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YY@yangyu
PhD-qualified data scientist specializing in machine learning and statistical modeling.
ChinaI am a PhD-qualified data scientist with a robust foundation in machine learning, statistical modeling, and mathematics. My expertise lies in transforming complex datasets into actionable insights, particularly in the realm of flood risk forecasting. I successfully delivered a 94% accurate prediction model that has been recognized by stakeholders as a baseline for future urban planning, showcasing my ability to address social challenges through data-driven solutions.
In my recent role as a Postdoctoral Research Associate at the University of Liverpool, I deployed a long COVID prediction model on interactive dashboards, significantly enhancing public health decision-making. My work has consistently focused on improving data processing efficiency, achieving a 67% reduction in processing time through parallel computing. I have also developed bespoke solutions for stakeholders, tailoring insights to inform local employment support strategies.
With a strong emphasis on Bayesian statistics and advanced statistical modeling, I have contributed to various projects that require innovative approaches to data analysis. My academic journey has equipped me with the skills to handle large-scale datasets and deliver impactful visualizations that drive strategic decisions.
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Work history, roles, and key accomplishments
University of Liverpool
Apr 2023 - Present (2 years 7 months)
Performed a multinomial logistic regression model and a multivariate correspondence analysis to identify key risk patterns in workless populations who accessed mental health services. Improved data processing speed by 55% through parallel computing.
NUDesigned and implemented an advanced statistical model to predict the age-distributed long COVID rate across local authorities. Visualised long COVID prediction on geographical charts and created an interactive real-time dashboard using R Shiny.
NUDeveloped a Gaussian Process emulator to forecast maximal flood depths spatially with 92% forecast accuracy. Designed Markov Chain Monte Carlo (MCMC) algorithms to estimate the model from a Bayesian perspective.
Beihang University
Sep 2014 - Jan 2019 (4 years 4 months)
Proposed a Bayesian nonparametric approach to perform local smoothing quantile regression, reducing estimate error by 26%. Developed Bayesian quantile regression and variable selection to estimate the partial linear single-index model.
Degrees, certifications, and relevant coursework
BUPhD in Management Science and Engineering, Management Science and Engineering
2014 - 2019
Proposed a Bayesian nonparametric approach to perform local smoothing quantile regression, designing MCMC algorithms for estimation and reducing estimate error by 26%. Developed a nonparametric Bernstein-Dirichlet approximation to estimate the single-index model, with MCMC algorithms for estimation and a 20% reduction in estimate error.
Visiting Student in Statistics, Statistics
2016 - 2016
Focused on advanced statistical methods and their applications. Gained exposure to new research methodologies in the field of statistics.
NEMSc in Probability and Mathematical Statistics, Probability and Mathematical Statistics
2012 - 2014
Proposed Bayesian quantile regression and variable selection for hierarchical linear models, implementing estimation using MCMC. Developed Bayesian quantile regression to model the ordinal categorical outcomes in a classification framework.
SUBSc in Information and Computing Science, Information and Computing Science
2008 - 2012
Studied the fundamentals of mathematics and computer science. Gained foundational knowledge in mathematics and programming.
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