generated by bibbase.org
  2021 (12)
A Method for Convex Black-Box Integer Global Optimization. Larson, J.; Leyffer, S.; Palkar, P.; and Wild, S. M. Journal of Global Optimization, 80: 439–477. 2021.
doi   bibtex  
Tuning Multigrid Methods with Robust Optimization and Local Fourier Analysis. Brown, J.; He, Y.; MacLachlan, S.; Menickelly, M.; and Wild, S. M. SIAM Journal on Scientific Computing, 43(1): A109–A138. 2021.
doi   bibtex  
libEnsemble: A Library to Coordinate the Concurrent Evaluation of Dynamic Ensembles of Calculations. Hudson, S.; Larson, J.; Navarro, J.; and Wild, S. M. IEEE Transactions on Parallel and Distributed Systems. 2021. To appear
doi   bibtex  
Scalable Statistical Inference of Photometric Redshift via Data Subsampling. Fadikar, A.; Wild, S. M.; and Chaves-Montero, J. In Computational Science – ICCS2021, pages 245–258, 2021. Springer
doi   bibtex  
Get on the BAND Wagon: A Bayesian Framework for Quantifying Model Uncertainties in Nuclear Dynamics. Phillips, D. R.; Furnstahl, R. J.; Heinz, U.; Maiti, T.; Nazarewicz, W.; Nunes, F. M.; Plumlee, M.; Pratola, M. T.; Pratt, S.; Viens, F. G.; and Wild, S. M. Journal of Physics G: Nuclear and Particle Physics, 48(7): 072001. 2021.
doi   bibtex  
Hierarchical Analysis of Halo Center in Cosmology. Di, Z. W.; Rangel, E.; Yoo, S.; and Wild, S. M. In Computational Science – ICCS 2021, pages 671–684, 2021. Springer
doi   bibtex  
Machine Learning-Based Inversion of Nuclear Responses. Raghavan, K.; Balaprakash, P.; Lovato, A.; Rocco, N.; and Wild, S. M. Physical Review C, 103(3): 035502. 2021.
doi   bibtex  
Randomized Algorithms for Scientific Computing. Buluc, A.; Kolda, T. G.; Wild, S. M.; Anitescu, M.; DeGennaro, A.; Jakeman, J.; Kamath, C.; Ramakrishnan; Kannan; Lopes, M. E.; Martinsson, P.; Myers, K.; Nelson, J.; Restrepo, J. M.; Seshadhri, C.; Vrabie, D.; Wohlberg, B.; Wright, S. J.; Yang, C.; and Zwart, P. Technical Report 2104.11079, ArXiv, 2021.
Randomized Algorithms for Scientific Computing [link]Paper   bibtex  
Lookahead Acquisition Functions for Finite-Horizon Time-Dependent Bayesian Optimization and Application to Quantum Optimal Control. Renganathan, S. A.; Larson, J.; and Wild, S. M. Technical Report 2105.09824, ArXiv, 2021.
Lookahead Acquisition Functions for Finite-Horizon Time-Dependent Bayesian Optimization and Application to Quantum Optimal Control [link]Paper   bibtex  
Sequential Learning of Active Subspaces. Wycoff, N.; Binois, M.; and Wild, S. M. Journal of Computational and Graphical Statistics. 2021. To appear
doi   bibtex  
Exploiting Symmetry Reduces the Cost of Training QAOA. Shaydulin, R.; and Wild, S. M. IEEE Transactions on Quantum Engineering, 2: 1–9. 2021.
doi   bibtex  
Optimization and Supervised Machine Learning Methods for Fitting Numerical Physics Models without Derivatives. Bollapragada, R.; Menickelly, M.; Nazarewicz, W.; O'Neal, J.; Reinhard, P.; and Wild, S. M. Journal of Physics G: Nuclear and Particle Physics, 48(2): 024001. 2021.
doi   bibtex  
  2020 (6)
Derivative-Free Robust Optimization by Outer Approximations. Menickelly, M.; and Wild, S. M. Mathematical Programming, 179(1–2): 157–193. 2020.
doi   bibtex  
A Survey of Nonlinear Robust Optimization. Leyffer, S.; Menickelly, M.; Munson, T.; Vanaret, C.; and Wild, S. M. INFOR: Information Systems and Operational Research, 58(2): 342–373. 2020.
doi   bibtex  
Calibration of Energy Density Functionals with Deformed Nuclei. Schunck, N.; O'Neal, J.; Grosskopf, M.; Lawrence, E.; and Wild, S. M. Journal of Physics G: Nuclear and Particle Physics, 47(7): 074001. 2020.
doi   bibtex  
Pufferscale: Rescaling HPC Data Services for High Energy Physics Applications. Cheriere, N.; Dorier, M.; Antoniu, G.; Wild, S. M.; Leyffer, S.; and Ross, R. In 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid 2020), 2020.
doi   bibtex  
Recursive Two-Step Lookahead Expected Payoff for Time-Dependent Bayesian Optimization. Renganathan, S. A.; Larson, J.; and Wild, S. M. Technical Report 2006.08037, ArXiv, 2020.
Recursive Two-Step Lookahead Expected Payoff for Time-Dependent Bayesian Optimization [link]Paper   bibtex  
Looking Ahead to the 2021 SIAM Conference on Computational Science and Engineering. Grigori, L.; Kilmer, M.; and Wild, S. M. SIAM News, 53(4). 2020.
