Ekaterina Rapinchuk (Merkurjev) CV

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 (June 2015), Advisor: Prof. Andrea Bertozzi, GPA: 4.00

* BS/MS (joint degree) in Mathematics, UCLA (June 2010), GPA: 3.954

Research Interests

* Graph-based methods, semi-supervised learning, image processing. Applications include classification of high-dimensional data.

Publications

[27] Bhusal, G., Lou, Y., Garcia Cardona, C., Merkurjev, E., Hyperspectral Image Unmixing with Endmember Bundles and Different Sparsity Promoting Functions, in preparation.
[26] Hayes, N., Merkurjev, E., Wei, G.-W., A Persistent Sheaf Laplacian Model for Protein Flexibility Analysis, in preparation.
[25] Bhusal, G., Miller, K., Merkurjev, E., MALADY: Multistage Active Learning with Auction Dynamics on Graphs, submitted. link
[24] Hayes, N., Merkurjev, E., Wei, G.-W., Graph-based Bidirectional Transformer Decision Threshold Algorithm for Class Imbalanced Molecular Data, Journal of Computational Biophysics and Chemistry, published online September 19, 2024. link
[23] Bhusal, G., Merkurjev, E., Wei, G.-W, Persistent Laplacian-enhanced Algorithm for Scarcely Labeled Data Classification, Machine Learning, 113, pp. 7267-7292, 2024. link
[22] Zhu, Z., Dou, B., Merkurjev, E., Ke, L., Chen, L., Jiang, J., Zhu, Y., Liu, J., Zhang, B., Wei, G.-W., Machine Learning Methods for Small Data Challenges in Molecular Science, Chemical Reviews, 123(13), pp. 8736-8780, 2023. link
[21] Hayes, N., Merkurjev, E., Wei, G.-W., Integrating Transformer and Autoencoder Techniques with Spectral Graph Algorithms for the Prediction of Scarcely Labeled Molecular Data, Computers in Biology and Medicine, Volume 153, February 2023 issue, Article 106479, 2023. link

[20] Merkurjev, E., Efficient Graph-based Spectral Techniques for Data with Few Labeled Samples, International Journal of Data Science and Analytics, 18(2), pp. 1-26, 2023. link
[19] Merkurjev, E., Similarity Graph-based Max-flow and Dual Approaches to Semi-supervised Data Classification and Image Segmentation, International Journal of Machine Learning and Cybernetics, 14(12), pp. 1-26, 2023. link
[18] Merkurjev, E., Nguyen, D., Wei, G.-W., Multiscale Laplacian Learning, Applied Intelligence, 53(12), pp. 15727-15746, 2023, published online November 28, 2022. link
[17] Balaji, A., Rapinchuk (Merkurjev), E., Similarity Graph-based Semi-Supervised Methods for Multiclass Data Classification, Journal of Emerging Investigators, 2021, with high school student Ashwin Balaji at Novi High School in Michigan.
[16] Merkurjev, E., A Graphical Approach for Multiclass Classification and for Correcting the Labeling Errors in Mislabeled Training Data, Intelligent Data Analysis, 25(4), pp. 879-906, 2021. link
[15] Merkurjev, E., A Fast Graph-Based Classification Method Applied to Unsupervised Classification of 3D Point Clouds, Pattern Recognition Letters, 136, pp. 154-160, 2020. link
[14] 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
[13] Waters, A., Merkurjev, E., Asymptotics for Optimal Design Problems for the Schrodinger Equation with a Potential, Journal of Optimization, 2018(3), pp. 1-16, 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] 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
[10] 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
[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,
Journal of Mathematical Imaging and Vision, 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 Transactions on Pattern Analysis and Machine Intelligence, 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 Journal on Imaging Sciences, 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, Advances in the Astronautical Sciences, 136 (1), pp. 1495-1510, presented at 20th AAS/AIAA Space Flight Mechanics Meeting, San Diego, CA, February 14-17, 2010. link

