Los Angeles Metropolitan Area
Computer Science student at UC Santa Cruz with a focus on building high-performance AI/ML systems and scalable full-stack applications. I specialize in bridging the gap between complex data science and intuitive user interfaces, leveraging modern tools to create impactful software solutions. Currently, I serve as a Project Researcher with the Overture Maps Foundation—a collaboration founded by Amazon (AWS), Meta, and Microsoft—where I contribute to large-scale geospatial machine learning initiatives. My background spans enterprise software development at Molina Healthcare, AI-driven automation at GPT Integrators, and technical instruction at UCLA. I am passionate about developing tools that solve real-world problems, from 3D visualization platforms for medical research to AI-powered consumer assistants. I thrive in environments that challenge me to optimize performance, enhance user experience, and deploy cutting-edge technologies at scale.
• Coordinated full-day event logistics for Baskin Day, managing scheduling and cross-team communication to ensure seamless execution. • Partnered with 5+ student organizations to design and set up STEM-focused activities that fostered community among Baskin School students. • Streamlined operational workflows and volunteer coordination, driving high participant engagement and attendance across event activities.
• Evaluated and graded Python programming assignments for CSE 101P under Professor Niloofar Montazeri, providing detailed feedback on code correctness, efficiency, and style. • Assessed student implementations of core data structures and algorithms including linked lists, trees, sorting algorithms, and graph traversal. • Provided constructive written feedback to help students improve their understanding of algorithm design and Python coding best practices. • Maintained consistent and fair grading standards across assignments, ensuring alignment with course rubrics and learning objectives.
• Supported students in CSE 101P, a Python-based Data Structures and Algorithms course, through tutoring sessions and office hours under Professor Niloofar Montazeri. • Held regular office hours and small-group sessions to help students implement and debug core data structures in Python including linked lists, stacks, queues, trees, and graphs. • Guided students through algorithm design and complexity analysis, reinforcing concepts like recursion, sorting algorithms, and Big-O notation. • Assisted students in translating algorithmic thinking into clean, efficient Python code during assignments and exam preparation.
• Designed and deployed a multi-signal ML system predicting operational status of 33M+ U.S. businesses at 90%+ accuracy, fusing geospatial features, satellite imagery (YOLO), and business metadata into a unified LightGBM inference engine. • Built scalable ETL pipelines joining multi-source datasets and engineered a full-stack platform (React + TypeScript + Python) surfacing real-time predictions on an H3 hexagonal grid with sub-second query performance across 33M+ records. • Processed and analyzed Mapillary street-level imagery at scale, engineering a computer vision pipeline to extract real-world business signals and feed downstream ML inference across 33M+ global locations.
• Diagnosed and resolved production issues in enterprise healthcare software processing 500+ claims daily, using SQL and ASP.NET to trace root causes and implement fixes in a live B2B system. • Optimized SQL queries and stored procedures against large-scale datasets, improving throughput and cutting report generation time for operations and compliance teams. • Collaborated with senior engineers across Agile sprints to deliver production-ready features, authoring internal documentation to share root-cause findings and best practices across the team.
• Developed and deployed scalable AI automation solutions using Python, OpenAI API, and GCP, collaborating cross-functionally to translate client requirements into production-ready LLM-powered applications. • Designed backend systems and REST API integrations to connect AI pipelines with enterprise products, improving operational efficiency for client deployments. • Prototyped and iterated on LLM prompt engineering strategies to optimize AI output quality and reliability across multiple client use cases.
• Provided one-on-one and small-group tutoring for CSE101, a data structures and algorithms course taught by Professor Patrick Tantalo, reinforcing core concepts such as sorting, searching, and graph algorithms. • Explained complex algorithmic ideas clearly using examples, pseudocode, and problem-solving strategies to improve student comprehension. • Supported students in developing stronger analytical and coding skills by guiding them through assignments, exam preparation, and debugging.