Looking Ahead to the 2021 SIAM Conference on Computational Science and Engineering [pdf]2   Looking Ahead to the 2021 SIAM Conference on Computational Science and Engineering [link]Paper   bibtex  
  2019 (10)
Scalable Reinforcement-Learning-Based Neural Architecture Search for Cancer Deep Learning Research. Balaprakash, P.; Egele, R.; Salim, M.; Vishwanath, V.; Wild, S. M.; Xia, F.; Brettin, T.; and Stevens, R. In International Conference for High Performance Computing, Networking, Storage, and Analysis (SC19), pages 37:1–37:33, 2019. ACM
doi   bibtex  
Workshop Report on Basic Research Needs for Scientific Machine Learning: Core Technologies for Artificial Intelligence. Baker, N.; Alexander, F.; Bremer, T.; Hagberg, A.; Kevrekidis, Y.; Najm, H.; Parashar, M.; Patra, A.; Sethian, J.; Wild, S. M.; and Willcox, K. Technical Report U.S.\ Department of Energy, ASCR, 2019.
doi   bibtex  
Simultaneous Sensing Error Recovery and Tomographic Inversion Using an Optimization-Based Approach. Austin, A.; Di, Z. W.; Leyffer, S.; and Wild, S. M. SIAM Journal on Scientific Computing, 41(3): B497–B521. 2019.
doi   bibtex  
Derivative-Free Optimization Methods. Larson, J.; Menickelly, M.; and Wild, S. M. Acta Numerica, 28: 287–404. 2019.
doi   bibtex  
libEnsemble User Manual, Version 0.5.2. Hudson, S.; Larson, J.; Wild, S. M.; Bindel, D.; and Navarro, J. Technical Report Argonne National Laboratory, 2019.
libEnsemble User Manual, Version 0.5.2 [link]Paper   bibtex  
Adaptive Sampling Quasi-Newton Methods for Derivative-Free Stochastic Optimization. Bollapragada, R.; and Wild, S. M. In Beyond First Order Methods in Machine Learning (NeurIPS 2019 Workshop), 2019.
Adaptive Sampling Quasi-Newton Methods for Derivative-Free Stochastic Optimization [link]Paper   bibtex  
Optimization-Based Simultaneous Alignment and Reconstruction in Fluorescence Tomography. Di, Z. W.; Chen, S.; Gursoy, D.; Paunesku, T.; Leyffer, S.; Wild, S. M.; and Vogt, S. Optics Letters, 44(17): 4331–4334. 2019.
doi   bibtex  
Large Area Imaging of Integrated Circuits Using Hard X-Ray Ptychography. Klug, J.; Deng, J.; Jiang, Y.; Preissner, C.; Di, Z. W.; Bicer, T.; Wild, S. M.; Vogt, S.; Sandberg, R.; and Honig, S. Technical Report 17-2, Argonne, 2019.
bibtex  
Calibrating Sensing Drift in Tomographic Inversion. Huang, X.; Di, Z. W.; and Wild, S. M. In 2019 IEEE International Conference on Image Processing (ICIP19), pages 1267–1271, 2019.
doi   bibtex  
Adaptive Learning for Concept Drift in Application Performance Modeling. Madireddy, S.; Balaprakash, P.; Carns, P.; Latham, R.; Lockwood, G.; Ross, R.; Snyder, S.; and Wild, S. M. In Proceedings of the 48th International Conference on Parallel Processing (ICPP 2019), pages 79:1–79:11, 2019.
doi   bibtex  
  2018 (10)
3D X-Ray Imaging of Continuous Objects beyond the Depth of Focus Limit. Gilles, M. A. T.; Nashed, Y. S. G.; Du, M.; Jacobsen, C.; and Wild, S. M. Optica, 5(9): 1078–1086. 2018.
doi   bibtex  
Doing Moore with Less – Leapfrogging Moore's Law with Inexactness for Supercomputing. Leyffer, S.; Wild, S. M.; Fagan, M.; Snir, M.; Palem, K.; Finkel, H.; and Yoshii, K. In 3rd International Workshop on Post-Moore Era Supercomputing (PMES), of SC18 Workshops, 2018.
Doing Moore with Less – Leapfrogging Moore's Law with Inexactness for Supercomputing [link]Paper   bibtex  
DeepHyper: Asynchronous Hyperparameter Search for Deep Neural Networks. Balaprakash, P.; Salim, M.; Uram, T. D.; Vishwanath, V.; and Wild, S. M. In 25th IEEE International Conference on High Performance Computing (HiPC18), 2018.
doi   bibtex  
Robust Learning of Trimmed Estimators via Manifold Sampling. Menickelly, M.; and Wild, S. M. In Modern Trends in Nonconvex Optimization for Machine Learning (ICML 2018 Workshop), 2018.
Robust Learning of Trimmed Estimators via Manifold Sampling [link]Paper   bibtex  
Accurate, Rapid Identification of Dislocation Lines in Coherent Diffractive Imaging via a Min-Max Optimization Formulation. Ulvestad, A.; Menickelly, M.; and Wild, S. M. AIP Advances, 8(1): 015114. 2018.
doi   bibtex  
Modeling I/O Performance Variability Using Conditional Variational Auto Encoders. Madireddy, S.; Balaprakash, P.; Carns, P.; Latham, R.; Ross, R.; Snyder, S.; and Wild, S. M. In IEEE Cluster Conference, pages 109–113, 2018.
doi   bibtex  
Understanding and Improving the Trust in Results of Numerical Simulations and Scientific Data Analytics. Cappello, F.; Gupta, R.; Di, S.; Constantinescu, E. M.; Peterka, T.; and Wild, S. M. In Euro-Par 2017: Parallel Processing Workshops, pages 545–556, 2018. Springer
doi   bibtex  
Asynchronously Parallel Optimization Solver for Finding Multiple Minima. Larson, J.; and Wild, S. M. Mathematical Programming Computation, 10(3): 303–332. 2018.
