Prasanna Balaprakash

213 papers A* 4A 12B 11C 15Misc 4Journal 124Unranked 40

Year	Rank	Type	Title / Venue / Authors
2026	—	`conf`	Scalable Federated Learning for Scientific Foundation Models on Leadership-Class Systems. EuroMLSys@EuroSys Olivera Kotevska, Trong Nguyen, Rafael Ferreira da Silva, Christian Engelmann, Prasanna Balaprakash
2025	J	`jnl`	Advancing anomaly detection in computational workflows with active learning. Future Gener. Comput. Syst. Krishnan Raghavan, George Papadimitriou, Hongwei Jin, Anirban Mandal, Mariam Kiran, Prasanna Balaprakash, Ewa Deelman
2025	J	`jnl`	Bayesian Optimized Deep Ensemble for Uncertainty Quantification of Deep Neural Networks: a System Safety Case Study on Sodium Fast Reactor Thermal Stratification Modeling. Reliab. Eng. Syst. Saf. Zaid Abulawi, Rui Hu, Prasanna Balaprakash, Yang Liu
2025	—	`conf`	Bridging Speed and Optimality in Job Scheduling: A Hybrid Ant Colony Optimization Approach for Distributed Systems. SC Workshops Hongwei Jin, Pawel Zuk, Krishnan Raghavan, Prachi Jadhav, Aiden Hamade, Ewa Deelman, Prasanna Balaprakash
2025	J	`jnl`	DeepHyper: A Python Package for Massively Parallel Hyperparameter Optimization in Machine Learning. J. Open Source Softw. Romain Egele, Prasanna Balaprakash, Gavin M. Wiggins, Brett Eiffert
2025	A	`conf`	Distributed Cross-Channel Hierarchical Aggregation for Foundation Models. SC Aristeidis Tsaris, Isaac Lyngaas, John H. Lagergren, Mohamed Wahib, Larry M. York, Prasanna Balaprakash, Dan Lu, Feiyi Wang, Xiao Wang
2025	J	`jnl`	Distributed Cross-Channel Hierarchical Aggregation for Foundation Models. CoRR Aristeidis Tsaris, Isaac Lyngaas, John H. Lagergren, Mohamed Wahib, Larry M. York, Prasanna Balaprakash, Dan Lu, Yi-Fei Wang, Xiao Wang
2025	J	`jnl`	Ensembles of Neural Surrogates for Parametric Sensitivity in Ocean Modeling. CoRR Yixuan Sun, Romain Egele, Sri Hari Krishna Narayanan, Luke Van Roekel, Carmelo Gonzales, Steven Brus, Balu Nadiga, Sandeep Madireddy, Prasanna Balaprakash
2025	—	`conf`	Evaluating the Efficacy of LLM-Based Reasoning for Multiobjective HPC Job Scheduling. SC Workshops Prachi Jadhav, Hongwei Jin, Ewa Deelman, Prasanna Balaprakash
2025	J	`jnl`	Evaluating the Efficacy of LLM-Based Reasoning for Multiobjective HPC Job Scheduling. CoRR Prachi Jadhav, Hongwei Jin, Ewa Deelman, Prasanna Balaprakash
2025	J	`jnl`	Exploring the Capabilities of the Frontier Large Language Models for Nuclear Energy Research. CoRR Ahmed Almeldein, Mohammed Alnaggar, Rick Archibald, Tom Beck, Arpan Biswas, Rike Bostelmann, Wes Brewer, Chris Bryan, Christopher Calle, Cihangir Celik, Rajni Chahal, Jong Youl Choi, Arindam Chowdhury, Mark R. Cianciosa, Franklin Curtis, Gregory G. Davidson, Sebastian De Pascuale, Lisa Fassino, Ana Gainaru, Yashika Ghai, Luke Gibson, Qian Gong, Christopher Greulich, Scott Greenwood, Cory D. Hauck, Ehab Hassan, Rinkle Juneja, Soyoung Kang, Scott Klasky, Atul Kumar, Vineet Kumar, M. Paul Laiu, Calvin Lear, Yan-Ru Lin, Jono McConnell, Furkan Oz, Rishi Pillai, Anant Raj, Pradeep Ramuhalli, Marie Romedenne, Samantha Sabatino, José Salcedo-Pérez, Nathan D. See, Arpan Sircar, Punam Thankur, Timothy Reed Younkin, Xiao-Ying Yu, Prashant Jain, Tom Evans, Prasanna Balaprakash
2025	J	`jnl`	HARNESS: Human-Agent Risk Navigation and Event Safety System for Proactive Hazard Forecasting in High-Risk DOE Environments. CoRR Ran Elgedawy, Sanjay Das, Ethan Seefried, Gavin M. Wiggins, Ryan Burchfield, Dana Hewit, Sudarshan Srinivasan, Todd Thomas, Prasanna Balaprakash, Tirthankar Ghosal
2025	J	`jnl`	How does ion temperature gradient turbulence depend on magnetic geometry? Insights from data and machine learning. CoRR Matt Landreman, Jong Youl Choi, Caio Alves, Prasanna Balaprakash, Randy Michael Churchill, Rory Conlin, Gareth Roberg-Clark
2025	J	`jnl`	Integrating ytopt and libEnsemble to autotune OpenMC. Int. J. High Perform. Comput. Appl. Xingfu Wu, John R. Tramm, Jeffrey Larson, John-Luke Navarro, Prasanna Balaprakash, Brice Videau, Michael Kruse, Paul D. Hovland, Valerie Taylor, Mary W. Hall
2025	—	`conf`	Is In-Context Learning Feasible for HPC Performance Autotuning? IPDPS (Workshops) Thomas Randall, Akhilesh Bondapalli, Rong Ge, Prasanna Balaprakash
2025	—	`conf`	LLM Agents for Interactive Workflow Provenance: Reference Architecture and Evaluation Methodology. SC Workshops Renan Souza, Timothy Poteet, Brian Etz, Daniel Rosendo, Amal Gueroudji, Woong Shin, Prasanna Balaprakash, Rafael Ferreira da Silva
2025	J	`jnl`	LLM Agents for Interactive Workflow Provenance: Reference Architecture and Evaluation Methodology. CoRR Renan Souza, Timothy Poteet, Brian Etz, Daniel Rosendo, Amal Gueroudji, Woong Shin, Prasanna Balaprakash, Rafael Ferreira da Silva
2025	J	`jnl`	Large Language Models Inference Engines based on Spiking Neural Networks. CoRR Adarsha Balaji, Sandeep Madireddy, Prasanna Balaprakash
2025	C	`conf`	Leveraging AI for Productive and Trustworthy HPC Software: Challenges and Research Directions. ISC Keita Teranishi, Harshitha Menon, William F. Godoy, Prasanna Balaprakash, David Bau, Tal Ben-Nun, Abhinav Bhatele, Franz Franchetti, Michael E. Franusich, Todd Gamblin, Giorgis Georgakoudis, Tom Goldstein, Arjun Guha, Steven E. Hahn, Costin Iancu, Zheming Jin, Terry R. Jones, Tze Meng Low, Het Mankad, Narasinga Rao Miniskar, Mohammad Alaul Haque Monil, Daniel Nichols, Konstantinos Parasyris, Swaroop Pophale, Pedro Valero-Lara, Jeffrey S. Vetter, Samuel Williams, Aaron R. Young
2025	J	`jnl`	Leveraging AI for Productive and Trustworthy HPC Software: Challenges and Research Directions. CoRR Keita Teranishi, Harshitha Menon, William F. Godoy, Prasanna Balaprakash, David Bau, Tal Ben-Nun, Abhinav Bhatele, Franz Franchetti, Michael E. Franusich, Todd Gamblin, Giorgis Georgakoudis, Tom Goldstein, Arjun Guha, Steven E. Hahn, Costin Iancu, Zheming Jin, Terry R. Jones, Tze Meng Low, Het Mankad, Narasinga Rao Miniskar, Mohammad Alaul Haque Monil, Daniel Nichols, Konstantinos Parasyris, Swaroop Pophale, Pedro Valero-Lara, Jeffrey S. Vetter, Samuel Williams, Aaron R. Young
