ISEAGE Capture the Flag Competition
I partcipated in the Iowa State International ISEAGE Capture the Flag / Cyber Defense Competition. Where we placed Top 20 on Febuary 22, 2025. This is my second read more about the first on ALL PROJECTS below! (ISEAGE stands for Internet-Scale Event and Attack Generation Environment)
UL Solutions Cybersecurity Internship
During my 12-week cybersecurity internship at UL Solutions, I created comprehensive testing documentation for EU Radio Equipment Directive (RED) compliance, specifically developing detailed guides for 18031-1 (Internet-Connected Radio Equipment) and 18031-2 (Personal Data Processing Equipment) testing procedures. Additionally, I researched and developed a proof-of-concept local LLM solution using LoRA fine-tuning techniques to automate documentation generation for cybersecurity testing standards.
The Implications of Quantum Computing in Cybersecurity and Cryptography in the Digital Age
Quantum computing represents a revolutionary advancement in computational power that threatens existing cybersecurity measures by potentially breaking current encryption standards like RSA through superior processing capabilities. This paper examines the implications of quantum computing on cybersecurity, highlighting the development of quantum-resistant encryption methods such as lattice-based algorithms and emphasizing the urgent need for collaboration between governments, businesses, and experts to protect financial systems, national security, and individual privacy in the emerging quantum era.
Machine Learning for Bank Failure Research: Enhancing Early Warning Systems
In this group research project, we are attempting to explore how machine learning models can best predict bank failures by evaluating financial health indicators. Our goal is to contribute to more reliable early warning systems for banking regulators, enabling proactive intervention that reduces systemic financial risk while providing financial institutions with more accurate, data-driven risk assessments.
Uploading Consciousness: Examining the Risks and Rewards of Superintelligent Beings
My paper examines the ethical implications of mind uploading, exploring whether uploaded superintelligent beings should be allowed to interact with humanity through various philosophical frameworks, including rights theory, virtue ethics, utilitarianism, and Kantian ethics. After analyzing compelling arguments from both perspectives, I conclude that preventing such integration presents the most defensible position, as the risks of radical cognitive imbalance, potential loss of human autonomy, and existential threats to non-enhanced humans outweigh the prospective benefits.
Archeway
Archeway is a full-stack web application that helps students streamline their internship search by providing a centralized platform to track job applications, manage deadlines, and analyze success rates through an intuitive dashboard, kanban board, and calendar integration. Originally developed as a Software Engineering class project, my team and I successfully brought this Flask-based platform to production, where it now serves real users with features like OAuth authentication, advanced analytics, and comprehensive security measures.
A Comparative Analysis of RSA and AES Encryption Algorithms
RSA and AES encryption algorithms serve as the cornerstones of modern cryptographic security, with RSA utilizing asymmetric key pairs for secure communication and digital signatures while AES employs symmetric block cipher encryption with substitution-permutation networks for superior data protection. Despite RSA's versatility and wide application, the paper concludes that AES offers greater long-term security against quantum computing threats and has become the gold standard in cybersecurity due to its resistance to various cryptanalytic attacks and its flexible implementation options with different key lengths.
Fine Grained Visual Categorization (FGVC): Bird Species Classification with Deep Learning
This project is on visual classification of bird species using deep learning. The goal is to predict the correct bird species given an input image and to compare the performance of three different neural network architectures: AlexNet, VGGNet, and ResNet in the process.