Systems, platform, backend, and applied AI engineering.
I build domain-specific software systems: geospatial simulation, health data apps, scientific ML pipelines, deterministic language scoring, OCR/ASR tooling, urban intelligence backends, and developer tooling.
- Python: ML/research pipelines, FastAPI services, CLIs, data processing, scientific computing.
- TypeScript/JavaScript: NestJS backend systems, React/Vite apps, data engines, browser tooling.
- Dart/Flutter: cross-platform mobile and health tracking applications.
- Swift/iOS: native iOS app surfaces and platform integration work.
- SQL: PostgreSQL schema design, migrations, indexing, materialized risk surfaces.
- C: low-level sliding-window rate limiter core and benchmark harnesses.
- Shell: CI validation, build scripts, migration runners, smoke-test automation.
- FastAPI, stdlib HTTP services, NestJS, Express-style service layering.
- REST APIs, app factories, route modules, DTO/schema validation, health checks.
- PostgreSQL, SQLite, Redis, JSONL/CSV event stores, migration-heavy backend design.
- Docker, Docker Compose, GitHub Actions, CI matrices, smoke checks, lint/type/test gates.
- Metrics endpoints, Prometheus client usage, structured run artifacts, operational runbooks.
- Rate limiting, event ingestion, deterministic assignment, API gateway patterns.
- PyTorch, TorchVision, CRNN/CTC OCR, ASR with faster-whisper, vector-field approximation.
- Computer vision: plant classification, fragment reconstruction, art classification/retrieval.
- Geospatial computing: raster/vector GIS, viewshed analysis, path tracing, GDAL/rasterio/geopandas.
- Simulation: SIR/Gillespie epidemic modeling, particle collision simulation, urban traffic/digital twins.
- NLP/text systems: deterministic scoring rubrics, ontology tagging, sentiment/mechanic extraction.
- Retrieval/embedding workflows: CLIP-style retrieval, TF-IDF, cosine similarity, FAISS-oriented design.
- React, Vite, Three.js, D3, Zustand, dashboard/data visualization surfaces.
- Flutter with local persistence, Riverpod/Provider patterns, Drift/SQLite.
- Native iOS/Swift project structure and mobile client integration.
- Static portfolio/web pages, browser-extension UI, game UI.
- Package-oriented Python layouts:
api,core,services,config,utils,tests. - CLI-first research tooling with reproducible artifact directories.
- Deterministic pipelines, audit logs, fixture-driven tests, benchmark scripts.
- Repository hygiene: generated artifact exclusion,
.gitignorebaselines, dependency manifests. - Documentation as infrastructure: architecture docs, setup docs, API docs, runbooks, validation reports.
| Repository | Domain | Skills Demonstrated |
|---|---|---|
| UDIE | Urban disruption intelligence platform | NestJS, PostgreSQL, Redis, migrations, metrics, Flutter/iOS clients, spatial/risk modeling, CI |
| LAMP | Archaeological geospatial modeling | Python, raster/vector GIS, GDAL, rasterio, geopandas, path tracing, viewsheds, Docker, tests |
| healingstone | 2D/3D fragment reconstruction | Python, geometry, Open3D/Torch runtime, deterministic artifacts, mypy/ruff/pytest CI |
| sira | Epidemic dynamics ML service | Gillespie simulation, neural vector fields, SINDy, FastAPI, PyTorch, regression tests |
| ParticleStimulator | Particle physics simulation platform | Monte Carlo simulation, WebSocket streaming, React/Three.js, ML event classification |
| LifeTrack | Health tracking mobile app | Flutter, Drift/SQLite, domain/data/presentation layering, health records, local-first storage |
| SmartAPILimiter | Low-level rate limiter | C, sliding-window algorithms, bounded memory design, tests, microbenchmarks |
| Repository | Domain | Skills Demonstrated |
|---|---|---|
| autoeit-suite | Spanish EIT transcription and scoring suite | ASR, workbook processing, deterministic rubrics, package split, CLI/test structure |
| AutoEIT-STS | Deterministic EIT scoring | Python packaging, pandas/openpyxl, audit logs, Streamlit/CLI interface |
| audio_transcription | Audio transcription pipeline | faster-whisper, alignment, workbook I/O, evaluation metrics |
| AutoTRandHD | Historical OCR/HTR | CRNN, CTC decoding, image preprocessing, FastAPI, Docker, benchmark scripts |
| ArtExtract | Art classification and retrieval | PyTorch, CLIP/FAISS-style retrieval, outlier detection, experiment scripts |
| ChoreoAI | Multimodal motion research | PyTorch, transformers, motion embeddings, dataset validation, generative-model scaffolding |
| TerraHerb | Plant identification and knowledge retrieval | CV inference, MobileNet/EfficientNet, FastAPI, React, botanical API enrichment |
| AI-PFI | Funding opportunity intelligence | Web extraction, PDF parsing, ontology tagging, embeddings, normalized records |
| AI4MH | Synthetic crisis signal monitoring | FastAPI, sentiment scoring, alert workflows, React dashboard, Docker Compose |
| Repository | Domain | Skills Demonstrated |
|---|---|---|
| agentskill | Agentfile parser/generator toolchain | Python packaging, JSON schema, deterministic generation, CI/security validation |
| SecureForg | Runtime exploit-behavior analyzer | Python execution harnesses, payload testing, AST analysis, security tooling |
| TrustLab | Trust-calibration experiment platform | stdlib HTTP server, deterministic assignment, rate limiting, JSONL/SQLite event storage |
| Noesis | Research/scene compiler experiments | Pydantic schemas, compiler IR, planning/runtime abstractions, autonomous research prototypes |
| gametrend-intelligence-engine | Game trend analysis engine | TypeScript, collectors, TF-IDF, k-means, trend scoring, concept generation |
| cognitron-game | Standalone browser game | React/Vite, TypeScript game state, lightweight UI |
| Repository | Role |
|---|---|
| my-portfolio | Static portfolio site |
| cv | Resume artifact |
| fallofpheonix | GitHub profile README |
- Domain-first architecture over generic CRUD.
- Deterministic pipelines where auditability matters.
- Small service layers around core domain logic.
- Reusable CLI and artifact contracts for research workflows.
- Explicit tests and smoke checks before claiming production readiness.
- Preference for simple deployable systems before distributed complexity.
- Consolidating repositories into a coherent engineering ecosystem.
- Extracting shared runtime, pipeline, CI, documentation, and security conventions.
- Promoting the strongest systems as capstone-grade repositories.
- Reducing public noise from learning, generated, and duplicate repositories.