LOCALIZING_MANIFOLD_NODE
Handshake Protocol // Active
System Online

HENRY STOOPEN

Physics Engineer

Applying differentiable physics totopological deep learning
andreal-world problems.

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λ
π
Core Logic // 01

System Intelligence

I am a Physics Engineering professional focused on quantitative research at the intersection of machine learning, optimization, and geometric modeling. My work explores how physical structure and mathematical insight can inform the design of more efficient learning systems.

I am particularly interested in physics-informed machine learning, manifold methods, and the application of differential geometry to real-world data and decision processes for business intelligence.

Geometric Machine Learning

  • Manifold learning concepts
  • Differential geometry fundamentals
  • Curvature-based representations
  • Dimensionality reduction (e.g., ISOMAP)
  • Topological intuition in machine learning
  • Embedding space analysis
  • Physics-informed modeling
  • Scientific visualization of manifolds
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Optimization & Learning Dynamics

  • Gradient-based optimization
  • Loss landscape analysis
  • Convergence behavior interpretation
  • Stochastic optimization intuition
  • Learning rate scheduling strategies
  • Momentum and adaptive optimization methods
  • Optimization diagnostics
  • Model performance analysis
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RHEM (Research Project)

  • Curvature analysis of financial assets
  • Dimensionality reduction pipelines
  • Time-series representation learning
  • Feature engineering for macroeconomic variables
  • Multi-model forecasting workflows
  • ARIMA / VAR conceptual modeling
  • Ensemble modeling strategies
  • Research pipeline design
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Process Optimization & BI

  • KPI design and monitoring
  • Data-driven process optimization
  • Fraud detection heuristics
  • Dashboard conceptualization
  • User behavior analysis
  • Chatbot system design
  • Product analytics interpretation
  • Operational decision support
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Simulation: Manifold Curvature
Artifacts // 02

Technical Artifacts

NODE_REF: RHEM-THESIS
RHEM — Final Bachelor Thesis
Geometric ML
Finance
Python
Manifolds

Geometric learning + manifold methods applied to real-world data. Curvature-driven representations and dimensionality reduction pipelines.

NODE_REF: MICRO-TSN
Micrometeorites — Extended Research
Research
Materials
Field Work
Analysis

Collection and analysis of micrometeorites with a research pipeline for classification, documentation, and scientific reporting.

Accreditation // 03

Validated Records

Education_Background

B.Eng. Physics Engineering

Universidad Iberoamericana (IBERO)

2020-2025VERIFY_NODE

Certificates

Connect // 05

Signal Interface

Initiate a high-priority transmission for research collaboration or technical inquiries.