

EKATERINA RAPINCHUK (MERKURJEV)
Curriculum Vitae
My vitae (pdf version) can be found at the following link:
vitae. I include it below.
Employment
* TenureTrack 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
* Semisupervised learning, unsupervised learning, image processing. Applications include classification of highdimensional data.
Google Scholar Profile: link
Publications
[16] Merkurjev, E., A Fast GraphBased Classification Method Applied to Unsupervised Classification of 3D Point Clouds, Pattern Recognition Letters, 136, pp. 154160, 2020.
[15] Bertozzi, A.L., Merkurjev, E., Graphbased 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. 503532, 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. 288310, 2018. link
[11] Bae, E. and Merkurjev, E., Convex Variational Methods on Graphs for Multiclass Segmentation of HighDimensional Data and Point Clouds, Journal of Mathematical Imaging and Vision, 58(3), pp. 468493, 2017. link
[10] Merkurjev, E., Bertozzi, A.L., Chung, F., A SemiSupervised Heat Kernel Pagerank MBO Algorithm for Data Classification, Communications in Mathematical Sciences, 16(5), pp. 12411265, 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. 218245, 2017. link
[8] Merkurjev, E., Bertozzi, A.L., Lerman, K., Yan, X., Modified Cheeger and Ratio Cut Methods Using the GinzburgLandau Functional for Classification of HighDimensional 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. 414435, 2015. link
[6] Merkurjev, E., Sunu, J. and Bertozzi, A.L., Graph MBO Method for Multiclass Segmentation of Hyperspectral Standoff Detection Video, IEEE International Conference on Image Processing, pp. 689693, Paris, France, October 2730, 2014. link
[5] Merkurjev, E., GarciaCardona, C., Bertozzi, A.L., Flenner, A. and Percus, A., Research
Announcement: Diffuse Interface Methods for Multiclass Segmentation of HighDimensional
Data, Applied Mathematics Letters, 33, pp. 2934, 2014. link
[4] GarciaCardona, C., Merkurjev, E., Bertozzi, A.L., Percus, A., Flenner, A., Multiclass
Segmentation Using the GinzburgLandau Functional and the MBO Scheme, IEEE
Trans. Pattern Anal. Mach. Intell., 36(8), pp. 16001614, 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 29May 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. 19031930, 2013. link
[1] Peterson, G.E., Campbell, E.T., Balbas, J., Ivy, S., Merkurjev, E., Rodriguez, P., "Relative Performance of Lambert Solvers 1: 0Revolution Methods, Adv Astronaut Sci", 136 (1), pp. 14951510, presented at 20th AAS/AIAA Space Flight Mechanics Meeting,
San Diego, CA, February 1417, 2010.
Grants and Awards
* AWMNSF Travel Grant (2017)
* AMS Simons Travel Grant (2017)
* UC President's Postdoctoral Fellowship (20152017)
* 2015 Pacific Journal of Mathematics Dissertation Prize
* Dissertation Year Fellowship (20142015)
* NSF Graduate Fellowship (20112014)
* EugeneCota Robles Fellowship (20102011)
* NSF Research and Training Grant (RTG) Fellowship in Applied Mathematics (20102011)
* Sherwood Award (for excellence in undergraduate studies) (2010)
* Departmental Scholar at UCLA (20092010)
* 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 15, 2021
* Theory and Algorithms in GraphBased Learning Workshop, University of Minnesota, September 1418, 2020, via Zoom
* SIAM Mathematics of Data Science Conference (MDS) in Cincinnati, May 57, 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 58, 2018
* Numerical Analysis and Approximation Theory meets Data Science, BIRS, Canada, April 2227, 2018
* Colloquium, Michigan State University, April 17, 2018
* TopSUM (Topical Seminar for Undergraduate Mathematicians), MSU, February 16, 2018
* Inverse Problems in Machine Learning, Caltech, February 911, 2018
* SIAM Conference on Analysis of Partial Differential Equations, December 912, 2017
* CMSE Department Seminar, Michigan State University, October 27, 2017
* Invited Speaker, John H. Barrett Memorial Lectures, University of Tennessee, May 13, 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 69, 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 2730, 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 1112, 2015
* IEEE International Conference on Image Processing, Paris, October 2730, 2014
* Keck Meeting, California NanoSystems Institute, Los Angeles, CA, August 18, 2014
* Algorithms for Threat Detection Workshop, Boulder, CO, March 1012, 2014
* Fall Western Sectional Meeting (#1095), UCR, Riverside, CA, Nov. 23, 2013
* ONR Math Data Science Program Review Meeting, Durham, NC, Sept. 1619, 2013
* Level Set Seminar, Institute for Pure and Applied Mathematics, CA, August 27, 2013
* Algorithms for Threat Detection Workshop, San Diego, CA, Nov. 2629, 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


