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relationships in biology, M. Aburidi and R. Marcia, Accepted to the 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society [125] Contrastive pre-training and multiple instance learning for predicting tumor microsatellite instability, R. Nap, M. Aburidi, and R. Marcia, Accepted to the 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society [124] Sparse negative binomial signal recovery for genomic variant prediction in diploid species, J. Ornelas-Muñoz, E. Rutter, M. Bañuelos, S. Sindi, and R. Marcia, Accepted to the 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society [123] Overcoming data sparsity to enable deep learning for radiographic non-destructive testing, J. Alvarez, K. Henderson, M. Aufderheide, B. Gallagher, R. Marcia, and M. Jiang, Accepted to the 2024 American Society for Nondestructive Testing Research Symposium: Beyond Boundaries. [122] Topological adversarial attacks on graph neural networks via projected meta learning, M. Aburidi and R. Marcia, Accepted to the 2024 IEEE International Conference on Evolving and Adaptive Intelligent Systems. [121] From observations to theoretical consistency: Decoder recovery in coded aperture imaging using convolutional neural networks, J. Ornelas-Muñoz, E. Rutter, and R. Marcia, Accepted to the 2024 NIST/IEEE Conference on Computational Imaging Using Synthetic Apertures. [120] Alternating direction method of multipliers for negative binomial model with the weighted difference of anisotropic and isotropic total variation, Y. Lu, K. Bui, and R. Marcia, Accepted to the 2024 IEEE International Conference on Multimedia and Expo. [119] Adversarial attack and training for graph convolutional networks using focal loss-projected momentum, M. Aburidi and R. Marcia, Accepted to the 2024 IEEE International Conference on Computing and Machine Intelligence. [118] Sparse signal reconstruction for overdispersed low-photon count biomedical imaging using ℓp total variation Y. Lu and R. Marcia, Accepted to the 2024 IEEE International Symposium on Biomedical Imaging. [117] Wasserstein distance-based graph kernel for drug safety and efficacy prediction, M. Aburidi and R. Marcia, Accepted to 2024 IEEE International Conference on Artificial Intelligence for Medicine, Health, and Care (Candidate for Best Paper Award). [116] Shape-changing trust-region methods using multipoint symmetric secant methods, J. Brust, J. Erway, and R. Marcia, Optimization Methods and Software, pp. 1-18, 2024. [doi]
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histopathology images, M. Aburidi and R. Marcia, Proceedings of the 2023 IEEE International Conference on Machine Learning and Applications. [doi] [114] Parameterized inverse eigenvalue problems for quantum sensing, K. Wright, R. Marcia, M. Scheibner, and B. Ilan, Proceedings of the 2023 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing. [doi] [113] Decoding the hidden: direct image classification using coded aperture imaging, J. Ornelas-Muñoz, E. Rutter, and R. Marcia, Proceedings of the 2023 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing. [doi] [112] Overdispersed photon-limited sparse signal recovery using nonconvex regularization, Y. Lu and R. Marcia, Proceedings of the 2023 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing. [doi] [111] Sparse overdispersed photon-limited signal recovery with upper and lower bounds, Y. Lu and R. Marcia, Proceedings of the 2023 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing. [doi] [110] Negative binomial optimization for low-count overdispersed sparse signal reconstruction, Y. Lu and R. Marcia, Proceedings of the 2023 European Signal Processing Conference. [doi] [109] CLOT: Contrastive learning-driven and optimal transport-based training for simultaneous clustering, M. Aburidi and R. Marcia, Proceedings of the 2023 IEEE International Conference in Image Processing. [doi] [108] Genetic variant detection over generations: sparsity-constrained optimization using block- coordinate descent, M. Aburidi, M. Banuelos, S. Sindi, and R. Marcia, Proceedings of the 2023 IEEE Conference on Medical Measurements and Applications. [doi]