2025	J	`jnl`	Matrix-free Neural Preconditioner for the Dirac Operator in Lattice Gauge Theory. CoRR Yixuan Sun, Srinivas Eswar, Yin Lin, William Detmold, Phiala E. Shanahan, Xiaoye Li, Yang Liu, Prasanna Balaprakash
2025	J	`jnl`	Multi-task parallelism for robust pre-training of graph foundation models on multi-source, multi-fidelity atomistic modeling data. CoRR Massimiliano Lupo Pasini, Jong Youl Choi, Pei Zhang, Kshitij Mehta, Rylie Weaver, Ashwin M. Aji, Karl W. Schulz, Jorda Polo, Prasanna Balaprakash
2025	A	`conf`	ORBIT-2: Scaling Exascale Vision Foundation Models for Weather and Climate Downscaling. SC Xiao Wang, Jong-Youl Choi, Takuya Kurihana, Isaac Lyngaas, Hong-Jun Yoon, Xi Xiao, David Pugmire, Ming Fan, Nasik Muhammad Nafi, Aristeidis Tsaris, Ashwin M. Aji, Maliha Hossain, Mohamed Wahib, Dali Wang, Peter E. Thornton, Prasanna Balaprakash, Moetasim Ashfaq, Dan Lu
2025	J	`jnl`	ORBIT-2: Scaling Exascale Vision Foundation Models for Weather and Climate Downscaling. CoRR Xiao Wang, Jong-Youl Choi, Takuya Kurihana, Isaac Lyngaas, Hong-Jun Yoon, Ming Fan, Nasik Muhammad Nafi, Aristeidis Tsaris, Ashwin M. Aji, Maliha Hossain, Mohamed Wahib, Dali Wang, Peter E. Thornton, Prasanna Balaprakash, Moetasim Ashfaq, Dan Lu
2025	J	`jnl`	ORCHID: Orchestrated Retrieval-Augmented Classification with Human-in-the-Loop Intelligent Decision-Making for High-Risk Property. CoRR Maria Mahbub, Vanessa Lama, Sanjay Das, Brian Starks, Christopher Polchek, Saffell Silvers, Lauren Deck, Prasanna Balaprakash, Tirthankar Ghosal
2025	—	`conf`	PROV-AGENT: Unified Provenance for Tracking AI Agent Interactions in Agentic Workflows. eScience Renan Souza, Amal Gueroudji, Stephen Dewitt, Daniel Rosendo, Tirthankar Ghosal, Robert B. Ross, Prasanna Balaprakash, Rafael Ferreira da Silva
2025	J	`jnl`	PROV-AGENT: Unified Provenance for Tracking AI Agent Interactions in Agentic Workflows. CoRR Renan Souza, Amal Gueroudji, Stephen Dewitt, Daniel Rosendo, Tirthankar Ghosal, Robert B. Ross, Prasanna Balaprakash, Rafael Ferreira da Silva
2025	J	`jnl`	Report of the 2025 Workshop on Next-Generation Ecosystems for Scientific Computing: Harnessing Community, Software, and AI for Cross-Disciplinary Team Science. CoRR Lois Curfman McInnes, Dorian Arnold, Prasanna Balaprakash, Mike Bernhardt, Beth Cerny, Anshu Dubey, Roscoe Giles, Denice Ward Hood, Mary Ann E. Leung, Vanessa Lopez-Marrero, Paul Messina, Olivia B. Newton, Chris Oehmen, Stefan M. Wild, James M. Willenbring, Lou Woodley, Tony Baylis, David E. Bernholdt, Chris Camano, Johanna Cohoon, Charles Ferenbaugh, Stephen M. Fiore, Sandra Gesing, Diego Gómez-Zará, James Howison, Tanzima Z. Islam, David Kepczynski, Charles Lively, Harshitha Menon, Bronson Messer, Marieme Ngom, Umesh Paliath, Michael E. Papka, Irene Qualters, Elaine M. Raybourn, Katherine Riley, Paulina Rodriguez, Damian W. I. Rouson, Michelle Schwalbe, Sudip K. Seal, Ozge Surer, Valerie Taylor, Lingfei Wu
2025	J	`jnl`	Revisiting the problem of learning long-term dependencies in recurrent neural networks. Neural Networks Liam Johnston, Vivak Patel, Yumian Cui, Prasanna Balaprakash
2025	J	`jnl`	SWARM: Reimagining scientific workflow management systems in a distributed world. Int. J. High Perform. Comput. Appl. Prasanna Balaprakash, Krishnan Raghavan, Franck Cappello, Ewa Deelman, Anirban Mandal, Hongwei Jin, Imtiaz Mahmud, Komal Thareja, Shixun Wu, Pawel Zuk, Mariam Kiran, Zizhong Chen, Sheng Di, Kesheng Wu
2025	J	`jnl`	Scalable training of trustworthy and energy-efficient predictive graph foundation models for atomistic materials modeling: a case study with HydraGNN. J. Supercomput. Massimiliano Lupo Pasini, Jong Youl Choi, Kshitij Mehta, Pei Zhang, David M. Rogers, Jonghyun Bae, Khaled Z. Ibrahim, Ashwin M. Aji, Karl W. Schulz, Jordà Polo, Prasanna Balaprakash
2025	A*	`conf`	Scaling Laws of Graph Neural Networks for Atomistic Materials Modeling. DAC Chaojian Li, Zhifan Ye, Massimiliano Lupo Pasini, Jong Youl Choi, Cheng Wan, Prasanna Balaprakash
2025	J	`jnl`	Scaling Laws of Graph Neural Networks for Atomistic Materials Modeling. CoRR Chaojian Li, Zhifan Ye, Massimiliano Lupo Pasini, Jong Youl Choi, Cheng Wan, Yingyan Celine Lin, Prasanna Balaprakash
2025	—	`conf`	The (R)evolution of Scientific Workflows in the Agentic AI Era: Towards Autonomous Science. SC Workshops Woong Shin, Renan Souza, Daniel Rosendo, Frédéric Suter, Feiyi Wang, Prasanna Balaprakash, Rafael Ferreira da Silva
2025	J	`jnl`	The (R)evolution of Scientific Workflows in the Agentic AI Era: Towards Autonomous Science. CoRR Woong Shin, Renan Souza, Daniel Rosendo, Frédéric Suter, Feiyi Wang, Prasanna Balaprakash, Rafael Ferreira da Silva
2025	J	`jnl`	The Iceberg Index: Measuring Workforce Exposure Across the AI Economy. CoRR Ayush Chopra, Santanu Bhattacharya, DeAndrea Salvador, Ayan Paul, Teddy Wright, Aditi Garg, Feroz Ahmad, Alice C. Schwarze, Ramesh Raskar, Prasanna Balaprakash
2025	J	`jnl`	ytopt: Autotuning Scientific Applications for Energy Efficiency at Large Scales. Concurr. Comput. Pract. Exp. Xingfu Wu, Prasanna Balaprakash, Michael Kruse, Jaehoon Koo, Brice Videau, Paul D. Hovland, Valerie Taylor, Brad Geltz, Siddhartha Jana, Mary W. Hall
2024	J	`jnl`	A Gradient-Aware Search Algorithm for Constrained Markov Decision Processes. IEEE Trans. Neural Networks Learn. Syst. Sami Khairy, Prasanna Balaprakash, Lin X. Cai
2024	J	`jnl`	AI Competitions and Benchmarks: Dataset Development. CoRR Romain Egele, Júlio C. S. Jacques Júnior, Jan N. van Rijn, Isabelle Guyon, Xavier Baró, Albert Clapés, Prasanna Balaprakash, Sergio Escalera, Thomas B. Moeslund, Jun Wan
2024	J	`jnl`	Advancing Anomaly Detection in Computational Workflows with Active Learning. CoRR Krishnan Raghavan, George Papadimitriou, Hongwei Jin, Anirban Mandal, Mariam Kiran, Prasanna Balaprakash, Ewa Deelman
2024	J	`jnl`	Automated defect identification in coherent diffraction imaging with smart continual learning. Neural Comput. Appl. Orcun Yildiz, Krishnan Raghavan, Henry Chan, Mathew J. Cherukara, Prasanna Balaprakash, Subramanian Sankaranarayanan, Tom Peterka
