Shozen Dan
Doctoral Student at Imperial College London
London, England, Japan
Summary
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Senior🎓
Top SchoolShozen Dan is a doctoral student in statistics at Imperial College London with nine years of applied data science experience spanning Japan, the US, and the UK. He builds statistical and ML solutions for industry and research, contributing to Julia packages for wavelet-based analysis and applying quantitative methods to veterinary and public health data. His background includes teaching statistics and R at Stanford Medicine and hands-on R&D roles at a Tokyo analytics firm, demonstrating an ability to translate theory into production-ready tools. Shozen combines rigorous academic training—a Distinction MSc in Mathematics and Statistics—with practical software development across multiple institutions. He is motivated to leverage statistical learning to address societal challenges, often working at the intersection of reproducible research and domain-specific applications. A less obvious strength is his cross-cultural fluency from studying and collaborating across four institutions on three continents, which helps him navigate multidisciplinary teams and global projects.
9 years of coding experience
3 years of employment as a software developer
University of California Education Abroad Program, Statistics, GPA: 3.91/4.00, University of California Education Abroad Program, Statistics, GPA: 3.91/4.00 at University of California, DavisMaster of Science - MS, Mathematics and Statistics, Distinction, Master of Science - MS, Mathematics and Statistics, Distinction at Imperial College LondonCenter for Asian Research and Education, Bio-Statistics, Epidemiology, Center for Asian Research and Education, Bio-Statistics, Epidemiology at Stanford University School of MedicineEnvironmental and Information Studies, Data Science, Environmental and Information Studies, Data Science at Keio University
Github Skills (34)
autocorrelation10
wavelet9
wavelet-transform9
filter9
julia8
high-resolution8
signal-processing8
survey7
pyro7
mcmc7
classification7
bayesian-inference7
probabilistic-programming6
mobility5
geodata5
Programming languages (8)
Github contributions (5)
ShozenD/fund-allocation-experiment
Oct 2019 - Apr 2020
This is a test repository to house HTML and JavaScript for an experiment created in LIONESS LAB.
Contributions:2 PRs, 202 pushes, 3 branches in 5 months
javascripthouselabtest-repository
MLGlobalHealth/bayes-rate-consistency
Nov 2022 - Mar 2023
High resolution human contact patterns in the German COVIMOD survey
Contributions:3 releases, 106 commits, 23 PRs in 4 months
surveyresolutionpatternshigh-resolution
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