AI/ML Engineer & Computer Science Graduate building intelligent systems that solve real-world problems.
I am a Computer Science graduate specializing in Artificial Intelligence and Machine Learning. I work with modern AI frameworks, neural networks, and large language models, with hands-on experience in fine-tuning and evaluating LLMs.
As a Legal Tech & Automation Intern at NYC Emergency Management, I build internal web tools, workflow automation, and legal technology systems that replace manual processes with reusable software.
I am passionate about leveraging AI to solve real-world problems and help organizations harness the power of artificial intelligence.
Designed and built a custom event registration and tracking website (HTML, CSS, JavaScript) integrated with the Zoom Events REST API, handling sign-up and attendance tracking for 1,000+ registrants across hybrid events and replacing a manual coordination process with a reusable template adopted by the department.
Authored a vendor evaluation framework benchmarking 8 competing legal matter management platforms (Thomson Reuters-class systems) on technical capabilities, cost, and integration fit; framework adopted by Chief Counsel and drove the department’s final vendor selection.
Designed and deployed an end-to-end OKR tracking and reporting system for the Office of the Chief Counsel: built a dynamic Power Apps form with cascading dropdowns backed by a SharePoint list of 63 pre-populated records across 4 attorneys, and orchestrated 3 Power Automate flows for quarterly reminders, 48-hour compliance follow-ups, and automated HTML report generation and email delivery.
Re-implemented a reverse-mode automatic differentiation engine in Python (inspired by Karpathy’s micrograd), then extended it beyond the tutorial with tensor operations, a numerical gradient checker using finite differences, and a PyTorch benchmark suite validating gradient correctness against torch.autograd on identical inputs.
Built a character-level language model on top of the custom engine with tokenization, embedding layers, and an MLP architecture following Bengio et al. (2003), trained end-to-end via backpropagation on a 32,000-name dataset.
Built a modular Python application for automated legal document classification, signature detection, and vendor-based file organization — deployed at NYC Emergency Management’s Office of the Chief Counsel to process a legal drive of 16,000+ contracts ahead of migration to a new legal management system.
Architected as a multi-component pipeline (processing engine, threaded GUI, CLI query tool, test suite) with OCR fallback via Tesseract, PDF text extraction via pdfplumber, and persistent metadata tracking for retention and destruction scheduling; released as MIT-licensed open-source with full installation and contribution documentation.
Python pipeline for next-day price prediction on AAPL using 9 technical indicators. Predicted next-day returns to handle non-stationarity, then reconstructed price for evaluation against a naive baseline on a held-out test set.
XGBoost achieved 1.54% MAPE vs. 1.05% for the baseline, a known finance-ML result.
Built a sanitized public version of the Disaster Law Symposium registration and tracking website used for hybrid event sign-up and attendance workflows.
Implemented reusable front-end registration flows with HTML, CSS, and JavaScript, designed around the same operational needs as the internal Zoom Events-integrated system.
I'm happy to discuss AI/ML, Cybersecurity, research opportunities, and professional collaborations. Feel free to reach out for with any inquiries or potential opportunities.