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ORCIDRoummel F. Marcia
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- affiliation: University of California, Merced, USA
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2020 – today
- 2024
[c63]Mohammed Aburidi, Roummel F. Marcia:
Topological Adversarial Attacks on Graph Neural Networks Via Projected Meta Learning. EAIS 2024: 1-8- [c62]Yu Lu, Kevin Bui, Roummel F. Marcia:
Alternating Direction Method of Multipliers for Negative Binomial Model with the Weighted Difference of Anisotropic and Isotropic Total Variation. ICME 2024: 1-6 - [c61]Yu Lu, Roummel F. Marcia:
Sparse Signal Reconstruction for Overdispersed Low-Photon Count Biomedical Imaging Using ℓp Total Variation. ISBI 2024: 1-5 - [i12]Yu Lu, Kevin Bui, Roummel F. Marcia:
Negative Binomial Matrix Completion. CoRR abs/2408.16113 (2024) - [i11]Yu Lu, Kevin Bui, Roummel F. Marcia:
Alternating Direction Method of Multipliers for Negative Binomial Model with The Weighted Difference of Anisotropic and Isotropic Total Variation. CoRR abs/2408.16117 (2024) - [i10]Yu Lu, Roummel F. Marcia:
Sparse Signal Reconstruction for Overdispersed Low-photon Count Biomedical Imaging Using ℓ Total Variation. CoRR abs/2408.16622 (2024) - 2023
- [c60]Yu Lu, Roummel F. Marcia:
Sparse Overdispersed Photon-Limited Signal Recovery with Upper and Lower Bounds. CAMSAP 2023: 181-185 - [c59]Yu Lu, Roummel F. Marcia:
Overdispersed Photon-Limited Sparse Signal Recovery Using Nonconvex Regularization. CAMSAP 2023: 191-195 - [c58]Kyle Wright, Roummel F. Marcia, Michael Scheibner
, Boaz Ilan:
Parameterized Inverse Eigenvalue Problem for Quantum Sensing. CAMSAP 2023: 351-355 - [c57]Jocelyn Ornelas Munoz, Erica M. Rutter, Roummel F. Marcia:
Decoding the Hidden: Direct Image Classification Using Coded Aperture Imaging. CAMSAP 2023: 361-365 - [c56]Yu Lu, Roummel F. Marcia:
Negative Binomial Optimization for Low-Count Overdispersed Sparse Signal Reconstruction. EUSIPCO 2023: 1948-1952 - [c55]Mohammed Aburidi, Roummel F. Marcia:
CLOT: Contrastive Learning-Driven and Optimal Transport-Based Training for Simultaneous Clustering. ICIP 2023: 1515-1519 - [c54]Mohammed Aburidi, Roummel F. Marcia:
Optimal Transport and Contrastive-Based Clustering for Annotation-Free Tissue Analysis in Histopathology Images. ICMLA 2023: 301-307 - [c53]Mohammed Aburidi, Mario Banuelos, Suzanne Sindi, Roummel F. Marcia:
Genetic Variant Detection Over Generations: Sparsity-Constrained Optimization Using Block-Coordinate Descent. MeMeA 2023: 1-5 - 2022
- [j23]Johannes J. Brust, Roummel F. Marcia, Cosmin G. Petra, Michael A. Saunders:
Large-Scale Optimization with Linear Equality Constraints Using Reduced Compact Representation. SIAM J. Sci. Comput. 44(1): 103- (2022) - [j22]Johannes Brust, Oleg Burdakov, Jennifer B. Erway, Roummel F. Marcia:
Algorithm 1030: SC-SR1: MATLAB Software for Limited-memory SR1 Trust-region Methods. ACM Trans. Math. Softw. 48(4): 48:1-48:33 (2022) - [c52]Aditya Ranganath, Omar DeGuchy, Fabian Santiago, Mukesh Singhal, Roummel F. Marcia:
Recurrent Nerual Imaging: An Evolutionary Approach for Mixed Possion-Gaussian Image Denoising. ICMLA 2022: 484-489 - [c51]Rogelio E. Garcia, Jacqueline Alvarez, Roummel F. Marcia:
Machine Learning for Classifying Images with Motion Blur. ICMLA 2022: 490-494 - [c50]Jason Van Tuinen, Aditya Ranganath, Goran Konjevod, Mukesh Singhal, Roummel F. Marcia:
Novel Adversarial Defense Techniques for White-Box Attacks. ICMLA 2022: 617-622 - [c49]Boaz Ilan, Aditya Ranganath, Jacqueline Alvarez, Shilpa Khatri, Roummel F. Marcia:
