Tianjian Qin

Tianjian Qin

PhD candidate, computational scientist
I study complex biological systems using probabilistic modeling, simulation, and machine learning.

What I do

In my current project, I build modeling and machine-learning pipelines to turn large spatiotemporal livestock movement data into realistic network “digital twins” and decision-relevant insights; in my spare time, I bring complex data to life through interactive visualizations and open tools—below is an overview of my skill set, and you can find out more in my blogs and projects.

Scientific Programming

R, Python, C++. Package development, data analysis, visualization, simulation.

Machine Learning / AI

Machine learning, deep learning, neural networks, with PyTorch, Sklearn, and more.

Modeling and Inference

Combining stochastic processes, graph theory, and network science

Big Data

Learning to use SQL, Spark, and other modern frameworks.
d3.js Icon

Web Development

HTML, CSS, JavaScript, interactive data visualization with D3.js.

GIS, Bioinformatics

Geospatial analysis, phylogeny reconstruction, metagenomics

Bash, Server, HPC

Server-side software maintenance, HPC management.

Scientific Writing

Typesetting, rendering and visualization with LaTeX, TikZ and PGFPlots.

Latest Blog Posts

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Sub-Species Level Diversification and Tree Coarsening

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Integrating Pre-Trained PyTorch Models into Your R Package

Pre-trained neural network models can significantly enhance your R projects. This tutorial will walk you through the process taking my R package EvoNN as an example...

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A Protocol to Bridge Phylogeny and GCN

Phylogenies are essentially graphs. Therefore, we can utilize a specific class of neural networks-the graph convolutional networks-to extract valuable information. But how? ...

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