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R. Garcia, J. Alvarez, and R. Marcia, Proceedings of the 2022 IEEE International Conference on Machine Learning and Applications. [doi] [106] Novel adversarial defense techniques for white-box attacks, J. Van Tuinen, A. Ranganath, G. Konjevod, M. Singhal, and R. Marcia, Proceedings of the 2022 IEEE International Conference on Machine Learning and Applications. [doi] [105] Interpretability of ReLU for inversion, B. Ilan, A. Ranganath, J. Alvarez, S. Khatri, and R. Marcia, Proceedings of the 2022 IEEE International Conference on Machine Learning and Applications. [doi] [104] Recurrent neural imaging: an evolutionary approach for mixed Poisson-Gaussian image denoising, A. Ranganath, O. DeGuchy, F. Santiago, M. Singhal, and R. Marcia, Proceedings of the 2022 IEEE International Conference on Machine Learning and Applications. [doi] [103] Numerical methods for a Schrödinger equation inverse eigenvalue problem, K. Wright, R. Marcia, and B. Ilan WAVES, Palaiseau, 2022. [link] [102] Image classification and training with severe data loss, D. Marquard, K. Wright, and R. Marcia, Proc. SPIE 12227, Applications of Machine Learning 2022. [doi] [101] Synthetic aperture radar image reconstruction using convolutional neural networks, J. Alvarez, A. Kim, R. Marcia, and C. Tsogka, Proc. SPIE 12227, Applications of Machine Learning 2022. [doi] [100] Algorithm 1030: SC-SR1: MATLAB Software for Limited-Memory SR1 Trust-Region Methods, J. Brust, O. Burdakov, J. Erway, and R. Marcia, ACM Transactions on Mathematical Software, 48:4, pp. 1-33, 2022. [doi] [99] Large-Scale Optimization with Linear Equality Constraints using Reduced Compact Representation, J. Brust, R. Marcia, C. Petra, and M. Saunders, SIAM Journal on Scientific Computing, 44:1, pp. A103-A127, 2022. [doi]
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A. Lazar, M. Banuelos, S. Sindi, and R. Marcia, Proceedings of the 55th Asilomar Conference on Signals, Systems & Computers, pp. 563-567, 2021. [doi] [97] Convolution padding in recurrent neural networks for image denoising with limited data, A. Ho, J. Alvarez, and R. Marcia, Proceedings of the 55th Asilomar Conference on Signals, Systems & Computers, pp. 1699-1703, 2021. [doi] [96] Multi-stage Gaussian denoising with recurrent neural networks, A. Ranganath, O. DeGuchy, M. Singhal, and R. Marcia, Proceedings of the 55th Asilomar Conference on Signals, Systems & Computers, pp. 135-139, 2021. [doi] [95] Data-limited deep learning methods for mild cognitive impairment classification in Alzheimer's disease patients, A. De Luna and R. Marcia, Proceedings of the 2021 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2641-2646, 2021. [doi] [94] Second-order trust-region optimization for data-limited inference, A. Ranganath, O. DeGuchy, M. Singhal, and R. Marcia, Proceedings of the 2021 29th European Signal Processing Conference, pp. 2059-2063, 2021. [doi] [93] Related inference: A supervised learning approach to detect signal variation in genome data, M. Banuelos, O. DeGuchy, S. Sindi, and R. Marcia, Proceedings of the 2020 28th European Signal Processing Conference, pp. 1215-1219, 2021. [doi] [92] Genomic signal processing for variant detection in diploid parent-child trios, M. Banuelos, M. Spence, R. Marcia, and S. Sindi, Proceedings of the 2020 28th European Signal Processing Conference, pp. 1318-1322, 2021. [doi]