2024	J	`jnl`	Bayesian optimized deep ensemble for uncertainty quantification of deep neural networks: a system safety case study on sodium fast reactor thermal stratification modeling. CoRR Zaid Abulawi, Rui Hu, Prasanna Balaprakash, Yang Liu
2024	—	`conf`	ChatBLAS: The First AI-Generated and Portable BLAS Library. SC Workshops Pedro Valero-Lara, William F. Godoy, Keita Teranishi, Prasanna Balaprakash, Jeffrey S. Vetter
2024	J	`jnl`	Efficient Mapping Between Void Shapes and Stress Fields Using Deep Convolutional Neural Networks With Sparse Data. J. Comput. Inf. Sci. Eng. Anindya Bhaduri, Nesar Ramachandra, Sandipp Krishnan Ravi, Lele Luan, Piyush Pandita, Prasanna Balaprakash, Mihai Anitescu, Changjie Sun, Liping Wang
2024	C	`conf`	Enhancing Power Distribution System Resilience with Fusion-GNN: A Dynamic Graph Representation Learning Approach. IECON Boming Liu, Chen Zhang, Jin Dong, Prasanna Balaprakash, Yan Liu, Brett Eiffert
2024	J	`jnl`	Generalizable Prediction Model of Molten Salt Mixture Density with Chemistry-Informed Transfer Learning. CoRR Julian Barra, Shayan Shahbazi, Anthony Birri, Rajni Chahal, Ibrahim Isah, Muhammad Nouman Anwar, Tyler Starkus, Prasanna Balaprakash, Stephen Lam
2024	J	`jnl`	Integrating ytopt and libEnsemble to Autotune OpenMC. CoRR Xingfu Wu, John R. Tramm, Jeffrey Larson, John-Luke Navarro, Prasanna Balaprakash, Brice Videau, Michael Kruse, Paul D. Hovland, Valerie Taylor, Mary W. Hall
2024	A	`conf`	Large Language Models for Anomaly Detection in Computational Workflows: From Supervised Fine-Tuning to In-Context Learning. SC Hongwei Jin, George Papadimitriou, Krishnan Raghavan, Pawel Zuk, Prasanna Balaprakash, Cong Wang, Anirban Mandal, Ewa Deelman
2024	J	`jnl`	Large Language Models for Anomaly Detection in Computational Workflows: from Supervised Fine-Tuning to In-Context Learning. CoRR Hongwei Jin, George Papadimitriou, Krishnan Raghavan, Pawel Zuk, Prasanna Balaprakash, Cong Wang, Anirban Mandal, Ewa Deelman
2024	J	`jnl`	Large language model evaluation for high-performance computing software development. Concurr. Comput. Pract. Exp. William F. Godoy, Pedro Valero-Lara, Keita Teranishi, Prasanna Balaprakash, Jeffrey S. Vetter
2024	J	`jnl`	Multi-fidelity reinforcement learning with control variates. Neurocomputing Sami Khairy, Prasanna Balaprakash
2024	J	`jnl`	Network architecture search of X-ray based scientific applications. CoRR Adarsha Balaji, Ramyad Hadidi, Gregory Kollmer, Mohammed E. Fouda, Prasanna Balaprakash
2024	A	`conf`	ORBIT: Oak Ridge Base Foundation Model for Earth System Predictability. SC Xiao Wang, Siyan Liu, Aristeidis Tsaris, Jong-Youl Choi, Ashwin M. Aji, Ming Fan, Wei Zhang, Junqi Yin, Moetasim Ashfaq, Dan Lu, Prasanna Balaprakash
2024	J	`jnl`	ORBIT: Oak Ridge Base Foundation Model for Earth System Predictability. CoRR Xiao Wang, Aristeidis Tsaris, Siyan Liu, Jong-Youl Choi, Ming Fan, Wei Zhang, Junqi Yin, Moetasim Ashfaq, Dan Lu, Prasanna Balaprakash
2024	C	`conf`	Optimizing Distributed Training on Frontier for Large Language Models. ISC Sajal Dash, Isaac Lyngaas, Junqi Yin, Xiao Wang, Romain Egele, J. Austin Ellis, Matthias Maiterth, Guojing Cong, Feiyi Wang, Prasanna Balaprakash
2024	J	`jnl`	Physics-Informed Heterogeneous Graph Neural Networks for DC Blocker Placement. CoRR Hongwei Jin, Prasanna Balaprakash, Allen Zou, Pieter Ghysels, Aditi S. Krishnapriyan, Adam Mate, Arthur K. Barnes, Russell Bent
2024	J	`jnl`	Refining computer tomography data with super-resolution networks to increase the accuracy of respiratory flow simulations. Future Gener. Comput. Syst. Xin Liu, Mario Rüttgers, Alessio Quercia, Romain Egele, Elisabeth Pfaehler, Rushikesh Shende, Marcel Aach, Wolfgang Schröder, Prasanna Balaprakash, Andreas Lintermann
2024	J	`jnl`	Scalable Training of Graph Foundation Models for Atomistic Materials Modeling: A Case Study with HydraGNN. CoRR Massimiliano Lupo Pasini, Jong Youl Choi, Kshitij Mehta, Pei Zhang, David M. Rogers, Jonghyun Bae, Khaled Z. Ibrahim, Ashwin M. Aji, Karl W. Schulz, Jorda Polo, Prasanna Balaprakash
2024	J	`jnl`	Sequence Length Scaling in Vision Transformers for Scientific Images on Frontier. CoRR Aristeidis Tsaris, Chengming Zhang, Xiao Wang, Junqi Yin, Siyan Liu, Moetasim Ashfaq, Ming Fan, Jong-Youl Choi, Mohamed Wahib, Dan Lu, Prasanna Balaprakash, Fei-Yue Wang
2024	J	`jnl`	Streamlining Ocean Dynamics Modeling with Fourier Neural Operators: A Multiobjective Hyperparameter and Architecture Optimization Approach. CoRR Yixuan Sun, Olaperi Yeside Sowunmi, Romain Egele, Sri Hari Krishna Narayanan, Luke Van Roekel, Prasanna Balaprakash
2024	J	`jnl`	The Unreasonable Effectiveness Of Early Discarding After One Epoch In Neural Network Hyperparameter Optimization. CoRR Romain Egele, Felix Mohr, Tom J. Viering, Prasanna Balaprakash
2024	J	`jnl`	The unreasonable effectiveness of early discarding after one epoch in neural network hyperparameter optimization. Neurocomputing Romain Egele, Felix Mohr, Tom J. Viering, Prasanna Balaprakash
2024	J	`jnl`	Transfer-Learning-Based Autotuning Using Gaussian Copula. CoRR Thomas Randall, Jaehoon Koo, Brice Videau, Michael Kruse, Xingfu Wu, Paul D. Hovland, Mary W. Hall, Rong Ge, Prasanna Balaprakash
2024	J	`jnl`	Uncertainty Quantification for Traffic Forecasting Using Deep-Ensemble-Based Spatiotemporal Graph Neural Networks. IEEE Trans. Intell. Transp. Syst. Tanwi Mallick, Jane MacFarlane, Prasanna Balaprakash
2023	J	`jnl`	Analyzing the impact of climate change on critical infrastructure from the scientific literature: A weakly supervised NLP approach. CoRR Tanwi Mallick, Joshua David Bergerson, Duane R. Verner, John K. Hutchison, Leslie-Anne Levy, Prasanna Balaprakash
2023	C	`conf`	Application - Hardware Co-Optimization of Crossbar-Based Neuromorphic Systems. ICMLA Adarsha Balaji, Prasanna Balaprakash
