New York City Metropolitan Area
I'm a passionate AI engineer and researcher currently pursuing my Master's in Information Science at Cornell University, with a strong track record of developing innovative AI solutions that drive measurable business outcomes. My experience spans the full AI development lifecycle, from research to production deployment. At Cotiviti, I engineered fine-tuned healthcare LLMs using LORA techniques, reducing costs while improving patient care analytics. I've also implemented RAG-based systems that decreased hallucination, enabling more reliable data-driven healthcare decisions. This summer, I'm worked as an AI Software Engineer Intern at Helport AI, where I contributed to developing core AI platform features and building technical demos that have helped secure recognition in competitive environments. My work focuses on optimizing AI workflows and creating cost-effective solutions with improved accuracy. As a Research Assistant at Cornell's Artifact Lab, I lead a team of 4 researchers developing LLM-enhanced applications for qualitative research analysis. My work has contributed to publications at top-tier HCI conferences including CSCW and UIST 2025. Core Expertise: AI/ML Engineering: LLM fine-tuning, RAG systems, prompt engineering Full-Stack Development: React, Python, JavaScript, API design Research & Publications: HCI, AI-human collaboration, qualitative analysis Database & Analytics: PostgreSQL, data visualization, statistical analysis I'm passionate about bridging the gap between cutting-edge AI research and practical business applications. Whether it's reducing operational costs, improving accuracy, or enhancing user experiences, I focus on delivering solutions that create real value. Always open to discussing AI innovations, research collaborations, or exciting opportunities in the field. Let's connect!
Course：Physical Computing & Rapid Prototyping, Data Driven Web Applications, Project Management
2 papers accepted by UIST 2025 and CSCW 2025