Projects
PhishGuard
Phishing detection tool
Utilized:
Python
SpaCY
LabelBox
SQL
GitHub
Email APIs (AOL, Yahoo, GMail)
Password Vulnerability Predictor
Predicts top passwords and analyzes vulnerability trends in user datasets
Utilized:
Python
TensorFlow
Google Colab
Kaggle
Work Experience
Software Developer (Contractor)
June 2025 – August 2025
United States Anesthesia Partners (USAP), Maryland Division
Prototyped a scheduling model using gradient trees and hierarchical clustering to forecast staffing needs up to 9 months in advance.
Details
- Collected and cleaned historical data for ingestion, training, and validation.
- Used gradient trees and exploring hierarchical clustering for analysis.
Education
| NC State University |  | 2021 – 2025 |
B.S. in Computer Science
Focus: Security
Minor: Criminology
Relevant Courses:
- Artificial Intelligence (Graduate Level)
- Automated Learning & Data Analysis
Other courses:
- Data Structures & Algorithms
- Data Science in Cybersecurity
- Network Security
- Software Engineering
- Operating Systems
- C/Software Tools
- Calculus III
- Cryptography
- Computer Security
- Cybersecurity Topics
| Harvard Online |  | May 2024 |
Large Language Models: Application through Production
A course for teaching developers, data scientists, and engineers looking to build LLM-centric applications with the latest and most popular frameworks.
Students in this course learn how to…
- use Hugging Face to solve natural language processing (NLP) problems
- leverage LangChain to perform complex, multi-stage tasks
- deep-dive into prompt engineering
- use data embeddings and vector databases to augment LLM pipelines
- fine-tune LLMs with domain-specific data to improve performance and cost, as well as identify the benefits and drawbacks of proprietary models.
- assess societal, safety, and ethical considerations of using LLMs
- deploy models at scale, leveraging LLMOps best practices
| DeepLearning.AI & Stanford Online |   | May 2024 |
Supervised Machine Learning: Regression and Classification
An introduction to modern machine learning, including supervised learning, unsupervised learning, and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation.
Students in this course…
- Learn about supervised learning: multiple linear regression, logistic regression, neural networks, and decision trees
- Learn about unsupervised learning: clustering, dimensionality reduction, recommender systems
- Learn about best practices: evaluating and tuning models, taking a data-centric approach to improving performance, and more
- Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn
- Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression
- Program simple games