Summary
Jinlu Liu is an accomplished statistician and researcher specializing in Bayesian nonparametric mixture models and their applications in single-cell sequencing data. With a PhD in Statistics and Artificial Intelligence from the University of Edinburgh, Jinlu has developed innovative methods for cell subtype detection and neuronal connectivity modeling. His expertise spans advanced statistical modeling, data analysis, and computational techniques, and he has made significant contributions through publications, poster presentations, and awards.
Education
- PhD in Statistics and Artificial Intelligence, University of Edinburgh (Sep 2019 – Feb 2024)
Research focused on constructing Bayesian nonparametric mixture models for single-cell sequencing data, with applications in detecting cell subtypes and genetic markers.
- MSc in Data Science (Distinction), University of Edinburgh (Sep 2018 – Sep 2019)
Specialized in Biomedical Data Science, Incomplete Data Analysis, Bayesian Theory, and Natural Language Processing.
- BSc in Mathematics with Statistical Science (First), University College London (Sep 2015 – Aug 2018)
Studied Mathematical Methods, Real Analysis, Bayesian Statistics, and Optimization Algorithms.
Professional Experience
- Postdoctoral Researcher, Centre for Discovery Brain Sciences, University of Edinburgh (Feb 2024 – Present)
Modeling neuronal connectivity using MAPseq data and Bayesian mixture models.
- Mathematics Tutor, University of Edinburgh (Sep 2019 – Feb 2024)
Tutored various courses, including Multivariate Analysis, Nonparametric Regression Models, and Statistical Computing.
- Information Analyst, Public Health Scotland, National Health Service (Jan 2022 – Jul 2022)
Developed time series algorithms and optimized R code for patient waiting times.
Research Interests
Jinlu Liu’s research interests include Bayesian nonparametric methods, statistical modeling of biological data, and computational algorithms for data analysis. He is particularly focused on applications in single-cell sequencing, neuronal connectivity, and healthcare data analysis. His work aims to develop efficient and reliable statistical methods to advance understanding in these fields.
Key Skills
- Advanced proficiency in R, LaTeX, OpenBUGS, JAGS, and Stan
- Intermediate skills in Python, SQL, and SAS
- Expertise in Bayesian modeling, statistical computing, and data analysis
- Proficient in using Slack, Zoom, and Microsoft Teams