AI/ML Engineer & Computer Science Student
I am a Computer Science student at Long Island University's Honors College with a strong focus on Artificial Intelligence, Machine Learning, and Cybersecurity. My technical expertise spans across modern AI frameworks, neural networks, and large language models, with hands-on experience in fine-tuning and evaluating LLMs.
Currently serving as a Legal Intern at NYC Emergency Management, I'm developing full-stack applications and intelligent document processing systems using AI/ML technologies. My passion lies in leveraging cutting-edge AI to solve real-world problems and create innovative solutions.
I actively engage in machine learning projects, from fine-tuning LLaMA models for specialized knowledge bases to building neural networks for financial forecasting. My goal is to contribute meaningfully to the AI/ML field and help organizations harness the power of artificial intelligence.
• Built a full-stack event registration website enabling 1,000+ participants to attend hybrid Disaster Law Symposium sessions, improving accessibility and operational efficiency.
• Supported the RFP process for a department-wide Legal Management System, collaborating with the Chief Counsel's office to refine requirements and evaluate vendor proposals.
• Contributed to the design of the case identifier schema, structured request intake, and document-handling logic.
• Led campus tours for 100+ families and contributed to 15+ annual programs, improving visitor engagement and communication workflows.
• Supported operational planning for a 200-student honors community using basic data insights to optimize event logistics.
• Coordinated outreach across 5+ digital platforms, improving communication efficiency through automated workflows.
Built a reverse-mode automatic differentiation engine from scratch inside Jupyter Notebooks, implementing dynamic computation graphs, custom gradient functions, and a full training loop for neural networks.
Developed a character-level language model inspired by Karpathy's makemore, including tokenizer creation, embedding layers, MLP architecture, and backpropagation using the custom autograd system.
Leveraged Jupyter for iterative debugging, visualizing gradients, inspecting graph structures, and benchmarking training performance.
Built a PyTorch LSTM for sequence modeling on financial data, using engineered time-series features and multi-API ingestion.
Improved forecasting accuracy via hyperparameter search and ensemble experimentation.
Engineered a high-throughput document-processing system for New York City Emergency Management to automate sorting, classification, and renaming across 16,000+ procurement contracts and legal documents.
Built an ML-assisted pipeline combining OCR extraction for scanned PDFs, text-pattern analysis and token matching, and hybrid rule-based + probabilistic classification for MSAs, SOWs, NDAs, and Purchase Orders.
Implemented robust vendor-name standardization, metadata extraction, and structured filename generation to eliminate manual inconsistencies across departments. Designed scalable preprocessing, exception-handling, and logging workflows to reliably process noisy, inconsistent, and multi-format legal documents at scale.
I'm happy to discuss AI/ML, Cybersecurity, research opportunities, and professional collaborations. Feel free to reach out with any inquiries or potential opportunities.