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Machine Learning Publications

Publications

  • Performance and power modeling and prediction using MuMMI and 10 machine learning methods, Xingfu Wu, Valerie Taylor, Zhiling Lan, Concurrency and Computation: Practice and Experience, 45(15) July 2023, special issue paper, 10.1002/cpe.7254.
  • Elastic deep learning through resilient collective operations, Paper: Jiali Li, George Bosilca, Aurelien Bouteiller, and Bogdan Nicolae, Workshop on Artificial Intelligence and Machine Leaerning for Scientific Applications, SC23, https://ai4s.github.io/#home.
  • Ciprijanovic, Aleksandra; Kafkes, Diana; Snyder, Gregory; Sanchez, F. Javier; Perdue, Gabriel; Pedro, Kevin; Nord, Brian; Madireddy, Sandeep; Wild, Stefan. DeepAdversaries: Examining the robustness of deep learning models for galaxy morphology classification. Machine Learning: Science and Technology 3(3) 2022.
  • Kudithipudi, Dhireesha; Aguilar-Simon, Mario; Babb, Jonathan; Bazhenov, Maxim; Blackiston, Douglas; Bongard, Josh; Brna, Andrew P.; Chakravarthi Raja, Suraj; Cheney, Nick; Clune, Jeff; Madireddy, Sandeep; Yanguas-Gil, Angel. Biological underpinnings for lifelong learning machines. Nature Machine Intelligence 4, 196–210, 2022.
  • Lao, Lang; Kruger, Scott E.; Akcay, Cihan; Balaprakash, Prasanna; Bechtel, Torrin; Howell, Eric; Koo, Jaehoon; Leddy, Jarrod; Leinhauser, Matthew; Liu, Yueqiang; Madireddy, Sandeep Application of machine learning and artificial intelligence to extend EFIT equilibrium reconstruction. Plasma Physics and Controlled Fusion 64, 074001, 2022.
  • Liu, Jie; Nicolae, Bogdan; Li, Dong; Wozniak, Justin; Bicer, Tekin; Liu, Zhengchun; Foster, Ian. Large scale caching and streaming of training data for online deep learning, in FlexScience '22: Proceedings of the 12th Workshop on AI and Scientific Computing at Scale using Flexible Computing Infrastructures, pp. 19–26, July 2022.
  • Renganathan, S. Ashwin; Maulik, Romit; Letizia, Stefano; lungo, Giacomo Valerio. Data-driven wind turbine wake modeling via probabilistic machine learning Neural Computing & Applications 34, 6171–6186, 2022.
  • Nicolae, Bogdan; Hobson, Tanner; Yildiz, Orcun; Peterka, Thomas; Morozov, Dmitriy. Towards low-overhead resilience for data parallel deep learning, in 2022 22nd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid), pp. 336-345, 2022.
  • Ciprijanovic, A.; Kafkes, D.; Downey, K.; Jenkins, S.; Perdue, G.N.; Madireddy, S.; Johnston, T.; Snyder, G. F.; Nord, B. DeepMerge II. Building robust deep learning algorithms for merging galaxy identification across domains. Monthly Notices of the Royal Astronomical Society, 506(1), 677–691, 2021.
  • Fukami, Kai; Maulik, Romit; Ramachandra, Nesar; Fukagata, Koji; Taira, Kunihiko. Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learning. Nature Machine Intelligence 3, 945–951, 2021.
  • Schmidt, David; Maulik, Romit; Lyras, Konstantinos. Machine learning accelerated turbulence modeling of transient flashing jets, Physics of Fluids 33(12), 127104, 2021.
  • Xie, Bing; Tan, Zilong; Carns, Philip; Chase, Jeff; Harms, Kevin; Lofstead, Jay; Oral, Sarp; Vazkudai, Sudharshan S.; Wang, Feiyi. Interpreting write performance of supercomputer I/O systems with machine intelligence. In 2021 IEEE International Parallel and Distributed Processing Symposium (IPDPS), pp. 557-566, 2021.
