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EllenSyuu/README.md

Ziyi (Ellen) Zhou — statistician and data engineer in Montréal. I model rare, high-impact events — floods, financial tail risk, gaps in education data — and build the software that makes those models trustworthy. Drawing: Planche 0, the Yasaka pagoda in Kyōto as an exploded isometric, pulled apart along its free-hanging core post, with a plan, a bracket-set detail and dimensions.

Ziyi (Ellen) Zhou · Statistical ML & Data Engineering · Montréal
I develop statistically rigorous methods and turn them into tested, reproducible software —
from multivariate extremes to large-scale education data.

Recent work: the last five weeks as a hand-drawn calendar, with a tick for every day of work. Latest from the workbench: my most recently updated public repositories and their latest commits, redrawn each morning.

02. Selected work

Planche II, covarchimax — M.Sc. thesis, McGill 2026. Does heavy rain hit several places at the same time, and is that changing? An R package for covariate-dependent Archimax copulas. Planche I, SDG 4 Data Revision Tracker — how much do education statistics change from one data release to the next? Python, Polars, strict typing and tests.

Planche IV, TailFM — can a neural network learn how heavy a tail is, and how often extremes arrive together? PyTorch, amortized inference. Planche VI, IMMCA — Canadian immigration pathways: federal, Québec and provincial routes and what they ask, in three languages, refreshed daily.

03. More work

Hybrid immunity in calendar time (VVI) — booster timing and Omicron infection, from my HKU final-year project. Bayesian Cohort Model, SDG 4.1.4 — an independent reproduction, work in progress. HosiCite — a citation atlas drawn as an old painted map. ellensyuu.github.io — the full portfolio.

ellensyuu.github.io  ·  LinkedIn  ·  Montréal · English · Français · 中文 · 粵語 · 日本語

Popular repositories Loading

  1. SAC-thesis-2026 SAC-thesis-2026 Public

    Semiparametric Analysis of Precipitation Extremes in Southern Québec using Archimax Copulas (Master's thesis, 2026)

    R

  2. ABC ABC Public

    Forked from AmeerD/ABC

    Adjusted Bayesian Completion Rates (ABC)

    R

  3. BCM BCM Public

    Independent reproduction of a Bayesian cohort model for SDG 4.1.4 out-of-school rates in NumPyro/JAX with a Stan reference. Synthetic end-to-end demo, resumable MCMC, tested in CI. Work in progress.

    Python

  4. LADR LADR Public

    Forked from jubnoske08/linear_algebra

    Updated Solutions to Linear Algebra Done Right, by Sheldon Axler.

    TeX

  5. SDG SDG Public

    Release-vintage analytics for SDG 4 education statistics: reporting lag, backfill, withdrawals and value revisions. Typed Polars pipeline validated against a synthetic ground truth.

    Python

  6. IMMCA IMMCA Public

    immigration eligibility check tool

    TypeScript