doi   bibtex  
Machine Learning Based Parallel I/O Predictive Modeling: A Case Study on Lustre File Systems. Madireddy, S.; Balaprakash, P.; Carns, P.; Latham, R.; Ross, R.; Snyder, S.; and Wild, S. M. In Lecture Notes in Computer Science: ISC High Performance 2018: High Performance Computing, pages 184–204, 2018. Springer
doi   bibtex  
Manifold Sampling for Optimization of Nonconvex Functions that are Piecewise Linear Compositions of Smooth Components. Khan, K. A.; Larson, J.; and Wild, S. M. SIAM Journal on Optimization, 28(4): 3001–3024. 2018.
doi   bibtex  
  2017 (9)
Advancing Cross-Cutting Ideas for Computational Climate Science. Evans, K.; Ng, E. G.; Caldwell, P.; Hoffman, F. M.; Jackson, C.; Kleese van Dam, K.; Leung, L.; Martin, D.; Ostrouchov, G.; Tuminaro, R.; Ullrich, P.; Wild, S. M.; and Williams, S. Technical Report U.S.\ Department of Energy, BER and ASCR, 2017.
doi   bibtex  
Bragg Coherent Modulation Imaging: Strain- and Defect-Sensitive Single Views of Extended Samples. Ulvestad, A.; Cha, W.; Calvo-Almazan, I.; Maddali, S.; Wild, S. M.; Maxey, E.; Dupraz, M.; and Hruszkewycz, S. O. Technical Report Argonne National Laboratory, MCS Division, 2017.
Bragg Coherent Modulation Imaging: Strain- and Defect-Sensitive Single Views of Extended Samples [link]Paper   bibtex  
Solving Derivative-Free Nonlinear Least Squares Problems with POUNDERS. Wild, S. M. In Terlaky, T.; Anjos, M. F.; and Ahmed, S., editor(s), Advances and Trends in Optimization with Engineering Applications, pages 529–540. SIAM, 2017.
doi   bibtex  
Analysis and Correlation of Application I/O Performance and System-Wide I/O Activity. Madireddy, S.; Balaprakash, P.; Carns, P.; Latham, R.; Ross, R.; Snyder, S.; and Wild, S. M. In 2017 International Conference on Networking, Architecture, and Storage (NAS), pages 1–10, 2017. IEEE
doi   bibtex  
CONORBIT: Constrained Optimization by Radial Basis Function Interpolation in Trust Regions. Regis, R. G.; and Wild, S. M. Optimization Methods and Software, 32(3): 552–580. 2017.
doi   bibtex  
Joint Reconstruction of X-Ray Fluorescence and Transmission Tomography. Di, Z. W.; Chen, S.; Hong, Y. P.; Jacobsen, C.; Leyffer, S.; and Wild, S. M. Optics Express, 25(12): 13107–13124. 2017.
doi   bibtex  
Improving FPGA Design Parameter Exploration: Timing, Power, and Area. Mametjanov, A.; Balaprakash, P.; Choudary, C.; Hovland, P. D.; Wild, S. M.; Sabin, G.; and Wolfe, G. Technical Report ANL/MCS-P7000-0117, Argonne National Laboratory, MCS Division, 2017.
Improving FPGA Design Parameter Exploration: Timing, Power, and Area [pdf]Paper   bibtex  
Uncertainty Quantification for Optical Model Parameters. Lovell, A. E.; Nunes, F. M.; Sarich, J.; and Wild, S. M. Physical Review C, 95(2): 024611. 2017.
doi   bibtex  
Computing Just What You Need: Online Data Analysis and Reduction at Extreme Scales. Foster, I.; Ainsworth, M.; Allen, B.; Bessac, J.; Cappello, F.; Choi, J. Y.; Constantinescu, E. M.; Davis, P. E.; Di, S.; Di, Z. W.; Guo, H.; Klasky, S.; Kleese van Dam, K.; Kurc, T.; Liu, Q.; Malik, A.; Mehta, K.; Mueller, K.; Munson, T.; Ostrouchov, G.; Parashar, M.; Peterka, T.; Pouchard, L.; Tao, D.; Tugluk, O.; Wild, S. M.; Wolf, M.; Wozniak, J. M.; Xu, W.; and Yoo, S. In Rivera, F. F.; Pena, T. F.; and Cabaleiro, J. C., editor(s), European Conference on Parallel Processing, pages 3–19, 2017. Springer
doi   bibtex  
  2016 (17)
Report of the DOE Workshop on Management, Analysis, and Visualization of Experimental and Observational Data – The Convergence of Data and Computing. Bethel, E. W.; and Greenwald (eds.), M. Technical Report U.S.\ Department of Energy, ASCR, 2016.
doi   bibtex  
AutoMOMML: Automatic Multi-Objective Modeling with Machine Learning. Balaprakash, P.; Tiwari, A.; Wild, S. M.; Carrington, L.; and Hovland, P. D. In Kunkel, M. J.; Balaji, P.; and Dongarra, J., editor(s), High Performance Computing: 31st International Conference, ISC High Performance 2016, Frankfurt, Germany, June 19-23, 2016, Proceedings, pages 219–239, 2016. Springer
doi   bibtex  
Doing Moore with Less – Leapfrogging Moore's Law with Inexactness for Supercomputing. Leyffer, S.; Wild, S. M.; Fagan, M.; Snir, M.; Palem, K.; Finkel, H.; and Yoshii, K. Technical Report ANL/MCS-P6077-1016, Argonne National Laboratory MCS, 2016.
