Krishnan Raghavan

36 papers A* 1A 1B 4Misc 1Journal 25Unranked 4

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Incentive-Aware Federated Averaging with Performance Guarantees under Strategic Participation. CoRR Fateme Maleki, Krishnan Raghavan, Farzad Yousefian
2026	J	`jnl`	Mitigating Task-Order Sensitivity and Forgetting via Hierarchical Second-Order Consolidation. CoRR Protik Nag, Krishnan Raghavan, Vignesh Narayanan
2026	J	`jnl`	SWARM+: Scalable and Resilient Multi-Agent Consensus for Fully-Decentralized Data-Aware Workload Management. CoRR Komal Thareja, Krishnan Raghavan, Anirban Mandal, Ewa Deelman
2026	J	`jnl`	The Effect of Architecture During Continual Learning. CoRR Allyson Hahn, Krishnan Raghavan
2025	B	`conf`	A Bilevel Optimization Framework for Dynamically Correcting Imbalance in Two-class Classification Problems. IJCNN Karen Medlin, Sven Leyffer, Krishnan Raghavan
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	—	`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`	On Understanding of the Dynamics of Model Capacity in Continual Learning. CoRR Supriyo Chakraborty, Krishnan Raghavan
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`	Sampling Imbalanced Data with Multi-objective Bilevel Optimization. CoRR Karen Medlin, Sven Leyffer, Krishnan Raghavan
2024	J	`jnl`	A Bilevel Optimization Framework for Imbalanced Data Classification. CoRR Karen Medlin, Sven Leyffer, Krishnan Raghavan
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`	Autonomy Loops for Monitoring, Operational Data Analytics, Feedback, and Response in HPC Operations. CoRR Francieli Boito, Jim M. Brandt, Valeria Cardellini, Philip H. Carns, Florina M. Ciorba, Hilary Egan, Ahmed Eleliemy, Ann C. Gentile, Thomas Gruber, Jeff Hanson, Utz-Uwe Haus, Kevin A. Huck, Thomas Ilsche, Thomas Jakobsche, Terry R. Jones, Sven Karlsson, Abdullah Mueen, Michael Ott, Tapasya Patki, Ivy Peng, Krishnan Raghavan, Stephen Simms, Kathleen Shoga, Michael T. Showerman, Devesh Tiwari, Torsten Wilde, Keiji Yamamoto
2024	J	`jnl`	Cooperative Deep Q-Learning Framework for Environments Providing Image Feedback. IEEE Trans. Neural Networks Learn. Syst. Krishnan Raghavan, Vignesh Narayanan, Sarangapani Jagannathan
2024	J	`jnl`	DGRO: Diameter-Guided Ring Optimization for Integrated Research Infrastructure Membership. CoRR Shixun Wu, Krishnan Raghavan, Sheng Di, Zizhong Chen, Franck Cappello
2024	B	`conf`	DISTRI: Development and Integration of Simulation Tools for Resilient Infrastructure. IEEE Big Data Imtiaz Mahmud, Pawel Zuk, Cong Wang, Mariam Kiran, Kesheng Wu, Komal Thareja, Krishnan Raghavan, Anirban Mandal, Ewa Deelman
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	B	`conf`	Privacy-Preserving Federated Learning for Science: Challenges and Research Directions. IEEE Big Data Kibaek Kim, Krishnan Raghavan, Olivera Kotevska, Matthieu Dorier, Ravi K. Madduri, Minseok Ryu, Todd S. Munson, Robert Ross, Thomas Flynn, Ai Kagawa, Byung-Jun Yoon, Christian Engelmann, Farzad Yousefian
2023	—	`conf`	Autonomy Loops for Monitoring, Operational Data Analytics, Feedback, and Response in HPC Operations. CLUSTER Workshops Francieli Boito, Jim M. Brandt, Valeria Cardellini, Philip H. Carns, Florina M. Ciorba, Hilary Egan, Ahmed Eleliemy, Ann C. Gentile, Thomas Gruber, Jeff Hanson, Utz-Uwe Haus, Kevin A. Huck, Thomas Ilsche, Thomas Jakobsche, Terry R. Jones, Sven Karlsson, Abdullah Mueen, Michael Ott, Tapasya Patki, Ivy Peng, Krishnan Raghavan, Stephen Simms, Kathleen Shoga, Michael T. Showerman, Devesh Tiwari, Torsten Wilde, Keiji Yamamoto
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	J	`jnl`	Forward Gradients for Data-Driven CFD Wall Modeling. CoRR Jan Hückelheim, Tadbhagya Kumar, Krishnan Raghavan, Pinaki Pal
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	J	`jnl`	Learning Continually on a Sequence of Graphs - The Dynamical System Way. CoRR Krishnan Raghavan, 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	Misc	`conf`	SF-SFD: Stochastic Optimization of Fourier Coefficients to Generate Space-Filling Designs. WSC Manisha Garg, Tyler H. Chang, Krishnan Raghavan
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
2022	J	`jnl`	A Game Theoretic Approach for Addressing Domain-Shift in Big-Data. IEEE Trans. Big Data Krishnan Raghavan, Sarangapani Jagannathan, V. A. Samaranayake
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	—	`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`	AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification. CoRR Romain Egele, Romit Maulik, Krishnan Raghavan, Prasanna Balaprakash, Bethany Lusch
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`	Learning to Control using Image Feedback. CoRR Krishnan Raghavan, Vignesh Narayanan, Jagannathan Saraangapani