  • Mihailo Isakov, Mikaela Currier, Eliakin del Rosario, Sandeep Madireddy, Prasanna Balaprakash, Philip Carns, Robert B. Ross, Glenn K. Lockwood, and Michel A. Kinsy. A Taxonomy of Error Sources in HPC I/O Machine Learning Models. In Proc. SC22 International Conf. for High Performance Computing, Networking, Storage and Analysis, pp. 1-14, 2022. DOI 10.1109/SC41404.2022.00021.
  • D. H. Kurniawan, L. Toksoz, M. Hao, A. Badam, T. Emami, S. Madireddy, R. B. Ross, H. Hoffmann, and H. S. Gunawi. IONET: Towards an Open Machine Learning Training Ground for I/O Performance Prediction. Technical Report, The University of Chicago, 2021.
  • Moosavi, Azam; Rao, Vishwas; Sandu, Adrian Machine Learning based algorithms for uncertainty quantification in numerical weather prediction models Journal of Computational Science (2021)
  • Park, Seongha; Kim, Yongho; Ferrier, Nicola; Collis, Scott; Sankaran, Rajesh; Beckman, Peter Prediction of Solar Irradiance and Photovoltaic Solar Energy Product Based on Cloud Coverage Estimation Using Machine Learning Methods Atmosphere (2021)
  • Venkatramanan, Srinivasan; Sadilek, Adam; Fadikar, Arindam; Barrett, Christopher L.; Biggerstaff, Matthew ; Chen, Jiangzhuo; Dotiwalla, Xerxes; Eastham, Paul; Gipson, Bryant ; Hidden, Dave Forecasting influenza activity using machine-learned mobility map Nature Communications (2021
  • Bollapragada, Raghu; Menickelly, Matt; Nazarewicz, Witold; ONeal, Jared; Reinhard, Paul-Gerhard; Wild, Stefan Optimization and Supervised Machine Learning Methods for Fitting Numerical Physics Models without Derivatives Journal of Physics G: Nuclear and Particle Physics (2021)
  • Binois, M.; Picheny, V.; Taillandier, P.; Habbal, A The Kalai-Smorodinsky solution for many-objective Bayesian optimization Journal of Machine Learning Research (2020)
  • Dey, Tonmoy; Sato, Kento; Nicolae, Bogdan; Guo, Jian; Domke, Jens; Yu, Weikuan; Cappello, Franck; Mohror, Kathryn Optimizing Asynchronous Multi-Level Checkpoint/Restart Configurations with Machine Learning (2020)
  • S. Khairy, P. Balaprakash, L. X. Cai, Yu Cheng, Constrained deep reinforcement learning for energy sustainable multi-UAV based random access IoT networks with NOMA, arXiv.2002.00073 (2020)
  • R Maulik, A Mohan, S Madireddy, B Lusch, P. Balaprakash, D Livescu, Machine learning of sequential data for non-intrusive reduced-order models, Bulletin of the American Physical Society, 64, no. 13 (2019)
  • S. Madireddy, D-W. Chung, T. Loeffler, S. K.R>S> Sankaranarayanan, D.N. Deidman, P. Balaprakash, and O. Heinonen, Phase segmentation in atom-probe tomography using deep learning-based edge detection, Scientific Reports 9, article 20140 (2019)
  • S. Pumma, M. Si, W. Feng, P. Balaji, Scalable Deep Learning via I/O Analysis and Optimization, ACM Transactions on Parallel Computing (TOPC), 6(5) 2019
  • R Maulik, A Mohan, S Madireddy, B Lusch, P Balaprakash, D Livescu, Machine learning of sequential data for non-intrusive reduced-order models Bulletin of the American Physical Society 64(13) abstract H10.00003, 2019
  • S Madireddy, P Balaprakash, P Carns, R Latham, GK Lockwood, R Ross, S. Snyder, S. Wild, Adaptive Learning for Concept Drift in Application Performance Modeling in Proceedings of the 48th International Conference on Parallel Processing, 1-11, 2019
  • S Madireddy, A Yanguas-Gil, P Balaprakash Neuromorphic Architecture Optimization for Task-Specific Dynamic Learning, In Proceedings of the International Conference on Neuromorphic Systems, 1-5, 2019
  • P. Balaprakash, R. Egele, M. Salim, S. Wild, V. Vishwanath, F. ZXia, T. Beettin, R. Stevens, Scalable Reinforcement-Learning-Based Neural Architecture Search for Cancer Deep Learning Research. In SC '19: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, November 2019, Article no. 37, pp. 1–3. https://doi.org/10.1145/3295500.3356202.
