REU, UCLA
* Winter 2011: Teaching Assistant for Calculus (Math 31B), UCLA

Industry Experience

* Image Scientist, GumGum (March 2015- June 2015)
        -implemented image classification algorithms in C++

        -trained existing image recognition models