OIST — Graph Neural Network Research
Scientific literature synthesis & Graph Neural Network representations for ecological food webs
The Problem
Complex ecological food webs exhibit non-linear predator-prey dynamics and trophic cascades that standard tabular modeling fails to represent accurately.
Engineering Approach
Employed graph neural network abstractions (GNNs) to model multi-trophic interactions and synthesized cross-disciplinary computational biology literature.
What I Built & My Contribution
Graph modeling, literature synthesis, and structural relationship mapping during an AI research internship at the Okinawa Institute of Science and Technology (OIST).
Architecture & Data Pipeline
Ecological Literature → Relationship Entity Extraction → Directed Graph Representation → Graph Neural Network Modeling
[ Ecological Research Literature ]
│
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┌───────────────────────────────┐
│ Entity-Relation Parser │
│ - Trophic Levels & Species │
│ - Biomass & Energy Exchange │
└───────────────┬───────────────┘
│
▼
┌───────────────────────────────┐
│ Graph Neural Network Model │
│ - Node Embeddings │
│ - Edge Weight Sensitivity │
└───────────────────────────────┘Verified Outcomes & Metrics
Structured graph modeling of multi-species food webs; published research overview and academic literature synthesis.
Verified Implementation
The preserved portfolio research account describes graph neural network formulations, trophic representations, and multi-model literature synthesis. Verification here represents code audit and local inspection, distinguishing functional code paths from external cloud provider accounts.
Boundaries & Known Limitations
No private proprietary datasets, commercial implementations, or publication guarantees are asserted. Grounded in the documented research account.