2023	J	`jnl`	Application of probabilistic modeling and automated machine learning framework for high-dimensional stress field. CoRR Lele Luan, Nesar Ramachandra, Sandipp Krishnan Ravi, Anindya Bhaduri, Piyush Pandita, Prasanna Balaprakash, Mihai Anitescu, Changjie Sun, Liping Wang
2023	B	`conf`	Asynchronous Decentralized Bayesian Optimization for Large Scale Hyperparameter Optimization. e-Science Romain Égelé, Isabelle Guyon, Venkatram Vishwanath, Prasanna Balaprakash
2023	—	`conf`	Comparing Llama-2 and GPT-3 LLMs for HPC Kernels Generation. LCPC Pedro Valero-Lara, Alexis Huante, Mustafa Al Lail, William F. Godoy, Keita Teranishi, Prasanna Balaprakash, Jeffrey S. Vetter
2023	J	`jnl`	Comparing Llama-2 and GPT-3 LLMs for HPC kernels generation. CoRR Pedro Valero-Lara, Alexis Huante, Mustafa Al-Lail, William F. Godoy, Keita Teranishi, Prasanna Balaprakash, Jeffrey S. Vetter
2023	J	`jnl`	DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies. CoRR Shuaiwen Leon Song, Bonnie Kruft, Minjia Zhang, Conglong Li, Shiyang Chen, Chengming Zhang, Masahiro Tanaka, Xiaoxia Wu, Jeff Rasley, Ammar Ahmad Awan, Connor Holmes, Martin Cai, Adam Ghanem, Zhongzhu Zhou, Yuxiong He, Pete Luferenko, Divya Kumar, Jonathan A. Weyn, Ruixiong Zhang, Sylwester Klocek, Volodymyr Vragov, Mohammed AlQuraishi, Gustaf Ahdritz, Christina Floristean, Cristina Negri, Rao Kotamarthi, Venkatram Vishwanath, Arvind Ramanathan, Sam Foreman, Kyle Hippe, Troy Arcomano, Romit Maulik, Maxim Zvyagin, Alexander Brace, Bin Zhang, Cindy Orozco Bohorquez, Austin Clyde, Bharat Kale, Danilo Perez-Rivera, Heng Ma, Carla M. Mann, Michael W. Irvin, J. Gregory Pauloski, Logan T. Ward, Valérie Hayot-Sasson, Murali Emani, Zhen Xie, Diangen Lin, Maulik Shukla, Ian T. Foster, James J. Davis, Michael E. Papka, Thomas S. Brettin, Prasanna Balaprakash, Gina Tourassi, John Gounley, Heidi A. Hanson, Thomas E. Potok, Massimiliano Lupo Pasini, Kate Evans, Dan Lu, Dalton D. Lunga, Junqi Yin, Sajal Dash, Feiyi Wang, Mallikarjun Shankar, Isaac Lyngaas, Xiao Wang, Guojing Cong, Pei Zhang, Ming Fan, Siyan Liu, Adolfy Hoisie, Shinjae Yoo, Yihui Ren, William Tang, Kyle Felker, Alexey Svyatkovskiy, Hang Liu, Ashwin M. Aji, Angela Dalton, Michael J. Schulte, Karl W. Schulz, Yuntian Deng, Weili Nie, Josh Romero, Christian Dallago, Arash Vahdat, Chaowei Xiao, Thomas Gibbs, Anima Anandkumar, Rick Stevens
2023	—	`conf`	Evaluation of OpenAI Codex for HPC Parallel Programming Models Kernel Generation. ICPP Workshops William F. Godoy, Pedro Valero-Lara, Keita Teranishi, Prasanna Balaprakash, Jeffrey S. Vetter
2023	J	`jnl`	Evaluation of OpenAI Codex for HPC Parallel Programming Models Kernel Generation. CoRR William F. Godoy, Pedro Valero-Lara, Keita Teranishi, Prasanna Balaprakash, Jeffrey S. Vetter
2023	J	`jnl`	Flow-Bench: A Dataset for Computational Workflow Anomaly Detection. CoRR George Papadimitriou, Hongwei Jin, Cong Wang, Krishnan Raghavan, Anirban Mandal, Prasanna Balaprakash, Ewa Deelman
2023	C	`conf`	Graph Pyramid Autoformer for Long- Term Traffic Forecasting. ICMLA Weiheng Zhong, Tanwi Mallick, Jane MacFarlane, Hadi Meidani, Prasanna Balaprakash
2023	J	`jnl`	Graph neural networks for detecting anomalies in scientific workflows. Int. J. High Perform. Comput. Appl. Hongwei Jin, Krishnan Raghavan, George Papadimitriou, Cong Wang, Anirban Mandal, Mariam Kiran, Ewa Deelman, Prasanna Balaprakash
2023	—	`conf`	Improving Performance in Continual Learning Tasks using Bio-Inspired Architectures. CoLLAs Sandeep Madireddy, Angel Yanguas-Gil, Prasanna Balaprakash
2023	J	`jnl`	Improving Performance in Continual Learning Tasks using Bio-Inspired Architectures. CoRR Sandeep Madireddy, Angel Yanguas-Gil, Prasanna Balaprakash
2023	B	`conf`	Is One Epoch All You Need For Multi-Fidelity Hyperparameter Optimization? ESANN Romain Egele, Isabelle Guyon, Yixuan Sun, Prasanna Balaprakash
2023	J	`jnl`	Is One Epoch All You Need For Multi-Fidelity Hyperparameter Optimization? CoRR Romain Egele, Isabelle Guyon, Yixuan Sun, Prasanna Balaprakash
2023	J	`jnl`	Learning Continually on a Sequence of Graphs - The Dynamical System Way. CoRR Krishnan Raghavan, Prasanna Balaprakash
2023	J	`jnl`	Learning Symbolic Expressions: Mixed-Integer Formulations, Cuts, and Heuristics. INFORMS J. Comput. Jongeun Kim, Sven Leyffer, Prasanna Balaprakash
2023	B	`conf`	Multi-Fidelity Reinforcement Learning with Control Variates. ESANN Sami Khairy, Prasanna Balaprakash
2023	J	`jnl`	Multi-fidelity reinforcement learning framework for shape optimization. J. Comput. Phys. Sahil Bhola, Suraj Pawar, Prasanna Balaprakash, Romit Maulik
2023	J	`jnl`	Optimizing Distributed Training on Frontier for Large Language Models. CoRR Sajal Dash, Isaac Lyngaas, Junqi Yin, Xiao Wang, Romain Egele, Guojing Cong, Feiyi Wang, Prasanna Balaprakash
2023	J	`jnl`	Parallel Multi-Objective Hyperparameter Optimization with Uniform Normalization and Bounded Objectives. CoRR Romain Egele, Tyler H. Chang, Yixuan Sun, Venkatram Vishwanath, Prasanna Balaprakash
2023	J	`jnl`	Quantifying uncertainty for deep learning based forecasting and flow-reconstruction using neural architecture search ensembles. CoRR Romit Maulik, Romain Egele, Krishnan Raghavan, Prasanna Balaprakash
2023	—	`conf`	Scalable Automated Design and Development of Deep Neural Networks for Scientific and Engineering Applications. IPDPS Workshops Prasanna Balaprakash
2023	J	`jnl`	Self-supervised Learning for Anomaly Detection in Computational Workflows. CoRR Hongwei Jin, Krishnan Raghavan, George Papadimitriou, Cong Wang, Anirban Mandal, Ewa Deelman, Prasanna Balaprakash
2023	A	`conf`	Sparsity-Inducing Categorical Prior Improves Robustness of the Information Bottleneck. AISTATS Anirban Samaddar, Sandeep Madireddy, Prasanna Balaprakash, Taps Maiti, Gustavo de los Campos, Ian Fischer
2023	J	`jnl`	Stabilized neural ordinary differential equations for long-time forecasting of dynamical systems. J. Comput. Phys. Alec J. Linot, Joshua W. Burby, Qi Tang, Prasanna Balaprakash, Michael D. Graham, Romit Maulik
