{"id":363,"date":"2020-07-09T16:51:59","date_gmt":"2020-07-09T16:51:59","guid":{"rendered":"http:\/\/sites.rutgers.edu\/farzad-yousefian\/?page_id=363"},"modified":"2025-09-06T17:05:52","modified_gmt":"2025-09-06T17:05:52","slug":"publications","status":"publish","type":"page","link":"https:\/\/sites.rutgers.edu\/farzad-yousefian\/publications\/","title":{"rendered":"Publications"},"content":{"rendered":"<h6><strong>Published\/Accepted Journal Articles<\/strong> (* and ** refer to graduate and undergraduate student advisee names, respectively)<\/h6>\n<ul>\n<li>S. Samadi* and F. Yousefian, <em>Improved Guarantees for Optimal Nash Equilibrium Seeking and Bilevel Variational Inequalities<\/em>, <strong>SIAM Journal on Optimization, <\/strong>35 (1), 369\u2013399, 2025. <a href=\"https:\/\/doi.org\/10.1137\/23M1589402\">doi<\/a>: 10.1137\/20M1357378\u00a0[<a href=\"https:\/\/arxiv.org\/abs\/2307.12511\">arXiv<\/a>]<\/li>\n<\/ul>\n<ul>\n<li>Z. Alizadeh, A. Jalilzadeh, and F. Yousefian, <em>Randomized Lagrangian Stochastic Approximation for Large-Scale Constrained Stochastic Nash Games<\/em>, <strong>Optimization Letters<\/strong>, vol. 18, pp. 377\u2013401, 2024, <a href=\"https:\/\/link.springer.com\/article\/10.1007\/s11590-023-02079-5\">doi<\/a>: 10.1007\/s11590-023-02079-5 [<a href=\"https:\/\/arxiv.org\/abs\/2304.07688\">arXiv<\/a>]<\/li>\n<\/ul>\n<ul>\n<li>A. Jalilzadeh, F. Yousefian, and M. Ebrahimi*, <em>Stochastic Approximation for Estimating the Price of Stability in Stochastic Nash Games<\/em>, <strong>ACM<\/strong> <strong>Transactions on Modeling and Computer Simulation<\/strong>, vol. 34, pp 1\u201424, 2024, <a href=\"https:\/\/doi.org\/10.1145\/3632525\">doi<\/a>: 10.1145\/3632525 [<a href=\"https:\/\/arxiv.org\/abs\/2203.01271\">arXiv<\/a>]<\/li>\n<\/ul>\n<ul>\n<li>D. Burbano and F. Yousefian, <em>A Fish Rheotaxis Mechanism as a Zero-Order Optimization Strategy,<\/em> <strong>IEEE Access<\/strong>, vol. 11, pp. 102781\u2013102795, 2023, <a href=\"https:\/\/ieeexplore.ieee.org\/abstract\/document\/10250765\">doi<\/a>: 10.1109\/ACCESS.2023.3315240<\/li>\n<\/ul>\n<ul>\n<li>H. D. Kaushik*, S. Samadi*, and F. Yousefian, <em>An Incremental Gradient Method for Optimization Problems with Variational Inequality Constraints<\/em>, <strong>IEEE Transactions on Automatic Control<\/strong>, 68 (12), 7879\u20137886, 2023. <a href=\"https:\/\/ieeexplore.ieee.org\/document\/10057966\">doi<\/a>: 10.1109\/TAC.2023.3251851 [<a href=\"https:\/\/arxiv.org\/abs\/2105.14205\">arXiv<\/a>]<\/li>\n<\/ul>\n<ul>\n<li>\n<p dir=\"ltr\" role=\"presentation\">S. Cui, U. V. Shanbhag, and F. Yousefian, <em>Complexity Guarantees for an Implicit Smoothing-Enabled Method for Stochastic MPECs<\/em>, <strong>Mathematical Programming<\/strong>, 198, 1153\u2013225, 2023. <a href=\"https:\/\/link.springer.com\/article\/10.1007\/s10107-022-01893-6\">doi<\/a>: 10.1007\/s10107-022-01893-6 [<a href=\"https:\/\/rdcu.be\/cW7yF\">Springer Link<\/a>] [<a href=\"https:\/\/arxiv.org\/abs\/2104.08406\">arXiv<\/a>]<\/p>\n<\/li>\n<li>\n<p dir=\"ltr\" role=\"presentation\">A. Jalilzadeh, A. Nedich, U. V. Shanbhag, and F. Yousefian, <em>A Variable Sample-Size