  • P Balaprakash, M Salim, T Uram, V Vishwanath, S Wild
    2018 IEEE 25th International Conference on High Performance Computing (HiPC) Dec. 2018, DOI: 10.1109/HiPC.2018.00014. DeepHyper: Asynchronous Hyperparameter Search for Deep Neural Networks
  • S. Madireddy, D. W. Chung, T. Loeffler, S. K R. S. Sankaranarayanan, D. N. Seidman, P. Balaprakash, O. Heinonen, Phase Segmentation in Atom-Probe Tomography Using Deep Learning-Based Edge Detection, Science Reports 9, article no. 20149, 2019
  • S. Aithal and P. Balaprakash. MaLTESE: Large-Scale Simulation-Driven Machine Learning for Transient Driving Cycles. In: Weiland, M., Juckeland, G., Trinitis, C., Sadayappan, P. (eds) High Performance Computing. ISC High Performance 2019. Lecture Notes in Computer Science, vol 11501. pp 186–205, Springer, Cham., 2019
  • ukreja, Navjot, Alena Shilova, Olivier Beaumont, Jan Hückelheim, Nicola Ferrier, Paul Hovland, and Gerard Gorman. "Training on the Edge: The why and the how." In 1st Workshop on Parallel AI and Systems for the Edge (PAISE). 2019.
  • Jun Han, Jun Tao, Hanqi Guo, Danny Z Chen, and Chaoli Wang. "Flow Field Reduction via Reconstructing Vector Data from 3D Streamlines Using Deep Learning." IEEE Computer Graphics and Applications (to appear), 2019.
  • Nathan Baker, Frank Alexander, Timo Bremer, Aric Hagberg, Yannis Kevrekidis, Habib Najm, Manish Parashar, Abani Patra, James Sethian, Stefan Wild, Karen Willcox Technical report, U.S. Department of Energy, DOI: 10.2172/1478744 2019. Workshop Report on Basic Research Needs for Scientific Machine Learning: Core Technologies for Artificial Intelligence
  • S. Sreepathi, J. Kumar, R. T. Mills, Forrest M. Hoffman, V. Sripathi, W. W. Hargrove, 2017. "Parallel Multivariate Spatio-Temporal Clustering of Large Ecological Datasets on Hybrid Supercomputers" https://www.climatemodeling.org/~rmills/pubs/Sreepathi_IEEECluster_2017… of IEEE Cluster 2017. doi: 10.1109/CLUSTER.2017.88.
  • JM Wozniak, R Jain, P Balaprakash, J Ozik, NT Collier, J Bauer, F Xia, Thomas Brettin, Rick Stevens, Jamaludin Mohd-Yusof, Cristina Garcia Cardona, Brian Van Essen, Matthew Baughman, BMC Bioinformatics 19 (18), 491, 2018. CANDLE/Supervisor: A workflow framework for machine learning applied to cancer research
  • R. T. Mills, V. Sripathi, J. Kumar, S. Sreepathi, F. M. Hargrove, W. W. Hargrove, 2018. "Parallel k-means Clustering of Geospatial Data Sets Using Manycore CPU Architectures" https://www.climatemodeling.org/~rmills/pubs/Mills_DMESS2018_20181117.p…. Eighth Workshop on Data Mining in Earth System Sciences (DMESS 2018), Proceedings of the IEEE International Conference on Data Mining (ICDM 2018) Workshops.
