Publications

Jun, 2026

Journal

Scaling open-weight large language models for hydropower regulatory information extraction: A systematic analysis

Information extraction from regulatory and technical documents using large language models (LLMs) involves practical trade-offs between extraction quality and computational cost. We evaluate eight open-weight LLMs spanning 0.6B–70B parameters on hydropower licensing documents and report deployment-oriented evidence under a unified extraction schema and evaluation protocol.…

Mar, 2026

Conference Paper

OFCnetLLM: Large Language Model for Network Monitoring and Alertness

Mar, 2026

Conference Paper

ORBIT-2: Scaling Exascale Vision Foundation Models for Weather and Climate Downscaling

Sparse observations and coarse-resolution climate models limit effective regional decision-making, underscoring the need for robust downscaling. However, existing AI methods struggle with generalization across variables and geographies and are constrained by the quadratic complexity of Vision Transformer (ViT) self-attention. We introduce ORBIT-2, a scalable foundation mod…

Mar, 2026

Conference Paper

A Decision Support System to Compile Environmental Mitigations from Hydropower Licensing Documents

The process of deciphering, extracting, and compiling information from texts dense with domain-specific terminology and technical jargon is a challenging endeavor. It demands considerable expertise and deep knowledge in the respective field, resulting in a labor-intensive process when executed by humans. Furthermore, the task of identifying multiple class labels in extensi…

Sep, 2024

Journal

Development of message passing-based graph convolutional networks for classifying cancer pathology reports

Jul, 2024

Journal

Improving Text Classification with Large Language Model-Based Data Augmentation

Large Language Models (LLMs) such as ChatGPT possess advanced capabilities in understanding and generating text. These capabilities enable ChatGPT to create text based on specific instructions, which can serve as augmented data for text classification tasks. Previous studies have approached data augmentation (DA) by either rewriting the existing dataset with ChatGPT or gen…

May, 2024

Conference Paper

Enhancing Diagnosis through AI-driven Analysis of Reflectance Confocal Microscopy

Reflectance Confocal Microscopy (RCM) is a non-invasive imaging technique used in biomedical research and clinical dermatology. It provides virtual high-resolution images of the skin and superficial tissues, reducing the need for physical biopsies. RCM employs a laser light source to illuminate the tissue, capturing the reflected light to generate detailed images of micros…

Apr, 2024

Conference Paper

Filter pruning of Convolutional Neural Networks for text classification: A case study of cancer pathology report comprehension

Convolutional Neural Networks (CNN) have recently demonstrated effective performance in many Natural Language Processing tasks. In this study, we explore a novel approach for pruning a CNN's convolution filters using our new data-driven utility score. We have applied this technique to an information extraction task of classifying a dataset of cancer pathology reports by ca…

Feb, 2024

ORNL Report

HyperKube: A Kubernetes based system for the automation of processing and analysis of hyperspectral data obtained from multiple hyperspectral imaging systems

Hyperspectral imagery is an emerging field of technology that has enormous potential for remote and proximal sensing in numerous areas of research. The plant phenotyping community is applying this technology to advance the throughput and accuracy of plant phenotypes based on airborne and lab-based hyperspectral imaging technology. Here we report an automated processing and…

Feb, 2024

Journal

Deep learning uncertainty quantification for clinical text classification

Dec, 2023

Journal

Privacy-Preserving Deep Learning NLP Models for Cancer Registries

Population cancer registries can benefit from Deep Learning (DL) to automatically extract cancer characteristics from the high volume of unstructured pathology text reports they process annually. The success of DL to tackle this and other real-world problems is proportional to the availability of large labeled datasets for model training. Although collaboration among cance…

Dec, 2023

Conference Paper

Deep Transfer Learning Across Cancer Registries for Information Extraction from Pathology Reports

Automated text information extraction from cancer pathology reports is an active area of research to support national cancer surveillance. A well-known challenge is how to develop information extraction tools with robust performance across cancer registries. In this study we investigated whether transfer learning (TL) with a convolutional neural network (CNN) can facilitat…