Published in 2025 DFRWS EU, 2025
In this work, we introduce ForensicLLM, a 4-bit quantized LLaMA-3.1-8B model fine-tuned on Q&A samples extracted from digital forensic research articles and curated digital artifacts. We evaluate the model’s performance, both quantitatively and qualitatively, against standard RAG and base-model performance.
Published in 2024 Workshop on AI for Cyber Threat Intelligence, 2024
In this paper, we explore leveraging Large Language Models (LLMs) for semantic malware analysis to expedite the analysis of known and novel samples. Built on GPT-4o-mini model, MalParse is designed to augment malware analysis for Android through a hierarchical-tiered summarization chain and strategic prompt engineering.
Published in 2023 International Conference on Neuromorphic Systems, 2023
This work reports on new results and insights from the optimization of spiking neural networks developed for gamma-ray radiation anomaly detection.
Published in PLoS ONE, 2023
This work evaluates a variety of neural network explainability approaches for explaining deep learning models trained to perform gamma-ray spectroscopy.
Published in 2022 International Conference on Neuromorphic Systems, 2022
In this work, we present initial results on the development of a neuromorphic spiking neural network for performing gamma-ray radiation anomaly detection, the first known application of neuromorphic computing to be applied to the radiation detection domain.
Published in AAAI 2024 Spring Symposium on Clinical Foundation Models, 2022
In this paper, we explore the application of Low-Rank Adaptation (LoRA) fine-tuning of small language models for performing TNM staging on unstructured pathology reports for triple negative breast cancer cases. We also attempt to develop a more generalized approach, so that our work can be applied to other NLP tasks within the medical field.
Published in Engineering Applications of Artificial Intelligence, 2022
In this work we demonstrate an in-depth analysis and characterization of the Autoencoder Radiation Anomaly Detection (ARAD) algorithm. ARAD is a deep convolutional autoencoder designed to detect anomalous radioactive signatures in gamma-ray spectra collected by NaI(Tl) detectors.
Published in Nature Scientific Data, 2020
To encourage the development of new detection, radioisotope identification, and source localization algorithms, a dataset consisting of realistic Monte Carlo–simulated radiation detection data from a 2 in. × 4 in. × 16 in. NaI(Tl) scintillation detector moving through a simulated urban environment based on Knoxville, Tennessee, was developed and made public in the form of a Topcoder competition.