Speakers

Jessica Dai is a field application engineer with experience in AI hardware demonstration and enterprise software development. Her work focuses on RISC-V-based AI server infrastructure, building inference demonstrations for quantized large language models and supporting technical outreach across universities, research institutions, and enterprise partners. She specializes in bridging emerging hardware architectures with practical AI deployment, combining frameworks like llama.cpp and Ollama with hands-on demos and go-to-market strategy. She previously worked on UI and testing infrastructure for enterprise applications, and developed NLP-based developer tools and compiler optimizations for AI accelerator chips. Jessica holds a B.S. in Computer Science, with a concentration in Security and Privacy, from Carnegie Mellon University. Her current interests include open ISA hardware for AI infrastructure, LLM inference optimization, and expanding access to AI compute through emerging chip architectures.