[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, Filed June 3, 2020. Granted June 4, 2024.
[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, Filed May 6, 2020, Approved July 30, 2024.
Pending Patents
[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).
[pdf] K. Kotsiopoulos, A. Miroshnikov, A. Ravi Kannan, Computing system and method for applying Monte Carlo estimation to determine the contribution of dependent input variable groups on the output of a data science model, application 18/111823, Pub. No. US 2024/0281450 A1, Aug. 22, 2024.
[pdf] K. Kotsiopoulos, A. Miroshnikov, A. Ravi Kannan, Computing system and method for applying Monte Carlo estimation to determine the contribution of independent input variables within dependent variable groups on the output of a data science model, application 18/111825, Pub. No. US 2024/0281669 A1, Aug. 22, 2024.
[pdf] K. Kotsiopoulos, A. Miroshnikov, A. Ravi Kannan, Computing system and method for applying Monte Carlo estimation to determine the contribution of independent input variables within dependent variable groups on the output of a data science model, application 18/111825, Pub. No. US 2024/0281670 A1, Aug. 22, 2024.
Publications
Submitted Papers and Preprints
[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. Under review in two parts:
[Stability I] + [Supplement] (June, 2024).
[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, 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. Volume 6, Issue 4: 395-467, (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
King Abdullah University of Science and Technology, Saudi Arabia, AMSC/STAT Graduate Seminar and Applied Mathematics Seminar, 2024. AIE Stability, AIE Mathematics.
SIAM Conference on Mathematics of Data Science, 2024. Mathematics of Explainable AI with Applications to Finance and Medicine (co-organizer). Stability of AI explanations. [pdf].
NC State University, Computational and Applied Mathematics Seminar, Nonlinear Analysis Seminar, March 2024. Stability of AI explanations. [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.