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Pages

Posts

Future Blog Post

less than 1 minute read

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This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

less than 1 minute read

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 2

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

portfolio

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.

talks

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.