Prasanna Balaprakash

Orcid: 0000-0002-0292-5715

According to our database1, Prasanna Balaprakash authored at least 137 papers between 2006 and 2024.

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Bibliography

2024
Efficient Mapping Between Void Shapes and Stress Fields Using Deep Convolutional Neural Networks With Sparse Data.
J. Comput. Inf. Sci. Eng., April, 2024

Multi-fidelity reinforcement learning with control variates.
Neurocomputing, 2024

The unreasonable effectiveness of early discarding after one epoch in neural network hyperparameter optimization.
Neurocomputing, 2024

Scalable Training of Graph Foundation Models for Atomistic Materials Modeling: A Case Study with HydraGNN.
CoRR, 2024

Sequence Length Scaling in Vision Transformers for Scientific Images on Frontier.
CoRR, 2024

Physics-Informed Heterogeneous Graph Neural Networks for DC Blocker Placement.
CoRR, 2024

Advancing Anomaly Detection in Computational Workflows with Active Learning.
CoRR, 2024

ORBIT: Oak Ridge Base Foundation Model for Earth System Predictability.
CoRR, 2024

Network architecture search of X-ray based scientific applications.
CoRR, 2024

AI Competitions and Benchmarks: Dataset Development.
CoRR, 2024

Streamlining Ocean Dynamics Modeling with Fourier Neural Operators: A Multiobjective Hyperparameter and Architecture Optimization Approach.
CoRR, 2024

Integrating ytopt and libEnsemble to Autotune OpenMC.
CoRR, 2024

2023
Graph neural networks for detecting anomalies in scientific workflows.
Int. J. High Perform. Comput. Appl., July, 2023

Multi-fidelity reinforcement learning framework for shape optimization.
J. Comput. Phys., June, 2023

Stabilized neural ordinary differential equations for long-time forecasting of dynamical systems.
J. Comput. Phys., February, 2023

Learning Symbolic Expressions: Mixed-Integer Formulations, Cuts, and Heuristics.
INFORMS J. Comput., 2023

Optimizing Distributed Training on Frontier for Large Language Models.
CoRR, 2023

DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies.
CoRR, 2023

Self-supervised Learning for Anomaly Detection in Computational Workflows.
CoRR, 2023

Parallel Multi-Objective Hyperparameter Optimization with Uniform Normalization and Bounded Objectives.
CoRR, 2023

Comparing Llama-2 and GPT-3 LLMs for HPC kernels generation.
CoRR, 2023

Uncertainty Quantification for Molecular Property Predictions with Graph Neural Architecture Search.
CoRR, 2023

Flow-Bench: A Dataset for Computational Workflow Anomaly Detection.
CoRR, 2023

Learning Continually on a Sequence of Graphs - The Dynamical System Way.
CoRR, 2023

Application of probabilistic modeling and automated machine learning framework for high-dimensional stress field.
CoRR, 2023

ytopt: Autotuning Scientific Applications for Energy Efficiency at Large Scales.
CoRR, 2023

Quantifying uncertainty for deep learning based forecasting and flow-reconstruction using neural architecture search ensembles.
CoRR, 2023

Analyzing the impact of climate change on critical infrastructure from the scientific literature: A weakly supervised NLP approach.
CoRR, 2023

Scalable Automated Design and Development of Deep Neural Networks for Scientific and Engineering Applications.
Proceedings of the IEEE International Parallel and Distributed Processing Symposium, 2023

iWAPT2023 Invited Speaker Optimizing HPC Systems for Scientific Applications: Machine Learning Approaches to Performance Tuning and Anomaly Detection.
Proceedings of the IEEE International Parallel and Distributed Processing Symposium, 2023

Transfer-learning-based Autotuning using Gaussian Copula.
Proceedings of the 37th International Conference on Supercomputing, 2023

Evaluation of OpenAI Codex for HPC Parallel Programming Models Kernel Generation.
Proceedings of the 52nd International Conference on Parallel Processing Workshops, 2023

Graph Pyramid Autoformer for Long- Term Traffic Forecasting.
Proceedings of the International Conference on Machine Learning and Applications, 2023

Application - Hardware Co-Optimization of Crossbar-Based Neuromorphic Systems.
Proceedings of the International Conference on Machine Learning and Applications, 2023

Is One Epoch All You Need For Multi-Fidelity Hyperparameter Optimization?
Proceedings of the 31st European Symposium on Artificial Neural Networks, 2023

Asynchronous Decentralized Bayesian Optimization for Large Scale Hyperparameter Optimization.
Proceedings of the 19th IEEE International Conference on e-Science, 2023

