Overview
PhD with extensive experience in perception systems through sensor fusion, Kalman filtering, and computer vision for autonomous applications. I specialize in state estimation, perception systems, and software development with expertise in Python, C++, and various ML frameworks.
Throughout my career, I've led the development of autonomous maritime perception systems, vehicle behavior analysis, and real-time state estimation solutions. I've worked across various industries, from autonomous vehicles to healthcare, implementing cutting-edge mathematical and computational solutions.
My approach combines rigorous mathematical foundations with practical implementation, ensuring robust solutions for complex real-world problems.
Professional Experience
- Created AI Enterprise Operations Platform serving as digital command center for private equity firms
- Architected core PaaS infrastructure that automates workflows and consolidates enterprise data
- Led cross-functional AI/ML project teams providing technical leadership on system architecture
- Developed complex routing algorithms and memory management systems for real-time AI applications
- Led end-to-end AI projects on Azure for PDF ingestion and data extraction
- Architected solutions with robust error handling and logging systems
- Drove internal capability development through codebase management and mentorship
- Engaged in pre-sales activities and client management for AI initiatives
- Served as thought leader in Generative AI policies and tooling development
- Led ML architecture for forward-facing moonshot project with JRCS managing international teams
- Designed novel computer vision solutions for nighttime detection applications
- Delivered models in AWS and Azure ecosystems using Python and SQL
- Developed sensor fusion and estimation algorithms for various client projects
- Created valuable IP including novel propensity score matching approach
- Measured outcome effects in observational experiments with statistical rigor
- Managed lifecycle from idea to platform deployment including scaling to Spark
- Architected unscented Kalman filter systems for real-time multivariate sensor fusion
- Achieved robust state estimation in noisy culinary environments
- Developed production-grade algorithms in Python using test-driven development
- Collaborated with hardware teams to optimize sensor calibration and PID controllers
- Developed real-time state estimation algorithms for vehicle behavior analysis
- Implemented OBD and phone sensor fusion for comprehensive data collection
- Created end-to-end perception pipeline for multi-sensor data streams
Education
Dissertation: "The Novikov-Veselov Equation, Stability of Solitary-Wave Solutions and a Numerical Solution"
Advisor: Dr. Jennifer Mueller
Technical Skills
State Estimation & Perception
- Bayesian Filtering: Kalman Filters (UKF, EKF)
- Multi-object Tracking & Motion Models
- Sensor Fusion & Real-time Processing
- Object Detection & Track Association
Software Development
- Python (Expert), C++ (Proficient), SQL, R
- PyTorch, TensorFlow, NumPy, Pandas
- Git, AWS, Docker, Test-Driven Development
Domain Expertise
- Real-time Systems & Sensor Data Processing
- PID Controllers & System Identification
- Optimization & Differential Equations
Mathematical Interests
- Signal Processing & Fourier/Laplace Transforms
- State Space Models & Numerical Methods
- Operations Research & History of Mathematics
Publications
Reinholz, Daniel L.; Cox, Murray; and Croke, Ryan (2015)
International Journal for the Scholarship of Teaching and Learning: Vol. 9: No. 2, Article 11.
Croke R, Mueller J L, Music M, Perry P, Siltanen S and Stahel A (2015)
Contemporary Mathematics 635, pp. 25-70.