  • A. Sanaullah, M. Herbordt, C. Yang, Y. Alexeev, K. Yoshii, "Real-Time Data Analysis for Medical Diagnosis using FPGA Accelerated Neural Networks", BMC special issue
  • A. Sanaullah, C. Yang, M. Herbordt, Y. Alexeev, K. Yoshii, "Application Aware Tuning of Reconfigurable Multi-Layer Perceptiron Architectures", 2018 IEEE High Performance Extreme Computing Conference (HPEC)
  • Y. Luo, X. Wang, S. Ogrenci-Memik, G. Memik, K. Yoshii, P. Beckman, "Minimizing Thermal Variation in Heterogeneous HPC Systems with FPGA Nodes", 2018 IEEE International Conference on Computer Design (ICCD), Florida, Oct 10
  • Omer Subasi, L. Bautista-Gomez,P. Balaprakash, O. Sabri Unsal, S. Krishnamoorthy, F. Cappello, A. Cristal, S. Di, J. Labarta, "Exploring the Capabilities of Support Vector Machines in Detecting Silent Data Corruptions", Sustainable Computing, Informatics and Systems, (SCIS) Elsevier, forthcoming, Volume 19, Pages 277-290, September 2018.
  • C. Wang, N. Dryden, F. Cappello, M. Snir, "Neural network based silent error detector", Best Paper, IEEE Cluster 2018
  • Madireddy, Sandeep, Prasanna Balaprakash, Philip Carns, Robert Latham, Robert Ross, Shane Snyder, and Stefan M. Wild. “Machine Learning Based Parallel I/O Predictive Modeling: A Case Study on Lustre File Systems”. In: High Performance Computing. Cham: Springer International Publishing, 184–204, 2018.
  • Madireddy, Sandeep, Prasanna Balaprakash, Philip Carns, Robert Latham, Robert Ross, Shane Snyder, andStefan M. Wild. “Modeling I/O Performance Variability Using Conditional Variational Autoencoders”. In: 2018 IEEE International Conference on Cluster Computing (CLUSTER), 2018.
  • R. T. Mills, V. Sripathi, J. Kumar, S. Sreepathi, F. M. Hargrove, W. W. Hargrove, 2018. "Parallel k-means Clustering of Geospatial Data Sets Using Manycore CPU Architectures". Eighth Workshop on Data Mining in Earth System Sciences (DMESS 2018), Proceedings of the IEEE International Conference on Data Mining (ICDM 2018) Workshops.
  • Peterka, T., Nashed, Y., Grindeanu, I., Mahadevan, V., Yeh, R., Tricoche, X.: Foundations of Multivariate Functional Approximation for Scientific Data. Proceedings of IEEE Symposium on Large Data Analysis and Visualization (LDAV) 2018, Berlin, Germany, 2018.
  • Cherukara, Mathew J., Youssef SG Nashed, and Ross J. Harder. "Real-time coherent diffraction inversion using deep generative networks." Scientific reports8, no. 1 (2018): 16520.
  • Yoo, Seunghwan, Pablo Ruiz, Xiang Huang, Kuan He, Xiaolei Wang, Itay Gdor, Alan Selewa et al. "Bayesian Approach for Automatic Joint Parameter Estimation in 3D Image Reconstruction from Multi-Focus Microscope." In 201825th IEEE International Conference on Image Processing (ICIP), pp. 3583-3587. IEEE, 2018.
  • Seunghwan Yoo, Pablo Ruiz, Xiang Huang, Kuan He, Nicola J. Ferrier, Mark Hereld, Alan Selewa, Matthew Daddysman, Norbert Scherer, Oliver Cossairt, Aggelos K. Katsaggelos, “3D Image reconstruction from multi-focus microscope: axial super-resolution and multiple-frame processing,” In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1453-1457. IEEE, 2018.
