About me

I'm Colin Penn, based in Toronto, Ontario. I hold an MSc in Statistics from Western University and a BSc in Computer Science from Shanghai Institute of Technology. My work connects statistical modelling with practical machine learning systems.

Research & applied work

My MSc research at Western University focused on fast variational Bayesian inference for right-censored Weibull accelerated failure time models. I independently derived the algorithm, implemented it in R, and evaluated it through simulations and a METABRIC breast-cancer application.

As an Algorithm Engineer at JSTI Group Limited, I owned the architecture and implementation of a highway traffic-balancing system. The work combined preprocessing for PB-scale multimodal data, incident-aware forecasting, and data-driven traffic imbalance analysis.

Earlier, as a Visiting Scholar at Shanghai Astronomical Observatory, Chinese Academy of Sciences, I applied statistical learning to astronomical spectra and time series. I contributed to qrpca and developed Yonder. My undergraduate thesis used Transformer attention for neutrino direction reconstruction and received Outstanding Undergraduate Thesis recognition.

Experience & education

Technical toolkit

• Programming: Python, R, C++.
• Statistical modelling: variational and Bayesian inference, survival analysis, simulation, Weibull AFT models.
• Machine learning: PyTorch, scikit-learn, Transformers, Bi-LSTM, STGCN, clustering, PCA.
• Data & visualization: pandas, NumPy, SciPy, Matplotlib, Seaborn, Plotly.
• Tools: Git, Docker, Linux, Stan.
• Languages: Mandarin (native); English (professional working proficiency).

Open source & community

• labml.ai, May 2024–present: translated and edited technical documentation for Chinese-speaking developers, with a focus on accurate terminology and clear explanations.
• Hello-Algo, October 2022–present: developed Python examples and exercises explaining data structures and algorithms to beginners.
• DataWhale, May 2023–present: supported AIGC Specialized Study and Dive into Deep Learning as a teaching assistant, contributing to educational initiatives that reached more than 10,000 learners.

Selected publications

My publications appear under the names Peng Chen and C. Peng.

• P. Chen and R. S. de Souza. Yonder: A Python Package for Data Denoising and Reconstruction. Research Notes of the AAS, 2022.
• R. S. de Souza, X. Quanfeng, S. Shen, C. Peng, and Z. Mu. qrpca: A Package for Fast Principal Component Analysis with GPU Acceleration. Astronomy and Computing, 2022.
• A. L. Chies-Santos et al., including P. Chen. J-PLUS: A Catalogue of Globular Cluster Candidates around the M81/M82/NGC 3077 Triplet of Galaxies. Monthly Notices of the Royal Astronomical Society, 2022.

Honours & leadership

• Outstanding Undergraduate Thesis, Shanghai Institute of Technology, 2024.
• Second-Prize Scholarship, Shanghai Institute of Technology, 2023.
• National Third Prize, Donghua Cup College Student Cyber Security Invitational, 2022.
• Shanghai Second Prize, Seventh Shanghai College Student Cyber Security Competition, 2022.
• President, Artificial Intelligence Club, Shanghai Institute of Technology, 2020–2024: taught Python and machine learning fundamentals and coordinated club activities.