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jeremy-wayland/README.md

πŸ‘‹ Hi, I'm Jeremy Wayland

PhD candidate & data scientist specializing in topological and geometric machine learning. Founder of Krv.


πŸ”¬ What I Do

  • Topological & geometric machine learning
  • Graph neural networks & knowledge graphs
  • Applied projects in Healthcare and Environmental Science
  • Representation Learning as a Service

πŸš€ Featured Projects

  • retire: & THEMA: Graph-based modeling and analysis for US Coal Plants (Nature Energy, 2025).
  • RINGS: Evaluating graph learning datasets with topological and structural priors.
  • APPARENT: Tools for interpretable patient representation learning in healthcare (IPLDSC 2024).
  • PRESTO: Topological analysis of latent representations in deep learning (ICML 2024).
  • Curvature Filtrations: Novel filtrations for probing latent space geometry (NeurIPS 2023).

πŸ› οΈ Tech Stack & Skills

  • Languages: Python, Typescript
  • Specialties: Graph neural networks, topological data analysis (TDA), representation learning
  • Tools: Knowledge graphs, graph databases (Neo4j), Docker, Kubernetes, Terraform

🌱 Beyond the Code

  • πŸ„ Surfer
  • 🎿 Skiing & Snowboarding
  • 🎻 Cello player
  • 🚴 Biking in Bavaria
  • 🌍 Travel explorer
  • πŸ‘ Advocate for open science & reproducible ML

πŸ“« Connect with Me


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  1. aidos-lab/rings aidos-lab/rings Public

    Relevant Information in Node features and Graph Structure (ICML 2025)

    Python 4 1

  2. aidos-lab/curvature-filtrations aidos-lab/curvature-filtrations Public

    SCOTT: Synthesizing Curvature Operations and Topological Tools

    Python 14 3

  3. aidos-lab/Presto aidos-lab/Presto Public

    Mapping the Multiverse of Latent Representations (ICML 2024)

    Python 7

  4. aidos-lab/apparent aidos-lab/apparent Public

    Analyzing Patient Physician Network Topology

    Python 3

  5. Krv-Analytics/Thema Krv-Analytics/Thema Public

    Our Topological Hyperparameter Evaluation Mapping Algorithm.

    Python 3

  6. Krv-Analytics/retire Krv-Analytics/retire Public

    Strategies to Accelerate US Coal Power Phaseout Using Contextual Retirement Vulnerabilities

    Python 1