Publications

Thesis Work

• (Ph.D. Thesis — In Progress) “AI-Driven Resource Optimization for High-Performance Computing: A Comprehensive Framework”
• (M.S. Thesis) “Hypernym Discovery over WordNet and English Corpora — using Hearst Patterns and Word Embeddings”, S. Vallabhajosyula, T. Pedersen. https://hdl.handle.net/11299/200144

Papers

• Hassan, A. Z., Vallabhajosyula, M. S., & Pedersen, T. (2018). UMDuluth-CS8761 at SemEval-2018 Task 9: Hypernym discovery using Hearst patterns, co-occurrence frequencies and word embeddings. arXiv:1805.10271.
• Vallabhajosyula, M. S., and Ramnath, R. “Towards Practical, Generalizable Machine-Learning Training Pipelines to build Regression Models for Predicting Application Resource Needs on HPC Systems.” PEARC 2022, pp. 1–5.
• Vallabhajosyula, S. and Ramnath, R. “Establishing a Generalizable Framework for Generating Cost-Aware Training Data and Building Unique Context-Aware Walltime Prediction Regression Models.” IEEE ISPA/BDCloud/SocialCom/SustainCom, Melbourne, Australia, 2022, pp. 497–506. doi: 10.1109/ISPA-BDCloud-SocialCom-SustainCom57177.2022.00070.
• [BEST PAPER] Vallabhajosyula, M. S., and Ramnath, R. “Insights from the HARP Framework: Using an AI-Driven Approach for Efficient Resource Allocation in HPC Scientific Workflows.” PEARC 2023, pp. 341–344.
• Vallabhajosyula, M. S., Guzman, C., Ramnath, R., and Stubbs, J. “Demonstrating HARP (HPC Application Resource Predictor) Framework for Predicting Walltime for Single Node Applications.”
• Vallabhajosyula, M. S., Budhya, S. S., and Ramnath, R. “Reference Implementation of Smart Scheduler: A CI-Aware, AI-Driven Scheduling Framework for HPC Workloads.” PEARC 2024, pp. 1–4.
• [BEST PAPER] Stubbs, J., Balasubramaniam, S., Khuvis, S., Withana, S., Vallabhajosyula, S., Cardone, R., Garcia, C., Freeman, N., Guzman, C., Plale, B., Ramnath, R., and Berger-Wolf, T. “ML Field Planner: Analyzing and Optimizing ML Pipelines For Field Research.” PEARC 2024: Human Powered Computing. 2025.
• Reddy, S., Davis, R., Vallabhajosyula, S., and Ramnath, R. “Building a Lab-scale Cyberinfrastructure for Fun and Profit.” PEARC 2024: Human Powered Computing. 2025.
• Cliffel, N., Vallabhajosyula, M. S., Wang, J., Zhang, Z., and Ramnath, R. “Unified Component API: Supporting Standardized Middleware Integration and Flexible Interoperability in Cyberinfrastructure.” Science Gateways 2025 (SG25), Green Bay, WI.
• [Extended Abstract – WHPC Workshop] Vallabhajosyula, M. S., and Ramnath, R. “Predicting Resources for AI Workloads in HPC: Methods, Challenges, and Opportunities.” SC’25, St. Louis, MI.