2023	A	`conf`	Transfer-learning-based Autotuning using Gaussian Copula. ICS Thomas Randall, Jaehoon Koo, Brice Videau, Michael Kruse, Xingfu Wu, Paul D. Hovland, Mary W. Hall, Rong Ge, Prasanna Balaprakash
2023	J	`jnl`	Uncertainty Quantification for Molecular Property Predictions with Graph Neural Architecture Search. CoRR Shengli Jiang, Shiyi Qin, Reid C. Van Lehn, Prasanna Balaprakash, Victor M. Zavala
2023	—	`conf`	iWAPT2023 Invited Speaker Optimizing HPC Systems for Scientific Applications: Machine Learning Approaches to Performance Tuning and Anomaly Detection. IPDPS Workshops Prasanna Balaprakash
2023	J	`jnl`	ytopt: Autotuning Scientific Applications for Energy Efficiency at Large Scales. CoRR Xingfu Wu, Prasanna Balaprakash, Michael Kruse, Jaehoon Koo, Brice Videau, Paul D. Hovland, Valerie Taylor, Brad Geltz, Siddhartha Jana, Mary W. Hall
2022	A	`conf`	A Taxonomy of Error Sources in HPC I/O Machine Learning Models. SC Mihailo Isakov, Mikaela Currier, Eliakin Del Rosario, Sandeep Madireddy, Prasanna Balaprakash, Philip H. Carns, Robert B. Ross, Glenn K. Lockwood, Michel A. Kinsy
2022	J	`jnl`	A Taxonomy of Error Sources in HPC I/O Machine Learning Models. CoRR Mihailo Isakov, Mikaela Currier, Eliakin Del Rosario, Sandeep Madireddy, Prasanna Balaprakash, Philip H. Carns, Robert B. Ross, Glenn K. Lockwood, Michel A. Kinsy
2022	J	`jnl`	A cross-study analysis of drug response prediction in cancer cell lines. Briefings Bioinform. Fangfang Xia, Jonathan E. Allen, Prasanna Balaprakash, Thomas S. Brettin, Cristina Garcia-Cardona, Austin Clyde, Judith D. Cohn, James H. Doroshow, Xiaotian Duan, Veronika Dubinkina, Yvonne A. Evrard, Ya Ju Fan, Jason Gans, Stewart He, Pinyi Lu, Sergei Maslov, Alexander Partin, Maulik Shukla, Eric A. Stahlberg, Justin M. Wozniak, Hyun Seung Yoo, George F. Zaki, Yitan Zhu, Rick Stevens
2022	J	`jnl`	Asynchronous Distributed Bayesian Optimization at HPC Scale. CoRR Romain Egele, Joceran Gouneau, Venkatram Vishwanath, Isabelle Guyon, Prasanna Balaprakash
2022	B	`conf`	AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification. ICPR Romain Egele, Romit Maulik, Krishnan Raghavan, Bethany Lusch, Isabelle Guyon, Prasanna Balaprakash
2022	—	`conf`	Automated Continual Learning of Defect Identification in Coherent Diffraction Imaging. AI4S Orcun Yildiz, Henry Chan, Krishnan Raghavan, William Judge, Mathew J. Cherukara, Prasanna Balaprakash, Subramanian Sankaranarayanan, Tom Peterka
2022	J	`jnl`	Autotuning PolyBench benchmarks with LLVM Clang/Polly loop optimization pragmas using Bayesian optimization. Concurr. Comput. Pract. Exp. Xingfu Wu, Michael Kruse, Prasanna Balaprakash, Hal Finkel, Paul D. Hovland, Valerie Taylor, Mary W. Hall
2022	B	`conf`	Continual Learning via Dynamic Programming. ICPR R. Krishnan, Prasanna Balaprakash
2022	J	`jnl`	Data-Driven Random Access Optimization in Multi-Cell IoT Networks Using NOMA. IEEE Trans. Wirel. Commun. Sami Khairy, Prasanna Balaprakash, Lin X. Cai, H. Vincent Poor
2022	J	`jnl`	Deep-Ensemble-Based Uncertainty Quantification in Spatiotemporal Graph Neural Networks for Traffic Forecasting. CoRR Tanwi Mallick, Prasanna Balaprakash, Jane MacFarlane
2022	J	`jnl`	Efficient data acquisition and training of collisional-radiative model artificial neural network surrogates through adaptive parameter space sampling. Mach. Learn. Sci. Technol. Nathan A Garland, Romit Maulik, Qi Tang, Xian-Zhu Tang, Prasanna Balaprakash
2022	J	`jnl`	Explainable Graph Pyramid Autoformer for Long-Term Traffic Forecasting. CoRR Weiheng Zhong, Tanwi Mallick, Hadi Meidani, Jane MacFarlane, Prasanna Balaprakash
2022	C	`conf`	HPC Storage Service Autotuning Using Variational- Autoencoder -Guided Asynchronous Bayesian Optimization. CLUSTER Matthieu Dorier, Romain Egele, Prasanna Balaprakash, Jaehoon Koo, Sandeep Madireddy, Srinivasan Ramesh, Allen D. Malony, Robert B. Ross
2022	J	`jnl`	HPC Storage Service Autotuning Using Variational-Autoencoder-Guided Asynchronous Bayesian Optimization. CoRR Matthieu Dorier, Romain Egele, Prasanna Balaprakash, Jaehoon Koo, Sandeep Madireddy, Srinivasan Ramesh, Allen D. Malony, Robert B. Ross
2022	J	`jnl`	Modeling design and control problems involving neural network surrogates. Comput. Optim. Appl. Dominic Yang, Prasanna Balaprakash, Sven Leyffer
2022	J	`jnl`	Multi-fidelity reinforcement learning framework for shape optimization. CoRR Sahil Bhola, Suraj Pawar, Prasanna Balaprakash, Romit Maulik
2022	J	`jnl`	Multifidelity Reinforcement Learning with Control Variates. CoRR Sami Khairy, Prasanna Balaprakash
2022	J	`jnl`	Sequential Bayesian Neural Subnetwork Ensembles. CoRR Sanket R. Jantre, Sandeep Madireddy, Shrijita Bhattacharya, Tapabrata Maiti, Prasanna Balaprakash
2022	J	`jnl`	Sparsity-Inducing Categorical Prior Improves Robustness of the Information Bottleneck. CoRR Anirban Samaddar, Sandeep Madireddy, Prasanna Balaprakash
2022	J	`jnl`	Stabilized Neural Ordinary Differential Equations for Long-Time Forecasting of Dynamical Systems. CoRR Alec J. Linot, Joshua W. Burby, Qi Tang, Prasanna Balaprakash, Michael D. Graham, Romit Maulik
2022	J	`jnl`	Unified Probabilistic Neural Architecture and Weight Ensembling Improves Model Robustness. CoRR Sumegha Premchandar, Sandeep Madireddy, Sanket R. Jantre, Prasanna Balaprakash
2022	—	`conf`	Workflow Anomaly Detection with Graph Neural Networks. WORKS@SC Hongwei Jin, Krishnan Raghavan, George Papadimitriou, Cong Wang, Anirban Mandal, Patrycja Krawczuk, Loïc Pottier, Mariam Kiran, Ewa Deelman, Prasanna Balaprakash
2021	J	`jnl`	A data-centric weak supervised learning for highway traffic incident detection. CoRR Yixuan Sun, Tanwi Mallick, Prasanna Balaprakash, Jane MacFarlane
2021	A	`conf`	AgEBO-tabular: joint neural architecture and hyperparameter search with autotuned data-parallel training for tabular data. SC Romain Égelé, Prasanna Balaprakash, Isabelle Guyon, Venkatram Vishwanath, Fangfang Xia, Rick Stevens, Zhengying Liu
2021	J	`jnl`	AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification. CoRR Romain Egele, Romit Maulik, Krishnan Raghavan, Prasanna Balaprakash, Bethany Lusch
