Computer Science @ University of Florida | Building intelligent systems at scale
I'm a Computer Science student at the University of Florida specializing in algorithm development and machine learning systems. Through internships at Amazon AWS and SharkNinja, I've built production-scale ML infrastructure, developed optimization algorithms, and engineered intelligent systems for real-world applications.
My expertise spans algorithmic problem solving, deep learning model development, and ML system optimization. From designing configurable alarming algorithms for AWS ML accelerator platforms to creating reinforcement learning agents for robotic swarms, I focus on building intelligent systems that scale efficiently and solve complex computational challenges.
Core Focus Areas: Algorithm Development, Machine Learning, Deep Learning, ML Infrastructure, Computer Vision, Reinforcement Learning, Distributed Systems
May 2025 - Present | Austin, TX
Developed a configurable alarming system using TypeScript, JavaScript, React, and Python for ML accelerator platforms. Reduced incident response time by 45% and degradable events by 30% by providing real-time notifications across thousands of servers.
Technologies: TypeScript, JavaScript, React, Python, ElasticSearch, PostgreSQL
January 2024 - May 2024 | Boston, MA
Engineered control systems for the Ninja Luxe Café (4.8-star rated product) using C/C++ PID controllers. Enhanced quality control by 60% through real-time data visualization tools in Python with Matplotlib, and designed ML algorithms for café-quality optimization.
Technologies: C, C++, Python, Matplotlib, Machine Learning
Production-grade benchmarking platform supporting 6+ LLM providers (OpenAI, Anthropic, Google Gemini, HuggingFace, Ollama, Mock) with real-time performance monitoring. Reduced LLM API costs by 80% through dynamic budget tiers and usage-aware throttling.
Delivered 99% reliability under load with intelligent error handling and auto-model installation. Built comprehensive cost tracking system with 3-tier budget management ($5-$100/day limits) and hardware-adaptive client selection.
Technologies: Python, Streamlit, Prometheus, Plotly, PyTorch, Transformers, Docker, AWS-ready
Impact: 99%+ success rate, sub-second latency, 80% cost reduction, 6 LLM providers
Machine Learning & Sensing Lab Research: Increased phenotype prediction accuracy by 35% using deep learning on 10,000+ hyperspectral images. Built automated feature extraction pipelines reducing manual processing time by 50%.
Technologies: TensorFlow, Scikit-learn, Pandas, Python, Computer Vision
Research: Sustainable switchgrass research funded by Department of Energy
Jain Lab Research: Leading innovative research on reinforcement learning-driven swarm robotic manipulation in immersive VR/MR environments. Designed and engineered a comprehensive experimental framework using Unity and C# specifically optimized for Meta Quest Pro, featuring real-time asset switching, dynamic swarm interactions, and adaptive behavior modeling.
The project focuses on developing intuitive human-computer interfaces that allow users to control and coordinate multiple autonomous agents through natural VR gestures and interactions. Implemented advanced algorithms for swarm intelligence, collision avoidance, and task allocation while maintaining seamless real-time performance in virtual reality.
Technologies: Unity, C#, Python, VR/MR, Reinforcement Learning, Meta Quest Pro SDK
Focus: Human-Computer Interaction, Adaptive Swarm Intelligence, Immersive Technologies
Developed ML model achieving 85% accuracy in classifying playlist moods across 1,000+ songs. Implemented web interface using Flask and deployed on AWS EC2 with real-time mood analysis and distribution visualization.
Technologies: Python, Scikit-learn, NLTK, Flask, AWS EC2, Spotify API
ML Techniques: NLP, Sentiment Analysis, Classification Models
Explore my repositories showcasing machine learning projects, algorithms, and software engineering solutions. Each project demonstrates different aspects of my technical skills and problem-solving approach.
Production-grade benchmarking platform for 6+ LLM providers with real-time monitoring. Achieved 80% cost reduction and 99% reliability through intelligent budget management and automated optimization.
This responsive portfolio website with modern design, smooth animations, and professional navigation system.
Full-stack web application helping low-income families find affordable housing. Built with Django backend and integrated housing data APIs for accessible search.
Complete compiler implementation featuring lexer, parser, interpreter, semantic analyzer, and code generator. Comprehensive solution for programming language concepts.
DEMO COMING SOON! Machine learning model for playlist mood classification using audio features and NLP sentiment analysis. Live demo and project details will be available soon.
VR interface for multi-robot swarm control using Unity and reinforcement learning algorithms. Research project at UF Jain Lab.
Built configurable alarming systems for ML accelerator platforms (Trainium/Inferentia). Improved monitoring accuracy by 40% and developed scalable AWS infrastructure solutions.
Leading plant phenotyping research using deep learning on 10,000+ hyperspectral images. Increased prediction accuracy by 35% and developed automated feature extraction pipelines.
Developed production-grade benchmarking platform supporting 6+ LLM providers with real-time monitoring. Achieved 80% cost reduction through intelligent budget management and delivered 99% reliability under load with automated optimization.
Developing VR interfaces for swarm robotics using Unity/C# and reinforcement learning algorithms for Meta Quest Pro platform.
Engineered control systems for the Ninja Luxe Café using C/C++ PID controllers. Enhanced quality control by 60% through real-time data visualization tools and designed ML algorithms for café-quality optimization.
Drove 50% increase in client acquisition by developing scalable AI-driven software solutions using Python, Azure, and Oracle AI platforms. Enhanced system interoperability and accelerated document processing efficiency by 30%.
Transferred from UT Austin to pursue Computer Science with focus on Machine Learning and Algorithms. Dean's List multiple semesters.
Started engineering journey in Mechanical Engineering, developing foundation in mathematics, physics, and engineering principles.
Active in robotics competitions and STEM programs. Developed early passion for engineering, programming, and problem-solving that shaped career direction.
Interested in discussing software engineering, machine learning, or potential opportunities? I'd love to connect!
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