
PhD, Tsinghua University
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Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
tch
October 26, 2025 – Present
Code for the paper "Testing Copula Hypothesis with Copula Entropy"
View Projectcpd
February 7, 2024 – June 15, 2025
Code for the paper "Change Point Detection with Copula Entropy based Two-Sample Test"
View Projectnhanes
July 6, 2023 – November 10, 2024
Code for the paper "Discovering Association with Copula Entropy"
View Projectsysid
April 25, 2023 – November 16, 2024
Code for the paper "System Identification with Copula Entropy"
View Projecttimelag
January 18, 2023 – October 2, 2025
Code for the paper "Identifying time lag in dynamical systems with copula entropy based transfer entropy"
View Projecteval
May 17, 2022 – September 21, 2024
Code for the Paper "Evaluating Independence and Conditional Independence Measures"
View Projecttransferentropy
November 26, 2020 – April 7, 2023
Code for the paper "Estimating Transfer Entropy via Copula Entropy"
View Projectaps2020
July 29, 2020 – May 9, 2022
Code for the paper 'Variable Selection with Copula Entropy' published on Chinese Journal of Applied Probability and Statistics
View Projectcopent
April 15, 2020 – June 7, 2024
R package for estimating copula entropy (mutual information), transfer entropy (conditional mutual information), and the statistic for multivariate normality test and two-sample test
View Projectpycopent
March 30, 2020 – October 2, 2024
Estimating Copula Entropy (Mutual Information), Transfer Entropy (Conditional Mutual Information), and the statistics for multivariate normality test and two-sample test, and change point detection in Python
View ProjectCultural Fit Analysis
The candidate's projects are heavily focused on academic research and development of statistical methodologies, primarily in R and Python. This indicates a strong inclination towards deep analytical work and scientific contribution. The alignment with a 'Data Scientist' role is strong in terms of analytical depth and statistical expertise. However, the lack of diverse project types (e.g., industry applications, large-scale data processing, MLOps) might suggest a narrower scope of experience in typical industry data science roles. The candidate's experience level is listed as 0, which contradicts the depth of their research projects, suggesting a potential misclassification or a focus on academic rather than industry experience.
Soft Skills & Operational Fit
Insufficient data to assess soft skills or operational fit. No psychometric test results or interview feedback provided.