Improving Performance in Continual Learning Tasks using Bio-Inspired Architectures.
Proceedings of the Conference on Lifelong Learning Agents, 2023

Sparsity-Inducing Categorical Prior Improves Robustness of the Information Bottleneck.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022
Efficient data acquisition and training of collisional-radiative model artificial neural network surrogates through adaptive parameter space sampling.
Mach. Learn. Sci. Technol., December, 2022

Data-Driven Random Access Optimization in Multi-Cell IoT Networks Using NOMA.
IEEE Trans. Wirel. Commun., 2022

Unified Probabilistic Neural Architecture and Weight Ensembling Improves Model Robustness.
CoRR, 2022

Explainable Graph Pyramid Autoformer for Long-Term Traffic Forecasting.
CoRR, 2022

Asynchronous Distributed Bayesian Optimization at HPC Scale.
CoRR, 2022

Multifidelity Reinforcement Learning with Control Variates.
CoRR, 2022

Sequential Bayesian Neural Subnetwork Ensembles.
CoRR, 2022

Deep-Ensemble-Based Uncertainty Quantification in Spatiotemporal Graph Neural Networks for Traffic Forecasting.
CoRR, 2022

Sparsity-Inducing Categorical Prior Improves Robustness of the Information Bottleneck.
CoRR, 2022

Autotuning PolyBench benchmarks with LLVM Clang/Polly loop optimization pragmas using Bayesian optimization.
Concurr. Comput. Pract. Exp., 2022

Modeling design and control problems involving neural network surrogates.
Comput. Optim. Appl., 2022

A cross-study analysis of drug response prediction in cancer cell lines.
Briefings Bioinform., 2022

Workflow Anomaly Detection with Graph Neural Networks.
Proceedings of the IEEE/ACM Workshop on Workflows in Support of Large-Scale Science, 2022

Automated Continual Learning of Defect Identification in Coherent Diffraction Imaging.
Proceedings of the IEEE/ACM International Workshop on Artificial Intelligence and Machine Learning for Scientific Applications, 2022

A Taxonomy of Error Sources in HPC I/O Machine Learning Models.
Proceedings of the SC22: International Conference for High Performance Computing, 2022

Continual Learning via Dynamic Programming.
Proceedings of the 26th International Conference on Pattern Recognition, 2022

AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification.
Proceedings of the 26th International Conference on Pattern Recognition, 2022

HPC Storage Service Autotuning Using Variational- Autoencoder -Guided Asynchronous Bayesian Optimization.
Proceedings of the IEEE International Conference on Cluster Computing, 2022

2021
In situ compression artifact removal in scientific data using deep transfer learning and experience replay.
Mach. Learn. Sci. Technol., 2021

Constrained Deep Reinforcement Learning for Energy Sustainable Multi-UAV Based Random Access IoT Networks With NOMA.
IEEE J. Sel. Areas Commun., 2021

A data-centric weak supervised learning for highway traffic incident detection.
CoRR, 2021

AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification.
CoRR, 2021

AutoPhaseNN: Unsupervised Physics-aware Deep Learning of 3D Nanoscale Coherent Imaging.
CoRR, 2021

Autotuning PolyBench Benchmarks with LLVM Clang/Polly Loop Optimization Pragmas Using Bayesian Optimization (extended version).
CoRR, 2021

Learning-Based Distributed Random Access for Multi-Cell IoT Networks with NOMA.
CoRR, 2021

AgEBO-tabular: joint neural architecture and hyperparameter search with autotuned data-parallel training for tabular data.
Proceedings of the International Conference for High Performance Computing, 2021

Customized Monte Carlo Tree Search for LLVM/Polly's Composable Loop Optimization Transformations.
Proceedings of the 2021 International Workshop on Performance Modeling, 2021

Formalizing the Generalization-Forgetting Trade-off in Continual Learning.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Learning to Switch Optimizers for Quadratic Programming.
Proceedings of the Asian Conference on Machine Learning, 2021

2020
AgEBO-Tabular: Joint Neural Architecture and Hyperparameter Search with Autotuned Data-Parallel Training for Tabular Data.
CoRR, 2020

Dynamic Graph Neural Network for Traffic Forecasting in Wide Area Networks.
CoRR, 2020

Meta Continual Learning via Dynamic Programming.
CoRR, 2020

Multilayer Neuromodulated Architectures for Memory-Constrained Online Continual Learning.
CoRR, 2020

Towards On-Chip Bayesian Neuromorphic Learning.
CoRR, 2020

A Gradient-Aware Search Algorithm for Constrained Markov Decision Processes.
CoRR, 2020

Site-specific graph neural network for predicting protonation energy of oxygenate molecules.
CoRR, 2020