Doing Moore with Less – Leapfrogging Moore's Law with Inexactness for Supercomputing [link]Paper   bibtex  
Exploiting Performance Portability in Search Algorithms for Autotuning. Roy, A.; Balaprakash, P.; Hovland, P. D.; and Wild, S. M. In 2016 IEEE International Parallel and Distributed Processing Symposium IPDPS Workshops, pages 1535–1544, 2016.
doi   bibtex  
Rejoinder: Modeling an Augmented Lagrangian for Blackbox Constrained Optimization. Gramacy, R. B.; Gray, G. A.; Le Digabel, S.; Lee, H. K. H.; Ranjan, P.; Wells, G.; and Wild, S. M. Technometrics, 58(1): 26–29. 2016.
doi   bibtex  
Novel Large Scale Simulation Process to Support DOT's CAFE Modeling System. Moawad, A.; Balaprakash, P.; Rousseau, A.; and Wild, S. M. International Journal of Automotive Technology, 17(6): 1067–1077. 2016.
doi   bibtex  
Origins and Optimization of Entanglement in Plasmonically Coupled Quantum Dots. Otten, M.; Larson, J.; Min, M.; Wild, S. M.; Pelton, M.; and Gray, S. K. Physical Review A, 94(2): 022312. 2016.
doi   bibtex  
Management, Analysis, and Visualization of Experimental and Observational Data – The Convergence of Data and Computing. Bethel, E. W.; Greenwald, M.; Kleese van Dam, K.; Parashar, M.; Wild, S. M.; and Wiley, H. S. In 2016 IEEE 12th International Conference on e-Science, pages 213–222, 2016.
doi   bibtex  
Bayesian Optimization under Mixed Constraints with a Slack-Variable Augmented Lagrangian. Picheny, V.; Gramacy, R. B.; Wild, S. M.; and Le Digabel, S. In Lee, D. D.; Sugiyama, M.; Luxburg, U. V.; Guyon, I.; and Garnett, R., editor(s), Advances in Neural Information Processing Systems 29, pages 1435–1443, 2016. Curran Associates, Inc.
Bayesian Optimization under Mixed Constraints with a Slack-Variable Augmented Lagrangian [pdf]Paper   bibtex  
Optimization-Based Approach for Joint X-Ray Fluorescence and Transmission Tomographic Inversion. Di, Z. W.; Leyffer, S.; and Wild, S. M. SIAM Journal on Imaging Sciences, 9(1): 1–23. 2016.
doi   bibtex  
Overcoming the Power Wall by Exploiting Application Inexactness and Emerging COTS Architectural Features: Trading Precision for Improving Application Quality. Fagan, M.; Schlachter, J.; Yoshii, K.; Leyffer, S.; Palem, K.; Snir, M.; Wild, S. M.; and Enz, C. In 29th IEEE International System-on-Chip Conference (SOCC), pages 241–246, 2016.
doi   bibtex  
Single-View Phase Retrieval of an Extended Sample by Exploiting Edge Detection and Sparsity. Tripathi, A.; McNulty, I.; Munson, T.; and Wild, S. M. Optics Express, 24(21): 24719–24738. 2016.
doi   bibtex  
Coherent Diffractive Imaging of Time-Evolving Samples with Improved Temporal Resolution. Ulvestad, A.; Tripathi, A.; Hruszkewycz, S. O.; Cha, W.; Fuoss, P. H.; Wild, S. M.; and Stephenson, G. B. Physical Review B, 93(18): 184105. 2016.
doi   bibtex  
Manifold Sampling for $\ell_1$ Nonconvex Optimization. Larson, J.; Menickelly, M.; and Wild, S. M. SIAM Journal on Optimization, 26(4): 2540–2563. 2016.
doi   bibtex  
A Batch, Derivative-Free Algorithm for Finding Multiple Local Minima. Larson, J.; and Wild, S. M. Optimization and Engineering, 17(1): 205–228. 2016.
doi   bibtex  
Modeling an Augmented Lagrangian for Blackbox Constrained Optimization. Gramacy, R. B.; Gray, G. A.; Le Digabel, S.; Lee, H. K. H.; Ranjan, P.; Wells, G.; and Wild, S. M. Technometrics, 58(1): 1–11. 2016.
doi   bibtex  
Non-Negative Matrix Analysis in X-Ray Spectromicroscopy: Choosing Regularizers. Mak, R.; Wild, S. M.; and Jacobsen, C. AIP Conference Proceedings, 1696: 020034. 2016. 12th International Conference on X-Ray Microscopy (XRM 2014)
doi   bibtex  
  2015 (18)
A Bayesian Approach for Parameter Estimation and Prediction using a Computationally Intensive Model. Higdon, D.; McDonnell, J. D.; Schunck, N.; Sarich, J.; and Wild, S. M. Journal of Physics G: Nuclear and Particle Physics, 42(3): 034009. 2015.
doi   bibtex  
Quantification of Uncertainties in Nuclear Density Functional Theory. Schunck, N.; McDonnell, J. D.; Sarich, J.; Wild, S. M.; and Higdon, D. Nuclear Data Sheets, 123: 115–118. 2015.
doi   bibtex  
Uncertainty Quantification and Propagation in Nuclear Density Functional Theory. Schunck, N.; McDonnell, J. D.; Higdon, D.; Sarich, J.; and Wild, S. M. European Physical Journal A, 51(12): 169(1–14). 2015.
doi   bibtex  
Error Analysis in Nuclear Density Functional Theory. Schunck, N.; McDonnell, J. D.; Sarich, J.; Wild, S. M.; and Higdon, D. Journal of Physics G: Nuclear and Particle Physics, 42(3): 034024. 2015.
doi   bibtex  
One-Nucleon Transfer Reactions and the Optical Potential. Nunes, F. M.; Lovell, A. E.; Ross, A.; Titus, L. J.; Charity, R. J.; Dickhoff, W. H.; Mahzoon, M. H.; Sarich, J.; and Wild, S. M. In Proceedings, 14th International Conference on Nuclear Reaction Mechanisms: Varenna, Italy, June 15-19, 2015, pages 123–130, 2015.
