Falcon Dai is a machine learning researcher with 14 years of experience bridging rigorous academic research and production ML systems, currently advancing automated theorem proving with LLMs and reinforcement learning at Symbolica AI. He combines a PhD-level background from TTI Chicago with hands-on work across perception pipelines, annotation ETL at Apple, and applied summarization prototypes for startups, showing fluency from low-level tooling to high-level generative models. His past projects include open-source contributions to OpenNMT-py, novel ECoG electrode registration software, and practical improvements to text autoencoders and latent diffusion through compression-theoretic insights. Comfortable in both research labs and engineering teams, he builds distributed proof corpora and verification toolchains (Ray-based tooling, parsers, unifiers) that make formal methods more scalable and auditable. An understated strength is his track record of turning complex academic ideas into reproducible, production-ready code and workflows.
14 years of coding experience
4 years of employment as a software developer
Doctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at Toyota Technological Institute at Chicago
High School Diploma, High School Diploma at The Affiliated High School of South China Normal University
Middle School Diploma, Middle School Diploma at Guangdong Experimental High School
Bachelor of Science (BS) with Honors Mathematics Physics, Bachelor of Science (BS) with Honors Mathematics Physics at University of Chicago
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Falcon Dai - Machine Learning Researcher at Symbolica AI