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Achievement: Pull Sharkx2Achievement: Pair ExtraordinaireAchievement: Quickdraw

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Cyriellewu/README.md

Hi, I'm Willow 👋

M.S. Computer Science (Machine Learning) at Columbia, graduating December 2026. I work on NLP, knowledge graphs and ML interpretability, and I build the data pipelines and tools around them.

Research

  • Explainable Dynamic GNNs for Predictive Maintenance in Vehicle Chassis Systems. First author, ACML 2025 (PMLR 304). paper · repo
  • FKGE-OSC: Fuzzy-Enhanced Uncertain Knowledge Graph Embeddings. First author, manuscript. repo
  • Research Data Analyst, Columbia Business School (Summer 2026): entity resolution over billion-row finance datasets

Selected projects

  • llm-lab: local-first, DuckDB-native workbench for evaluating LLMs and agents
  • duckdb-torch-pipeline: out-of-core Parquet → DuckDB → PyTorch streaming DataLoader
  • evidenceoflife-v2: visual day planner with draggable timelines and map-based memories

Open source

Stack: Python · PyTorch · PyG · DuckDB · TypeScript / React

📫 yw4643@columbia.edu · LinkedIn

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  1. evidenceoflife-v2 evidenceoflife-v2 Public

    ADHD-friendly visual planning with draggable timelines, planned-versus-actual tracking, and map-based memories.

    TypeScript 2 4

  2. duckdb-torch-pipeline duckdb-torch-pipeline Public

    Out-of-core Parquet->DuckDB->PyTorch streaming DataLoader with pushdown, Arrow-native tensors, bounded-memory shuffle, and a benchmark vs pandas full-load

    Python

  3. llm-lab llm-lab Public

    A local-first, DuckDB-native workbench for evaluating LLMs and agents: suites, multi-model runs, pluggable scorers (incl. SQL-execution + drift), and a dashboard.

    Python

  4. oracle-os oracle-os Public

    A React/TypeScript reflection app with deterministic symbolic engines, a local journal, and optional LLM interpretation.

    TypeScript

  5. explainable-gnn-predictive-maintenance explainable-gnn-predictive-maintenance Public

    ACML 2025 (PMLR 304) · Explainable hybrid GCN + Random Forest for predicting high-cost vehicle faults from SPN/FMI fault-code graphs. 98.9% accuracy.

  6. fuzzy-uncertain-kg-embeddings fuzzy-uncertain-kg-embeddings Public

    FKGE-OSC: uncertain knowledge graph embeddings with fuzzy logic, a Lee oscillator layer and PSL for confidence-score prediction.