One-Nucleon Transfer Reactions and the Optical Potential [pdf]Paper   bibtex  
Statistical Uncertainties of a Chiral Interaction at Next-to-Next-To Leading Order. Ekström, A.; Carlsson, B. D.; Wendt, K.; Forssén, C.; Hjorth-Jensen, M.; Machleidt, R.; and Wild, S. M. Journal of Physics G: Nuclear and Particle Physics, 42(3): 034003. 2015.
doi   bibtex  
Nuclear Energy Density Optimization: UNEDF2. Kortelainen, M.; McDonnell, J. D.; Nazarewicz, W.; Olsen, E.; Reinhard, P.; Sarich, J.; Schunck, N.; Wild, S. M.; Davesne, D.; Erler, J.; and Pastore, A. In JPS Conference Proceedings, volume 6, pages 020018, 2015.
doi   bibtex  
Collective I/O Tuning Using Analytical and Machine Learning Models. Isaila, F.; Balaprakash, P.; Wild, S. M.; Kimpe, D.; Latham, R.; Ross, R.; and Hovland, P. D. In Proceedings of IEEE International Conference on Cluster Computing (CLUSTER 2015), pages 128–137, 2015.
doi   bibtex  
Using Multiobjective Optimization for Automotive Component Sizing. Vijayagopal, R.; Chen, R.; Sharer, P.; Wild, S. M.; and Rousseau, A. World Electric Vehicle Journal, 7(2): 261–269. 2015.
doi   bibtex  
Dynamic Model-Driven Parallel I/O Performance Tuning. Behzad, B.; Byna, S.; Wild, S. M.; Prabhat; and Snir, M. In Proceedings of IEEE International Conference on Cluster Computing (CLUSTER 2015), pages 184–193, 2015.
doi   bibtex  
Uncertainty Quantification for Nuclear Density Functional Theory and Information Content of New Measurements. McDonnell, J. D.; Schunck, N.; Higdon, D.; Sarich, J.; Wild, S. M.; and Nazarewicz, W. Physical Review Letters, 114(12): 122501. 2015.
doi   bibtex  
ACCOLADES: A Scalable Workflow Framework for Large-Scale Simulation and Analyses of Automotive Engines. Aithal, S. M.; and Wild, S. M. In Kunkel, J. M.; and Ludwig, T., editor(s), High Performance Computing (ISC-HPC 2015), volume 9137, pages 87–95, 2015. Springer
doi   bibtex  
Analysis of the Tradeoffs between Energy and Run Time for Multilevel Checkpointing. Balaprakash, P.; Bautista Gomez, L. A.; Bouguerra, M.; Wild, S. M.; Cappello, F.; and Hovland, P. D. In Jarvis, S. A.; Wright, S. A.; and Hammond, S. D., editor(s), High Performance Computing Systems. Performance Modeling, Benchmarking, and Simulation, volume 8966, pages 249–263, 2015. Springer
doi   bibtex  
Derivative-Free Optimization for Parameter Estimation in Computational Nuclear Physics. Wild, S. M.; Sarich, J.; and Schunck, N. Journal of Physics G: Nuclear and Particle Physics, 42(3): 034031. 2015.
doi   bibtex  
A Taxonomy of Constraints in Black-Box Simulation-Based Optimization. Le Digabel, S.; and Wild, S. M. Technical Report ANL/MCS-P5350-0515, Argonne National Laboratory, MCS, 2015.
A Taxonomy of Constraints in Black-Box Simulation-Based Optimization [pdf]Paper   bibtex  
Development of a Reduced-Order Design/Optimization Tool for Automotive Engines Using Massively Parallel Computing. Aithal, S. M.; and Wild, S. M. In Proceedings of the SAE 12th International Conference on Engines & Vehicles, 2015. SAE
doi   bibtex  
Visualizing and Improving the Robustness of Phase Retrieval Algorithms. Tripathi, A.; Leyffer, S.; Munson, T.; and Wild, S. M. Procedia Computer Science (ICCS 2015), 51: 815–824. 2015.
doi   bibtex  
Autotuning FPGA Design Parameters for Performance and Power. Mametjanov, A.; Balaprakash, P.; Choudary, C.; Hovland, P. D.; Wild, S. M.; and Sabin, G. In Proceedings of the 23rd IEEE International Symposium on Field-Programmable Custom Computing Machines (FCCM 2015), pages 84–91, 2015.
doi   bibtex  
  2014 (10)
Energy-Performance Tradeoffs in Multilevel Checkpoint Strategies (Poster). Bautista Gomez, L. A.; Balaprakash, P.; Bouguerra, M.; Wild, S. M.; Cappello, F.; and Hovland, P. D. In Proceedings of IEEE International Conference on Cluster Computing (CLUSTER 2014), pages 278–279, 2014.
doi   bibtex  
Empirical Performance Modeling of GPU Kernels Using Active Learning. Balaprakash, P.; Rupp, K.; Mametjanov, A.; Gramacy, R. B.; Hovland, P. D.; and Wild, S. M. In Bader, M.; Bode, A.; Bungartz, H.; Gerndt, M.; Joubert, G. R.; and Peters, F., editor(s), Parallel Computing: Accelerating Computational Science and Engineering (ParCo2013), volume 25, pages 646–655, 2014. IOS Press
doi   bibtex  
SciDAC: Accelerating Scientific Discovery, Transforming Computational Science. D'Azevedo, E.; Ng, E. G.; and Wild, S. M. SIAM News, 47(3). 2014.
