2025 · Platform · Architect, Engineer & Technical Product Lead
AI as lab partner, not oracle — hyperbolic GNN + provenance that makes every inference retraceable, not plausible.
hgnn-protein-ligand ↗Scientific assistants fail when they sit next to the work instead of in it — inventing plausible chemistry, ignoring the open structure, and leaving no provenance. Tokyo Eye had to bind agents to the current graph, selected residues, and preregistered metric gates, and keep geometry, data, and session state on one governed write path.
Built the platform in tokyo-eye-agenticpoincare. Science extra: PyTorch/PyG, geoopt Poincaré, e3nn SE(3), GUDHI TDA, MoE routing, evidential heads, RDKit, OpenMM, PDB/dehydron ingestion. Agent: FastAPI, JWT, rate-limited sessions, WebSocket viewport directives, 20-tool coordinator, Bedrock → Anthropic → Mock, hypothesis + literature tools. Data: pgvector hyperbolic similarity, 30-schema migrations local↔Aurora, dimensional model, single-write-path normalizer, provenance_run. Infra: Terraform Aurora+S3, MLflow+Optuna, property-gate CI (corpus/smoke/curv). Visualizer: three XState machines (viewport, discovery phase, hypothesis lifecycle).
Rejected chatbot-beside-the-work. The agent must see current structures, selected residues, workflow stage, and visual state before it is allowed to speak.
Poincaré / geoopt embeddings so hierarchical residue, fragment, and pocket structure stay continuous instead of flattening into a Euclidean bag of atoms.
DTIE v3–v6 live in tree. Production inference is the V5 GNN path; training continues on v6 with MLflow governance. MoE and e3nn are in the science extra, not a slide overlay.
All data writes go through the normalizer. provenance_run is recorded before results. The 20-tool coordinator cannot skip that contract.
XState owns the React Poincaré dashboard in the platform repo. This site is the write-up, not the live visualizer.
FastAPI coordinator with JWT, rate limits, and a WebSocket viewport. XState in the visualizer plus the open structure constrain what the LLM may say.
PyTorch, geoopt Poincaré, e3nn SE(3), GUDHI persistence, MoE routing, evidential (epistemic + aleatoric) heads, RDKit/OpenMM, PDB/dehydron ingestion. Agent container stays lightweight — no PyTorch on the API image.
PostgreSQL 16 / pgvector hyperbolic similarity, 30-schema migrations local↔Aurora, dimensional model, single-write-path normalizer, provenance_run lineage, Terraform Aurora + S3.
MLflow + Optuna. Mandatory training schema in mlflow_governance.py. Smoke proves field emission. Multi-stage parent/child lineage is documented as not yet proved. Property-gate CI: corpus / smoke / curv.
gates.yml property tests (corpus / smoke / curvature). Findings markdown records abandoned claims (λ₂ as cancer discriminator) alongside surviving dehydron physics.
Hypergraph spectrum, protein geometry, contact maps, and energy landscape on one immersive console.
Protein in a Poincaré-style field with residue focus, fragment screening, epistemic uncertainty, and literature DOIs.
Proves: I can bind scientific agents to a governed write path — hyperbolic geometry, provenance, and property gates in one platform.
Private source of truth is tokyo-eye-agenticpoincare. Public research showcase is hgnn-protein-ligand. Findings docs include abandoned claims as well as surviving physics; unproven capability stays labeled unproven.
FastAPI / pgvector control plane, 20-tool coordinator, Terraform Aurora+S3, MLflow property-gate CI, and Poincaré GNN + MoE science extra.