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A. Lazar, M. Banuelos, S. Sindi, and R. Marcia, Proceedings of the 54th Asilomar Conference on Signals, Systems & Computers, pp. 511-515, 2020. [doi] [90] Deep convolutional autoencoders for deblurring and denoising low-resolution images, M. Mendez Jimenez, O. DeGuchy, and R. Marcia, Proceedings of the 2020 International Symposium on Information Theory and Its Applications. [IEEE Xplore link] [89] A neural network approach for anomaly detection in genomic signals, E. Sawyer, M. Banuelos, R. Marcia, S. Sindi, Proceedings of the 2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, pp. 968-971, 2020. [IEEE Xplore link] [88] Image classification in synthetic aperture radar using reconstruction from learned inverse scattering, J. Alvarez, O. DeGuchy, and R. Marcia, Proceedings of the 2020 IEEE International Geoscience and Remote Sensing Symposium. [doi] [87] Machine learning for direct and inverse scattering in synthetic aperture radar, O. DeGuchy, J. Alvarez, A. Kim, R. Marcia, and C. Tsogka, Proc. SPIE 11511, Applications of Machine Learning 2020, 115110S (19 August 2020). [doi] [86] Computationally efficient decompositions of oblique projection matrices, J. Brust, R. Marcia, and C. Petra, SIAM Journal on Matrix Analysis and Applications, 41:2, pp. 852-870, 2020. [doi] [85] Quasi-Newton optimization methods for deep learning applications, J. Rafati and R. Marcia, In Deep Learning Applications, M. A. Wani, M. Kantardzic, and M. Mouchaweh, eds., Springer, 2020. [Springer link] [84] Detecting inherited and novel structural variants in low-coverage parent-child sequencing data, M. Spence, M. Banuelos, and R. Marcia, and S. Sindi, Methods, 173, p. 61-68, 2020. [doi] [83] Trust-Region Algorithms for Training Responses: Machine Learning Methods Using Indefinite Hessian Approximations, J. Erway, J. Griffin, R. Marcia, and R. Omheni, Optimization Methods and Software, 35:3, pp. 460-487, 2020. [doi]
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X. Chen, H. Perez, M. Spence, R. Marcia, and S. Sindi, Proceedings of the 2019 IEEE International Conference on Bioinformatics and Biomedicine [doi] [81] Large-scale quasi-Newton trust-region methods with low-dimensional linear equality constraints, J. Brust, R. Marcia, and C. Petra, Computational Optimization and Applications, 74:3, pp. 669-701, 2019. [doi] [80] Dense initializations for limited-memory quasi-Newton methods, J. Brust, O. Burdakov, J. Erway, and R. Marcia, Computational Optimization and Applications, 74:1, pp. 121-142, 2019. [doi] [79] Parameter tuning using asynchronous parallel pattern search in sparse signal reconstruction, O. DeGuchy and R. Marcia, Proceedings of the 2019 SPIE Wavelets and Sparsity XVIII in San Diego, CA. [doi] [78] Image disambiguation with deep neural networks, O. DeGuchy, A. Ho, and R. Marcia, Proceedings of the 2019 SPIE Applications of Machine Learning in San Diego, CA. [doi] [77] Predicting novel and inherited variants in parent-child trios, M. Spence, M. Banuelos, and R. Marcia, and S. Sindi, Proceedings of the 2019 IEEE International Symposium on Medical Measurements and Applications in Istanbul, Turkey. [doi] [76] Deep neural networks for low-resolution photon-limited imaging, O. DeGuchy, F. Santiago, M. Banuelos, and R. Marcia, Proceedings of the 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing in Brighton, UK. [doi]
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M. Spence, M. Banuelos, and R. Marcia, and S. Sindi, Proceedings of the 2018 IEEE International Conference on Bioinformatics and Biomedicine in Madrid, Spain. [doi] [74] Improving L-BFGS initialization for trust-region methods in deep learning, J. Rafati and R. Marcia, Proceedings of the 2018 IEEE Conference on Machine Learning and Applications in Orlando, FL. [doi] [73] Trust-Region Minimization Algorithm for Training Responses (TRMinATR): The Rise of Machine Learning Techniques, J. Rafati, O. DeGuchy, and R. Marcia, Proceedings of the the 26th European Signal Processing Conference (EUSIPCO 2018) in Rome, Italy. [doi] [72] Structural variant prediction in extended pedigrees through sparse negative binomial genome signal recovery, M. Banuelos, S. Sindi, and R. Marcia, Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society in Honolulu, HI. [doi] [71] Negative binomial optimization for biomedical structural variant signal reconstruction, M. Banuelos, S. Sindi, and R. Marcia, Proceedings of the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing in Calgary, Canada. [doi] [70] Compact representation of the full Broyden class of quasi-Newton updates, O. DeGuchy, J. Erway, and R. Marcia, Numerical Linear Algebra with Applications, 25:5, p. e2186, 2018. [doi] [bibtex]
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O. DeGuchy, L. Adhikari, A. Kim, and R. Marcia, Proceedings of the 2017 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) in Curacao, Dutch Antilles. [doi] [68] Trust-region methods for sparse relaxation, L. Adhikari, O. DeGuchy, J. Erway, S. Lockhart, and R. Marcia, Proceedings SPIE 10394,Wavelets and Sparsity XVII, 103940, 2017. [doi] [67] Non-convex Shannon entropy for photon-limited imaging, L. Adhikari, R. Baikejiang, O. DeGuchy, and R. Marcia, Proceedings SPIE 10394,Wavelets and Sparsity XVII, 103940, 2017. [doi] [66] Mixture regression as subspace clustering, D. Pimentel-Alarcon, L. Balzano, R. Marcia, R. Nowak, and R. Willett, Proceedings of 2017 International Conference on Sampling Theory and Applications (SampTA). [doi] [65] Sparse diploid signal recovery for genomic variant detection, M. Banuelos, L. Adhikari, A. Fujikawa, J. Sahagun, K. Sanderson, M. Spence, R. Almanza, S. Sindi, and R. Marcia, Proceedings of the 2017 IEEE International Symposium on Medical Measurements and Applications. [doi] [64] Nonconvex regularization for sparse genomic variant detection, M. Banuelos, L. Adhikari, A. Fujikawa, J. Sahagun, K. Sanderson, M. Spence, R. Almanza, S. Sindi, and R. Marcia, Proceedings of the 2017 IEEE International Symposium on Medical Measurements and Applications. [doi] [63] Nonconvex regularization based sparse recovery and demixing with application to color image inpainting, F. Wen, L. Adhikari, L. Pei, R. Marcia, P. Liu, and R. Qiu, IEEE Access, 5, pp. 11513-11527, 2017. [doi] [62] Biomedical signal recovery: Genomic variant detection in family lineages, M. Banuelos, L. Adhikari, R. Almanza, S. Sindi, and R. Marcia, Proceedings of the 2017 IEEE 5th Portuguese Meeting on Bioengineering (ENBENG). [doi] [61] On solving L-SR1 trust-region subproblems, J. Brust, J. Erway and R. Marcia, Computational Optimization and Applications, 66:2, pp. 245- 266, 2017. [doi] [bibtex] [60] On solving limited-memory quasi-Newton equations, J. Erway, and R. Marcia, Linear Algebra and its Applications, 515, pp. 196-225, 2017. [doi] [bibtex]
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L. Adhikari, A. Kim, and R. Marcia, Proceedings of the 2016 IEEE Global Conference on Signal and Information Processing in Washington, DC. [doi] [58] Nonconvex sparse Poisson intensity reconstruction for time-dependent bioluminescence tomography, L. Adhikari, A. Kim, and R. Marcia, Proceedings of the 2016 International Symposium on Information Theory and Its Applications in Monterey, CA. [link] [57] Trust-region methods for nonconvex sparse recovery optimization, L. Adhikari, J. Erway, R. Marcia, and R. Plemmons, Proceedings of the 2016 International Symposium on Information Theory and Its Applications in Monterey, CA. [link] [56] Bounded sparse photon-limited image recovery, L. Adhikari and R. Marcia, Proceedings of the 2016 IEEE International Conference on Image Processing. [doi] [55] Constrained variant detection with SPaRC: Sparsity, parental relatedness, and coverage, M. Banuelos, R. Almanza, L. Adhikari, R. Marcia, and S. Sindi, Proceedings of the 2016 International Conference of the IEEE Engineering