SciDAC: Accelerating Scientific Discovery, Transforming Computational Science [link]Paper   bibtex  
Unsupervised Cell Identification on Multidimensional X-ray Fluorescence Datasets. Wang, S.; Ward, J.; Leyffer, S.; Vogt, S.; Wild, S. M.; and Jacobsen, C. Journal of Synchrotron Radiation, 21(3): 568–579. 2014.
doi   bibtex  
Improving Parallel I/O Autotuning with Performance Modeling. Behzad, B.; Byna, S.; Wild, S. M.; Prabhat; and Snir, M. In Proceedings of the 23rd International Symposium on High-Performance Parallel and Distributed Computing (HPDC14), pages 253–256, 2014. ACM
doi   bibtex  
Applied Mathematics Research for Exascale Computing. Dongarra, J.; Hittinger, J.; Bell, J.; Chacón, L.; Falgout, R.; Heroux, M.; Hovland, P. D.; Ng, E. G.; Webster, C.; and Wild, S. M. Technical Report U.S.\ Department of Energy, ASCR, 2014.
doi   bibtex  
Non-Negative Matrix Analysis for Effective Feature Extraction in X-ray Spectromicroscopy. Mak, R.; Lerotic, M.; Fleckenstein, H.; Vogt, S.; Wild, S. M.; Leyffer, S.; Sheynkin, Y.; and Jacobsen, C. Faraday Discussions, 171: 357–371. 2014.
doi   bibtex  
Multi-Objective Optimization of HPC Kernels for Performance, Power, and Energy. Balaprakash, P.; Tiwari, A.; and Wild, S. M. In Jarvis, S. A.; Wright, S. A.; and Hammond, S. D., editor(s), High Performance Computing Systems. Performance Modeling, Benchmarking and Simulation, volume 8551, pages 239–260, 2014. Springer
doi   bibtex  
Nuclear Energy Density Optimization: Shell Structure. Kortelainen, M.; McDonnell, J. D.; Nazarewicz, W.; Olsen, E.; Reinhard, P.; Sarich, J.; Schunck, N.; Wild, S. M.; Davesne, D.; Erler, J.; and Pastore, A. Physical Review C, 89(5): 054314. 2014.
doi   bibtex  
Do You Trust Derivatives or Differences?. Moré, J. J.; and Wild, S. M. Journal of Computational Physics, 273: 268–277. 2014.
doi   bibtex  
  2013 (12)
Framework for Optimizing Power, Energy, and Performance (Poster). Balaprakash, P.; Tiwari, A.; and Wild, S. M. In International Conference for High Performance Computing, Networking, Storage, and Analysis (SC13), 2013.
Framework for Optimizing Power, Energy, and Performance (Poster) [link]Paper   bibtex  
Unsupervised Cell Identification on Multidimensional X-ray Fluorescence Datasets. Wang, S.; Ward, J.; Leyffer, S.; Wild, S. M.; Jacobsen, C.; and Vogt, S. In ACM SIGGRAPH 2013 Posters, pages 88:1, 2013.
doi   bibtex  
Active-Learning-Based Surrogate Models for Empirical Performance Tuning. Balaprakash, P.; Gramacy, R. B.; and Wild, S. M. In Proceedings of IEEE International Conference on Cluster Computing (CLUSTER 2013), pages 1–8, 2013.
doi   bibtex  
An Experimental Study of Global and Local Search Algorithms in Empirical Performance Tuning. Balaprakash, P.; Wild, S. M.; and Hovland, P. D. In High Performance Computing for Computational Science - VECPAR 2012, 10th International Conference, Kobe, Japan, July 17-20, 2012, Revised Selected Papers, volume 7851, of Lecture Notes in Computer Science, pages 261–269. Springer, 2013.
doi   bibtex  
Axially Deformed Solution of the Skyrme-Hartree-Fock-Bogolyubov Equations Using the Transformed Harmonic Oscillator Basis (II) HFBTHO v2.00d: A New Version of the Program. Stoitsov, M. V.; Schunck, N.; Kortelainen, M.; Michel, N.; Nam, H.; Olsen, E.; Sarich, J.; and Wild, S. M. Computer Physics Communications, 184(6): 1592–1604. 2013.
doi   bibtex  
Non-Intrusive Termination of Noisy Optimization. Larson, J.; and Wild, S. M. Optimization Methods and Software, 28(5): 993–1011. 2013.
doi   bibtex  
Computational Nuclear Quantum Many-Body Problem: The UNEDF Project. Bogner, S.; Bulgac, A.; Carlson, J.; Enge, J.; Fann, G.; Furnstahl, R. J.; Gandolfi, S.; Hagen, G.; Horoi, M.; Johnson, C.; Kortelainen, M.; Lusk, E.; Maris, P.; Nam, H.; Navratil, P.; Nazarewicz, W.; Ng, E. G.; Nobre, G. P. A.; Ormand, E.; Papenbrock, T.; Pei, J. C.; Pieper, S. C.; Quaglioni, S.; Roche, K. J.; Sarich, J.; Schunck, N.; Sosonkina, M.; Terasaki, J.; Thompson, I.; Vary, J. P.; and Wild, S. M. Computer Physics Communications, 184(10): 2235–2250. 2013.