Funded Grants

[5] SP (Senior Personnel): NSF 23-519: Major Research Instrumentation (MRI) Program, PI: Mohammed A Ben-Idris, co-PIs: Joydeep Mitra, Subir K Biswas, Kristen Cetin, Woongkul Lee
- Title: Equipment: MRI: Track 1 Acquisition of an Interactive and Multi-functional Real-time Simulator for Smart Power and Energy Systems
- Award Amount: $762,500
- Dates: 9/2024- 8/2027
[4] PI (Principal Investigator): NSF DMS CDS&E-MSS, Co-PI: Guowei Wei
- Title: Collaborative research: Integrating algebraic topology, graph theory, and multiscale analysis for learning complex and diverse datasets
- Award Amount: $350,000
- Dates: 9/2021- 8/2024
[3] Single PI (Principal Investigator): Simons Foundation Collaboration Grant for Mathematicians (Declined due to award of NSF grant)
- Title: Design of algorithms for machine learning tasks using graph-based and semisupervised frameworks
- Award Amount: $42,000
- Dates: 9/2021- 8/2026
[2] Single PI (Principal Investigator): AMS Simons Travel Grant
- Title: A fusion of graphical and optimization approaches applied to machine learning
- Award Amount: $4,000
- Dates: 7/2017- 6/2019
[1] Single PI (Principal Investigator): AWM-NSF Travel Grant
- funding to attend the workshop "Numerical Analysis and Approximation Theory meets Data Science" in Banff, Alberta, Canada, 4/22/2018-4/27/2018

Awards

* J. Sutherland Frame Excellence-in-Teaching Award (April 2022)
* 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 highest score on the William Lowell Putnam Examination among UCLA students (2008)

Expertise

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

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

Featured Talks

* John H. Barrett Memorial Lectures, University of Tennessee, TN, May 1-3, 2017
* Association for Women in Mathematics Research Symposium, MD, April 11, 2015
* IEEE International Conference on Image Processing, Paris, October 27-30, 2014

Invited Talks/Conference Presentations/Posters

* Joint Mathematics Meetings, Seattle, Washington, January 8 - 11, 2025
* SIAM Conference on Imaging Science, Atlanta, Georgia, May 28 - 31, 2024
* One World Seminar Series on the Mathematics of Machine Learning, July 5th, 2023, via Zoom
* SIAM Conference on Mathematics of Data Science, San Diego, September 26-30, 2022, via Zoom
* ACRES REU research presentation, MSU, June 2022
* Data Science Group Meeting, UCLA (led by Andrea Bertozzi), May 24, 2022, via Zoom
* SIAM Conference on Imaging Science, Berlin, Germany, March 22-25, 2022, via Zoom
* ECRE program for underrepresented minorities, MSU, May 24, 2021, via Zoom
* UC Davis Mathematics of Data and Decisions Seminar, May 18, 2021, via Zoom
* SIAM Conference on Computational Science and Engineering, Fort Worth, Texas, US, March 1-5, 2021
* Seminar (Math 285J), University of California, Los Angeles, November 20, 2020, via Zoom
* 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
* Seminar (Arjun Krishnan lab), 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

Conference Organization

* Organizer of a mini-symposium "Graph-based Techniques in Machine Learning"" of the SIAM Great Lakes Section Annual Meeting, MSU, October 14, 2023

Teaching Experience

* Fall 2024: Matrix Algebra I (Math 314), MSU
* Fall 2024: Optimization Methods in Data Science (CMSE 382), MSU
* Spring 2024: Instructor for Capstone Seminar in Mathematics (Math 496), MSU
* Fall 2023: Instructor for Numerical Analysis I (Math 850), MSU, graduate course
* Spring 2023: Matrix Algebra I (Math 314), MSU
* Fall 2022: Instructor for Optimization Methods in Data Science (CMSE 382), MSU
* Spring 2022: Instructor for Capstone Seminar: Machine Learning (Math 496), MSU
* Fall 2021: Instructor for College Algebra I (Math 103A), MSU
* Spring 2021: Instructor for Capstone Seminar: Machine Learning (Math 496), MSU
* 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

Graduate Student Mentoring

* Nicole Hayes - (December 2020 - present)
- I am a PhD advisor of Nicole Hayes (a Ph.D. student in the MSU Mathematics Department), also co-advised by Prof. Guowei Wei. She is studying graph-based topological techniques for applications such as biological data.

