Chantilly, Virginia, United States
I am a Computer Science student at Virginia Tech with hands-on experience in software development, AI/ML research, and full-stack web applications. I’ve built and optimized AI systems at IBM, conducted research on multi-agent frameworks, and developed award-winning projects that integrate scalable cloud, virtualization, and chatbot solutions. I enjoy solving complex technical problems, optimizing performance, and collaborating in fast-paced, cross-functional teams. I bring strong technical skills, a research-driven mindset, and a passion for building impactful, high-performance systems to any role.
• Analyzed cross-contamination of document sources for an AI assistant, identifying data quality issues across enterprise platforms • Utilized SQL and Snowflake to extract and process large-scale datasets, enabling comprehensive analysis of document source integrity • Developed Python scripts using pandas to quantify cross-contamination levels between Cloudability and ApptioOne platforms, improving data accuracy assessment capabilities • Analyzed chunk sizing configurations to optimize RAG performance while preserving URL metadata in chunks to maintain traceability to original content sources • Enhanced data retrieval system with contextual retrieval by Anthropic techniques to improve chunk quality and semantic coherence for AI assistant responses, resulting in better context-aware information extraction • Deployed prompt caching optimization to improve system efficiency, reducing query processing time by 14% and enhancing overall AI assistant performance through intelligent cache management
• Developing an LLM agent to generate efficient parallel versions of critical code, achieving a 30% improvement in matrix multiplication performance for torch.matmul • Implementing progressive optimizations including parallelization, vectorization, and other techniques for 40% greater speed • Utilizing CUDA, SLURM, and OpenMP for testing on NVIDIA GPUs, benchmarking optimized code against baseline implementations with a 20% performance gain
• Building 5+ AI apps and APIs using Next.js, OpenAI, Pinecone, StripeAI with 97% accuracy as seen by 1000 users • Developing projects from design to deployment leading 4+ engineering fellows using MVC design patterns • Coached by Amazon, Bloomberg, and Capital One engineers on Agile, CI/CD, Git, and microservice patterns
• Managing 5 teams of 5-7 Software Engineering interns developing specific sections of HokieHub • Integrating 2-week team sprints to address bugs, concerns, and readjusting targets • Developed a comprehensive plan to complete by late August 2024
• Measured neural network model efficiency utilizing ONNX runtime-optimized model for 10% greater scalability. • Designed increased scalability of neural network models for usage in ARM and RISC-V processors. • Optimized MySQL database queries, reducing page load times by 15% and enhancing overall application performance.