doi   bibtex  
Development of a Fast, Robust Numerical Tool for the Design, Optimization, and Control of IC Engines. Aithal, S. M.; and Wild, S. M. In Proceedings of the SAE 11th International Conference on Engines & Vehicles, 2013. SAE
doi   bibtex  
Optimized Chiral Nucleon-Nucleon Interaction at Next-to-Next-to-Leading Order. Ekström, A.; Baardsen, G.; Forssén, C.; Hagen, G.; Hjorth-Jensen, M.; Jansen, G. R.; Machleidt, R.; Nazarewicz, W.; Papenbrock, T.; Sarich, J.; and Wild, S. M. Physical Review Letters, 110(19): 192502. 2013.
doi   bibtex  
Novel Process to Use Vehicle Simulations Directly as Inputs to the VOLPE Model. Moawad, A.; Halbach, S.; Pagerit, S.; Rousseau, A.; Balaprakash, P.; and Wild, S. M. Technical Report ANL/ESD-13/13, Argonne National Laboratory, 2013.
Novel Process to Use Vehicle Simulations Directly as Inputs to the VOLPE Model [pdf]Paper   bibtex  
Variable Selection and Sensitivity Analysis via Dynamic Trees with an Application to Computer Code Performance Tuning. Gramacy, R. B.; Taddy, M. A.; and Wild, S. M. Annals of Applied Statistics, 7(1): 51–80. 2013.
doi   bibtex  
Global Convergence of Radial Basis Function Trust-Region Algorithms for Derivative-Free Optimization. Wild, S. M.; and Shoemaker, C. A. SIAM Review, 55(2): 349–371. 2013.
doi   bibtex  
  2012 (10)
SPAPT: Search Problems in Automatic Performance Tuning. Balaprakash, P.; Wild, S. M.; and Norris, B. Procedia Computer Science (ICCS 2012), 9: 1959–1968. 2012.
doi   bibtex  
Quality Input for Microscopic Fission Theory. Nazarewicz, W.; Schunck, N.; and Wild, S. M. Stockpile Stewardship Quarterly, 2(1). 2012.
bibtex  
Estimating Derivatives of Noisy Simulations. Moré, J. J.; and Wild, S. M. ACM Transactions on Mathematical Software, 38(3): 19:1–19:21. 2012.
doi   bibtex  
Occupation Number-based Energy Functional for Nuclear Masses. Bertolli, M. G.; Papenbrock, T.; and Wild, S. M. Physical Review C, 85(1): 014322. 2012.
doi   bibtex  
Report on the Workshop on Extreme-Scale Solvers: Transition to Future Architectures. Ang, J.; Evans, K.; Geist, A.; Heroux, M.; Hovland, P. D.; Marques, O.; Curfman McInnes, L.; Ng, E. G.; and Wild, S. M. Technical Report U.S.\ Department of Energy, ASCR, 2012.
Report on the Workshop on Extreme-Scale Solvers: Transition to Future Architectures [pdf]Paper   bibtex  
The TAO Linearly Constrained Augmented Lagrangian Method for PDE-Constrained Optimization. Gawlik, E.; Munson, T.; Sarich, J.; and Wild, S. M. Technical Report ANL/MCS-P2003-0112, Argonne National Laboratory, MCS Division, 2012.
The TAO Linearly Constrained Augmented Lagrangian Method for PDE-Constrained Optimization [pdf]Paper   bibtex  
Benefits of Deeper Analysis in Simulation-based Groundwater Optimization Problems. Kannan, A.; and Wild, S. M. In Proceedings of the XIX International Conference on Computational Methods in Water Resources (CMWR 2012), 2012.
Benefits of Deeper Analysis in Simulation-based Groundwater Optimization Problems [pdf]2   Benefits of Deeper Analysis in Simulation-based Groundwater Optimization Problems [pdf]Paper   bibtex  
TAO 2.0 Users Manual. Munson, T.; Sarich, J.; Wild, S. M.; Benson, S.; and Curfman McInnes, L. Technical Report ANL/MCS-TM-322, Argonne National Laboratory, 2012.
TAO 2.0 Users Manual [pdf]Paper   bibtex  
Nuclear Energy Density Optimization: Large Deformations. Kortelainen, M.; McDonnell, J. D.; Nazarewicz, W.; Reinhard, P.; Sarich, J.; Schunck, N.; Stoitsov, M. V.; and Wild, S. M. Physical Review C, 85: 024304. 2012.
doi   bibtex  
UNEDF: Advanced Scientific Computing Collaboration Transforms the Low-Energy Nuclear Many-Body Problem. Nam, H.; Stoitsov, M. V.; Nazarewicz, W.; Bulgac, A.; Hagen, G.; Kortelainen, M.; Maris, P.; Pei, J. C.; Roche, K. J.; Schunck, N.; Thompson, I.; Vary, J. P.; and Wild, S. M. Journal of Physics: Conference Series, 402: 012033. 2012.
UNEDF: Advanced Scientific Computing Collaboration Transforms the Low-Energy Nuclear Many-Body Problem [link]2   doi   bibtex  
  2011 (7)
Advancing Nuclear Physics Through TOPS Solvers and Tools. Ng, E. G.; Sarich, J.; Munson, T.; Wild, S. M.; Aktulga, H.; Yang, C.; Maris, P.; Vary, J. P.; Kortelainen, M.; Nazarewicz, W.; Papenbrock, T.; Schunck, N.; Stoitsov, M. V.; and Bertolli, M. G. In Proceedings of SciDAC 2011, 2011.
