Projects

Underwriting teams receive messy, multi-format claim packs. The goal is to convert those artifacts into an auditable, typed case summary with linked evidence so reviewers can reason faster and more consistently.

Problem

Scanned PDFs, photos, and dashboard screenshots resist search and comparison, and rarely map cleanly to policy clauses. This creates manual re-keying, fragmented provenance, and variability in how similar cases are assessed.

Approach

Action: a modular intake pipeline that chains OCR (DeepSeek), visual reasoning (Qwen3‑VL), local retrieval over uploaded artifacts, and a typed report generator. The Next.js app exposes both mock and live paths so the end-to-end flow is reproducible in demos and safe without external credentials.

Impact

• • Produces a structured, citation-rich memo with links back to the original artifacts for auditability.
• • Reduces copy-paste by normalizing extracted facts into a typed schema that downstream tools can consume.
• • Pairs naturally with ParaEval: Aegis extracts and justifies evidence; ParaEval adjudicates formal trigger decisions.

A narrative authoring runtime that turns Orchid from a research concept into an operational tool: card decks, stage boards, prompt inspection, and admin controls in one coherent workspace.

Problem

Narrative AI tools often stop at playful generation. They rarely give authors a real production surface for structuring worlds, inspecting prompt/runtime behavior, and managing story state as a system.

Approach

Action: evolve the original Orchid card-and-graph idea into a multi-surface workbench with deck creation, stage orchestration, LLM/runtime inspection, JSON import-export, and admin settings for deployment defaults.

Impact

• • Repositions Orchid as a credible product system rather than only a paper prototype.
• • Makes the authoring pipeline legible: writers can move from world ingredients to stage logic to prompt/runtime debugging in one place.
• • Creates a stronger portfolio bridge from earlier interactive narrative research to current operational AI product work.

A workbench to adjudicate parametric trigger decisions against multi-source evidence. It makes the decision path explicit and expandable so reviewers can trace Situation → Task → Action → Result for each case.

Problem

Policies trigger on index values (gauges, satellite, APIs) rather than on verified loss. Near-threshold disagreements across sources create ambiguity and hidden basis risk without a shared reasoning frame.

Approach

Action: normalize heterogeneous sources, apply a deterministic decision algorithm, surface disagreement as first-class basis risk, and regression-test the engine against golden cases. The contract layer (Zod) mirrors future Pydantic models for a clean TS⇄Python bridge.

Impact

• • Transparent rule-trace and narrative output suitable for review memos and audits.
• • Explicit basis-risk classification highlights why sources diverge and how that affects confidence.
• • 31 unit tests cover algorithm branches to prevent silent regressions as cases and rules evolve.

Students and practitioners struggle to build intuition for CRP/HDP morphology. The task is to make the abstract process tangible and testable so people can connect symbols to outcomes.

Problem

Without an interactive mental model, reuse vs. novelty, concentration parameters, and segmentation quality feel like disconnected equations rather than one system.

Approach

Action: animate the CRP/HDP generative story (reuse vs. novelty) and pair it with runnable train/test experiments on English, Finnish, and Turkish datasets so users can tune priors and observe effects.

Impact

• • Moves from metaphor to measurable results in one place (no context‑switching).
• • Reduces ramp‑up time by letting users observe how priors change segmentation quality.
• • Bridges paper‑level theory and reproducible interaction in the same learning flow.

An interactive 3D explorable that turns embedding math into spatial intuition. Visitors can fly through clusters and watch familiar analogies line up in space.

Problem

300‑D embeddings are powerful but opaque; it is hard to build intuition for neighborhoods, axes, and analogies from numbers alone.

Approach

Action: reduce 300‑D Word2Vec vectors to 3D via PCA, then render an efficient Three.js scene with labeled clusters and vector hints (e.g., king − man + woman → queen).

Impact

• • Reveals countries, capitals, emotions, and tools as stable spatial groupings.
• • Shows classic analogy vectors directly in the scene to connect equations with perception.

Group storytelling with AI collapses without shared memory and social logic. The task: let humans and agents improvise together while the world stays coherent.

Problem

When improvisation outruns memory and governance, characters forget, norms erode, and continuity breaks ruin the experience.

Approach

Action: model the world like a small society—lifecycle, scheduling, memory, and reputation agents beneath an LLM dialogue layer—so play remains believable at human tempo on commodity hardware.

Impact

• • Reputation signals increased perceived naturalness and trust in controlled comparisons.
• • Sustained engagement from casual play up to 100+ turns/session demonstrates stability.