Stochastic Quasi-Newton Method for Smooth and Nonsmooth Stochastic Convex Optimization<\/em>, <strong>Mathematics of Operations Research<\/strong>, 47 (1), pp. 690 \u2013719, 2022. <a href=\"https:\/\/doi.org\/10.1287\/moor.2021.1147\">doi<\/a>: 10.1287\/moor.2021.1147 [<a href=\"https:\/\/arxiv.org\/abs\/1804.05368\">arXiv<\/a>]<\/p>\n<\/li>\n<li>\n<p dir=\"ltr\" role=\"presentation\">H. D. Kaushik* and F. Yousefian, <em>A Method with Convergence Rates for Optimization Problems with Variational Inequality Constraints<\/em>, <strong>SIAM Journal on Optimization<\/strong>, 31 (3), 2171\u20132198, 2021. <a href=\"https:\/\/doi.org\/10.1137\/20M1357378\">doi<\/a>: 10.1137\/20M1357378 [<a href=\"https:\/\/arxiv.org\/abs\/2007.15845\">arXiv<\/a>]<\/p>\n<\/li>\n<li>\n<p dir=\"ltr\" role=\"presentation\">F. Yousefian, A. Nedich, and U. V. Shanbhag, <em>On Stochastic and Deterministic Quasi-Newton Methods for Nonstrongly Convex Optimization: Asymptotic Convergence and Rate Analysis<\/em>, <strong>SIAM Journal on Optimization<\/strong>, 30 (2), 1144-1172, 2020. <a href=\"https:\/\/doi.org\/10.1137\/17M1152474\">doi<\/a>: 10.1137\/17M1152474 [<a href=\"https:\/\/arxiv.org\/abs\/1710.05509\">arXiv<\/a>]<\/p>\n<\/li>\n<li>\n<p dir=\"ltr\" role=\"presentation\">N. Majlesinasab*, F. Yousefian, and A. Pourhabib, <em>Self-tuned Stochastic Mirror Descent Methods for Smooth and Nonsmooth High-Dimensional Stochastic Optimization<\/em>, <strong>IEEE Transactions on Automatic Control<\/strong>, 64 (10), 4377-4384, 2019. <a href=\"https:\/\/ieeexplore.ieee.org\/document\/8636190\">doi<\/a>: 10.1109\/TAC.2019.2897889 [<a href=\"https:\/\/arxiv.org\/abs\/1709.08308\">arXiv<\/a>]<\/p>\n<\/li>\n<li>\n<p dir=\"ltr\" role=\"presentation\">F. Yousefian, A. Nedich, and U. V. Shanbhag, <em>On Stochastic Mirror-Prox Algorithms for Stochastic Cartesian Variational Inequalities: Randomized Block Coordinate and Optimal Averaging Schemes<\/em>, <strong>Set-Valued and Variational Analysis<\/strong>, 26 (4), 789-819, 2018. <a href=\"https:\/\/doi.org\/10.1007\/s11228-018-0472-9\">doi<\/a>: 10.1007\/s11228-018-0472-9 [<a href=\"http:\/\/rdcu.be\/Jr2U\">Springer Link<\/a>]<\/p>\n<\/li>\n<li>\n<p dir=\"ltr\" role=\"presentation\">F. Yousefian, A. Nedich, and U. V. Shanbhag, <em>On Smoothing, Regularization, and Averaging in Stochastic Approximation Methods for Stochastic Variational Inequality Problems<\/em>, <strong>Mathematical Programming<\/strong>, 165 (1), 391-431, 2017.\u00a0 <a href=\"https:\/\/link.springer.com\/article\/10.1007%2Fs10107-017-1175-y\">doi<\/a>: 10.1007\/s10107-017-1175-y [<a href=\"https:\/\/arxiv.org\/abs\/1411.0209\">arXiv<\/a>]<\/p>\n<\/li>\n<li>\n<p dir=\"ltr\" role=\"presentation\">F. Yousefian, A. Nedich, and U. V. Shanbhag, <em>Self-Tuned Stochastic Approximation Schemes for Non-Lipschitzian Stochastic Multi-User Optimization and Nash Games<\/em>, <strong>IEEE Transactions on Automatic Control<\/strong>, 61 (7), 1753-1766, 2016. <a href=\"https:\/\/www.doi.org\/10.1109\/TAC.2015.2478124\">doi<\/a>: 10.1109\/TAC.2015.2478124 [<a href=\"https:\/\/arxiv.org\/abs\/1301.1711\">arXiv<\/a>]<\/p>\n<\/li>\n<li>\n<p