I am now the founder and Chief Scientist of Sea Labs.
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
首先关于15-721的课程介绍请参见我的上一篇文章。昨天晚上刷完了CMU 15-721 2023 Spring课程的全部视频，也看了一部分的推荐论文，这里做一下课程总结。
2022真是魔幻的一年，开年的时候怎么也没想到今年会在阳中结束这一年。正好在家养病没事做，遂写一篇年度总结，总结一下这一年的林林总总。
总得来说，比起去年，这一年在工作上投入的精力更多了一些。事情的起因要从去年7月说起。当时我刚完成了项目存储引擎TSDB的数据读取与计算 …
今天突发奇想，看了下我在DolphinDB智臾科技入职的时间，看到是2021年3月3日，算了算距今刚好一年零几天，遂写篇文章，作为对过去一年工作的总结，同时展望一下未来一年的工作计划。在工业界工作，工作内容往往由一个个项目组成，故这篇文章也借一个个我在DolphinDB …
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:
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.