Gauge: An Interactive Data-Driven Visualization Tool for HPC Application I/O Performance Analysis.
Proceedings of the Fifth IEEE/ACM International Parallel Data Systems Workshop, 2020

Recurrent neural network architecture search for geophysical emulation.
Proceedings of the International Conference for High Performance Computing, 2020

HPC I/O throughput bottleneck analysis with explainable local models.
Proceedings of the International Conference for High Performance Computing, 2020

Toward Generalizable Models of I/O Throughput.
Proceedings of the 2020 IEEE/ACM International Workshop on Runtime and Operating Systems for Supercomputers, 2020

FIdelity: Efficient Resilience Analysis Framework for Deep Learning Accelerators.
Proceedings of the 53rd Annual IEEE/ACM International Symposium on Microarchitecture, 2020

Transfer Learning with Graph Neural Networks for Short-Term Highway Traffic Forecasting.
Proceedings of the 25th International Conference on Pattern Recognition, 2020

Graph Neural Network Architecture Search for Molecular Property Prediction.
Proceedings of the 2020 IEEE International Conference on Big Data (IEEE BigData 2020), 2020

Learning to Optimize Variational Quantum Circuits to Solve Combinatorial Problems.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019
Value-Added Chemical Discovery Using Reinforcement Learning.
CoRR, 2019

Reinforcement-Learning-Based Variational Quantum Circuits Optimization for Combinatorial Problems.
CoRR, 2019

Modular Deep Learning Analysis of Galaxy-Scale Strong Lensing Images.
CoRR, 2019

Graph-Partitioning-Based Diffusion Convolution Recurrent Neural Network for Large-Scale Traffic Forecasting.
CoRR, 2019

Using recurrent neural networks for nonlinear component computation in advection-dominated reduced-order models.
CoRR, 2019

Balsam: Automated Scheduling and Execution of Dynamic, Data-Intensive HPC Workflows.
CoRR, 2019

MaLTESE: Large-Scale Simulation-Driven Machine Learning for Transient Driving Cycles.
Proceedings of the High Performance Computing - 34th International Conference, 2019

Scalable reinforcement-learning-based neural architecture search for cancer deep learning research.
Proceedings of the International Conference for High Performance Computing, 2019

A Framework for Enabling OpenMP Autotuning.
Proceedings of the OpenMP: Conquering the Full Hardware Spectrum, 2019

Adaptive Learning for Concept Drift in Application Performance Modeling.
Proceedings of the 48th International Conference on Parallel Processing, 2019

Neuromorphic Acceleration for Approximate Bayesian Inference on Neural Networks via Permanent Dropout.
Proceedings of the International Conference on Neuromorphic Systems, 2019

Neuromorphic Architecture Optimization for Task-Specific Dynamic Learning.
Proceedings of the International Conference on Neuromorphic Systems, 2019

Improving Scalability of Parallel CNN Training by Adjusting Mini-Batch Size at Run-Time.
Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019

2018
Exploring the capabilities of support vector machines in detecting silent data corruptions.
Sustain. Comput. Informatics Syst., 2018

Autotuning in High-Performance Computing Applications.
Proc. IEEE, 2018

CANDLE/Supervisor: a workflow framework for machine learning applied to cancer research.
BMC Bioinform., 2018

Machine Learning Based Parallel I/O Predictive Modeling: A Case Study on Lustre File Systems.
Proceedings of the High Performance Computing - 33rd International Conference, 2018

Benchmarking Machine Learning Methods for Performance Modeling of Scientific Applications.
Proceedings of the 2018 IEEE/ACM Performance Modeling, 2018

Building a Wide-Area File Transfer Performance Predictor: An Empirical Study.
Proceedings of the Machine Learning for Networking - First International Conference, 2018

DeepHyper: Asynchronous Hyperparameter Search for Deep Neural Networks.
Proceedings of the 25th IEEE International Conference on High Performance Computing, 2018

Modeling I/O Performance Variability Using Conditional Variational Autoencoders.
Proceedings of the IEEE International Conference on Cluster Computing, 2018

2017
Analysis and Correlation of Application I/O Performance and System-Wide I/O Activity.
Proceedings of the 2017 International Conference on Networking, Architecture, and Storage, 2017

Explaining Wide Area Data Transfer Performance.
Proceedings of the 26th International Symposium on High-Performance Parallel and Distributed Computing, 2017

MACORD: Online Adaptive Machine Learning Framework for Silent Error Detection.
Proceedings of the 2017 IEEE International Conference on Cluster Computing, 2017

Analytical Performance Modeling and Validation of Intel's Xeon Phi Architecture.
Proceedings of the Computing Frontiers Conference, 2017