Interpretability of ReLU for Inversion. ICMLA 2022: 1190-1195 - 2021
- [c48]Aditya Ranganath, Omar DeGuchy, Mukesh Singhal, Roummel F. Marcia:
Multi-Stage Gaussian Noise Reduction with Recurrent Neural Networks. ACSCC 2021: 135-139 - [c47]Andrew Lazar, Mario Banuelos, Suzanne Sindi, Roummel F. Marcia:
Novel structural variant genome detection in extended pedigrees through negative binomial optimization. ACSCC 2021: 563-567 - [c46]Alex Ho, Jacqueline Alvarez, Roummel F. Marcia:
Convolution Padding in Recurrent Neural Networks for Image Denoising with Limited Data. ACSCC 2021: 1699-1703 - [c45]Ashley De Luna, Roummel F. Marcia:
Data-Limited Deep Learning Methods for Mild Cognitive Impairment Classification in Alzheimer's Disease Patients. EMBC 2021: 2641-2646 - [c44]Aditya Ranganath, Omar DeGuchy, Mukesh Singhal, Roummel F. Marcia:
Second-Order Trust-Region Optimization for Data-Limited Inference. EUSIPCO 2021: 2059-2063 - [i9]Johannes J. Brust, Roummel F. Marcia, Cosmin G. Petra, Michael A. Saunders:
Large-scale Optimization with Linear Equality Constraints using Reduced Compact Representation. CoRR abs/2101.11048 (2021) - 2020
- [j21]Jennifer B. Erway, Joshua D. Griffin, Roummel F. Marcia, Riadh Omheni:
Trust-region algorithms for training responses: machine learning methods using indefinite Hessian approximations. Optim. Methods Softw. 35(3): 460-487 (2020) - [j20]Johannes Brust, Roummel F. Marcia, Cosmin G. Petra:
Computationally Efficient Decompositions of Oblique Projection Matrices. SIAM J. Matrix Anal. Appl. 41(2): 852-870 (2020) - [c43]Andrew Lazar, Mario Banuelos, Suzanne Sindi, Roummel F. Marcia:
Detecting novel genomic structural variants through negative binomial optimization. ACSSC 2020: 511-515 - [c42]Erica Sawyer, Mario Banuelos, Roummel F. Marcia, Suzanne Sindi:
A Neural Network Approach for Anomaly Detection in Genomic Signals. APSIPA 2020: 968-971 - [c41]Mario Banuelos, Omar DeGuchy, Suzanne Sindi, Roummel F. Marcia:
Related Inference: A Supervised Learning Approach to Detect Signal Variation in Genome Data. EUSIPCO 2020: 1215-1219 - [c40]Melissa Spence, Mario Banuelos, Roummel F. Marcia, Suzanne Sindi:
Genomic Signal Processing for Variant Detection in Diploid Parent-Child Trios. EUSIPCO 2020: 1318-1322 - [c39]Jacqueline Alvarez, Omar DeGuchy, Roummel F. Marcia:
Image Classification in Synthetic Aperture Radar Using Reconstruction from Learned Inverse Scattering. IGARSS 2020: 2867-2870 - [c38]Michael Fernando Mendez Jimenez, Omar DeGuchy, Roummel F. Marcia:
Deep Convolutional Autoencoders for Deblurring and Denoising Low-Resolution Images. ISITA 2020: 549-553
2010 – 2019
- 2019
- [j19]Johannes Brust, Oleg Burdakov, Jennifer B. Erway, Roummel F. Marcia:
A dense initialization for limited-memory quasi-Newton methods. Comput. Optim. Appl. 74(1): 121-142 (2019) - [j18]Johannes Brust, Roummel F. Marcia, Cosmin G. Petra:
Large-scale quasi-Newton trust-region methods with low-dimensional linear equality constraints. Comput. Optim. Appl. 74(3): 669-701 (2019) - [c37]Xiaolong Chen, Hansell Perez, Melissa Spence, Roummel F. Marcia, Suzanne Sindi:
Fine Tuning Sparsity Penalties to Improve Structural Variant Detection. BIBM 2019: 1182-1184 - [c36]Omar DeGuchy, Fabian Santiago, Mario Banuelos, Roummel F. Marcia:
Deep Neural Networks for Low-resolution Photon-limited Imaging. ICASSP 2019: 3247-3251 - [c35]Melissa Spence, Mario Banuelos, Roummel F. Marcia, Suzanne Sindi:
Predicting Novel and Inherited Variants in Parent-Child Trios. MeMeA 2019: 1-6 - [i8]Jacob Rafati, Roummel F. Marcia:
Quasi-Newton Optimization Methods For Deep Learning Applications. CoRR abs/1909.01994 (2019) - 2018
- [j17]Omar DeGuchy, Jennifer B. Erway, Roummel F. Marcia:
Compact representation of the full Broyden class of quasi-Newton updates. Numer. Linear Algebra Appl. 25(5) (2018) - [c34]Melissa Spence, Mario Banuelos, Roummel F. Marcia, Suzanne Sindi:
Detecting Novel Structural Variants In Genomes By Leveraging Parent-Child Relatedness. BIBM 2018: 943-950 - [c33]Mario Banuelos, Suzanne Sindi, Roummel F. Marcia:
Structural Variant Prediction in Extended Pedigrees Through Sparse Negative Binomial Genome Signal Recovery. EMBC 2018: 1311-1314 - [c32]Jacob Rafati, Omar DeGuchy, Roummel F. Marcia:
Trust-Region Minimization Algorithm for Training Responses (TRMinATR): The Rise of Machine Learning Techniques. EUSIPCO 2018: 2015-2019 - [c31]Mario Banuelos, Suzanne Sindi, Roummel F. Marcia:
Negative Binomial Optimization for Biomedical Structural Variant Signal Reconstruction. ICASSP 2018: 906-910 - [c30]Jacob Rafati, Roummel F. Marcia:
Improving L-BFGS Initialization for Trust-Region Methods in Deep Learning. ICMLA 2018: 501-508 - [i7]Jennifer B. Erway, Joshua D. Griffin, Roummel F. Marcia, Riadh Omheni:
Trust-Region Algorithms for Training Responses: Machine Learning Methods Using Indefinite Hessian Approximations. CoRR abs/1807.00251 (2018) - [i6]Jacob Rafati, Roummel F. Marcia:
Quasi-Newton Optimization in Deep Q-Learning for Playing ATARI Games. CoRR abs/1811.02693 (2018) - 2017
- [j16]Fei Wen, Lasith Adhikari, Ling Pei, Roummel F. Marcia, Peilin Liu, Robert C. Qiu:
Nonconvex Regularization-Based Sparse Recovery and Demixing With Application to Color Image Inpainting. IEEE Access 5: 11513-11527 (2017) - [j15]Johannes Brust, Jennifer B. Erway, Roummel F. Marcia:
On solving L-SR1 trust-region subproblems. Comput. Optim. Appl. 66(2): 245-266 (2017) - [c29]Omar DeGuchy, Lasith Adhikari, Arnold D. Kim, Roummel F. Marcia:
Photon-Limited fluorescence lifetime imaging microscopy signal recovery with known bounds. CAMSAP 2017: 1-5 - [c28]Mario Banuelos, Lasith Adhikari, Rubi Almanza, Andrew Fujikawa, Jonathan Sahagun, Katharine Sanderson, Melissa Spence, Suzanne Sindi, Roummel F. Marcia:
Sparse diploid spatial biosignal recovery for genomic variation detection. MeMeA 2017: 275-280 - [c27]Mario Banuelos, Lasith Adhikari, Rubi Almanza, Andrew Fujikawa, Jonathan Sahagun, Katharine Sanderson, Melissa Spence, Suzanne Sindi, Roummel F. Marcia:
Nonconvex regularization for sparse genomic variant signal detection. MeMeA 2017: 281-286 - [i5]Fei Wen, Lasith Adhikari, Ling Pei, Roummel F. Marcia, Peilin Liu, Robert C. Qiu:
Nonconvex Regularization Based Sparse Recovery and Demixing with Application to Color Image Inpainting. CoRR abs/1703.07967 (2017) - 2016
- [c26]Mario Banuelos, Rubi Almanza, Lasith Adhikari, Roummel F. Marcia, Suzanne Sindi:
Constrained variant detection with SPaRC: Sparsity, parental relatedness, and coverage. EMBC 2016: 3490-3493 - [c25]Lasith Adhikari, Arnold D. Kim, Roummel F. Marcia:
Sparse reconstruction for fluorescence lifetime imaging microscopy with poisson noise. GlobalSIP 2016: 262-266 - [c24]Mario Banuelos, Rubi Almanza, Lasith Adhikari, Suzanne Sindi, Roummel F. Marcia:
Sparse signal recovery methods for variant detection in next-generation sequencing data. ICASSP 2016: 864-868 - [c23]Aramayis Orkusyan, Lasith Adhikari, Joanna Valenzuela, Roummel F. Marcia:
