Mike Wang is a postdoctoral researcher and quantitative data scientist with eight years’ experience applying mathematical modeling, statistical analysis, and high-performance distributed computing to terabyte-scale heterogeneous datasets. At the University of Edinburgh he led development of an end-to-end galaxy survey analysis pipeline and built research software packages now used across international consortia, translating complex cosmological inference into reproducible tools. His background combines a PhD in Cosmology, an MMath from Cambridge, and hands-on industry experience deploying big-data stacks such as Hadoop and SQL, making him comfortable at the intersection of research-grade code and production-scale data engineering. He seeks to bring rigorous probabilistic thinking and scalable computation to quantitative research or financial services teams, and his early work applying random matrix theory to S&P 500 time series hints at a practical appetite for cross-domain problems beyond astrophysics.
8 years of coding experience
4 years of employment as a software developer
High School Diploma, Chinese, Mathematics, English, Physics, Chemistry, History, Geography, Politics, A (Outstanding), High School Diploma, Chinese, Mathematics, English, Physics, Chemistry, History, Geography, Politics, A (Outstanding) at Chengdu Experimental Foreign Languages School
Master of Mathematics (MMath), Applied Mathematics and Theoretical Physics, Distinction, Master of Mathematics (MMath), Applied Mathematics and Theoretical Physics, Distinction at University of Cambridge
Doctor of Philosophy (PhD), Cosmology, Doctor of Philosophy (PhD), Cosmology at University of Portsmouth
Advanced Level in General Certificate of Education (GCE A-Level), Mathematics, Futher Maths, Physics, Chemistry, Chinese, Economics, Extended Project Qualification, 4A*, A, A, A*, Advanced Level in General Certificate of Education (GCE A-Level), Mathematics, Futher Maths, Physics, Chemistry, Chinese, Economics, Extended Project Qualification, 4A*, A, A, A* at Canford School
Common European Framework of Reference for Languages (CEFR), German, A2.2, Common European Framework of Reference for Languages (CEFR), German, A2.2 at Eurocentres Language Schools
A Python/C++ package for three-point clustering measurements in LSS analyses
Contributions:21 releases, 107 reviews, 886 commits in 1 year 11 months
clusteringcpppythoncythonclustering-statistics
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