Yike Shi is a Carnegie Mellon junior studying Computer Science with a 4.0 GPA who blends strong systems and algorithms coursework with hands-on AI infrastructure and HCI research experience. They have optimized GPU inference kernels (int4/int8) and profiled real-world pipelines to improve latency at DeepLang, and built a web platform that treats requirements as first-class entities to streamline LLM prompt engineering at CMU HCI. Comfortable across systems programming, databases, and web development, Yike also brings a design-minded developer perspective from their GitHub bio, helping craft user-facing tools like visual feedback and interactive validation. Based in Pittsburgh, they pair rigorous academic achievement with practical deployment experience and a knack for turning high-level requirements into verifiable, iterated AI workflows.
10 years of coding experience
2 years of employment as a software developer
Bachelor of Science - BS, Computer Science, Bachelor of Science - BS, Computer Science at Carnegie Mellon University
High School Diploma, High School Diploma at Keystone Academy, Beijing
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