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Alejandro de la Fuente
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Projects

If I can't measure that it works, I don't ship it. The rule holds for everything here: engineering systems, peer-reviewed research and practice labs. Filter by type, or browse them all, newest first.

  • Engineering2026

    Deck Optimisation Engine

    Mines published Magic Online decklists to inform one Modern deck's flex slots. Its own audit retired the performance readings and kept the adoption ones.

    PythonDuckDBuvpytest

  • Engineering2026

    7PH Graph: A Knowledge Graph of a Competitive Metagame

    A graph of a competitive Magic format answers what stats tables cannot: which cards travel together. Charts across 4,591 decks carry provenance and statistical guards.

    PythonCypherGradioPlotly

  • Engineering2026

    Digital Twin

    An agent answers questions about my work, and a second model reviews every answer. Accuracy scores 4.56 of 5 on a 149-question evaluation.

    PythonChromaDBLiteLLMGradio

  • Engineering2026

    AI-JIE: Extraction and Evaluation Pipeline

    The extraction layer of the Job Intelligence Engine tells a genuine requirement from a nice-to-have. Human review scored the final prompt at 4.11 of 5.

    PythonOpenAI APIPydanticasyncio

  • Engineering2026

    LLM Engineering Lab

    11 projects spanning retrieval, fine-tuning and autonomous agents. The flagship ensemble predicts product prices with a mean absolute error of $29.95.

    PythonQLoRAChromaDBModal

  • Engineering2026

    Job Intelligence Engine

    The engine ranks 6,100 postings into 2 shortlists: target now, or worth a stretch. Its skill-demand models score 0.88 to 0.95 area under the curve.

    Pythonscikit-learnSBERTXGBoostStreamlit

  • Lab2025

    MLB Analytics with SQL

    A reusable SQL workflow answered 4 questions on 150 years of baseball data. One finding: some low-payroll teams consistently beat expectations.

    PostgreSQLSQLPython

  • Lab2025

    Python Labs

    3 lab collections cover the fundamentals: object-oriented Python, data analysis and machine learning. The machine learning collection spans 12 sections, from regression to neural networks.

    Pythonpandasscikit-learn

  • Research2025

    Seasonal altitudinal migration in rainforest birds

    Rainforest birds move uphill each summer and back down each winter. 16 years of counts make it the first system-wide measure of this movement.

    RJAGSN-mixture models

  • Research2025

    Forest gap effects on tropical birds

    Forest gaps changed which bird species lived where, while total numbers stayed level. One specialist, the Hill Blue Flycatcher, increased with gap size.

    RGLMs

  • Research2025

    Physiological stress and rainforest mammal declines

    2 possum species collapsed, each through a different mix of heat stress and foraging limits. A model chain traced the causes across 30 years.

    RJAGSBiophysical models

  • Research2024

    Climate, foliage chemistry and herbivory

    Climate and geology set the stage for how much insects eat rainforest leaves. Single soil nutrients, the expected drivers, predicted little after accounting for geology.

    RJAGSHierarchical models

  • Research2023

    Climatic drivers of rainforest bird change

    Warming and shifting rainfall drove opposite trends in lowland and upland birds across 47 species. Cyclones and droughts had only marginal effects.

    RJAGSRemote sensing

  • Research2022

    Ringtail possum viability forecast

    A model on 30 years of surveys forecasts possum collapse by 2050, heatwaves doing most of the damage. The forecast fed a national protection nomination.

    RJAGSForecasting

  • Research2022

    Community reshuffling under elevational shifts

    A simulation of 7,613 wildlife communities predicts mass local extinctions as warming pushes mountain-top species out of habitat. Each species climbs at its own speed.

    RSpatial forecasting

  • Research2021

    Rainforest bird declines

    17 years of bird monitoring showed upland populations nearly halving. The evidence supported protection nominations for 14 rainforest species.

    RGLMsShiny

  • Research2021

    Predicting abundance from environmental suitability

    Maps of where a species can live predicted about half the variation in local numbers. The models faced 50 species and places they never saw.

    REnsemble MLMaxEnt

Alejandro de la Fuente

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Melbourne, Australia · Terms