2021	J	`jnl`	AutoPhaseNN: Unsupervised Physics-aware Deep Learning of 3D Nanoscale Coherent Imaging. CoRR Yudong Yao, Henry Chan, Subramanian Sankaranarayanan, Prasanna Balaprakash, Ross J. Harder, Mathew J. Cherukara
2021	J	`jnl`	Autotuning PolyBench Benchmarks with LLVM Clang/Polly Loop Optimization Pragmas Using Bayesian Optimization (extended version). CoRR Xingfu Wu, Michael Kruse, Prasanna Balaprakash, Hal Finkel, Paul D. Hovland, Valerie Taylor, Mary W. Hall
2021	J	`jnl`	Constrained Deep Reinforcement Learning for Energy Sustainable Multi-UAV Based Random Access IoT Networks With NOMA. IEEE J. Sel. Areas Commun. Sami Khairy, Prasanna Balaprakash, Lin X. Cai, Yu Cheng
2021	—	`conf`	Customized Monte Carlo Tree Search for LLVM/Polly's Composable Loop Optimization Transformations. PMBS Jaehoon Koo, Prasanna Balaprakash, Michael Kruse, Xingfu Wu, Paul D. Hovland, Mary W. Hall
2021	J	`jnl`	Customized Monte Carlo Tree Search for LLVM/Polly's Composable Loop Optimization Transformations. CoRR Jaehoon Koo, Prasanna Balaprakash, Michael Kruse, Xingfu Wu, Paul D. Hovland, Mary W. Hall
2021	A*	`conf`	Formalizing the Generalization-Forgetting Trade-off in Continual Learning. NeurIPS Krishnan Raghavan, Prasanna Balaprakash
2021	J	`jnl`	Formalizing the Generalization-Forgetting Trade-off in Continual Learning. CoRR Krishnan Raghavan, Prasanna Balaprakash
2021	J	`jnl`	In situ compression artifact removal in scientific data using deep transfer learning and experience replay. Mach. Learn. Sci. Technol. Sandeep Madireddy, Ji Hwan Park, Sunwoo Lee, Prasanna Balaprakash, Shinjae Yoo, Wei-keng Liao, Cory D. Hauck, M. Paul Laiu, Richard Archibald
2021	J	`jnl`	Learning Symbolic Expressions: Mixed-Integer Formulations, Cuts, and Heuristics. CoRR Jongeun Kim, Sven Leyffer, Prasanna Balaprakash
2021	C	`conf`	Learning to Switch Optimizers for Quadratic Programming. ACML Grant Getzelman, Prasanna Balaprakash
2021	J	`jnl`	Learning-Based Distributed Random Access for Multi-Cell IoT Networks with NOMA. CoRR Sami Khairy, Prasanna Balaprakash, Lin X. Cai, H. Vincent Poor
2021	J	`jnl`	Modeling Design and Control Problems Involving Neural Network Surrogates. CoRR Dominic Yang, Prasanna Balaprakash, Sven Leyffer
2020	J	`jnl`	A Gradient-Aware Search Algorithm for Constrained Markov Decision Processes. CoRR Sami Khairy, Prasanna Balaprakash, Lin X. Cai
2020	J	`jnl`	AgEBO-Tabular: Joint Neural Architecture and Hyperparameter Search with Autotuned Data-Parallel Training for Tabular Data. CoRR Romain Egele, Prasanna Balaprakash, Venkatram Vishwanath, Isabelle Guyon, Zhengying Liu
2020	—	`conf`	Autotuning PolyBench Benchmarks with LLVM Clang/Polly Loop Optimization Pragmas Using Bayesian Optimization. PMBS@SC Xingfu Wu, Michael Kruse, Prasanna Balaprakash, Hal Finkel, Paul D. Hovland, Valerie Taylor, Mary W. Hall
2020	J	`jnl`	Autotuning PolyBench Benchmarks with LLVM Clang/Polly Loop Optimization Pragmas Using Bayesian Optimization. CoRR Xingfu Wu, Michael Kruse, Prasanna Balaprakash, Hal Finkel, Paul D. Hovland, Valerie Taylor, Mary W. Hall
2020	J	`jnl`	Constrained Deep Reinforcement Learning for Energy Sustainable Multi-UAV based Random Access IoT Networks with NOMA. CoRR Sami Khairy, Prasanna Balaprakash, Lin X. Cai, Yu Cheng
2020	J	`jnl`	Dynamic Graph Neural Network for Traffic Forecasting in Wide Area Networks. CoRR Tanwi Mallick, Mariam Kiran, Bashir Mohammed, Prasanna Balaprakash
2020	A*	`conf`	FIdelity: Efficient Resilience Analysis Framework for Deep Learning Accelerators. MICRO Yi He, Prasanna Balaprakash, Yanjing Li
2020	—	`conf`	Gauge: An Interactive Data-Driven Visualization Tool for HPC Application I/O Performance Analysis. PDSW@SC Eliakin Del Rosario, Mikaela Currier, Mihailo Isakov, Sandeep Madireddy, Prasanna Balaprakash, Philip H. Carns, Robert B. Ross, Kevin Harms, Shane Snyder, Michel A. Kinsy
2020	—	`conf`	Graph Neural Network Architecture Search for Molecular Property Prediction. IEEE BigData Shengli Jiang, Prasanna Balaprakash
2020	J	`jnl`	Graph Neural Network Architecture Search for Molecular Property Prediction. CoRR Shengli Jiang, Prasanna Balaprakash
2020	A	`conf`	HPC I/O throughput bottleneck analysis with explainable local models. SC Mihailo Isakov, Eliakin Del Rosario, Sandeep Madireddy, Prasanna Balaprakash, Philip H. Carns, Robert B. Ross, Michel A. Kinsy
2020	A*	`conf`	Learning to Optimize Variational Quantum Circuits to Solve Combinatorial Problems. AAAI Sami Khairy, Ruslan Shaydulin, Lukasz Cincio, Yuri Alexeev, Prasanna Balaprakash
2020	J	`jnl`	Meta Continual Learning via Dynamic Programming. CoRR R. Krishnan, Prasanna Balaprakash
2020	J	`jnl`	Multilayer Neuromodulated Architectures for Memory-Constrained Online Continual Learning. CoRR Sandeep Madireddy, Angel Yanguas-Gil, Prasanna Balaprakash
2020	A	`conf`	Recurrent neural network architecture search for geophysical emulation. SC Romit Maulik, Romain Egele, Bethany Lusch, Prasanna Balaprakash
2020	J	`jnl`	Site-specific graph neural network for predicting protonation energy of oxygenate molecules. CoRR Romit Maulik, Rajeev Surendran Array, Prasanna Balaprakash
2020	—	`conf`	Toward Generalizable Models of I/O Throughput. ROSS@SC Mihailo Isakov, Eliakin Del Rosario, Sandeep Madireddy, Prasanna Balaprakash, Philip H. Carns, Robert B. Ross, Michel A. Kinsy
2020	J	`jnl`	Towards On-Chip Bayesian Neuromorphic Learning. CoRR Nathan Wycoff, Prasanna Balaprakash, Fangfang Xia
2020	B	`conf`	Transfer Learning with Graph Neural Networks for Short-Term Highway Traffic Forecasting. ICPR Tanwi Mallick, Prasanna Balaprakash, Eric Rask, Jane MacFarlane
2020	J	`jnl`	Transfer Learning with Graph Neural Networks for Short-Term Highway Traffic Forecasting. CoRR Tanwi Mallick, Prasanna Balaprakash, Eric Rask, Jane MacFarlane
2019	—	`conf`	A Framework for Enabling OpenMP Autotuning. IWOMP Vinu Sreenivasan, Rajath Javali, Mary W. Hall, Prasanna Balaprakash, Thomas R. W. Scogland, Bronis R. de Supinski