Posters and Presentations

1. Vallabhajosyula, M. S. (2018): Identifying Hypernym for a New Sense in WordNet. figshare. https://doi.org/10.6084/m9.figshare.22565089.v1
2. Vallabhajosyula, M. S.; Ramnath, R. (2020): EAGER: Bridging the Last Mile. figshare. https://doi.org/10.6084/m9.figshare.11777808.v3
3. Vallabhajosyula, M. S.; Ramnath, R. (2021): Modeling A Framework To Estimate Resource Requirements For Scientific Workflows. figshare. https://doi.org/10.6084/m9.figshare.22363183.v1
4. Chanana, R.; Vallabhajosyula, M. S.; Ramnath, R. (2021): Using synthesized data to train machine learning models used in genome engineering pipeline. figshare. https://doi.org/10.6084/m9.figshare.22565113.v1
5. Vallabhajosyula, M. S.; Ramnath, R. (2022): Building an AI-powered Assistant for Computational Scientists. figshare. https://doi.org/10.6084/m9.figshare.11777796.v1
6. Vallabhajosyula, M. S.; Ramnath, R. (2023): Modeling A Framework To Estimate Resource Requirements For Scientific Workflows. figshare. https://doi.org/10.6084/m9.figshare.22363183.v1
7. Vallabhajosyula, M. S., and Ramnath, R. “Towards Characterizing DNNs to Estimate Training Time using HARP.” PEARC 2023, pp. 483–485.
8. Vallabhajosyula, M. S., Ramnath, R., and Stubbs, J. “Custom Cost, Loss, And Reward Functions to Train Regression Models for Estimating Execution Resources using HARP.” Science Gateways 2023 (SG23), Pittsburgh, PA.
9. Vallabhajosyula, S., Budhya, S. S., Baig, M., Jain, A., and Ramnath, R. “Orchestrating a DNN training job using an iScheduler Framework: a use case.” PEARC 2024.
10. Vallabhajosyula, S., Freeman, N., Garcia, C., Stubbs, J., and Ramnath, R. “Orchestrating End-to-End AI-Model Development using TAPIS and Smart Scheduler.” Science Gateways 2023 (SG23), Bozeman, MO.
11. Vallabhajosyula, M. S., Vaidhya, R., Ramasamy, N., and Ramnath, R. Hybrid black-box and white-box approaches for efficient resource prediction for AI workloads in high-performance computing [Project poster]. ISC-HPC 2025, Hamburg, Germany.
12. Vallabhajosyula, M. S., Tomko, K., and Ramnath, R. Intelligence Plane: A framework for machine learning application life-cycle management [Research poster]. ISC-HPC 2025, Hamburg, Germany.
13. Vallabhajosyula, M. S. Motivated by challenges: Harnessing AI to revolutionize resource and workflow management in high-performance computing [Women in HPC poster]. ISC-HPC 2025, Hamburg, Germany.
14. “AI-Driven Resource Optimization for High-Performance Computing: A Comprehensive Framework.” IHPCSS 2025, Lisbon, Portugal.
15. Vallabhajosyula, S., Molakalmuru, G., Cliffel, N., and Ramnath, R. “Beyond Automation: Integrating Agentic Capabilities into MLOps with ICICLE Infrastructure.” PEARC 2025.
16. Molakalmuru, G. G., Vallabhajosyula, M. S., Khuvis, S., Stubbs, J. F., and Ramnath, R. “Operational Considerations for Real-Time ML Pipelines on Edge Devices.” SG25, Green Bay, WI.
17. Vallabhajosyula, M. S., Molakalmuru, G. G., Karthikeyan, N., Gamage, A. I., Khuvis, S., Freeman, N., Stubbs, J., Plale, B., and Ramnath, R. “Beyond Accuracy: The ML Field Planner’s Framework for AI Model Selection in Conservation.” SG25, Green Bay, WI.
18. [Portal] Vallabhajosyula, M. S., et al. “ML Field Planner with TAPIS: Configuring and Analyzing AI Models for Animal Ecology.” SG25, Green Bay, WI.
19. [Doctoral Showcase] Vallabhajosyula, M. S., and Ramnath, R. “AI-Driven Resource Optimization for High-Performance Computing: A Comprehensive Framework.” SC’25, St. Louis, MI.

Mentored Student Project Reports

1. “Information Extraction from Gene Sequencing Scientific Documents”, Rishabh Chanana, May 2021.
2. “Generating Knowledge Graphs on Scientific Execution Workspace”, Akhilesh Gulati, December 2020.
3. “HARP – HPC Application Runtime Predictor”, Prasanna, Saishree Miriyala, December 2022.
4. “Reference Implementation of Smart Scheduler: Configuring a pre-SLURM Database for Enabling Smart Scheduler”, Akanksha Jain, May 2024.
5. “Reference Implementation of Smart Scheduler: Establishing a Smart Scheduler Framework Backend – Intelligence Plane”, Sandeep Satish Budhya, May 2024.
6. “AI-Enabled Smart Scheduler for Optimizing Resource Utilization in HPC”, Bhargavi Dwivedi (MS-ECE).
7. “Exploring and Building Profilers for HPC Environments” (Bachelor’s), Maaz Baig.
8. “Parallelizing Training and Inferencing for Machine Learning Models: A Study on HLO Graphs for Network Optimization”, Shashwat Rao.
9. “GPU Resource Prediction Dataset for DNN Workloads”, Rahul Vaidhya.
10. “Exploring Different Regression Models for Runtime Prediction on Black-Box and White-Box Resource Profiles for DNN Training Time Estimation”, Nachiappan Ramasamy.