Advancing Nuclear Physics Through TOPS Solvers and Tools [pdf]Paper   bibtex  
Obtaining Quadratic Models of Noisy Functions. Kannan, A.; and Wild, S. M. Technical Report ANL/MCS-P1975-1111, Argonne National Laboratory, MCS Division, 2011.
Obtaining Quadratic Models of Noisy Functions [pdf]Paper   bibtex  
Can Search Algorithms Save Large-Scale Automatic Performance Tuning?. Balaprakash, P.; Wild, S. M.; and Hovland, P. D. Procedia Computer Science (ICCS 2011), 4: 2136–2145. 2011.
doi   bibtex  
UNEDF: Advanced Scientific Computing Transforms the Low-Energy Nuclear Many-Body Problem. Stoitsov, M. V.; Nam, H.; Nazarewicz, W.; Bulgac, A.; Hagen, G.; Kortelainen, M.; Pei, J. C.; Roche, K. J.; Schunck, N.; Thompson, I.; Vary, J. P.; and Wild, S. M. In Proceedings of SciDAC 2011, 2011.
UNEDF: Advanced Scientific Computing Transforms the Low-Energy Nuclear Many-Body Problem [pdf]Paper   bibtex  
Computing Heavy Elements. Schunck, N.; Baran, A.; Kortelainen, M.; McDonnell, J. D.; Moré, J. J.; Nazarewicz, W.; Pei, J. C.; Sarich, J.; Sheikh, J.; Staszczak, A.; Stoitsov, M. V.; and Wild, S. M. In Proceedings of SciDAC 2011, 2011.
Computing Heavy Elements [pdf]Paper   bibtex  
Estimating Computational Noise. Moré, J. J.; and Wild, S. M. SIAM Journal on Scientific Computing, 33(3): 1292–1314. 2011.
doi   bibtex  
Global Convergence of Radial Basis Function Trust Region Derivative-Free Algorithms. Wild, S. M.; and Shoemaker, C. A. SIAM Journal on Optimization, 21(3): 761–781. 2011.
doi   bibtex  
  2010 (1)
Nuclear Energy Density Optimization. Kortelainen, M.; Lesinski, T.; Moré, J. J.; Nazarewicz, W.; Sarich, J.; Schunck, N.; Stoitsov, M. V.; and Wild, S. M. Physical Review C, 82(2): 024313. 2010.
doi   bibtex  
  2009 (2)
Towards The Universal Nuclear Energy Density Functional. Stoitsov, M. V.; Moré, J. J.; Nazarewicz, W.; Pei, J. C.; Sarich, J.; Schunck, N.; Staszczak, A.; and Wild, S. M. Journal of Physics: Conference Series, 180: 012082. 2009.
doi   bibtex  
Benchmarking Derivative-Free Optimization Algorithms. Moré, J. J.; and Wild, S. M. SIAM Journal on Optimization, 20(1): 172–191. 2009.
doi   bibtex  
  2008 (4)
MNH: A Derivative-Free Optimization Algorithm Using Minimal Norm Hessians. Wild, S. M. In Tenth Copper Mountain Conference on Iterative Methods, 2008.
MNH: A Derivative-Free Optimization Algorithm Using Minimal Norm Hessians [pdf]Paper   bibtex  
Derivative-Free Optimization Algorithms for Computationally Expensive Functions. Wild, S. M. Ph.D. Thesis, Cornell University, 2008.
Derivative-Free Optimization Algorithms for Computationally Expensive Functions [link]Paper   bibtex  
Bayesian Calibration of Computationally Expensive Models Using Optimization and Radial Basis Function Approximation. Bliznyuk, N.; Ruppert, D.; Shoemaker, C. A.; Regis, R. G.; Wild, S. M.; and Mugunthan, P. Journal of Computational and Graphical Statistics, 17(2): 270–294. 2008.
doi   bibtex  
ORBIT: Optimization by Radial Basis Function Interpolation in Trust-Regions. Wild, S. M.; Regis, R. G.; and Shoemaker, C. A. SIAM Journal on Scientific Computing, 30(6): 3197–3219. 2008.
doi   bibtex  
  2007 (1)
Maximizing Influence in a Competitive Social Network: A Follower's Perspective. Carnes, T.; Nagarajan, C.; Wild, S. M.; and van Zuylen, A. In ICEC '07: Proceedings of the Ninth International Conference on Electronic Commerce, pages 351–360, 2007. ACM
doi   bibtex  
  2004 (1)
Improving Non-Negative Matrix Factorizations Through Structured Initialization. Wild, S. M.; Curry, J. H.; and Dougherty, A. Pattern Recognition, 37(11): 2217–2232. 2004.
doi   bibtex  
  2003 (2)
Seeding Non-Negative Matrix Factorizations with the Spherical K-Means Clustering. Wild, S. M. Master's thesis, University of Colorado, 2003.
Seeding Non-Negative Matrix Factorizations with the Spherical K-Means Clustering [link]Paper   bibtex  
Motivating Non-Negative Matrix Factorizations. Wild, S. M.; Curry, J. H.; and Dougherty, A. In Proceedings of the Eighth SIAM Conference on Applied Linear Algebra, 2003.
Motivating Non-Negative Matrix Factorizations [pdf]Paper   bibtex  
  2002 (1)
Probabilistically Optimized Airline Overbooking Strategies, or ``Anyone Willing to Take a Later Flight?!". Leder, K. Z.; Spagnolie, S. E.; and Wild, S. M. Journal of Undergraduate Mathematics and Its Applications, 23(3): 317–338. 2002.
Probabilistically Optimized Airline Overbooking Strategies, or ``Anyone Willing to Take a Later Flight?!" [link]Paper   bibtex