2019	B	`conf`	Adaptive Learning for Concept Drift in Application Performance Modeling. ICPP Sandeep Madireddy, Prasanna Balaprakash, Philip H. Carns, Robert Latham, Glenn K. Lockwood, Robert B. Ross, Shane Snyder, Stefan M. Wild
2019	J	`jnl`	Balsam: Automated Scheduling and Execution of Dynamic, Data-Intensive HPC Workflows. CoRR Michael A. Salim, Thomas D. Uram, J. Taylor Childers, Prasanna Balaprakash, Venkatram Vishwanath, Michael E. Papka
2019	J	`jnl`	Graph-Partitioning-Based Diffusion Convolution Recurrent Neural Network for Large-Scale Traffic Forecasting. CoRR Tanwi Mallick, Prasanna Balaprakash, Eric Rask, Jane MacFarlane
2019	—	`conf`	Improving Scalability of Parallel CNN Training by Adjusting Mini-Batch Size at Run-Time. IEEE BigData Sunwoo Lee, Qiao Kang, Sandeep Madireddy, Prasanna Balaprakash, Ankit Agrawal, Alok N. Choudhary, Richard Archibald, Wei-keng Liao
2019	J	`jnl`	Learning to Optimize Variational Quantum Circuits to Solve Combinatorial Problems. CoRR Sami Khairy, Ruslan Shaydulin, Lukasz Cincio, Yuri Alexeev, Prasanna Balaprakash
2019	C	`conf`	MaLTESE: Large-Scale Simulation-Driven Machine Learning for Transient Driving Cycles. ISC Shashi M. Aithal, Prasanna Balaprakash
2019	J	`jnl`	MaLTESE: Large-Scale Simulation-Driven Machine Learning for Transient Driving Cycles. CoRR Shashi M. Aithal, Prasanna Balaprakash
2019	J	`jnl`	Modular Deep Learning Analysis of Galaxy-Scale Strong Lensing Images. CoRR Sandeep Madireddy, Nan Li, Nesar Ramachandra, Prasanna Balaprakash, Salman Habib
2019	—	`conf`	Neuromorphic Acceleration for Approximate Bayesian Inference on Neural Networks via Permanent Dropout. ICONS Nathan Wycoff, Prasanna Balaprakash, Fangfang Xia
2019	J	`jnl`	Neuromorphic Acceleration for Approximate Bayesian Inference on Neural Networks via Permanent Dropout. CoRR Nathan Wycoff, Prasanna Balaprakash, Fangfang Xia
2019	—	`conf`	Neuromorphic Architecture Optimization for Task-Specific Dynamic Learning. ICONS Sandeep Madireddy, Angel Yanguas-Gil, Prasanna Balaprakash
2019	J	`jnl`	Neuromorphic Architecture Optimization for Task-Specific Dynamic Learning. CoRR Sandeep Madireddy, Angel Yanguas-Gil, Prasanna Balaprakash
2019	J	`jnl`	Reinforcement-Learning-Based Variational Quantum Circuits Optimization for Combinatorial Problems. CoRR Sami Khairy, Ruslan Shaydulin, Lukasz Cincio, Yuri Alexeev, Prasanna Balaprakash
2019	J	`jnl`	Scalable Reinforcement-Learning-Based Neural Architecture Search for Cancer Deep Learning Research. CoRR Prasanna Balaprakash, Romain Egele, Misha Salim, Stefan M. Wild, Venkatram Vishwanath, Fangfang Xia, Tom Brettin, Rick Stevens
2019	A	`conf`	Scalable reinforcement-learning-based neural architecture search for cancer deep learning research. SC Prasanna Balaprakash, Romain Egele, Misha Salim, Stefan M. Wild, Venkatram Vishwanath, Fangfang Xia, Tom Brettin, Rick Stevens
2019	J	`jnl`	Using recurrent neural networks for nonlinear component computation in advection-dominated reduced-order models. CoRR Romit Maulik, Vishwas Rao, Sandeep Madireddy, Bethany Lusch, Prasanna Balaprakash
2019	J	`jnl`	Value-Added Chemical Discovery Using Reinforcement Learning. CoRR Peihong Jiang, Hieu Doan, Sandeep Madireddy, Rajeev Surendran Assary, Prasanna Balaprakash
2018	J	`jnl`	Autotuning in High-Performance Computing Applications. Proc. IEEE Prasanna Balaprakash, Jack J. Dongarra, Todd Gamblin, Mary W. Hall, Jeffrey K. Hollingsworth, Boyana Norris, Richard W. Vuduc
2018	—	`conf`	Benchmarking Machine Learning Methods for Performance Modeling of Scientific Applications. PMBS@SC Preeti Malakar, Prasanna Balaprakash, Venkatram Vishwanath, Vitali A. Morozov, Kalyan Kumaran
2018	—	`conf`	Building a Wide-Area File Transfer Performance Predictor: An Empirical Study. MLN Zhengchun Liu, Rajkumar Kettimuthu, Prasanna Balaprakash, Nageswara S. V. Rao, Ian T. Foster
2018	J	`jnl`	CANDLE/Supervisor: a workflow framework for machine learning applied to cancer research. BMC Bioinform. Justin M. Wozniak, Rajeev Jain, Prasanna Balaprakash, Jonathan Ozik, Nicholson T. Collier, John Bauer, Fangfang Xia, Thomas S. Brettin, Rick Stevens, Jamaludin Mohd-Yusof, Cristina Garcia-Cardona, Brian Van Essen, Matthew Baughman
2018	Misc	`conf`	DeepHyper: Asynchronous Hyperparameter Search for Deep Neural Networks. HiPC Prasanna Balaprakash, Michael Salim, Thomas D. Uram, Venkat Vishwanath, Stefan M. Wild
2018	J	`jnl`	Exploring the capabilities of support vector machines in detecting silent data corruptions. Sustain. Comput. Informatics Syst. Omer Subasi, Sheng Di, Leonardo Bautista-Gomez, Prasanna Balaprakash, Osman S. Unsal, Jesús Labarta, Adrián Cristal, Sriram Krishnamoorthy, Franck Cappello
2018	C	`conf`	Machine Learning Based Parallel I/O Predictive Modeling: A Case Study on Lustre File Systems. ISC Sandeep Madireddy, Prasanna Balaprakash, Philip H. Carns, Robert Latham, Robert B. Ross, Shane Snyder, Stefan M. Wild
2018	C	`conf`	Modeling I/O Performance Variability Using Conditional Variational Autoencoders. CLUSTER Sandeep Madireddy, Prasanna Balaprakash, Philip H. Carns, Robert Latham, Robert B. Ross, Shane Snyder, Stefan M. Wild
2017	—	`conf`	Analysis and Correlation of Application I/O Performance and System-Wide I/O Activity. NAS Sandeep Madireddy, Prasanna Balaprakash, Philip H. Carns, Robert Latham, Robert B. Ross, Shane Snyder, Stefan M. Wild
2017	—	`conf`	Analytical Performance Modeling and Validation of Intel's Xeon Phi Architecture. Conf. Computing Frontiers Sudheer Chunduri, Prasanna Balaprakash, Vitali A. Morozov, Venkatram Vishwanath, Kalyan Kumaran
2017	A	`conf`	Explaining Wide Area Data Transfer Performance. HPDC Zhengchun Liu, Prasanna Balaprakash, Rajkumar Kettimuthu, Ian T. Foster
2017	C	`conf`	MACORD: Online Adaptive Machine Learning Framework for Silent Error Detection. CLUSTER Omer Subasi, Sheng Di, Prasanna Balaprakash, Osman S. Unsal, Jesús Labarta, Adrián Cristal, Sriram Krishnamoorthy, Franck Cappello
2016	C	`conf`	AutoMOMML: Automatic Multi-objective Modeling with Machine Learning. ISC Prasanna Balaprakash, Ananta Tiwari, Stefan M. Wild, Laura Carrington, Paul D. Hovland
