EMBEDDED AI
Real-Time IR Target Detection on Embedded Compute
Designing AI/ML perception for infrared imagery under strict latency, memory, and false-alarm constraints.
Computer VisionInfraredCNNsEmbedded AI
AI/ML · AGENTIC AUTOMATION · AUTONOMY · GN&C
I build AI/ML systems and agentic engineering workflows that shorten the path from data to decision.
I’m Dr. Jean-Elie Pierre, an AI/ML systems engineer who connects model development, embedded inference, simulation, and AI-driven automation into reliable workflows for high-consequence systems.
profile.yamlengineer:
focus:
- AI/ML perception
- agentic engineering automation
- autonomous systems
- embedded AI
strengths:
- data → model → decision
- simulation → test → iteration
- algorithm → hardware → evidence
operating_mode:
reliability + traceability + latency + performanceSELECTED WORK
Engineering loops built from data, model behavior, test evidence, and execution.
ML SYSTEMS
Neural Architecture Search for Accuracy-Efficiency Tradeoffs
Using NAS to search CNN design space with deployment constraints in mind rather than optimizing accuracy in isolation.
NASPyTorchCNNsOptimization
EMBEDDED COMPUTE
Custom SIMD Inference Engine for Resource-Constrained Hardware
Reducing ML latency and memory use by treating inference as a data-movement and microkernel problem, not just a model problem.
SIMDC/C++InferencePerformance
AUTONOMY RESEARCH
Safe Multi-UAV Coordination with Deep Reinforcement Learning
Doctoral research on decentralized multi-agent coordination under partial observability and explicit safety / temporal-spatial constraints.
Ph.D. Computer Engineering
MARL decentralized coordination
Reinforcement LearningMulti-Agent SystemsUAVsSafety
ML INFRASTRUCTURE
Flight-Test MLOps and Data Pipeline
Turning fragmented test imagery into a repeatable, traceable ML data path for faster preprocessing, training, deployment, and engineering analysis.
MLOpsETLData EngineeringPython
GN&C SYSTEMS
6-DOF Simulation, Digital Twins, and Monte Carlo Automation
Building simulation and DevOps infrastructure to evaluate dynamic-system performance, detect defects earlier, and automate repeatable release analysis.
6-DOF dynamic simulation
CI/CD automated analysis
GN&CSimulationDigital TwinMonte Carlo
WHAT I BRING
A systems view of AI/ML and agentic engineering automation.
Engineering Automation + GN&C
Python and MATLAB workflow automation, dynamics, estimation, guidance, control, 6-DOF simulation, Monte Carlo campaigns, and system-level evaluation for autonomous and mission-critical systems.
AI/ML + Computer Vision
Detection and classification, neural architecture search, TensorFlow Lite, TinyML, SIMD kernels, memory-aware inference, and deployment under real-time, resource-constrained conditions.
Data, Simulation + Test Loops
MLOps, ETL, flight-test data systems, digital twins, CI/CD, software-in-the-loop validation, and repeatable evidence from build through analysis.
Agentic AI + Technical Leadership
Staff-level R&D direction, cross-functional product teams, mentoring, verification strategy, and translating AI and autonomy research into deployable, mission-ready capability.
AI AGENTS + ML SYSTEMS
Engineering tools → simulation → data → AI/ML → agentic workflows → verified decisions.
I build the connective tissue that helps engineering teams move from analysis to action: repeatable data pipelines, automated simulation and test loops, deployed ML systems, and AI-enabled automation that remains grounded in evidence.
See experience timeline