PNNL

Sai Munikoti is a data scientist at Pacific Northwest National Laboratory focusing on artificial intelligence (AI) and machine learning. His work primarily involves the development and application of multimodal large language models. His projects include STEEL THREAD and EXPERT 2.0 (fiscal year 2023) to advance the U.S. Department of Energy National Nuclear Security Administration’s nonproliferation mission via trustworthy AI reasoning models and PolicyAI and WindAI to develop generative AI capability for streamlining and improving federal permitting and siting operations. He has published in multiple journals and conference proceedings, including the Association for the Advancement of Artificial Intelligence and Knowledge Discovery in Databases Scholar. Sai received his PhD in electrical and computer engineering from Kansas State University in 2022. In addition to his doctorate, Sai also holds an MS from the Indian Institute of Technology Gandhinagar and a BE from the Birla Institute of Technology Sindri.

• Large language models
• Multimodal models
• Knowledge graphs

• AI
• Data science
• Deep learning
• High performance computing
• Machine learning
• Natural language processing
• Reinforcement learning

• PhD in electrical and computer engineering, Kansas State University
• MS in electrical engineering, Indian Institute of Technology Gandhinagar
• BS in electrical engineering, Birla Institute of Technology Sindri

2025

• Munikoti S., D.M. Nally, S.D. Koneru, S. Das, K. Bhattacharjee, A.C. Buchko, and T.C. Edwards, et al. 2025. NEPATEC v2.0: Standardized Metadata and Text Corpus of National Environmental Policy Act Documents. PNNL-38163. Richland, WA: Pacific Northwest National Laboratory. NEPATEC v2.0: Standardized Metadata and Text Corpus of National Environmental Policy Act Documents
• Nally D.M., M.J. Parker, M. Aumeier, K. Murphy, M. Rau, J. McWalter, and J. Titus, et al. 2025. Workshop Summary Report on Using AI Tools to Improve the Efficiency and Outcomes of the NEPA Process: AI for Permitting Workshop at the 2025 National Association of Environmental Professionals (NAEP) Annual Conference. PNNL-37758. Richland, WA: Pacific Northwest National Laboratory. Workshop Summary Report on Using AI Tools to Improve the Efficiency and Outcomes of the NEPA Process: AI for Permitting Workshop at the 2025 National Association of Environmental Professionals (NAEP) Annual Conference

2024

• Horawalavithana Y.S., S. Munikoti, I.B. Stewart, H.J. Kvinge, and K. Pazdernik. 2024. "SCITUNE: Aligning Large Language Models with Human-Curated Scientific Multimodal Instructions." In Proceedings of the First Workshop on Natural Language Processing for Science (NLP4Science 2024), November 12-16, 2024, Miami, FL, 58-72. Stroudsburg, Pennsylvania:Association for Computational Linguistics. PNNL-SA-186641.
• Mahapatra K., S. Munikoti, and J.D. Follum. 2024. "Transmission Data-Driven User-Defined Model for Inverter-based and Conventional Power Plants." In Resilience Week (RWS 2024), December 3-5, 2024, Austin, TX, 1-6. Piscataway, New Jersey:IEEE. PNNL-SA-200436. doi:10.1109/RWS62797.2024.10799382
• Munikoti S., A. Acharya, S.N. Wagle, and Y.S. Horawalavithana. 2024. "Evaluating the Effectiveness of Retrieval-Augmented Large Language Models in Scientific Document Reasoning." In Proceedings of the 4th Workshop on Scholarly Document Processing (SDP 2024), August 16, 2024, Bangkok, Thailand, edited by T. Ghosal, et al, 84-89. Kerrville, Texas:Association for Computational Linguistics. PNNL-SA-189029.
• Munikoti S., B. Natarajan, and M. Halappanavar. 2024. "GraMeR: Graph Meta Reinforcement Learning for Multi-Objective Influence Maximization." Journal of Parallel and Distributed Computing 192. PNNL-SA-198119. doi:10.1016/j.jpdc.2024.104900
• Munikoti S., D. Agarwal, L. Das, M. Halappanavar, and B. Natarajan. 2024. "Challenges and Opportunities in Deep Reinforcement Learning with Graph Neural Networks: A Comprehensive review of Algorithms and Applications." IEEE Transactions on Neural Networks and Learning Systems 35, no. 11:15051 - 15071. PNNL-SA-174409. doi:10.1109/TNNLS.2023.3283523
• Rahman M., K.O. Piryani, A.M. Sanchez, S. Munikoti, L. De La Torre, M.S. Levin, and M. Akbar, et al. 2024. Retrieval Augmented Generation for Robust Cyber Defense. PNNL-36792. Richland, WA: Pacific Northwest National Laboratory. Retrieval Augmented Generation for Robust Cyber Defense

2023

• Tharzeen A., S. Munikoti, P. Prakash, J. Kim, and B. Natarajan. 2023. "A General Spatiotemporal Imputation Framework for Missing Sensor Data." In IEEE Conference on Artificial Intelligence (CAI 2023), June 5-6, 2023, Santa Clara, CA, 55-58. Los Alamitos, California:IEEE Computer Society. PNNL-SA-183063. doi:10.1109/CAI54212.2023.00032