in Medicine and Biology Society. [doi] [54] Group-sparse subspace clustering with missing data, D. Pimentel-Alarcon, L. Balzano, R. Marcia, R. Nowak, and R. Willett, Proceedings of the 2016 IEEE Workshop on Statistical Signal Processing. [doi] [53] Sparse genomic structural variant detection: Exploiting parent-child relatedness for signal recovery, M. Banuelos, R. Almanza, L. Adhikari, R. Marcia, and S. Sindi, Proceedings of the 2016 IEEE Workshop on Statistical Signal Processing. [doi] [52] Analysis of p-norm regularized subproblem minimization for sparse photon-limited image recovery, A. Orkusyan, L. Adhikari, J. Valenzuela, and R. Marcia, Proceedings of the 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, Shanghai China. [doi] [51] Sparse signal recovery methods for variant detection in next-generation sequencing data, M. Banuelos, R. Almanza, L. Adhikari, S. Sindi, and R. Marcia, Proceedings of the 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, Shanghai, China. [doi]
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L. Adhikari, D. Zhu, C. Li, and R. Marcia, Proceedings of the 2015 IEEE International Conference on Image Processing, Quebec, Canada. [doi] [49] On efficiently computing the eigenvalues of limited-memory quasi-Newton matrices, J. Erway and R. Marcia, SIAM Journal on Matrix Analysis and Applications, 36:3, pp. 1338-1359, 2015. [doi] [48] p-th power total variation regularization in photon-limited imaging via iterative reweighting, L. Adhikari and R. Marcia, Proceedings of the 2015 European Signal Processing Conference, Nice, France. [doi] [pdf] [47] Nonconvex relaxation for Poisson intensity reconstruction, L. Adhikari and R. Marcia, Proceedings of 2015 IEEE International Conference on Acoustics, Speech and Signal Processing, Brisbane, Australia. [doi]
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J. Erway and R. Marcia, ACM Transactions on Mathematical Software, 40:4, pp. 1-12, 2014. [doi] [45] Shifted L-BFGS systems, J. Erway, V. Jain, and R. Marcia, Optimization Methods and Software, 29:5, pp. 992-1004, 2014. [doi]
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J. Erway, V. Jain, and R. Marcia, Proceedings of 2013 Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA. [arXiv] [doi] [43] Compressive coded aperture keyed exposure imaging with optical flow reconstruction, Z. Harmany, R. Marcia, and R. Willett, [arXiv] [42] Dual-scale masks for spatio-temporal compressive imaging, Z. Harmany, R. Marcia, and R. Willett, Proceedings of 2013 IEEE GlobalSIP Symposium on New Sensing and Statistical Inference Methods. [doi] [41] Linear Algebra, Sparsity, and Compressive Sensing, R. Marcia, IMAGE: The Bulletin of the International Linear Algebra Society, 50, pp. 10-15, 2013. [link]
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R. Marcia and S. Senay, IMAGE: The Bulletin of the International Linear Algebra Society, 49, pp. 17-24, 2012. [link] [39] Sequential anomaly detection in the presence of noise and limited feedback, M. Raginsky, R. Willett, C. Horn, J. Silva and R. Marcia, IEEE Transactions on Information Theory, 58:8, pp. 5544-5562, 2012. [doi] [bibtex] [38] Dimensionality reduction and recovery for hyperspectral images using discrete prolate spheroidal sequences, S. Senay and R. Marcia, Proceedings of 2012 IASTED International Conference on Signal and Image Processing, Honolulu, HI. [link] [37] Limited-memory BFGS systems with diagonal updates, J. Erway and R. Marcia, Linear Algebra and its Applications, 437:1, pp. 333-344, 2012. [doi] [bibtex] [software] [36] Solving limited-memory BFGS systems with generalized diagonal updates, J. Erway and R. Marcia, Proceedings of 2012 International Conference of Applied and Engineering Mathematics, London, UK. [link] [35] This is SPIRAL-TAP: Sparse Poisson intensity reconstruction algorithms - Theory and practice, Z. Harmany, R. Marcia, and R. Willett, IEEE Transactions in Signal Processing, 21:3, pp. 1084-1096, 2012. [doi] [bibtex] [software] [34] Compressive video recovery with upper and lower bound constraints, D. Jones, R. Schlick, and R. Marcia, Proceedings of 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, Kyoto, Japan. [doi] [bibtex]