dir=\"ltr\" role=\"presentation\">F. Yousefian, A. Nedich, and U. V. Shanbhag, <em>On Stochastic Gradient and Subgradient Methods with Adaptive Steplength Sequences<\/em>, <strong>Automatica<\/strong>, 48 (1), 56-67, 2012. <a href=\"https:\/\/doi.org\/10.1016\/j.automatica.2011.09.043\">doi<\/a>: 10.1016\/j.automatica.2011.09.043 [<a href=\"https:\/\/arxiv.org\/abs\/1105.4549\">arXiv<\/a>]<\/p>\n<\/li>\n<\/ul>\n<h5><strong>Book Chapters<\/strong><\/h5>\n<ul>\n<li>\n<p dir=\"ltr\" role=\"presentation\">D. Newton, F. Yousefian, and R. Pasupathy, <em>Stochastic Gradient Descent: Recent Trends<\/em>, <strong>INFORMS TutORials in Operations Research<\/strong>, pp. 193-220, 2018. <a href=\"https:\/\/doi.org\/10.1287\/educ.2018.0191\">doi<\/a>: 10.1287\/educ.2018.0191 [<a href=\"https:\/\/www.dropbox.com\/s\/pcj0h4jbyjqbqi5\/INFORMS%20Tutorial%202018%20on%20SGD.pdf?dl=0\">Slides<\/a>]<\/p>\n<\/li>\n<\/ul>\n<h5><\/h5>\n<h5><strong>Under Review\u00a0<\/strong><\/h5>\n<ul>\n<li>Y. Qiu*, U. V. Shanbhag, and F. Yousefian, <em>Zeroth-Order Federated Methods for Stochastic MPECs and Nondifferentiable Nonconvex Hierarchical Optimization, <\/em>under revision (July 2025) [<a href=\"https:\/\/arxiv.org\/pdf\/2309.13024.pdf\">arXiv<\/a>]<\/li>\n<\/ul>\n<ul>\n<li>M. Ebrahimi*, U. V. Shanbhag, and F. Yousefian, <em>On the Resolution of Stochastic MPECs over Networks: Distributed Implicit Zeroth-Order Gradient Tracking Methods, <\/em>submitted (May 2025) [<a href=\"https:\/\/arxiv.org\/abs\/2505.22916\">arXiv<\/a>]<\/li>\n<\/ul>\n<ul>\n<li>S. Samadi*, D. Burbano, and F. Yousefian, <em>On Iteratively Regularized First-order Methods for Simple Bilevel Optimization, <\/em>submitted (April 2025) [<a href=\"https:\/\/arxiv.org\/abs\/2504.08079\">arXiv<\/a>]<\/li>\n<\/ul>\n<ul>\n<li>M. Ebrahimi*, Y. Qiu*, S. Cui, and F. Yousefian, <em>Regularized Federated Methods with Universal Guarantees for Simple Bilevel Optimization, <\/em>submitted (March 2025) [<a href=\"https:\/\/arxiv.org\/pdf\/2503.08634\">arXiv<\/a>]<\/li>\n<\/ul>\n<ul>\n<li>Y. Qiu*, F. Yousefian, and B. Zhang**, <em>Iteratively Regularized Gradient Tracking Methods for Optimal Equilibrium Seeking, <\/em>under review (Dec. 2024) [<a href=\"https:\/\/arxiv.org\/abs\/2411.18883\">arXiv<\/a>]<\/li>\n<\/ul>\n<ul>\n<li>L. Marrinan, U. V. Shanbhag, and F. Yousefian, <em>Zeroth-order Gradient and Quasi-Newton Methods for Nonsmooth Nonconvex Stochastic Optimization<\/em>, under review\u00a0 [<a href=\"https:\/\/arxiv.org\/abs\/2401.08665\">arXiv<\/a>] (March 2025)<\/li>\n<\/ul>\n<h5><\/h5>\n<h5><strong>Peer-Reviewed Conference Proceedings<\/strong><\/h5>\n<ul>\n<li>Y. Qiu*, K. Kim, and F. Yousefian, <em>A Randomized Zeroth-Order Hierarchical Framework for Heterogeneous Federated Learning, Proceedings of the 2025 <strong>Conference on Decision and Control (CDC), <\/strong>accepted<\/em>\u00a0[<a href=\"https:\/\/arxiv.org\/abs\/2504.01839\">arXiv<\/a>]<\/li>\n<\/ul>\n<ul>\n<li>\n<p dir=\"ltr\" role=\"presentation\">M. Ebrahimi*, U. V. Shanbhag, and F. Yousefian, <em>Distributed Gradient Tracking Methods with Guarantees for Computing a Solution to Stochastic MPECs<\/em>, Proceedings of the 2024 <strong>American Control