* Gokul Bhusal - (Summer 2022 - present)
- I am a PhD advisor of Gokul Bhusal (a Ph.D. student in the MSU Mathematics Department). He is studying semi-supervised graph-based techniques and deep learning.

Undergraduate Student Mentoring

- Bao Hoang, Undergraduate Student, MSU Math Department - (Independent Study from December 2023- present)
- Yizhen Wang, Undergraduate Student, the Southeast University, Nanjing, China - (August 2023- December 2023, Math Exchange Program with China)
- Marco Abat, Undergraduate Student, MSU Math Department - (August 2023- December 2023, Math Exchange Program with China)
- Aidan Gollan, Undergraduate Student, MSU Math Department - (August 2023- December 2023, Math Exchange Program with China)
- Xianglin Chen, Undergraduate Student, Jilin University - (August 2022- December 2022, Math Exchange Program with China)
- Bao Hoang, Undergraduate Student, MSU Math Department - (August 2022- December 2022, Math Exchange Program with China)
- Ziwen Meng, Undergraduate Student, Jilin University - (August 2022- December 2022, Math Exchange Program with China)
- Alexander Sietsema, Undergraduate Student, MSU Math Department - (Independent Study during the 2021-2022 Academic Year)
- Nyssa Gaitor, Undergraduate Student at a college in the Michigan area - (May 2021- present, ECRE Program)
- Alexis Braswell, Undergraduate Student at a college in the Michigan area - (May 2021- present, ECRE program)
- Yunzhang Hu, Undergraduate Student, Xi'an Jiaotong University - (January 2021 - May 2021, Math Exchange Program with China)
- Nicholas Grabill, Undergraduate Student, MSU Math Department - (January 2021- May 2021, Math Exchange Program with China)
- Alexander Sietsema, Undergraduate Student, MSU Math Department - (January 2021- May 2021, Math Exchange Program with China)
- Calarina Muslimani, Undergraduate Student, University of Maryland - (June 2019 - August 2019, ACRES REU)
- Daria Garkavtseva, Undergraduate Student, University of Massachusetts, Amherst - (June 2019 - August 2019, ACRES REU)
- Elena Komesu, Undergraduate Student, MSU Math Department - (April 2018 - May 2019, Honors College Professorial Assistantship program)

Involvement in Summer or Semester Programs

[9] Fall 2023: Faculty Mentor for the MSU Exchange Program with China (Southeast University)
- Project: Semi-supervised and supervised machine learning techniques for data classification
- Mentor for Yizhen Wang, Marco Abat and Aidan Gollan

[8] Summer 2023: Virtually advised team led by Yifei Lou and Cristina Garcia-Cardona during the Research Collaboration Workshop, Women in Data Science and Mathematics (August 7 - 11, 2023)
- Project: Feature learning and optimization techniques for machine learning tasks

[7] Fall 2022: Faculty Mentor for the MSU Exchange Program with China (Jilin University)
- Project: Comparison of Machine Learning Methods
- Mentor for Xianglin Chen, Bao Hoang and Ziwen Meng

[6] Summer 2021: Faculty Mentor for ECRE program
- Project: Comparing Machine Learning Methods
- ECRE program at MSU is an opportunity for undergraduates from underrepresented minority groups (often from community colleges) to engage in a 10-week part-time computing research project to solve real-world scientific problems.
- Mentor for Nyssa Gaitor and Alexis Braswell

[5] Spring 2021: Faculty Mentor for the MSU Exchange Program with China (Jiaotong University)
- Project: Semi-Supervised and Supervised Learning Methods
- Mentor for Alexander Sietsema, Nicholas Grabill and Yunzhang Hu

[4] Summer 2019: Faculty Mentor for the 2019 ACRES program, MSU
- Project: Data Classification and Image Segmentation
- Mentor for Calarina Muslimani and Daria Garkavtseva