2016
AutoMOMML: Automatic Multi-objective Modeling with Machine Learning.
Proceedings of the High Performance Computing - 31st International Conference, 2016

Exploiting Performance Portability in Search Algorithms for Autotuning.
Proceedings of the 2016 IEEE International Parallel and Distributed Processing Symposium Workshops, 2016

Improving Data Transfer Throughput with Direct Search Optimization.
Proceedings of the 45th International Conference on Parallel Processing, 2016

Spatial Support Vector Regression to Detect Silent Errors in the Exascale Era.
Proceedings of the IEEE/ACM 16th International Symposium on Cluster, 2016

2015
Estimation-based metaheuristics for the single vehicle routing problem with stochastic demands and customers.
Comput. Optim. Appl., 2015

Generating Efficient Tensor Contractions for GPUs.
Proceedings of the 44th International Conference on Parallel Processing, 2015

Autotuning FPGA Design Parameters for Performance and Power.
Proceedings of the 23rd IEEE Annual International Symposium on Field-Programmable Custom Computing Machines, 2015

Collective I/O Tuning Using Analytical and Machine Learning Models.
Proceedings of the 2015 IEEE International Conference on Cluster Computing, 2015

2014
Machine-Learning-Based Load Balancing for Community Ice Code Component in CESM.
Proceedings of the High Performance Computing for Computational Science - VECPAR 2014 - 11th International Conference, Eugene, OR, USA, June 30, 2014

Analysis of the Tradeoffs Between Energy and Run Time for Multilevel Checkpointing.
Proceedings of the High Performance Computing Systems. Performance Modeling, Benchmarking, and Simulation, 2014

Energy-performance tradeoffs in multilevel checkpoint strategies.
Proceedings of the 2014 IEEE International Conference on Cluster Computing, 2014

2013
Exascale workload characterization and architecture implications.
Proceedings of the 2013 Spring Simulation Multiconference, SpringSim '13, 2013

Multi Objective Optimization of HPC Kernels for Performance, Power, and Energy.
Proceedings of the High Performance Computing Systems. Performance Modeling, Benchmarking and Simulation, 2013

Empirical performance modeling of GPU kernels using active learning.
Proceedings of the Parallel Computing: Accelerating Computational Science and Engineering (CSE), 2013

Active-learning-based surrogate models for empirical performance tuning.
Proceedings of the 2013 IEEE International Conference on Cluster Computing, 2013

2012
SPAPT: Search Problems in Automatic Performance Tuning.
Proceedings of the International Conference on Computational Science, 2012

An Experimental Study of Global and Local Search Algorithms in Empirical Performance Tuning.
Proceedings of the High Performance Computing for Computational Science, 2012

Poster: An Exascale Workload Study.
Proceedings of the 2012 SC Companion: High Performance Computing, 2012

Abstract: An Exascale Workload Study.
Proceedings of the 2012 SC Companion: High Performance Computing, 2012

2011
Can search algorithms save large-scale automatic performance tuning?
Proceedings of the International Conference on Computational Science, 2011

2010
Estimation-based metaheuristics for the probabilistic traveling salesman problem.
Comput. Oper. Res., 2010

F-Race and Iterated F-Race: An Overview.
Proceedings of the Experimental Methods for the Analysis of Optimization Algorithms., 2010

2009
Estimation-based ant colony optimization and local search for the probabilistic traveling salesman problem.
Swarm Intell., 2009

Adaptive sample size and importance sampling in estimation-based local search for the probabilistic traveling salesman problem.
Eur. J. Oper. Res., 2009

2008
Engineering Stochastic Local Search Algorithms: A Case Study in Estimation-Based Local Search for the Probabilistic Travelling Salesman Problem.
Proceedings of the Recent Advances in Evolutionary Computation for Combinatorial Optimization, 2008

Estimation-Based Local Search for Stochastic Combinatorial Optimization Using Delta Evaluations: A Case Study on the Probabilistic Traveling Salesman Problem.
INFORMS J. Comput., 2008

Iterated Greedy Algorithms for a Real-World Cyclic Train Scheduling Problem.
Proceedings of the Hybrid Metaheuristics, 5th International Workshop, 2008

2007
Improvement Strategies for the F-Race Algorithm: Sampling Design and Iterative Refinement.
Proceedings of the Hybrid Metaheuristics, 4th International Workshop, 2007

The ACO/F-Race Algorithm for Combinatorial Optimization Under Uncertainty.
Proceedings of the Metaheuristics, 2007

2006
Incremental Local Search in Ant Colony Optimization: Why It Fails for the Quadratic Assignment Problem.
Proceedings of the Ant Colony Optimization and Swarm Intelligence, 2006


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