Sandeep Madireddy
Sandeep Madireddy
Mathematics and Computer Science Division, Argonne National Laboratory
Verified email at - Homepage
Cited by
Cited by
Time-series learning of latent-space dynamics for reduced-order model closure
R Maulik, A Mohan, B Lusch, S Madireddy, P Balaprakash, D Livescu
Physica D: Nonlinear Phenomena 405, 132368, 2020
A Bayesian approach to selecting hyperelastic constitutive models of soft tissue
S Madireddy, B Sista, K Vemaganti
Computer Methods in Applied Mechanics and Engineering 291, 102-122, 2015
Bayesian calibration of hyperelastic constitutive models of soft tissue
S Madireddy, B Sista, K Vemaganti
Journal of the Mechanical Behavior of Biomedical Materials, Accepted for …, 2016
Machine learning based parallel I/O predictive modeling: A case study on Lustre file systems
S Madireddy, P Balaprakash, P Carns, R Latham, R Ross, S Snyder, ...
International Conference on High Performance Computing, 184-204, 2018
Analysis and correlation of application I/O performance and system-wide I/O activity
S Madireddy, P Balaprakash, P Carns, R Latham, R Ross, S Snyder, ...
2017 International Conference on Networking, Architecture, and Storage (NAS …, 2017
Phase segmentation in atom-probe tomography using deep learning-based edge detection
S Madireddy, DW Chung, T Loeffler, SKRS Sankaranarayanan, ...
Scientific reports 9 (1), 1-10, 2019
DeepMerge–II. Building robust deep learning algorithms for merging galaxy identification across domains
A Ćiprijanović, D Kafkes, K Downey, S Jenkins, GN Perdue, S Madireddy, ...
Monthly Notices of the Royal Astronomical Society 506 (1), 677-691, 2021
Modeling I/O performance variability using conditional variational autoencoders
S Madireddy, P Balaprakash, P Carns, R Latham, R Ross, S Snyder, ...
2018 IEEE international conference on cluster computing (CLUSTER), 109-113, 2018
Improving Scalability of Parallel CNN Training by Adjusting Mini-Batch Size at Run-Time
S Lee, Q Kang, S Madireddy, P Balaprakash, A Agrawal, A Choudhary, ...
2019 IEEE International Conference on Big Data (Big Data), 830-839, 2019
Applications and techniques for fast machine learning in science
AMC Deiana, N Tran, J Agar, M Blott, G Di Guglielmo, J Duarte, P Harris, ...
Frontiers in big Data 5, 2022
A Modular Deep Learning Pipeline for Galaxy-Scale Strong Gravitational Lens Detection and Modeling
S Madireddy, N Li, N Ramachandra, P Balaprakash, S Habib, J Butler, ...
arXiv preprint arXiv:1911.03867, 2020
HPC I/O throughput bottleneck analysis with explainable local models
M Isakov, E Del Rosario, S Madireddy, P Balaprakash, P Carns, RB Ross, ...
SC20: International Conference for High Performance Computing, Networking …, 2020
Adaptive Learning for Concept Drift in Application Performance Modeling
S Madireddy, P Balaprakash, P Carns, R Latham, GK Lockwood, R Ross, ...
Proceedings of the 48th International Conference on Parallel Processing, 1-11, 2019
On the Inference of Viscoelastic Constants from Stress Relaxation Experiments
K Vemaganti, S Madireddy, S Kedari
Mechanics of Time-Dependent Materials, 2019
Biological underpinnings for lifelong learning machines
D Kudithipudi, M Aguilar-Simon, J Babb, M Bazhenov, D Blackiston, ...
Nature Machine Intelligence 4 (3), 196-210, 2022
Gauge: An Interactive Data-Driven Visualization Tool for HPC Application I/O Performance Analysis
E del Rosario, M Currier, M Isakov, S Madireddy, P Balaprakash, P Carns, ...
Neuromodulated Neural Architectures with Local Error Signals for Memory-Constrained Online Continual Learning
S Madireddy, A Yanguas-Gil, P Balaprakash
arXiv preprint arXiv:2007.08159, 2021
In situ compression artifact removal in scientific data using deep transfer learning and experience replay
S Madireddy, JH Park, S Lee, P Balaprakash, S Yoo, W Liao, CD Hauck, ...
Machine Learning: Science and Technology 2 (2), 025010, 2020
Using recurrent neural networks for nonlinear component computation in advection-dominated reduced-order models
R Maulik, V Rao, S Madireddy, B Lusch, P Balaprakash
arXiv preprint arXiv:1909.09144, 2019
Uncertainty quantification using the nearest neighbor gaussian process
H Shi, EL Kang, BA Konomi, K Vemaganti, S Madireddy
New Advances in Statistics and Data Science, 89-107, 2017
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