I am a PhD student studying Cybersecurity at the University of Florida. I am looking for a Fall 2025 Employment, as I will be graduating that Summer. I am researching the use of Generative AI and Machine Learning in Cybersecurity. My primary research is in Steganography, and how Generative AI, NLP, and Audio Generation can be used to exchange information. I have also researched Differential Privacy, Voice anonymization, User Modeling, Membership Inference Attacks, Captcha Generation, Adversarial Attacks, LiDAR in self-driving vehicles, and more. I have published papers in CODASPY, ACSAC, WiSEC, and NDSS. My most recent first-author paper at CODASPY, examines realistic Steganography on large public platforms such as X and Reddit, using Large Language Models such as GPT. We use an LLM to generate realistic looking text content, which has a covert message encoded within it. I have also published a paper examining Membership Inference Attacks against survey data, and how Differential Privacy can be used to defend against them. I have other papers in submission or in progress examining the use of Stable Diffusion in Image Steganography, Audio Generative Steganography, Audio X-Vector Anonymization, and Social Media User Modeling. I have taken part in other papers examining Captcha Generation and usability and Adversarial Attacks against LiDAR used in Self-Driving Vehicles. I have interned at the Royal Bank of Canada (RBC), where I evaluated the feasibility of Federated Learning on Trusted Platform Modules, and helped develop Federated Learning for LLMs on Mobile Devices.My current work is primarily Machine Learning, Generative AI, and Data Privacy, but I also have previous experience with Network Security, Android Development and Design, and Mobile Security. I am experienced with Python, Java, C, C++, Tensorflow, PyTorch, and SQL.