Analysis of p-norm regularized subproblem minimization for sparse photon-limited image recovery. ICASSP 2016: 1407-1411 - [c22]Lasith Adhikari, Roummel F. Marcia:
Bounded sparse photon-limited image recovery. ICIP 2016: 3508-3512 - [c21]Jennifer B. Erway, Robert J. Plemmons, Lasith Adhikari, Roummel F. Marcia:
Trust-region methods for nonconvex sparse recovery optimization. ISITA 2016: 275-279 - [c20]Lasith Adhikari, Arnold D. Kim, Roummel F. Marcia:
Nonconvex sparse poisson intensity reconstruction for time-dependent bioluminescence tomography. ISITA 2016: 280-284 - [c19]Mario Banuelos, Rubi Almanza, Lasith Adhikari, Roummel F. Marcia, Suzanne Sindi:
Sparse genomic structural variant detection: Exploiting parent-child relatedness for signal recovery. SSP 2016: 1-5 - [c18]Daniel L. Pimentel-Alarcón, Laura Balzano, Roummel F. Marcia, Robert D. Nowak, Rebecca Willett:
Group-sparse subspace clustering with missing data. SSP 2016: 1-5 - 2015
- [j14]Jennifer B. Erway, Roummel F. Marcia:
On Efficiently Computing the Eigenvalues of Limited-Memory Quasi-Newton Matrices. SIAM J. Matrix Anal. Appl. 36(3): 1338-1359 (2015) - [c17]Lasith Adhikari, Roummel F. Marcia:
p-th Power total variation regularization in photon-limited imaging via iterative reweighting. EUSIPCO 2015: 1621-1625 - [c16]Lasith Adhikari, Roummel F. Marcia:
Nonconvex relaxation for Poisson intensity reconstruction. ICASSP 2015: 1483-1487 - [c15]Lasith Adhikari, Dianwen Zhu, Changqing Li, Roummel F. Marcia:
Nonconvex reconstruction for low-dimensional fluorescence molecular tomographic poisson observations. ICIP 2015: 2404-2408 - 2014
- [j13]Jennifer B. Erway, Vibhor Jain, Roummel F. Marcia:
Shifted L-BFGS systems. Optim. Methods Softw. 29(5): 992-1004 (2014) - [j12]Jennifer B. Erway, Roummel F. Marcia:
Algorithm 943: MSS: MATLAB Software for L-BFGS Trust-Region Subproblems for Large-Scale Optimization. ACM Trans. Math. Softw. 40(4): 28:1-28:12 (2014) - 2013
- [c14]Jennifer B. Erway, Vibhor Jain, Roummel F. Marcia:
Shifted limited-memory DFP systems. ACSSC 2013: 1033-1037 - [c13]Zachary T. Harmany, Roummel F. Marcia, Rebecca M. Willett:
Dual-scale masks for spatio-temporal compressive imaging. GlobalSIP 2013: 1045-1048 - [i4]Zachary T. Harmany, Roummel F. Marcia, Rebecca M. Willett:
Compressive Coded Aperture Keyed Exposure Imaging with Optical Flow Reconstruction. CoRR abs/1306.6281 (2013) - 2012
- [j11]Zachary T. Harmany, Roummel F. Marcia, Rebecca M. Willett:
This is SPIRAL-TAP: Sparse Poisson Intensity Reconstruction ALgorithms - Theory and Practice. IEEE Trans. Image Process. 21(3): 1084-1096 (2012) - [j10]Maxim Raginsky, Rebecca M. Willett, Corinne Horn, Jorge G. Silva, Roummel F. Marcia:
Sequential Anomaly Detection in the Presence of Noise and Limited Feedback. IEEE Trans. Inf. Theory 58(8): 5544-5562 (2012) - [c12]David R. Jones, Rachel O. Schlick, Roummel F. Marcia:
Compressive video recovery with upper and lower bound constraints. ICASSP 2012: 3325-3328 - 2011
- [j9]Jennifer B. Erway, Roummel F. Marcia:
A backward stability analysis of diagonal pivoting methods for solving unsymmetric tridiagonal systems without interchanges. Numer. Linear Algebra Appl. 18(1): 41-54 (2011) - [j8]Maxim Raginsky, Sina Jafarpour, Zachary T. Harmany, Roummel F. Marcia, Rebecca M. Willett, A. Robert Calderbank:
Performance Bounds for Expander-Based Compressed Sensing in Poisson Noise. IEEE Trans. Signal Process. 59(9): 4139-4153 (2011) - [c11]James Hernandez, Zachary T. Harmany, Daniel Thompson, Roummel F. Marcia:
