Publications

Refereed journals and book chapters

  • J. Prince, J. Maidens, A. Shapiro, M. Kanzawa, L. Le, S. Pham, MD; Subramaniam Venkatraman, Ph.D, A Cross-Cohort Assessment of Multiple Arrhythmia Detection using Machine Learning, Heart Rhythm, submitted.

  • O. Ozyegen, S. Mohammadjafari, E. Kavurmacioglu, J. Maidens, A. Bener, Experimental Results on the Impact of Memory in Neural Networks for Spectrum Prediction in Land Mobile Radio Bands, IEEE Transactions on Cognitive Communications and Networking, accepted.

  • J. Maidens, J. W. Gordon, H.-Y. Chen, I. Park, M. Van Criekinge, E. Milshteyn, R. Bok, R. Aggarwal, M. Ferrone, J. B. Slater, J. Kurhanewicz, D. B. Vigneron, M. Arcak, P. E. Z. Larson, Spatio-temporally constrained reconstruction for hyperpolarized carbon-13 MRI using kinetic models, IEEE Transactions on Medical Imaging, vol. 37, pp. 2603-2612, 2018. Preprint. doi:10.1109/TMI.2018.2844246.

  • P. E. Z. Larson, H.-Y. Chen, J. W. Gordon, N. Korn, J. Maidens, M. Arcak, S. Tang, M. Van Criekinge, L. Carvajal, D. Mammoli, R. Bok, R. Aggarwal, M. Ferrone, J. B. Slater, S. J. Nelson, J. Kurhanewicz, D. B. Vigneron, Investigation of Analysis Methods for Hyperpolarized 13C-pyruvate Metabolic MRI in Prostate Cancer Patients, NMR in Biomedicine, pp. e3997, 2018. Preprint. doi:10.1002/nbm.3997.

  • M. Arcak, J. Maidens, Simulation-based reachability analysis for nonlinear systems using componentwise contraction properties, in Principles of Modeling, eds. M. Lohstroh, P. Derler, M. Sirjani, Lecture Notes in Computer Science, vol 10760. Springer, Cham, pp. 61-76, 2018. Preprint. Software. doi:10.1007/978-3-319-95246-8_4.

  • J. Maidens, M. Arcak, Control and optimization problems in hyperpolarized carbon-13 MRI, in Emerging Applications of Control and System Theory, eds. R. Tempo, S. Yurkovich, and P. Misra, Lecture Notes in Control and Information Sciences - Proceedings, Springer, Cham, pp. 29-40, 2018. Preprint. doi:10.1007/978-3-319-67068-3_3.

  • J. Maidens, J. W. Gordon, M. Arcak, P. E. Z. Larson, Optimizing flip angles for metabolic rate estimation in hyperpolarized carbon-13 MRI, IEEE Transactions on Medical Imaging, vol. 35, pp. 2403-2412, 2016. Preprint. Software. doi:10.1109/TMI.2016.2574240.

  • J. Maidens, S. Kaynama, I. M. Mitchell, M. Oishi, G. A. Dumont, Lagrangian methods for approximating the viability kernel in high-dimensional systems, Automatica, vol. 49, pp. 2017-2029, 2013. Preprint. Software. doi:10.1016/j.automatica.2013.03.020.

Refereed conference proceedings

  • S. Mohammadjafari, E. Kavurmacioglu, J. Maidens, A. Bener, Neural network based spectrum prediction in land mobile radio bands for IoT deployments, 2019 IFIP/IEEE Symposium on Integrated Network and Service Management, Washington, DC, pp. 31–36, 2019. Best Paper Award. Preprint. Publisher Version.

  • K. El Mokhtari, J. Maidens, A. Bener, Predicting Commentaries on a Financial Report with Recurrent Neural Networks, Advances in Artificial Intelligence. Canadian AI 2019, eds. M. J. Meurs, and F. Rudzicz, pp. 531–542, Lecture Notes in Computer Science, vol 11489, Springer, 2019. Preprint. doi:10.1007/978-3-030-18305-9_56.

  • F. Palma, T. Abdou, A. Bener, J. Maidens, S. Liu, An improvement to test case failure prediction in the context of test case prioritization, Proceedings of the 14th International Conference on Predictive Models and Data Analytics in Software Engineering, Oulu, Finland, pp. 80–89, 2018. Preprint. doi:10.1145/3273934.3273944.

  • J. Maidens, A. Barrau, S. Bonnabel, M. Arcak, Symmetry reduction for dynamic programming and application to MRI. American Control Conference, Seattle, WA, pp. 4625-4630, 2017. Preprint. Software. doi:10.23919/ACC.2017.7963669.

  • J. Maidens, A. Packard, M. Arcak, Parallel dynamic programming for optimal experiment design in nonlinear systems. Conference on Decision and Control, Las Vegas, NV, pp. 2894-2899, 2016. Preprint. Software. doi:10.1109/CDC.2016.7798700.

  • J. Maidens, M. Arcak, Semidefinite relaxations in optimal experiment design with application to substrate injection for hyperpolarized MRI (Invited Paper). American Control Conference, Boston, MA, pp. 2023-2028, 2016. Preprint. Software. doi:10.1109/ACC.2016.7525216.