2016	—	`conf`	Exploiting Performance Portability in Search Algorithms for Autotuning. IPDPS Workshops Amit Roy, Prasanna Balaprakash, Paul D. Hovland, Stefan M. Wild
2016	B	`conf`	Improving Data Transfer Throughput with Direct Search Optimization. ICPP Prasanna Balaprakash, Vitali A. Morozov, Rajkumar Kettimuthu, Kalyan Kumaran, Ian T. Foster
2016	B	`conf`	Spatial Support Vector Regression to Detect Silent Errors in the Exascale Era. CCGrid Omer Subasi, Sheng Di, Leonardo Bautista-Gomez, Prasanna Balaprakash, Osman S. Ünsal, Jesús Labarta, Adrián Cristal, Franck Cappello
2015	Misc	`conf`	Autotuning FPGA Design Parameters for Performance and Power. FCCM Azamat Mametjanov, Prasanna Balaprakash, Chekuri Choudary, Paul D. Hovland, Stefan M. Wild, Gerald Sabin
2015	C	`conf`	Collective I/O Tuning Using Analytical and Machine Learning Models. CLUSTER Florin Isaila, Prasanna Balaprakash, Stefan M. Wild, Dries Kimpe, Robert Latham, Robert B. Ross, Paul D. Hovland
2015	J	`jnl`	Estimation-based metaheuristics for the single vehicle routing problem with stochastic demands and customers. Comput. Optim. Appl. Prasanna Balaprakash, Mauro Birattari, Thomas Stützle, Marco Dorigo
2015	B	`conf`	Generating Efficient Tensor Contractions for GPUs. ICPP Thomas Nelson, Axel Rivera, Prasanna Balaprakash, Mary W. Hall, Paul D. Hovland, Elizabeth R. Jessup, Boyana Norris
2014	—	`conf`	Analysis of the Tradeoffs Between Energy and Run Time for Multilevel Checkpointing. PMBS@SC Prasanna Balaprakash, Leonardo Arturo Bautista-Gomez, Mohamed-Slim Bouguerra, Stefan M. Wild, Franck Cappello, Paul D. Hovland
2014	C	`conf`	Energy-performance tradeoffs in multilevel checkpoint strategies. CLUSTER Leonardo Arturo Bautista-Gomez, Prasanna Balaprakash, Mohamed-Slim Bouguerra, Stefan M. Wild, Franck Cappello, Paul D. Hovland
2014	—	`conf`	Machine-Learning-Based Load Balancing for Community Ice Code Component in CESM. VECPAR Prasanna Balaprakash, Yuri Alexeev, Sheri A. Mickelson, Sven Leyffer, Robert L. Jacob, Anthony P. Craig
2013	C	`conf`	Active-learning-based surrogate models for empirical performance tuning. CLUSTER Prasanna Balaprakash, Robert B. Gramacy, Stefan M. Wild
2013	—	`conf`	Empirical performance modeling of GPU kernels using active learning. PARCO Prasanna Balaprakash, Karl Rupp, Azamat Mametjanov, Robert B. Gramacy, Paul D. Hovland, Stefan M. Wild
2013	B	`conf`	Exascale workload characterization and architecture implications. ISPASS Prasanna Balaprakash, Darius Buntinas, Anthony Chan, Apala Guha, Rinku Gupta, Sri Hari Krishna Narayanan, Andrew A. Chien, Paul D. Hovland, Boyana Norris
2013	—	`conf`	Exascale workload characterization and architecture implications. SpringSim (HPC) Prasanna Balaprakash, Darius Buntinas, Anthony Chan, Apala Guha, Rinku Gupta, Sri Hari Krishna Narayanan, Andrew A. Chien, Paul D. Hovland, Boyana Norris
2013	—	`conf`	Multi Objective Optimization of HPC Kernels for Performance, Power, and Energy. PMBS@SC Prasanna Balaprakash, Ananta Tiwari, Stefan M. Wild
2012	—	`conf`	Abstract: An Exascale Workload Study. SC Companion Prasanna Balaprakash, Darius Buntinas, Anthony Chan, Apala Guha, Rinku Gupta, Sri Hari Krishna Narayanan, Andrew A. Chien, Paul D. Hovland, Boyana Norris
2012	—	`conf`	An Experimental Study of Global and Local Search Algorithms in Empirical Performance Tuning. VECPAR Prasanna Balaprakash, Stefan M. Wild, Paul D. Hovland
2012	—	`conf`	Poster: An Exascale Workload Study. SC Companion Prasanna Balaprakash, Darius Buntinas, Anthony Chan, Apala Guha, Rinku Gupta, Sri Hari Krishna Narayanan, Andrew A. Chien, Paul D. Hovland, Boyana Norris
2012	Misc	`conf`	SPAPT: Search Problems in Automatic Performance Tuning. ICCS Prasanna Balaprakash, Stefan M. Wild, Boyana Norris
2011	Misc	`conf`	Can search algorithms save large-scale automatic performance tuning? ICCS Prasanna Balaprakash, Stefan M. Wild, Paul D. Hovland
2010	J	`jnl`	Estimation-based metaheuristics for the probabilistic traveling salesman problem. Comput. Oper. Res. Prasanna Balaprakash, Mauro Birattari, Thomas Stützle, Marco Dorigo
2010	—	`ch.`	F-Race and Iterated F-Race: An Overview. Experimental Methods for the Analysis of Optimization Algorithms Mauro Birattari, Zhi Yuan, Prasanna Balaprakash, Thomas Stützle
2009	J	`jnl`	Adaptive sample size and importance sampling in estimation-based local search for the probabilistic traveling salesman problem. Eur. J. Oper. Res. Prasanna Balaprakash, Mauro Birattari, Thomas Stützle, Marco Dorigo
2009	J	`jnl`	Estimation-based ant colony optimization and local search for the probabilistic traveling salesman problem. Swarm Intell. Prasanna Balaprakash, Mauro Birattari, Thomas Stützle, Zhi Yuan, Marco Dorigo
2008	—	`ch.`	Engineering Stochastic Local Search Algorithms: A Case Study in Estimation-Based Local Search for the Probabilistic Travelling Salesman Problem. Recent Advances in Evolutionary Computation for Combinatorial Optimization Prasanna Balaprakash, Mauro Birattari, Thomas Stützle
2008	J	`jnl`	Estimation-Based Local Search for Stochastic Combinatorial Optimization Using Delta Evaluations: A Case Study on the Probabilistic Traveling Salesman Problem. INFORMS J. Comput. Mauro Birattari, Prasanna Balaprakash, Thomas Stützle, Marco Dorigo
2008	—	`conf`	Iterated Greedy Algorithms for a Real-World Cyclic Train Scheduling Problem. Hybrid Metaheuristics Zhi Yuan, Armin Fügenschuh, Henning Homfeld, Prasanna Balaprakash, Thomas Stützle, Michael Schoch
2007	—	`conf`	Improvement Strategies for the F-Race Algorithm: Sampling Design and Iterative Refinement. Hybrid Metaheuristics Prasanna Balaprakash, Mauro Birattari, Thomas Stützle
2007	—	`ch.`	The ACO/F-Race Algorithm for Combinatorial Optimization Under Uncertainty. Metaheuristics Mauro Birattari, Prasanna Balaprakash, Marco Dorigo
2006	—	`conf`	Incremental Local Search in Ant Colony Optimization: Why It Fails for the Quadratic Assignment Problem. ANTS Workshop Prasanna Balaprakash, Mauro Birattari, Thomas Stützle, Marco Dorigo