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Z. Harmany, A. Oh, R. Marcia, and R. Willett, Proc. SPIE 8165, 81651C, San Diego, CA. [doi] [bibtex] [32] Performance bounds for expander-based compressed sensing in Poisson noise, M. Raginsky, S. Jafarpour, Z. Harmany, R. Marcia, R. Willett, and R. Calderbank, IEEE Transactions in Signal Processing, 59:9, pp. 4139-4153, 2011. [doi] [bibtex] [31] Compressive sensing for practical optical imaging systems: A tutorial, R. Willett, R. Marcia, and J. Nichols, Optical Engineering, 50:7, pp. 1-13, 2011. [doi] [bibtex] (Featured here) [30] Compressive Optical Imaging: Algorithms and Architectures, R. Marcia, Z. Harmany, and R. Willett, Optical and Digital Image Processing: Fundamentals and Applications, Eds. Gabriel Cristobal, Peter Schelkens, and Hugo Thienpont. Wiley, ISBN: 978-3-527-40956-3, April, 2011. [doi] [bibtex] [29] Bounded gradient projection methods for sparse signal recovery, J. Hernandez, Z. Harmany, D. Thompson, and R. Marcia, Proceedings of 2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, Prague, Czech Republic. [doi] [bibtex] [28] Sparse video recovery using linearly constrained gradient projection, D. Thompson, Z. Harmany, and R. Marcia, Proceedings of 2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, Prague, Czech Republic. [doi] [bibtex] [27] A backward stability analysis of diagonal pivoting methods for solving unsymmetric tridiagonal systems without interchanges, J. Erway and R. Marcia, Numerical Linear Algebra with Applications, 18:1, pp. 41-54, 2011. [pdf] [doi] [abstract]
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J. Erway, R. Marcia, and J. Tyson, IAENG International Journal of Applied Mathematics, 40:4, pp. 269-275, 2010. [link] [bibtex] [25] Gradient projection for linearly constrained convex optimization in sparse signal recovery, Z. Harmany, D. Thompson, R. Willett, and R. Marcia, Proceedings of 2010 IEEE International Conference on Image Processing, Hong Kong, China. [doi] [bibtex] [24] Poisson image reconstruction with total variation regularization, R. Willett, Z. Harmany, and R. Marcia, Proceedings of 2010 IEEE International Conference on Image Processing, Hong Kong, China. [doi] [bibtex] [23] Compressed sensing performance bounds under Poisson noise, M. Raginsky, R. WIllett, Z. Harmany, and R. Marcia, IEEE Transactions on Signal Processing, vol. 58, no. 8, pp. 3990-4002, 2010. [doi] [bibtex] [22] On solving unsymmetric tridiagonal systems without interchanges, J. Erway, R. Marcia, and J. Tyson, Proceedings of 2010 International Conference of Applied and Engineering Mathematics, London, UK. (Best Paper Award) [pdf] [bibtex] [21] Sparsity-regularized photon-limited imaging, Z. Harmany, R. Marcia, and R. Willett, Proceedings of 2010 IEEE International Symposium on Biomedical Imaging, Rotterdam, The Netherlands. [pdf] [doi] [bibtex] [20] Compressive coded apertures for high-resolution imaging, R. Marcia, Z. Harmany, and R. Willett, Proceedings of 2010 SPIE Photonics Europe, Brussels, Belgium. [pdf] [doi] [bibtex] [19] SPIRAL out of convexity: Sparsity-regularized algorithms for photon-limited imaging, Z. Harmany, R. Marcia, and R. Willett, Proceedings of 2010 IS&T/SPIE Electronic Imaging: Computational Imaging VIII, San Jose, CA. [pdf] [doi] [bibtex]
