PNNL

Sridevi Wagle is a machine learning (ML) engineer with a strong background in data analysis using interpretable ML and interactive data visualization. Wagle has experience building interactive artificial intelligence (AI)-driven applications, employing ML algorithms to aid domain scientists with comprehensive information processing, and working with large language models (LLMs) and retrieval augmented language models. She is proficient in data science and software engineering tasks, bridging the gap between data analysis and the development of robust AI solutions. Wagle has proficiency in developing end-to-end interactive visual analytics tools, such as Python, Plotly Dash, React, Explainable AI, scikit-learn, LLMs, C++, and OpenGL. These are some of the projects she has worked on while at Pacific Northwest National Laboratory:

• Implemented multimodal search and chat applications for wind energy related research articles. 
• Developed interactive analytics dashboard for comparing differently trained models using dimension reduction to reveal structural patterns among observations with similar differences in model outputs. 
• Implemented AI-driven analytics dashboard to enable predictive inferences used to monitor global nuclear nonproliferation. 
• Worked on LLMs for generating embeddings for large research articles and developed an interactive Jupyter widget for displaying uncertainty quantification of autoregressive styled LLMs. 

• Data Science
• Data Visualization
• Exploratory Data Analysis
• Machine Learning
• Python

• MS in computational sciences, Central Washington University
• BS in electronics and communication engineering, Visvesvaraya Technological University

Rounak Meyur, Hung Phan, Sridevi Wagle, Jan Strube, Mahantesh Halappanavar, Sameera Horawalavithana, Anurag Acharya, and Sai Munikoti. 2024. “WeQA: A Benchmark for Retrieval Augmented Generation in Wind Energy Domain.” arXiv. doi.org/10.48550/arXiv.2408.11800

Sridevi Wagle, Sai Munikoti, Anurag Acharya, Sara Smith, and Sameera Horawalavithana. 2023. “Empirical evaluation of Uncertainty Quantification in Retrieval-Augmented Language Models for Science.” arXiv. 
doi.org/10.48550/arXiv.2311.09358

Anurag Acharya, Sai Munikoti, Aaron Hellinger, Sara Smith, Sridevi Wagle, and Sameera Horawalavithana. 2023. “NuclearQA: A Human-Made Benchmark for Language Models for the Nuclear Domain.” arXiv. 
https://doi.org/10.48550/arXiv.2310.10920

Alex Worland, Sridevi Wagle, and Boris Kovalerchuk. 2022. “Visualization of Decision Trees based on General Line Coordinates to Support Explainable Models.” 26th International Conference Information Visualisation. DOI 10.1109/IV56949.2022.00065

Sridevi Narayana Wagle and Boris Kovalerchuk. 2020. “Interactive Visual Self-service Data Classification Approach to Democratize Machine Learning.” 24th International Conference Information Visualisation. DOI 10.1109/IV51561.2020.00052