I am now the founder and Chief Scientist of Sea Land.
I graduated as a master student of artificial intelligence from the Zhejiang University CAD&CG National Key Lab ZJULearning Group. I was very fortunate to be advised by Prof. Deng Cai. My research includes machine learning, data mining, deep learning, computer vision, operating system, system programming, and database. I have worked as a system developer in Optiver Shanghai and have interned as a machine learning engineer in Hangzhou FABU and Google. I was also an software engineer @ DolphinDB Inc.
MEng in Artificial Intelligence, 2020
Zhejiang University
BSc in Aerospace Engineering, 2017
Northwestern Polytechnical University
Responsibilities include:
Designing and building the storage engine for Time-Series Database, which is extremely efficient both for analytics, writing data, and point-query.
Leading System/DB for AI, add textDB(text search in DolphinDB), vectorDB, and etc.
Maintaining and extending the existing computing engine
Responsibilities include:
Responsibilities include:
Developing the rule-based autotraders.
Improving the machine learning pipeline.
Improving the testing environment for binaries.
Exploring extended application for Tesseract with some development.
Responsibilities include:
Responsibilities include:
This paper presents a formal proof for the validity of General Relativity and Quantum Mechanics based on the ‘Axiom of Finite Information.’ We demonstrate that if the speed of information transfer were infinite, or if space-time were perfectly continuous, the universe would contain states of infinite information density, leading to logical singularities. Furthermore, we prove that because physical laws must be encoded within the universe itself, a ‘Theory of Everything’ is precluded by Godelian incompleteness, ensuring that a universal theory can never exist.
We propose a conceptual framework to resolve the dichotomy of the Millennium Prize Problems by categorizing mathematical systems based on their capacity for logical simulation. We distinguish between Class I (Structural) problems (e.g., Poincaré, Hodge, Yang-Mills), which rely on symmetries, conservation laws, and coercivity estimates that constrain degrees of freedom effectively, and Class II (Simulational) problems (e.g., P vs NP, Navier-Stokes), which theoretically possess the fidelity to simulate Universal Turing Machines. While not a formal proof of independence, we argue that Class II problems face obstructions isomorphic to the Halting Problem, inhibiting standard analytic techniques. We posit that the ‘intractability’ of these problems arises because they inhabit a complexity class where asymptotic behavior is determined by generalized computation rather than geometric structure.