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Z. Harmany, R. Marcia, and R. Willett, Proceedings of 2009 IEEE Workshop on Statistical Signal Processing, Cardiff, Wales, UK. [pdf] [doi] [bibtex] [17] Sequential probability assignment via online convex programming using exponential families, M. Raginsky, R. Marcia, J. Silva, and R. Willett, Proceedings of 2009 IEEE International Symposium on Information Theory, Seoul, South Korea. [pdf] [doi] [bibtex] [16] Compressive coded aperture imaging, R. Marcia, Z. Harmany, and R. Willett, Proceedings of 2009 IS&T/SPIE Electronic Imaging: Computational Imaging VII, San Jose, CA. [pdf] [doi] [bibtex]
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R. Marcia, C. Kim, C. Eldeniz, J. Kim, D. Brady, and R. Willett, Optics Express, 16, pp. 16352-16363, 2008. [doi] [bibtex] [14] On solving sparse symmetric linear systems whose definiteness is unknown, R. Marcia, Applied Numerical Mathematics, 58:4, pp. 449-458, 2008. [pdf] [doi] [bibtex] [13] Fast disambiguation of superimposed images for increased field of view, R. Marcia, C. Kim, J. Kim, D. Brady, and R. Willett, Proceedings of 2008 IEEE International Conference on Image Processing, San Diego, CA. [pdf] [doi] [bibtex] [12] Compressive coded aperture video reconstruction, R. Marcia and R. Willett, Proceedings of 2008 Sixteenth European Signal Processing Conference, Lausanne, Switzerland. [pdf] [bibtex] [11] Compressive coded aperture superresolution image reconstruction, R. Marcia and R. Willett, Proceedings of 2008 IEEE International Conference on Acoustics, Speech, and Signal Processing, Las Vegas, NV. [pdf] [doi] [slides] [bibtex]
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R. Marcia, J. Mitchell, and S. J. Wright, Optimization Methods and Software, 22:5, pp. 803-811, 2007. [pdf] [doi] [bibtex] [9] Synaptotagmin C2A loop 2 mediates Ca2+-dependent SNARE interactions essential for Ca2+-triggered vesicle exocytosis, K. Lynch, R. Gerona, E. Larsen, R. Marcia, J. Mitchell, and T. Martin, Molecular Biology of the Cell, 18:12, pp. 4957-68. Epub 2007 Oct 3, 2007. [doi] [bibtex] [8] Multi-funnel optimization using Gaussian underestimation, R. Marcia, J. Mitchell, and J. B. Rosen, Journal of Global Optimization, 39:1, pp. 39-48, 2007 [pdf] [doi] [bibtex]
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J. Bunch and R. Marcia, Numerical Linear Algebra with Applications, 13, pp. 865-867, 2006. [pdf] [doi] [bibtex]
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R. Marcia, J. Mitchell, and J. B. Rosen, Computational Optimization and Applications, 32, pp. 285-297, 2005. [pdf] [doi] [bibtex] [5] A pivoting strategy for symmetric tridiagonal matrices, J. Bunch and R. Marcia, Numerical Linear Algebra with Applications, 12, pp. 911-922, 2005. [pdf] [doi] [bibtex]
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J. B. Rosen and R. Marcia, Computational Optimization and Applications, 28, pp. 173-187, 2004. [pdf] [doi] [bibtex]
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R. Bank, P. Gill, and R. Marcia, Large-scale PDE-constrained Optimization, L.T. Biegler, O. Ghattas, M. Heinkenschloss, and B. van Bloemen Waanders, eds., vol. 30 of Lecture Notes in Computational Science and Engineering, Berlin, Heidleberg and New York, Springer-Verlag, pp. 218-235, 2003. [pdf] [doi] [bibtex]
2002
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R. Marcia, Ph.D. dissertation, Department of Mathematics, University of California, San Diego.
2000
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D. Dunlavy, S. Joo, R. Lin, R. Marcia, A. Minut, and J. Sun, Mathematical Modeling in Industry Report, Institute for Mathematics and its Applications, 2000. [doi]
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Note: Copyrights may have been transferred to the appropriate publishers. Reprints are provided here for personal use only. Any opinions, findings and conclusions or recommendations expressed in the publications supported by the National Science Foundation (NSF) grants DMS-0811062, DMS-09-65711, and IIS-1741490 are those of the author(s) and do not necessarily reflect the views of the NSF. |