Conference (ACC), <\/strong>accepted [<a href=\"https:\/\/arxiv.org\/abs\/2310.09356\">arXiv<\/a>]<\/p>\n<\/li>\n<\/ul>\n<ul>\n<li>\n<p dir=\"ltr\" role=\"presentation\">S. Samadi*, D. Burbano, and F. Yousefian, <em>Achieving Optimal Complexity Guarantees for a Class of Bilevel Convex Optimization Problems<\/em>, Proceedings of the 2024 <strong>American Control Conference (ACC), <\/strong>accepted [<a href=\"https:\/\/arxiv.org\/abs\/2310.12247\">arXiv<\/a>]<\/p>\n<\/li>\n<\/ul>\n<ul>\n<li>\n<p dir=\"ltr\" role=\"presentation\">Y. Qiu*, U. V. Shanbhag, and F. Yousefian, <em>Zeroth-Order Methods for Nondifferentiable, Nonconvex, and Hierarchical Federated Optimization<\/em>, <strong>The\u00a0<b>37th Annual Conference on Neural Information Processing Systems<\/b> (NeurIPS 2023)<\/strong>\u00a0[<a href=\"https:\/\/arxiv.org\/abs\/2309.13024v2\">arXiv<\/a>] <span style=\"font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, 'Helvetica Neue', Arial, sans-serif;font-size: 1rem\">[<\/span><a style=\"background-color: #ffffff;font-size: 1rem\" href=\"https:\/\/nips.cc\/media\/PosterPDFs\/NeurIPS%202023\/72874.png?t=1699387657.060764\">poster<\/a><span style=\"font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, 'Helvetica Neue', Arial, sans-serif;font-size: 1rem\">]<\/span><\/p>\n<\/li>\n<\/ul>\n<ul>\n<li>\n<p dir=\"ltr\" role=\"presentation\">U. V. Shanbhag and F. Yousefian, <em>Zeroth-Order Randomized Block Methods for Constrained Minimization of Expectation-Valued Lipschitz Continuous Functions<\/em>, <strong>The Seventh Indian Control Conference (ICC)<\/strong>\u00a0[<a href=\"https:\/\/arxiv.org\/abs\/2107.07174\">arXiv<\/a>]<\/p>\n<\/li>\n<li>\n<p dir=\"ltr\" role=\"presentation\">F. Yousefian, <em>Bilevel Distributed Optimization in Directed Networks<\/em>, Proceedings of the 2021 <strong>American Control Conference (ACC)<\/strong> [<a href=\"https:\/\/arxiv.org\/pdf\/2006.07564v3.pdf\">arXiv<\/a>]<\/p>\n<\/li>\n<li>\n<p dir=\"ltr\" role=\"presentation\">H. D. Kaushik* and F. Yousefian, <em>An Incremental Gradient Method for Large-scale Distributed Nonlinearly Constrained Optimization<\/em>, Proceedings of the 2021 <strong>American Control Conference (ACC)<\/strong> [<a href=\"https:\/\/arxiv.org\/abs\/2006.07956v3\">arXiv<\/a>]<\/p>\n<\/li>\n<li>\n<p dir=\"ltr\" role=\"presentation\">\u00a0N. Majlesinasab*, F. Yousefian, and M. J. Feizollahi, <em>A First-Order Method for Monotone Stochastic Variational Inequalities on Semidefinite Matrix Spaces<\/em>, Proceedings of the 2019<strong> American Control Conference (ACC)<\/strong> [<a href=\"https:\/\/arxiv.org\/abs\/1809.09155\">arXiv<\/a>]<\/p>\n<\/li>\n<li>\n<p dir=\"ltr\" role=\"presentation\">\u00a0H. D. Kaushik* and F. Yousefian, <em>A Randomized Block Coordinate Iterative Regularized Subgradient Method for High-Dimensional Ill-Posed Convex Optimization<\/em>, Proceedings of the 2019<strong> American Control Conference (ACC)<\/strong> [<a href=\"https:\/\/arxiv.org\/abs\/1809.10035\">arXiv<\/a>]<\/p>\n<\/li>\n<li>\n<p dir=\"ltr\" role=\"presentation\">M. Amini* and F. Yousefian, <em>An Iterative Regularized Incremental Projected Subgradient Method for a