I'm a PhD candidate in Statistics at the University of Manchester, where I apply mathematical modelling, statistical learning, and sequence analysis to complex biological systems — from birdsong to ancient DNA. I work in the intersection of statistics, machine learning and biology, with a focus on building tools that are both theoretically grounded and practically useful.
My research explores how cultural and genetic traits evolve over time using probabilistic models, machine learning, and stochastic processes.
birdsong.tools: An R package for analysing the temporal features and rhythmic structures of birdsong.
You can find it in the pinned repositories below.
- Languages: R, Python, C++
- Methods: Machine Learning (SVM, Random Forest, Deep Learning), Time Series, SDEs, Bayesian Modelling, Hidden Markov Models
- Tools: Rcpp, Stan, Git,LaTeX
- Applications: Statistical Modelling, Population Genetics, Ancient DNA,Bioinformatics, Cultural Evolution, Animal Behaviour
I'm passionate about statistical communication, data-driven discovery, and creating research software that lasts. I’m open to opportunities in:
- Research & Development
- Data Science / Quantitative Analysis
- Computational Biology / Behavioural Science
- Science Communication / Open Source Projects
Let's connect:
📧 [shingyan.kwong@manchester.ac.uk]
🌐 LinkedIn
“All models are wrong, but some are useful.” – George Box