  • J. Maidens, P. E. Z. Larson, M. Arcak, Optimal experiment design for physiological parameter estimation using hyperpolarized carbon-13 magnetic resonance imaging. American Control Conference, Chicago, IL, pp. 5770-5775, 2015. Preprint. Software. doi:10.1109/ACC.2015.7172243.

  • J. Maidens, M. Arcak, Trajectory-based reachability analysis of switched nonlinear systems using matrix measures, Conference on Decision and Control, Los Angeles, CA, pp. 6358-6364, 2014. Preprint. doi:10.1109/CDC.2014.7040386.

  • J. Maidens, M. Y. Li, Global Lyapunov functions and a hierarchical control scheme for networks of robotic agents, American Control Conference, Washington, DC, pp. 4050-4055, 2013. Preprint. Software. doi:10.1109/ACC.2013.6580460.

  • S. Kaynama, J. Maidens, M. Oishi, I. M. Mitchell, G. A. Dumont, Computing the Viability Kernel Using Maximal Reachable Sets, Hybrid Systems Computation and Control, Beijing, China, pp. 55-63, 2012. Preprint. doi:10.1145/2185632.2185644.

Refereed conference abstracts

  • Z. Attia, J. Dugan, J. Maidens, A. Rideout, S. Venkatraman, F. Lopez Jimenez, P. A. Noseworthy, S. J. Asirvatham, P. A. Pelikka, D. J. Ladewig, G. A. Satam, S. L. Pham, P. A. Friedman, S. Kapa, Prospective analysis of utility of signals from an ECG-enabled stethoscope to automatically detect a low ejection fraction using neural network techniques trained from the standard 12-lead ECG. American Heart Association Scientific Sessions, Philadelphia, PA, p. 13447, 2019. doi:10.1161/circ.140.suppl_1.13447.

  • B. E. White, A. M. Shapiro, M. M. Kanzawa, S. Venkatraman, J. Paek, S. Pham, J. Maidens, P. M. McCarthy, and J. D. Thomas, Handheld Wireless Digital Phonocardiography for Machine Learning-Based Detection of Mitral Regurgitation. American Heart Association Scientific Sessions, Philadelphia, PA, p. 13831, 2019. doi:10.1161/circ.140.suppl_1.13831.

  • B. E. White, J. Paek, S. Pham, J. Maidens, P. M. McCarthy, and J. D. Thomas, Handheld Digital Wireless Phonocardiography for Machine Learning-Based Detection of Aortic Stenosis: the PEA-Valve Study. American Society for Echocardiograhpy Scientific Sessions, Portland, OR, Abstract P2-068, 2019. Top Investigator Award. Online Abstract.

  • J. Maidens, N. B. Slamon, Artificial Intelligence Detects Pediatric Heart Murmurs with Cardiologist-Level Accuracy, American Heart Association Scientific Sessions, Chicago, IL, p. 12591, 2018. doi:10.1161/circ.138.suppl_1.12591.

  • P. E. Z. Larson, H.-Y. Chen, J. W. Gordon, J. Maidens, D. Mammoli, M. Van Criekinge, R. Bok, R. Aggarwal, M. Ferrone, J. B. Slater, J. Kurhanewicz, D. B. Vigneron, Analysis Methods for Human Hyperpolarized 13C-pyruvate Studies, ISMRM Annual Meeting, Paris, France, p. 3850, 2018. Online Abstract.

  • J. Maidens, J. W. Gordon, M. Arcak, H.-Y. Chen, I. Park, M. Van Criekinge, E. Milshteyn, R. Bok, R. Aggarwal, M. Ferrone, J. B. Slater, J. Kurhanewicz, D. B. Vigneron, and P. E. Z. Larson, Spatio-temporally constrained reconstruction for hyperpolarized carbon-13 MRI using kinetic models, ISMRM Annual Meeting, Honolulu, HI, p. 3040, 2017. Online Abstract.

  • J. Maidens, J. W. Gordon, M. Arcak, P. E. Z. Larson, Optimizing flip angles for metabolic rate estimation in hyperpolarized carbon-13 MRI, ISMRM Annual Meeting, Singapore, p. 2341, 2016. Online Abstract.

  • P. E. Z. Larson, J. Gordon, J. Maidens, M. Arcak, H.-Y. Chen, G. Reed, I. Park, R. Aggarwal, R. Bok, S. J. Nelson, J. Kurhanewicz, and D. B. Vigneron, Robust Quantitative Methods Applied to Clinical Hyperpolarized C-13 MR of Prostate Cancer Patients, ISMRM Annual Meeting, Singapore, p. 2347, 2016. Online Abstract.

Patents

  • C. Landgraf, P. Goolkasian, J. Maidens, T. Crouch, J. Bellet, S. L. Pham. Methods and systems for determining a physiological or biological state or condition of a subject, United States Provisional Patent, Filed August 21, 2018.

Theses

  • J. Maidens, “Optimal Control for Learning with Applications in Dynamic MRI,” Doctor of Philosophy Dissertation, University of California, Berkeley, August 2017. Berkeley EECS Technical Report.

  • J. Maidens, “Scalable computation of viability kernels and a viability-theoretic approach to guaranteeing safety for closed-loop medical devices,” Master of Applied Science Thesis, The University of British Columbia, 2012. doi:10.14288/1.0072881.