United States
Software Engineer specializing in AI Systems, GPU Acceleration, and HPC. I build performance-critical software across semiconductor inspection and AI infrastructure spanning CUDA computer vision, LLM/RAG systems, and scalable microservices. Interested in GPU compute, generative AI, and large-scale intelligent systems. I value people-first engineering human insight, thoughtful collaboration, and real-world impact.
1. Led weekly lab sessions throughout the semester by running hands on coding workshops where students built modern LLM systems including transformer based pipelines and agentic architectures while guiding implementation debugging and real world engineering practices 2. Delivered a guest lecture on multi agent collaboration explaining how autonomous agents coordinate make decisions and create tools to solve complex tasks and showing how this research connects to practical systems and publishable outcomes
1. Achieved 15× performance improvement in pattern alignment system (600ms → 50ms) by architecting and implementing CUDA-accelerated solution leveraging GPU parallel processing; developed custom cross-correlation and phase correlation algorithms with advanced image processing pipeline including bilateral filtering and parabolic interpolation for sub-pixel accuracy 2. Designed high-performance memory management framework with CUDA pinned memory pools and asynchronous transfer pipelines, delivering 3× faster data throughput compared to traditional CPU-GPU transfer methods; established reusable architecture blueprint for migrating compute-intensive algorithms to GPU, enabling team-wide adoption of parallel processing solutions Division: eBeam
1. At KLA’s Fast Division, Developed a state visualizer for log traces, automating test case generation and verification with state machines, reducing debugging time by 60%. Engineered a high-performance C++/C# WPF library for the CFS application, resolving 25+ critical bugs and enhancing processing speed by 20%, positively impacting TSMC and Samsung fabrication 2. Built an Information Retrieval system using Graphs and RAG, boosting ticket resolution speed by 50%, enhancing accuracy with fine-tuned embeddings, streamlining ticketing and issue tracking across teams, earned Special Mention at the KLA 2024 Hackathon Division: FaST
1. Redesigned and revamped an old configuration system, switching the UI from MFC C++ to WPF C# and the backend from COM to gRPC service, thereby improving performance and functionality. 2. Designed project utilizing both MVVM and Plugin architecture patterns to enhance scalability. Division: FaST
1. Served as President for the CodeChef PSG Chapter and conducted successful events. 2. It helped improving the coding culture and increased our college participation by double on CodeChef and Other Competitive Programming platform.