[pdf] A. Miroshnikov, K. Kotsiopoulos, A. Ravi Kannan, Mutual information-based group explainers with coalition structure for machine learning model explanations, (2021). arXiv preprint (2021), arXiv:2102.10878.
[pdf] A. Miroshnikov, K. Kotsiopoulos, R. Franks, A. Ravi Kannan, Wasserstein-based fairness interpretability framework for machine learning models, (2020). arXiv preprint (2020), arXiv:2011.03156.
[pdf] A. Miroshnikov, K. Kotsiopoulos, E. Conlon, Asymptotic properties and approximation of Bayesian logspline density estimators for communication-free parallel methods. arXiv preprint (2020),
[pdf] A. Miroshnikov, Stability of Fully Discrete Variational Schemes for Elastodynamics with a Polyconvex Stored Energy. arXiv preprint (2018), arXiv:1611.02888.
Published and Accepted Papers
[pdf] A. Miroshnikov, E. Savelev, Asymptotic properties of parallel Bayesian kernel density estimators. Annals of the Institute of Statistical Mathematics, Volume 71, pp 711-810 (2019), https://doi.org/10.1007/s10463-018-0662-0.
A. Miroshnikov, M. Steinrücken, Computing the joint distribution of the total tree length across loci in populations with variable size. Theoretical Population Biology. (2017). Vol. 118, p.1-19.
[pdf] P.-E. Jabin, A. Miroshnikov, R. Young, Cellulose Biodegradation Models; an Example of Cooperative Interactions in Structured Populations. ESAIM: Mathematical Modelling and Numerical Analysis (2017), 51-6, 2289-2318.
[pdf] A. Miroshnikov, R. Young, Weak* Solutions II: The Vacuum in Lagrangian Gas Dynamics. SIAM Journal on Mathematical Analysis (2017), 49(3), 1810-1843.
[pdf] A. Miroshnikov, R. Young, Weak* Solutions I: A New Perspective on Solutions to Systems of Conservation Laws. Methods and Applications of Analysis (2017). Vol. 24-3, 351-382.
[pdf] A. Miroshnikov, K. Trivisa, Stability and Convergence of Relaxation Schemes to Hyperbolic Balance Laws via a Wave Operator, Journal of Hyperbolic Differential Equations (2015), Vol. 12, No. 1, 189-219.
[pdf] A. Miroshnikov, A. Tzavaras, On the Construction and Properties of Weak Solutions Describing Dynamic Cavitation, Journal of Elasticity (2015), 118-2, 141-185.
[pdf] A. Miroshnikov, Z. Wei, E. Conlon, Parallel Markov Chain Monte Carlo for Non-Gaussian Posterior Distributions, Stat (2015), Vol. 4, Issue 1, 304-319. DOI:10.1002/sta4.97.
[pdf] J. Philips, A. Miroshnikov, P.-J. Haest, D. Springael and E. Smolders, Motile Geobacter Dechlorinators Migrate into a Model Source Zone of Trichloroethene Dense Non-aqueous Phase Liquid: Experimental Evaluation and Modeling, Journal of Contaminant Hydrology (2014), 170, 28-38.
[pdf] A. Miroshnikov, E. Conlon, parallelMCMCcombine: An R Package for Bayesian Methods for Big Data and Analytics, PLoS ONE (2014), 9(9): e108425. DOI:10.1371/journal.pone.0108425.
[pdf] A. Miroshnikov, K. Trivisa, Relative Entropy in Hyperbolic Relaxation for Balance Laws, Communications in Mathematical Sciences (2014), 12-6, 1017-1043.
[pdf] A. Miroshnikov, A. Tzavaras, Convergence of Variational Approximation Schemes for Elastodynamics with Polyconvex Energy," with A. Tzavaras, Journal of Analysis and its Applications (ZAA) (2014), 33-1, 43-64.
[pdf] J. Giesselmann, A. Miroshnikov, A. Tzavaras, The problem of Dynamic Cavitation in Nonlinear Elasticity, Séminaire Laurent Schwartz - EDP et applications (2012-2013), Exp. No. 14, 1-17.
[pdf] A. Miroshnikov, A. Tzavaras, A Variational Approximation Scheme for Radial Polyconvex Elasticity That Preserves the Positivity of Jacobians, Communications in Mathematical Sciences (2012), 10-1, 87-115.
R-package parallelMCMCcombine: Methods for combining independent subset Markov chain Monte Carlo (MCMC) posterior samples to estimate a posterior density given the full data set. (with E. Conlon) (2014).
R-package BayesSummaryStatLM: Methods for generating Markov Chain Monte Carlo (MCMC) posterior samples of Bayesian linear regression model parameters that require only summary statistics of data as input. (with E. Conlon and E. Savelev) (2015).
University of Maryland, College Park, Maryland, 2018.
Iowa State University, Ames, Iowa, 2018.
Broad Institute of MIT and Harvard, Cambridge, Massachusetts, 2017.
Analysis and PDE Seminar, University of Southern California, Los Angeles, California, 2017
PDE and Applied Mathematics Seminar, University of California, Davis, California, 2016
Level Set Collective Seminar, UCLA, Los Angeles, California, 2016
11-th AIMS International Conference on Dynamical Systems, Differential Equations and Applications, Florida, 2016.
British Applied Mathematics Colloquium, University of Oxford, Oxford, UK, 2016.
SIAM-SEAS Conference, University of Alabama Birmingham, Alabama, 2015.
SAND Lab seminar, Massachusetts Institute of Technology, Cambridge, Massachusetts 2014.
Participant of IdeaLab 2014: Program for Early Career Researchers "Toward a more realistic model of ciliated and flagellated organisms," ICERM, Brown University, Providence, Rhode Island, 2014.
IMA Workshop: Mathematics at the Interface of Partial Differential Equations, the Calculus of Variations, and Materials Science, IMA, University of Minnesota, 2014.
AMS Spring Western Section Meeting, Albuquerque, University of New Mexico, 2014.
SIAM Conference on Analysis of Partial Differential Equations, Orlando, Florida. 2013.
PDE seminar, University of Connecticut, Connecticut. 2013.