Class of Bilevel Optimization Problems<\/em>, Proceedings of the 2019 <strong>American Control Conference (ACC)<\/strong> [<a href=\"https:\/\/arxiv.org\/abs\/1809.10050\">arXiv<\/a>]<\/p>\n<\/li>\n<li>\n<p dir=\"ltr\" role=\"presentation\">D. Newton, R. Pasupathy, and F. Yousefian, <em>Recent Trends in Stochastic Gradient Descent for Machine Learning and Big Data<\/em>, <strong>Proceedings of the 2018 Winter Simulation Conference (WSC)<\/strong> [<a href=\"https:\/\/dl.acm.org\/citation.cfm?id=3320569\">Link<\/a>]<\/p>\n<\/li>\n<li>\n<p dir=\"ltr\" role=\"presentation\">A. Jalilzadeh, A. Nedich, and U. V. Shanbhag, F. Yousefian, <em>A Variable Sample-Size Stochastic Quasi-Newton Method for Smooth and Nonsmooth Stochastic Convex Optimization<\/em>, 2018<strong> IEEE Conference on Decision and Control (CDC)<\/strong> [<a href=\"https:\/\/ieeexplore.ieee.org\/abstract\/document\/8619209\">Link<\/a>]<\/p>\n<\/li>\n<li>\n<p dir=\"ltr\" role=\"presentation\">F. Yousefian, A. Nedich, and U. V. Shanbhag, <em>A Smoothing Stochastic Quasi-Newton Method for Non-Lipschitzian Stochastic Optimization Problems<\/em>, Proceedings of the 2017<strong> Winter Simulation Conference (WSC)<\/strong> [<a href=\"https:\/\/ieeexplore.ieee.org\/document\/8247960\">Link<\/a>]<\/p>\n<\/li>\n<li>\n<p dir=\"ltr\" role=\"presentation\">F. Yousefian, A. Nedich, and U. V. Shanbhag, <em>Stochastic Quasi-Newton Methods for Non-Strongly Convex Problems: Convergence and Rate Analysis<\/em>, 2016 <strong>IEEE Conference on Decision and Control (CDC)<\/strong> [<a href=\"https:\/\/ieeexplore.ieee.org\/document\/7798953\">Link<\/a>]<\/p>\n<\/li>\n<li>\n<p dir=\"ltr\" role=\"presentation\">F. Yousefian, A. Nedich, and U. V. Shanbhag, <em>Optimal Robust Smoothing Extragradient Algorithms for Stochastic Variational Inequality Problems<\/em>, 2014 <strong>IEEE Conference on Decision and Control (CDC)<\/strong> [<a href=\"https:\/\/ieeexplore.ieee.org\/document\/7040302\">Link<\/a>]<\/p>\n<\/li>\n<li>\n<p dir=\"ltr\" role=\"presentation\">F. Yousefian, A. Nedich, and U. V. Shanbhag, <em>A Regularized Smoothing Stochastic Approximation (RSSA) Algorithm for Stochastic Variational Inequality Problems<\/em>, Proceedings of the 2013 <strong>Winter Simulation Conference (WSC)<\/strong> [<a href=\"https:\/\/ieeexplore.ieee.org\/document\/6721484\">Link<\/a>]<\/p>\n<ul>\n<li>\n<p dir=\"ltr\" role=\"presentation\">Awarded the Best Theoretical Paper in the 2013 Winter Simulation Conference<\/p>\n<\/li>\n<\/ul>\n<\/li>\n<li>\n<p dir=\"ltr\" role=\"presentation\">F. Yousefian, A. Nedich, and U. V. Shanbhag, <em>A Distributed Adaptive Steplength Stochastic Approximation Method for Monotone Stochastic Nash Games<\/em>, Proceedings of the 2013 <strong>American Control Conference (ACC)<\/strong> [<a href=\"https:\/\/ieeexplore.ieee.org\/document\/6580575\">Link<\/a>]<\/p>\n<ul>\n<li>\n<p dir=\"ltr\" role=\"presentation\">Awarded the Best Presentation in Session<\/p>\n<\/li>\n<\/ul>\n<\/li>\n<li>\n<p dir=\"ltr\" role=\"presentation\">F. Yousefian, A. Nedich, and U. V. Shanbhag, <em>A Regularized Adaptive Steplength Stochastic Approximation Scheme for Monotone Stochastic Variational