Bounded gradient projection methods for sparse signal recovery. ICASSP 2011: 949-952 - [c10]Daniel Thompson, Zachary T. Harmany, Roummel F. Marcia:
Sparse video recovery using Linearly Constrained Gradient Projection. ICASSP 2011: 1329-1332 - 2010
- [j7]Maxim Raginsky, Rebecca Willett, Zachary T. Harmany, Roummel F. Marcia:
Compressed sensing performance bounds under Poisson noise. IEEE Trans. Signal Process. 58(8): 3990-4002 (2010) - [c9]Zachary T. Harmany, Roummel F. Marcia, Rebecca Willett:
SPIRAL out of convexity: sparsity-regularized algorithms for photon-limited imaging. Computational Imaging 2010: 75330 - [c8]Zachary T. Harmany, Daniel Thompson, Rebecca Willett, Roummel F. Marcia:
Gradient projection for linearly constrained convex optimization in sparse signal recovery. ICIP 2010: 3361-3364 - [c7]Rebecca Willett, Zachary T. Harmany, Roummel F. Marcia:
Poisson image reconstruction with total variation regularization. ICIP 2010: 4177-4180 - [c6]Zachary T. Harmany, Roummel F. Marcia, Rebecca Willett:
Sparsity-regularized photon-limited imaging. ISBI 2010: 772-775 - [i3]Maxim Raginsky, Sina Jafarpour, Zachary T. Harmany, Roummel F. Marcia, Rebecca Willett, A. Robert Calderbank:
Performance bounds for expander-based compressed sensing in Poisson noise. CoRR abs/1007.2377 (2010)
2000 – 2009
- 2009
- [c5]Roummel F. Marcia, Zachary T. Harmany, Rebecca Willett:
Compressive coded aperture imaging. Computational Imaging 2009: 72460 - [c4]Maxim Raginsky, Roummel F. Marcia, Jorge G. Silva, Rebecca M. Willett:
Sequential probability assignment via online convex programming using exponential families. ISIT 2009: 1338-1342 - [i2]Maxim Raginsky, Zachary T. Harmany, Roummel F. Marcia, Rebecca Willett:
Compressed sensing performance bounds under Poisson noise. CoRR abs/0910.5146 (2009) - [i1]Maxim Raginsky, Roummel F. Marcia, Jorge G. Silva, Rebecca Willett:
Sequential anomaly detection in the presence of noise and limited feedback. CoRR abs/0911.2904 (2009) - 2008
- [c3]Roummel F. Marcia, Rebecca M. Willett:
Compressive coded aperture video reconstruction. EUSIPCO 2008: 1-5 - [c2]Roummel F. Marcia, Rebecca Willett:
Compressive coded aperture superresolution image reconstruction. ICASSP 2008: 833-836 - [c1]Roummel F. Marcia, Changsoon Kim, Jungsang Kim, David J. Brady, Rebecca Willett:
Fast disambiguation of superimposed images for increased field of view. ICIP 2008: 2620-2623 - 2007
- [j6]Roummel F. Marcia, Julie C. Mitchell, J. Ben Rosen:
Multi-funnel optimization using Gaussian underestimation. J. Glob. Optim. 39(1): 39-48 (2007) - [j5]Roummel F. Marcia, Julie C. Mitchell, Stephen J. Wright:
Global optimization in protein docking using clustering, underestimation and semidefinite programming. Optim. Methods Softw. 22(5): 803-811 (2007) - 2006
- [j4]James R. Bunch, Roummel F. Marcia:
A simplified pivoting strategy for symmetric tridiagonal matrices. Numer. Linear Algebra Appl. 13(10): 865-867 (2006) - 2005
- [j3]Roummel F. Marcia, Julie C. Mitchell, J. Ben Rosen:
Iterative Convex Quadratic Approximation for Global Optimization in Protein Docking. Comput. Optim. Appl. 32(3): 285-297 (2005) - [j2]James R. Bunch, Roummel F. Marcia:
A pivoting strategy for symmetric tridiagonal matrices. Numer. Linear Algebra Appl. 12(9): 911-922 (2005) - 2004
- [j1]J. Ben Rosen, Roummel F. Marcia:
Convex Quadratic Approximation. Comput. Optim. Appl. 28(2): 173-184 (2004)
Coauthor Index
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