Profile

I'm glad you found my profile! I'm an assistant professor of AI and data science at AIU where I have been since 2024.

I completed by undergraduate studies at The Ohio State University in 2019 with a double major in mathematics and physics. Immediately after graduation, I began a PhD in statistics at North Carolina State University where I was advised by Brian Reich and Srijan Sengupta. In 2023, I spent seven months at Tokyo Institute of Technology on a JSPS fellowship working with Petter Holme and Tusyoshi Murata. I finished my PhD in 2023 and started at AIU in 2024.

Research Field

I have two main research interests which recently have found some overlap. First, I study networks. Networks are a simple way to represent complex relationships between entities. I consider well-known network structures, e.g., community and core-periphery, and seek to formalize them from a statistical angle.

Second, I am interested in Bayesian inference, particularly in deriving principle prior distributions for scale parameters in hierarchical models. It is a well-known challenge to elicit priors for scale parameters, and I have applied my work to generalized linear mixed models and spatial regression models. Recently, I have also been interested in Bayesian Optimization (BO), a paradigm for optimizing complicated objective functions. I have applied this work to the influence maximization (IM) problem in network science, and can be applied to many other interesting domains.

Recent Activities

Please see my website or Google Scholar for a complete list of publications.

Yanchenko, E., Bondell, H.D. and Reich, B.J. (2024+) The R2D2 prior for generalized linear mixed models, The American Statistician, accepted, arXiv link: arxiv.org/abs/2111.10718

Yanchenko, E. and Sengupta, S. (2024+) A generalized hypothesis test for community structure in networks​, Network Science, 1-17. https://doi.org/10.1017/nws.2024.1

Yanchenko, E., Stevens, S.R., Burns, L., Wruck, L. and Hong, H. (2024+) Effect of imbalanced treatment allocation ratio on combining multiple historical controls in clinical trials, Submitted.

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

Yanchenko, E. (2023+) BOPIM: Bayesian Optimization for influence maximization on temporal networks, arXiv link: http://arxiv.org/abs/2308.04700

Yanchenko, E., Murata, T. and Holme, P. (2023+) Influence maximization on temporal networks: a review, arXiv link: https://arxiv.org/abs/2307.00181

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