[3] Fall 2018, Spring 2019: Honors College Professorial Assistantship program
- Project: Machine Learning and TensorFlow
- Mentor for Elena Komesu

[2] Summer 2014: Mentor for RIPS program, Institute for Pure and Applied Mathematics
- Project: Google LA- Text Classification
- Mentor for Simona Boyadzhiyska, Wei Qian, Laura Asaro and Lorena Maxwell

[1] Summer 2012: Mentor for Applied Mathematics REU, UCLA
- Project: Hyperspectral Image Segmentation
- Mentor for Torin Gerhart, Justin Sunu and Lauren Lieu

Open Source Code

Open source code and software is available at:
[1] my webpage linked here
[2] my Github account linked here
[3] IPOL webpage linked here

Outreach

[5] Mentor of high school student Veena Sundararajan (March 2023- present)
- Veena is a sophomore high school student in Tennessee, and I am mentoring her on machine learning and deep learning techniques.

[4] Mentor of high school student Ashwin Balaji (June 2020 - August 2021)
- Ashwin is a senior in Novi High School in Michigan, and I am mentoring him through Zoom meetings on machine learning techniques. His research work with me was accepted at the NCUR and Sigma Xi conferences and published at the Journal of Emerging Investigators.

[3] Presenter at the MSU Science Festival (Spring 2024 and Spring 2021)
- The presentation in Spring 2024 and Spring 2021 was titled "AI School: An Introduction to Machine Learning" and was an introduction to fundamental concepts of machine learning, a study that has created a "fourth industrial revolution". I also helped organize the event "Kids Verses Machine Learning" at the MSU Science Festival in 2024. The event involved machine learning activities for children, such as using machine learning computational resources to identify handwritten digits.

[2] One of the leaders of Machine Learning Consultancy (MLC)
- MLC founded by Guowei Wei, at MSU. MLC integrates MSU's expertise in machine learning and provides free consulting services on machine learning techniques related to research at MSU.

[1] Member of Math Alliance
- Math Alliance is an organization seeking to increase

Enhancement of Curricula

* Development of new course Math 483 on machine learning to be offered every Spring semester. This course will focus on mathematical theory and theoretical performance guarantees of the covered machine learning methods.

* Development of complete curriculum for Math 496: Capstone Seminar on Machine Learning taught in Fall 2018, Spring 2021 and Spring 2022. The course covers fundamental aspects of machine learning and deep learning, including k-nearest neighbors, support vector machines, neural networks, convolutional neural networks, autoencoders, generative adversarial networks, decision trees, random forests, regression, clustering, dimension reduction, density estimation, anomaly detection, etc.

Department Service

* Undergrad Studies Committee, Mathematics Department (2023-2024 Academic Year)
* Modernizing the Computational Math BA/BS Committee (2022-2023 Academic Year)
* Frame Teaching Award Committee (2022-2023 Academic Year)
* Awards Committee, Department of CMSE (2022-2023, 2023-2024 Academic Years)
* NatSci representative to the University Committee on Faculty Affairs (2022-2023, 2023-2024, 2024-2025 Academic Years)
* Grad Studies Committee (2021-2022, 2022-2023, 2024-2025 Academic Years)
* Seminar/Colloquiua Committee (2021-2022, 2020-2021, 2019-2020, 2024-2025 Academic Years)
* Organizer of the MSU Applied Mathematics Seminar (2018-2019 Academic Year)
* Learning Technology Committee (2018-2019 Academic Year)
* Dissertation committee of David Esteban Munoz Ramirez (2024), Ben Jones (2023), Mushal Zia (2023), Faisal Abdulaziz Suwayyid (2023), Bowen Su (2023), Marcus Djokic (2023), Gengzhuo Liu (2023), Li Shen (2023), Gokul Bhusal (2023), Nicole Hayes (2021), Daniel Griffin (2022), Eric Flynn (2021), Liping Yin (2021), Albert Chua (2021), Yuta Hozumi (2021), Azzam Alfarraj (2021), Xiaoqi Wei (2021), Christopher Grow (2021), Mark Roach (2020), Nazanin Donyapour (2020), Leo Li (2020)

Industry Experience

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