Research Area

  • Mathematics of AI/ML, Data Science, Applied Analysis, Applied Probability, Statistics

Research Interests

  • Machine Learning Explainability and Game Theory
  • Fairness of Machine Learning Algorithms
  • Statistics and Probability with applications to Data Science
  • Mathematical Finance
  • Computational and Mathematical Biology
    • Population genomics, including stochastic modeling and inference
    • Population dynamics, including structured populations
  • Partial differential equations with applications to
    • Materials science, including elastodynamics and gas dynamics
    • Singularity formations: vacuums, cavities and fractures
    • Hyperbolic balance laws, including shocks

Collaborators

Patents

    Approved Patents
  • [pdf] A. Miroshnikov, K. Kotsiopoulos, A. Ravi Kannan, R. Kulkarni, S. Dickerson, System and method for mitigating bias in classification scores generated by machine learning models, application 16/891989, Pub. No. US 2021/0383268 A1, Dec. 9, 2021.
    Pending Patents
  • [pdf] A. Miroshnikov, K. Kotsiopoulos, A. Ravi Kannan, R. Kulkarni, S. Dickerson, System and method for utlizing grouped partial depenendence plots and shapley additive explanations in the generation of adverse action reason codes, application 16/868019, Pub. No. US 2021/0350272 A1, Nov. 11, 2021,
  • [pdf] A. Miroshnikov, K. Kotsiopoulos, A. Ravi Kannan, R. Kulkarni, S. Dickerson, System and method for utlizing grouped partial depenendence plots and game-theoretic concepts and their extensions in the generation of adverse action reason codes, application 17/322828, Pub. No. US 2021/0383275 A1, Dec. 9, 2021 (continuation-in-part of application 16/868019).
  • [pdf] A. Miroshnikov, K. Kotsiopoulos, A. Ravi Kannan, R. Kulkarni, S. Dickerson, R. Franks, Computing system and method for creating a data science model having reduced bias, application 17/900753, Pub. No. US 2022/0414766 A1, Dec. 29, 2022 (continuation-in-part of application 16/891989).

Publications

    Submitted Papers and Preprints
  • [pdf] K. Kotsiopoulos, A. Miroshnikov, E. Conlon, Asymptotic properties and approximation of Bayesian logspline density estimators for communication-free parallel methods, arXiv preprint (2023), arXiv:1710.09071v3 (under review, 2023).
  • [pdf] K. Kotsiopoulos, A. Miroshnikov, K. Filom, A. Ravi Kannan, Approximation of group explainers with coalition structure using Monte Carlo sampling on the product space of coalitions and features, arXiv preprint v2 (2024), arXiv:2303.10216v2.
  • [pdf] A. Miroshnikov, K. Kotsiopoulos, A. Ravi Kannan, Stability theory of game-theoretic group feature explanations for machine learning models, arXiv preprint v5 (2024) arXiv:2102.10878v5.
  • [pdf] A. Miroshnikov, K. Kotsiopoulos, R. Franks, A. Ravi Kannan, Model-agnostic bias mitigation methods with regressor distribution control for Wasserstein-based fairness metrics, arXiv preprint (2021) , arXiv:2111.11259v1.
    Published and Accepted Papers
  • [pdf] K. Filom, A. Miroshnikov, K. Kotsiopoulos, A. Ravi Kannan, On marginal feature attributions of tree-based models. Foundations of Data Science, AIMS. (accepted, 2024).
  • [pdf] A. Miroshnikov, K. Kotsiopoulos, R. Franks, A. Ravi Kannan, Wasserstein-based fairness interpretability framework for machine learning models, Machine Learning Journal, Springer. (2022), https://link.springer.com/article/10.1007/s10994-022-06213-9
  • [pdf] A. Miroshnikov, E. Savelev, Asymptotic properties of parallel Bayesian kernel density estimators. Annals of the Institute of Statistical Mathematics, Volume 71, 771-810 (2019), https://doi.org/10.1007/s10463-018-0662-0.
  • [pdf] 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.
    Software Publications
  • 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).

Talks &
Conferences

  • NC State University, Computational and Applied Mathematics Seminar, Nonlinear Analysis Seminar, March 2024. Stability of AI explanations. [pdf].
  • NC State University, Numerical Analysis Seminar, March 2022. Fairness Interpretability [pdf].
  • Penn State University, Applied Mathematics Seminar, March 2022. Fairness Interpretability [pdf].
  • Los Alamos National Lab, ML reading seminar, January 2022. Fairness Interpretability [pdf].
  • Los Alamos National Lab, ML reading seminar, November 2021. Notes on ML interpretability [pdf].
  • Boston University, CISE, Massachusetts, 2021. [pdf], (BU-CISE).
  • 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.
  • PDE seminar, Brown University, Providence, Rhode Island. 2013.
  • SIAM Conference on Mathematical Aspects of Material Science, Philadelphia, Pennsylvania. 2013.
  • Research Workshop "Hyperbolic Conservation Laws and Infinite-Dimensional Dynamical Systems," Department of Mathematics, University of Pittsburgh, Pittsburgh, Pennsylvania. 2012.
  • SIAM Conference on Analysis of PDE, San Diego, California. 2011.
  • AMS Fall Western Section Meeting, University of Utah, Salt Lake City, Utah. 2011
  • "Kinetic Description of Multiscale Phenomena: Modeling, Theory, and Computation," Annual Kinetic FRG meeting, University of Wisconsin-Madison, Madison, Wisconsin. 2011
  • "Conference on Hyperbolic Conservation Laws and Continuum Mechanics" in Honor of Constantine Dafermos' 70-th Birthday, Brown University, Providence, Rhode Island. 2011
  • Research Workshop "Hyperbolic Conservation Laws and Fluid Dynamics," Department of Mathematics, University of Parma, Parma, Italy. 2010
  • The 3rd Annual Meeting of Marie Curie Initial Training Network "DEASE", Institute of Applied and Computational Mathematics, FORTH, Crete, Greece. 2009.
  • PDE Seminar, Virginia Polytechnic Institute and State University, Blacksburg, Virginia. 2008.
  • SIAM Conference on Computational Science & Engineering, Orlando, Florida. 2005.