AI engineer. Agentic systems, multimodal evaluation, reinforcement learning.
I build agentic AI systems end to end, from multimodal VLM benchmarks and reinforcement learning training pipelines to production LLM applications and MCP tool services that plug into real enterprise workflows. Currently an AI Engineering Intern at HCA Healthcare, with an M.S. in computer science and three IEEE papers across LLM agents, autonomous vehicle perception, and robotics security.
Right now: building the MCP service that lets a voice agent book imaging appointments.
Where I have worked and what I built there.
Building agentic AI systems end to end: an MCP scheduling service that lets a voice agent book imaging appointments, a production LLM assistant for data governance and the secured MCP tool service behind it, and a benchmark and reinforcement learning environment for evaluating multimodal VLM agents on real enterprise workflows.
Autonomous vehicle perception and robotics security research, covering YOLOv8 and LiDAR-camera fusion in the CARLA simulator and a supply chain attack against Secure ROS 2. Produced two IEEE papers, at ISCAS 2025 and MILCOM 2025.
Mentored students in algorithms, machine learning, and data analysis, and designed assignments around live LLM inference and real-world API datasets.
Systems I have built, most significant first. Each one has a write-up covering how it works.
A proof of concept at HCA Healthcare: a voice agent that books imaging appointments by calling an MCP scheduling service I built, plus a dashboard for following each call.
3 to 5 minute calls · every completed demo call booked · offline mock backend
An LLM agent I added to a production governance web app at HCA Healthcare. It answers questions through grounded SQL over a secured MCP tool service and controls the dashboard from plain language.
Production web app · deny-by-default tool authorization · read-only SQL
An internal platform at HCA Healthcare that containerizes a full Linux desktop to evaluate multimodal VLM agents on real enterprise workflows, later extended into a reinforcement learning training environment.
Containerized Linux desktop · deterministic checkpoint scoring · NeMo RL rollouts
Research code behind a first-author IEEE ISCAS 2025 paper on defending autonomous vehicles against camouflaged adversarial attacks, built on CARLA and ROS 2.
Attacked stop sign at 85 km/h · 10 m overshoot · two defenses restore safe stopping
An installable RLCard extension that lets decoder-only LLMs play UNO as agents, from single-GPU models to distributed 70B inference. Basis for a first-author IEEE ICMLA 2025 paper.
1B to 70B parameter models · up to 10,000 distributed games per configuration
A co-first-author IEEE MILCOM 2025 proof of concept: a trojanized Secure ROS 2 package that steals keystore credentials over DNS and hijacks a real autonomous vehicle.
Keystore exfiltration over DNS · 60 Hz spoofing on a physical Quanser QCar2
Deep Convolutional GAN in TensorFlow trained on CelebA to generate realistic human face images.
View on GitHubClassic Snake game implemented with the Dyna-Q reinforcement learning algorithm.
View on GitHubA Unity VR reconstruction of World War I battlefields built with a six-person team for Alvin C. York State Park, winner of the Distinguished Design Award.
Peer-reviewed papers across LLM agents, autonomous vehicle perception, and robotics security. Each one links to the project it came from.