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publications
Data for Training and Testing Radiation Detection Algorithms in an Urban Environment
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.
Characterization of the Autoencoder Radiation Anomaly Detection (ARAD) model
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.
TNM Tumor Classification from Unstructured Breast Cancer Pathology Reports using LoRA Finetuning of Mistral 7B
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.
A Neuromorphic Algorithm for Radiation Anomaly Detection
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.
Explaining machine-learning models for gamma-ray detection and identification
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.
Performance Optimization Study of the Neuromorphic Radiation Anomaly Detector
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.
Exploring Large Language Models for Semantic Analysis and Categorization of Android Malware
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.
ForensicLLM: A Local Large Language Model for Digital Forensics
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.
talks
Talk 1 on Relevant Topic in Your Field
Published:
This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Radiation Detection Data Analytics
Short Course, The Consortium for Enabling Technologies & Innovation (ETI) 2020 Annual Summer School, 2020
This course introduced traditional and emerging technologies for radiation detection data analytics. A synopsis of the 2020 ETI Annual Summer School was presented at the 2021 ASEE Virtual Annual Conference.
Machine Learning: An Introduction Through Nuclear Science
Short Course, IEEE Nuclear Science Symposium (NSS) 2022, 2022
This course provided students with a broad introduction to modern machine learning concepts. The course will consist of lectures, discussion, and interactive code demonstrations. The course materials and code is available on Github.
HNRS 3035/25: Large Language Models Development and Deployment for Real-World Applications
Senior-level Undergraduate Course, Louisiana State University, 2023
This course provides university Honors College seniors from various disciplines, including computer science and business, with hands-on experience in developing Large Language Model (LLM) applications to tackle real-world challenges within the Baton Rouge area. This course is led by the LSU Provost, with Dr. Ghawaly leading the technical/computer science parts.
CSC 2262: Numerical Methods
Undergraduate Course, Louisiana State University, 2024
Computer- oriented methods for solving numerical problems in science and engineering; numerical solutions to systems of simultaneous linear equations, nonlinear algebraic equations (root solving), differentiation and integration, ordinary differential equations, interpolation and curve fitting.