PNNL

Anurag Acharya is a data scientist at Pacific Northwest National Laboratory (PNNL). He is an active researcher in the overlapping fields of artificial intelligence (AI) and natural language processing, with a key focus on evaluating and analyzing large language models (LLMs) in their application for scientific and national security domains and in their correctness and bias. He has a record of using AI to detect misinformation and disinformation surrounding major world events, from the White Helmets operations in Syria to the COVID-19 pandemic. During his work in EXPERT, Acharya led the development of the first ever expert-crafted evaluation benchmark for LLMs for the nuclear nonproliferation domain, while in MegaAI, he developed foundational models for molecular chemistry and to detect and classify vulnerabilities in code to protect critical cyber infrastructure. His current work includes leading the Model and Benchmark thrust of PermitAI, which is developing a new AI-driven capability to help make faster decisions in the National Environmental Policy Act (NEPA) process by streamlining environmental reviews; and ACCELERATE, an effort to analyze and predict the degradation of catalysts for sustainable conversion of alternate feedstocks to fuels and chemicals; and THESEUS, which focuses on research of foundation models that prioritize applications to computational science.

In addition to these works, Acharya’s research interest is in understanding and mitigating biases in AI systems, and working towards building ethical AI. In addition to Department of Energy agencies, his research works have been funded by the Defense Advanced Research Projects Agency, the Air Force Research Laboratory, and IBM.

• Artificial Intelligence
• AI Safety and Trustworthiness
• AI Ethics
• Natural Language Processing
• Large Language Models
• Generative AI
• Computational Linguistics
• Computational Social Sciences

• PhD in computer science, Florida International University
• MS in computer science, Florida International University
• BEng in computer engineering, Tribhuvan University, Nepal
• BA in English and political science, Tribhuvan University, Nepal

Professional Membership

• Association for the Advancement of Artificial Intelligence
• Association for Computational Linguistics
• Association for Computing Machinery

Program and Organizing Committee

• Organizing Committee, Social Development through NLP-driven Interdisciplinary Collaborations (SocioNLP) Workshop, 2024–2025
• Program Committee, The 43rd International Conference on Conceptual Modeling, 2024
• Reviewer, Association for Computational Linguistics (ACL) Rolling Review, 2024
• Program Committee, Workshop on Responsible Language Models (ReLM), 2024
• Program Committee, Seventh International Workshop on Narrative Extraction from Texts, 2024
• Ethics Reviewer, Conference on Neural Information Processing Systems, 2023–2024
• Session Chair, Ninth Annual Conference on Advances in Cognitive Systems Conference, 2021
• Organizing Committee, Communicating Science Workshop for Graduate Students, 2021

Review Committee (Journals)

• Natural Language Engineering, 2023–Present
• IEEE Transactions on Artificial Intelligence, 2023–Present
• Humanities & Social Sciences Communications, 2023–Present
• International Journal of Data Science and Analytics, 2023–Present

• Best Paper Award, Advanced Engineering and ICT-Convergence Proceedings, Transfer Learned Mobilenets with Shrinking Hyperparameters for Classifying Covid-19 Based on X-ray Images, 2021

• Saldanha E.G., A. Acharya, M. Ocal, J. Eshun, M.F. Glenski, and S. Volkova. 2025. "Detecting and Summarizing Narratives in the Information Environment: A Case Study of Misinformation and Disinformation Campaigns." In Detecting Online Propaganda and Misinformation, edited by Mark Last, Marina Litvak, Miao Lin. PNNL-SA-171527.  doi:10.1142/13556
• Munikoti S., A. Acharya, S. Wagle, and Y. S. Horawalavithana. 2024. “Evaluating the Effectiveness of Retrieval-Augmented Large Language Models in Scientific Document Reasoning.” Proceedings of the Fourth Workshop on Scholarly Document Processing (SDP 2024), The 62nd Annual Meeting of the Association for Computational Linguistics (ACL). Bangkok, Thailand.
• Acharya A., D. Castro Estrada, S. Dahal, W.H. Yarlott, D. Gomez, and M.A. Finlayson. 2024. "Discovering Implicit Associations of Cultural Motifs from Text." Sixth Workshop on NLP and Computational Social Science (NLP+CSS), 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2024). Mexico City, Mexico. PNNL-SA-193395.
• Yarlott, W.V.H., A. Acharya, D. Castro-Estrada, D. Gomez, and M.A. Finlayson. 2024. “GOLEM: GOld standard for Learning and Evaluation of Motifs.” The 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (COLING-LREC). Torino, Italy.
• Munikoti, S., A. Acharya, S. Wagle, and Y. S. Horawalavithana. 2024. “ATLANTIC: Structure-Aware Retrieval-Augmented Language Model for Interdisciplinary Science.” Workshop on AI to Accelerate Science and Engineering, The Thirty-Eighth Annual AAAI Conference on Artificial Intelligence. Vancouver, Canada.
• Wagle, S., S. Munikoti, A. Acharya, S. Smith, and Y. S. Horawalavithana. 2024. “Empirical evaluation of Uncertainty Quantification in Retrieval-Augmented Language Models for Science.” Workshop on Scientific Document Understanding, The Thirty-Eighth Annual AAAI Conference on Artificial Intelligence. Vancouver, Canada.
• Yarlott, W.V.H., A. Ochoa, A. Acharya, L. Bobrow, D. Castro-Estrada, D. Gomez, J. Zheng, D. McDonald, C. Miller, and M.A. Finlayson. 2021. “Finding Trolls Under Bridges: Preliminary Work on a Motif Detector.” Advances in Cognitive Systems. Virtual Conference
• Yarlott, W.V.H., A. Ochoa, A. Acharya, L. Bobrow, D. Castro-Estrada, D. Gomez, J. Zheng, D. McDonald, C. Miller, and M.A. Finlayson. 2021. “AI models for detecting motifs in a text collection” Literature & Culture and/as Intelligent Systems. Stuttgart, Germany.
• Acharya, A., K. Talamadupula, and M.A. Finlayson. 2021. “Towards an Atlas of Cultural Commonsense for Machine Reasoning.” Workshop on Common Sense Knowledge Graphs, The Thirty-Fifth AAAI Conference on Artificial Intelligence. Virtual Conference.
• KC, K., A. Acharya, A. Acharya, and S. Shrestha. 2021. “Transfer Learned Mobilenets with shrinking hyperparameters for classifying Covid-19 based on X-ray images.” Advanced Engineering and ICT-Convergence Proceedings. Vol 4, No. 2. Bangkok, Thailand.v