Inequalities<\/em>, Proceedings of the 2011 <strong>Winter Simulation Conference (WSC)<\/strong> [<a href=\"https:\/\/ieeexplore.ieee.org\/document\/6148100\">Link<\/a>]<\/p>\n<\/li>\n<li>\n<p dir=\"ltr\" role=\"presentation\">F. Yousefian, A. Nedich, and U. V. Shanbhag, <em>Convex Nondifferentiable Stochastic Optimization: A Local Randomized Smoothing Technique<\/em>, Proceedings of the 2010 <strong>American Control Conference (ACC)<\/strong> [<a href=\"https:\/\/ieeexplore.ieee.org\/document\/5530908\">Link<\/a>]<\/p>\n<ul>\n<li>\n<p dir=\"ltr\" role=\"presentation\">Awarded the Best Paper in Session<\/p>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<h5><\/h5>\n<h5><strong>Preprint Articles and Technical Notes<\/strong><\/h5>\n<ul>\n<li>\n<p dir=\"ltr\" role=\"presentation\">F. Yousefian, J. Yevale*, and H. D. Kaushik*, <em>Distributed Randomized Block Stochastic Gradient Tracking Method<\/em>, [<a href=\"http:\/\/arxiv.org\/abs\/2110.06575\">arXiv<\/a>]<\/p>\n<\/li>\n<li>\n<p dir=\"ltr\" role=\"presentation\">N. Majlesinasab*, F. Yousefian, and M. J. Feizollahi, <em>First-Order Methods with Convergence Rates for Multi-Agent Systems on Semidefinite Matrix Spaces<\/em> [<a href=\"https:\/\/arxiv.org\/abs\/1902.05900\">arXiv<\/a>]<\/p>\n<\/li>\n<li>\n<p dir=\"ltr\" role=\"presentation\">M. Amini* and F. Yousefian, <em>An Iterative Regularized Mirror Descent Method for Ill-Posed Nondifferentiable Stochastic Optimization<\/em> [<a href=\"https:\/\/arxiv.org\/abs\/1901.09506\">arXiv<\/a>]<\/p>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h5><strong>Farzad Yousefian&#8217;s PhD Dissertation<\/strong><\/h5>\n<ul>\n<li>\n<p dir=\"ltr\" role=\"presentation\"><em>Stochastic Approximation Schemes for Stochastic Optimization and Variational Problems: Adaptive Steplengths, Smoothing, and Regularization<\/em> [<a href=\"https:\/\/www.ideals.illinois.edu\/handle\/2142\/45385\">Link<\/a>]<\/p>\n<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Published\/Accepted Journal Articles (* and ** refer to graduate and undergraduate student advisee names, respectively) S. Samadi* and F. Yousefian, Improved Guarantees for Optimal Nash Equilibrium Seeking and Bilevel Variational &hellip; <a href=\"https:\/\/sites.rutgers.edu\/farzad-yousefian\/publications\/\" class=\"\">Read More<\/a><\/p>\n","protected":false},"author":21,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"template-custom.php","meta":{"_acf_changed":false,"footnotes":""},"class_list":["post-363","page","type-page","status-publish","hentry"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v23.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Publications - Farzad Yousefian<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sites.rutgers.edu\/farzad-yousefian\/publications\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Publications - Farzad Yousefian\" \/>\n<meta property=\"og:description\" content=\"Published\/Accepted Journal Articles (* and ** refer to graduate and undergraduate student advisee names, respectively) S. 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Samadi* and F. 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