Trilingual Computer Science undergraduate at NYU focusing on AI Security, Systems Optimization, and Quantitative Modeling. Experienced in high-throughput enterprise infrastructure and probabilistic simulation.
My journey into software engineering and computer science is driven by a fascination with how complex mathematical frameworks and systems can be optimized, scaled, and secured. As a trilingual speaker fluent in English, Spanish, and Mandarin Chinese, I bring a global perspective to collaborative engineering teams.
At NYU's OSIRIS Lab, I research AI security defenses against indirect prompt injection in autonomous agents. Across campus, I stay active in Tech@NYU, Chess Team, Robotics Club, Stern Finance Society, and Philosophy Club.
Bachelor of Science in Computer Science
In Progress: Introduction to Programming and Problem Solving; Calculus I; General Chemistry for Engineers
Planned (Spring 2027): Data Structures and Algorithms; Calculus II; Introduction to Engineering and Design
Activities & Societies: Tech@NYU, Chess Team, Robotics Club, Stern Finance Society, Philosophy Club
International Baccalaureate (IB) Diploma
Predictive machine learning pipeline for urban infrastructure fail-safes on enterprise hardware.
Formed and led a six-person IBM Z Datathon team (recruiting members including graduate students) to predict heating-failure risks across NYC during extreme weather.
Developed an end-to-end ML pipeline integrating historical city housing records with live weather feeds, mapping risk priority for municipal inspectors.
Configured an enterprise IBM LinuxONE test environment and benchmarked batch inference performance at over 480,000+ building records/sec.
Probabilistic modeling & rigorous statistical investigation of player expected value (EV%).
Investigated how accurately the Hi-Lo card counting system predicts player advantage (EV%) across True Count (TC) levels (-2 to +6) in a 6-deck shoe when 1 deck remains.
Programmed a Monte Carlo simulation running 180,000 total rounds (20,000 rounds per TC level). Solved edge-case bugs in deck normalization, dealer S17 rules, and multi-split hand logic.
Achieved R² = 0.996 (Pearson r = 0.998). Calculated Standard Errors (SE = 0.675%), 95% Confidence Intervals (±1.35%), Z-values (<0.36), and physics-based slope bounds (0.5525 vs 0.543 theoretical).
Research defenses against indirect prompt injection in AI-native applications, focusing on local AI agents processing documents, logs, and tool outputs. Explore when agents should run additional security checks to balance protection, response latency, computational cost, and energy use.
Helped teach chess fundamentals to 30+ beginner students. Developed an interactive Python trainer featuring 80 original tactical puzzles for classroom instruction and practice.
Founded and scaled an online chess league connecting 100+ players across 23 schools. Organized competitive tournaments, managed weekly pairings, match schedules, and complete league operational logistics.
Python, HTML, CSS
Linux, IBM LinuxONE, Microsoft Excel
AI Security, Computer Networking, Data Visualization, Statistical Analysis
English, Spanish, Mandarin Chinese