ERIC YANCHENKO
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Research

Publications
  • Yanchenko, E. (2025+) Pseudo-R2D2 prior for high-dimensional ordinal regression, Statistics and Computing​ (accepted), arXiv link: arxiv.org/abs/2502.17491
  • Yanchenko, E. (2025+) BOPIM: Bayesian Optimization for influence maximization on temporal networks, Technometrics, 1-20. https://doi.org/10.1080/00401706.2025.2505483
  • Yanchenko, E. (2025) Graph sub-sampling for divide-and-conquer algorithms in large networks, Statistics and Computing, 35 (4), 87. https://doi.org/10.1007/s11222-025-10620-y
  • Yanchenko, E., Bondell, H.D. and Reich, B.J. (2025) The R2D2 Prior for Generalized Linear Mixed Models, The American Statistician, 79 (1), 40-49. https://doi.org/10.1080/00031305.2024.2352010
  • Yanchenko, E., Chappell, T.M. and Huseth, A.S. (2025) Bayesian Optimization of Insect Trap Distribution for Pest Monitoring Efficiency in Agroecosystems, Frontiers in Insect Science, 4, 1509942. https://doi.org/10.3389/finsc.2024.1509942
  • Yanchenko, E. (2024+) Statistics, in Introducing the Liberal Arts: A Guidebook for English Learners, Information Age Publishing, ​In Press.
  • Yanchenko, E., Murata, T. and Holme, P. (2024) Influence maximization on temporal networks: a review, Applied Network Science, 9, 16. https://doi.org/10.1007/s41109-024-00625-3 ​
  • Yanchenko, E. and Sengupta, S. (2024) A generalized hypothesis test for community structure in networks​, Network Science, 12 (2), 122-138. https://doi.org/10.1017/nws.2024.1
  • Yanchenko, E., Bondell, H.D. and Reich, B.J. (2024) Spatial regression modeling via the R2D2 framework,  Environmetrics, 35 (2), e2829. http://doi.org/10.1002/env.2829
  • Yanchenko, E., Murata, T. and Holme, P. (2023) Link prediction for ex ante influence maximization on temporal networks, Applied Network Science, 8, 70. doi.org/10.1007/s41109-023-00594-z
  • Swaminathan, A.C., Snyder, L.D., Hong, H., Stevens, S.R., Long, A.S., Yanchenko, E., Qiu, Y., Liu, R., Zhang, H., Fischer, A., Burns, L., Wruck, L., Palmer, S.M. (2023) Generalizability of External Clinical Trial and Electronic Health Record Control Arms in Idiopathic Pulmonary Fibrosis. ​American Journal of Respiratory and Critical Care Medicine, 208 (5), 579-588.​https://doi.org/10.1164/rccm.202210-1947OC  
  • Yanchenko, E. and Sengupta, S. (2023) Core-periphery structure in networks: a statistical exposition, Statistics Surveys​, 17, 42-74. https://doi.org/10.1214/23-SS141 
  • Yanchenko, E. (2022) A divide-and-conquer algorithm for core-periphery identification in large networks. Stat. pp. e475.​ https://doi.org/10.1002/sta4.475​
​
In review
  • Yanchenko, E. and Sengupta, S. (2025+) A label-switching algorithm for fast core-periphery identification, arXiv link: http://arxiv.org/abs/2506.02069
  • Yanchenko, E., Sengupta, S. and Mukherjee, D. (2025+) Statistical inference of core-periphery structures (under review).
  • Feng, B.R., Yanchenko, E., Hill, K.L., Rosman, L.A., Reich, B.J. and Rappold, A.G. (2024+) Mediation analysis of community context effects on heart failure using the survival R2D2 prior, Journal of the Royal Statistical Society: Series C (reject and resubmit), arXiv link: https://arxiv.org/abs/2411.04310
  • Yanchenko, E. (2024+) Oral exams in introductory statistics class with non-native English speakers, Teaching Statistics (major revisions), arXiv link: https://arxiv.org/abs/2409.16613
  • Yanchenko, E., Irie, K. and Sugasawa, S. (2024+) The Group R2D2 Shrinkage Prior for Sparse Linear Models with Grouped Covariates, arXiv link: arxiv.org/abs/2412.15293

Grants
  • KAKENHI Grant-in-Aid for Research Activity Start-up, The R2D2 Shrinkage Prior for Grouped Sparse Linear Models,
    Principal Investigator, Japan Society for the Promotion of Science, 1,430,000 yen, ​2024-2025 (link)

​​Presentations
  • BOPIM: Bayesian Optimization for influence maximization on temporal networks, CCSS Workshop on Computational Social Science: Methods and Applications, Kobe University, Kobe, Japan, 2024 (invited)
  • Graph sub-sampling for divide-and-conquer algorithms in large networks, Economics Workshop, Keio University, Tokyo, Japan, ​2024 (invited)
  • Core-periphery hypothesis testing in networks, Faculty of Economics, The University of Tokyo, Tokyo, Japan, ​2024 (invited)
  • The R2D2 prior for generalized linear mixed models, 6th International Conference on Statistics and Econometrics (EcoSta 2023), Waseda University, Tokyo, Japan, 2023
  • Spatial regression modeling via the R2D2 framework, Workshop on Bayesian Statistics and Econometrics, Temple University Japan, Tokyo, Japan, 2023 (invited)​
  • Comparing Bayesian methods for combining multiple historical controls in clinical trials, Annual Meeting of the Japanese Society of Biometrics, Sapporo, Japan, 2023
  • The R2D2 prior for generalized linear mixed models, Faculty of Economics, The University of Tokyo, Tokyo, Japan, 2023 (invited)
  • A generalized hypothesis test for community structure in networks, Center for Computational Social Science, Kobe University, Kobe, Japan, 2023 (invited)
  • A divide-and-conquer algorithm for core-periphery identification in large networks, North Carolina State University, Raleigh, NC, ​2022 (invited)
  • A divide-and-conquer algorithm for core-periphery identification in large networks (poster), SRCOS Summer Research Conference, Jekyll Island, GA, ​2022
  • A generalized hypothesis test for community structure and homophily in networks, Sunbelt 2022, International Network for Social Network Analysis, Cairns, Australia, 2022.
  • Quantifying the presence/absence of meso-scale structures in networks, North Carolina State University​, Raleigh, NC, 2022.
  • A model-agnostic hypothesis test for community structure and homophily in networks (poster), SRCOS Summer Research Conference, Jekyll Island, GA, 2021. ​Poster link. (won best graduate student poster)
  • A model-agnostic hypothesis test for community structure and homophily in networks, Joint Statistical Meeting, Seattle, WA, 2021. Recording link. Slides link.
  • Big Data Summer Institute Symposium and Poster Presentation, Department of Biostatistics, University of Michigan, 2018. Poster link.

Physics Publications
  • Asel, T., Yanchenko, E., Yang, X., Jiang, S., Krymowski, K., Wang, Y., Trout, A., McComb, D., Windl, W., Goldberger, J., Brillson, L., (2018) Identification of Ge Vacancies as Electronic Defects in Methyl- and Hydrogen-Terminated Germanane, Applied Physics Letters, 113, 061110. 
  • Jiang, S., Krymowski, K., Asel, T., Arguilla, M., Cultrara, N., Yanchenko, E., Yang, X., Brillson, L., Windl W., Goldberger, J.G., (2016) Tailoring the Electronic Structure of Covalently Functionalized Germanane via the Interplay of Ligand Strain and Electronegativity, Chemistry of Materials, 28, 8071-8077. 













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