  • Agarwal, Nitin, Ferrier, Nicola and Mark Hereld. "Towards Automated Transcription of Label Text from Pinned Insect Collections." 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 189-198, March 2018.
  • Zhang, Yan, Dilshan Godaliyadda, Nicola Ferrier, Emine Gulsoy, Charles Bouman and Charudatta Phatak. "SLADS-Net: Supervised Learning Approach for Dynamic Sampling using Deep Neural Networks." 2018 Imaging Science and Technology International Symposium on Electronic Imaging, January 28, 2018–February 1, 2018.
  • Zhang, Yan, G.M. Godaliyadda, Nicola Ferrier, Emine Gulsoy, Charles Bouman and Charudatta Phatak. "Reduced Electron Exposure for Energy-Dispersive Spectroscopy using Dynamic Sampling." Ultramicroscopy, Vol 184, Part B, Jan 2018, Pages 90–97. DOI: https://doi.org/10.1016/j.ultramic.2017.10.015
  • K. Kulshreshtha, S.H.K. Narayanan, J. Bessac & K. MacIntyre (2018 )Efficient computation of derivatives for solving optimization problems in R and Python using SWIG-generated interfaces to ADOL-C, Optimization Methods and Software,33:4-6,1173-1191,DOI: 10.1080/10556788.2018.1425861
  • P. Balaprakash, M. Salim, T. Uram, V. Vishwanath, and S. M. Wild. DeepHyper: Asynchronous Hyperparameter Search for Deep Neural Networks. In 25th IEEE International Conference on High Performance Computing, Data, and Analytics. IEEE, 2018. DOI: 10.1109/HiPC.2018.00014
  • Z. Liu, R. Kettimuthu, P. Balaprakash, and I. Foster. Building a wide-area data transfer performance predictor: An empirical study. In the 1st International Conference on Machine Learning for Networking, MLN 2018. Springer, 2018.
  • P. Balaprakash, J. Dongarra, T. Gamblin, M. Hall, J. K. Hollingsworth, B. Norris, and R. Vuduc. Autotuning in high-performance computing applications. Proceedings of the IEEE, pages 1–16, 2018. http://dx.doi.org/10.1109/JPROC.2018.2841200
  • Sunwoo Lee, Ankit Agrawal, Prasanna Balaprakash, Alok Choudhary, Wei-keng Liao, Communication-efficient parallelization strategy for deep convolutional neural network training, Workshop: 4th Workshop on Machine Learning in HPC Environments)- SC18
  • Xin Liang, Sheng Di, Sihuan Li, Dingwen Tao, Zizhong Chen, Franck Cappello, Exploring Best Lossy Compression Strategy by Combining SZ with Spatiotemporal Decimation, Workshop: Machine Learning in HPC Environments - SC18
  • Xin-Chuan Wu, Sheng Di, Franck Cappello, Hal Finkel, Yuri Alexeev, Frederic T. Chong, Amplitude-Aware Lossy Compression for Quantum Circuit Simulation, Workshop: 4th International Workshop on Data Reduction for Big Scientific Data, DRBSD-4 - SC18
  • Preeti Malakar, Prasanna Balaprakash, Venkatram Vishwanath, Vitali Morozov, Kalyan Kumaran, Benchmarking Machine Learning Methods for Performance Modeling of Scientific Application – (Workshop: 9th IEEE International Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems. PMBS’18)

Presentations

  • Justin M. Wozniak, Rajeev Jain, Prasanna Balaprakash, Jonathan Ozik, Nicholson Collier, John Bauer, Fangfang Xia, Thomas Brettin, Rick Stevens, Jamaludin Mohd-Yusof, Cristina, Garcia Cardona, Brian Van Essen, and Matthew Baughman, “CANDLE/Supervisor: A workflow framework for machine learning applied to cancer research,” SC17 Workshop: Computational Approaches for Cancer

Posters

Preprints