NASA Ames Research Center · Airspace Operations Laboratory (via San José State University Research Foundation)
I build research-grade AI and autonomous systems that connect machine intelligence, simulation, operational data, and human decision-making, bringing an AI research software engineer's approach to real-world autonomy.
I work at the intersection of AI research, autonomous systems, and production-quality research software.
Grounded in a software engineering background, I've grown drawn to building the systems around intelligent models: the agent loops, tools, retrieval pipelines, evaluation infrastructure, simulations, interfaces, and experimental software required to make AI useful in complex real-world environments.
At NASA Ames, my research engineering work has supported advanced airspace concepts including Advanced Air Mobility (AAM), Upper Class E / Higher Airspace Traffic Management, Urban Air Mobility, vertiport automation, and increasingly autonomous operations.
Long-term question: How do we build intelligent systems that can reason, use tools, collaborate with humans, and operate reliably in complex physical environments?
Supporting software engineering stack
I also have deep experience building end-to-end software systems with TypeScript, JavaScript, React, React Native, Node.js, GraphQL, MongoDB, REST APIs, and web application architectures, useful for turning research algorithms and AI systems into testable tools, operator interfaces, and deployable prototypes.
| 📟 Console | Readout |
|---|---|
| 🔭 Currently | Building Extensible Traffic Management System for NASA's Advanced Air Mobility Mission |
| 🎓 Certified | NVIDIA Certified Associate · Generative AI & LLMs |
| 🌱 Learning | LLM Agents · RAG · Transformer Internals · Fine-Tuning |
| 👯 Collaborate | AI/ML research projects |
| 💬 Ask me | Anything here! |
| Domain | Research / Engineering Focus |
|---|---|
| Advanced Air Mobility (AAM) | Scalable operations, autonomy, and future airspace concepts |
| Higher Airspace / Upper Class E | Cooperative traffic management for high-altitude operations |
| Urban Air Mobility (UAM) | Human-in-the-loop simulation and terminal-area operations |
| Vertiport Automation | Automated coordination and high-density operations |
| Air Traffic Management | Decision support, conflict management, system integration, and evaluation |
| Human-AI Systems | Intelligent assistants and AI-enabled operational workflows |
| Year | Publication | Venue |
|---|---|---|
| 2026 | Coordinating the Sky Above: NASA's Development and Evaluation of a Cooperative Higher Airspace Traffic Management (HATM) Concept | AIAA AVIATION 2026 · San Diego |
| 2025 | Advancing the Upper Class E Traffic Management (ETM) Concept: NASA's First ETM Collaborative Evaluation with Industry Partners | AIAA AVIATION 2025 · Las Vegas |
| 2024 | A Human-In-The-Loop Simulation for Urban Air Mobility in the Terminal Area | DASC 2024 · San Diego |
| 2024 | Initial Integration of a Conflict Probabilities Service for Upper Class E Traffic Management | DASC 2024 · San Diego |
| 2024 | Initial Development of an Upper Class E Traffic Management (ETM) System for Stratospheric Flight Operations | AIAA AVIATION 2024 · Las Vegas |
| 2023 🏅 | Airspace Performance Observations of Scalable Autonomous Operations in a High Density Vertiplex Simulation | DASC 2023 · Barcelona |
| 2023 | Initial Development and Integration of a Vertiport Automation System for Advanced Air Mobility Operations | AIAA AVIATION 2023 · San Diego |
🏅 Best Paper Award · DASC 2023, Barcelona Airspace Performance Observations of Scalable Autonomous Operations in a High Density Vertiplex Simulation
I write and publish courses that turn what I learn about AI into structured, executable material for other engineers.
| Course (authored by me) | What it teaches |
|---|---|
| AI for Research Engineer · From First Principles | Mathematics, probability, neural networks, optimization, and Transformer internals, each with derivations, interactive labs, and executable Python |
| Applied AI for Software Engineers | An end-to-end, tutorial-style course on LLM APIs, agentic systems, RAG, evaluation, safety, and fine-tuning for engineers moving into applied AI |
- How should AI agents be evaluated when correctness is not captured by a single benchmark?
- How can LLM-based systems remain grounded, observable, and controllable in safety-critical environments?
- What architectures best combine models, tools, memory, retrieval, simulation, and human oversight?
- How can AI accelerate scientific and engineering workflows without hiding uncertainty?
- How do we move from impressive AI demos to reliable operational systems?
- What does scalable autonomy look like when many intelligent agents must share a physical environment?
Research → Prototype → Experiment → Evaluate → Iterate