I am a Security-focused ML Engineer and IEEE-published researcher specializing in fault-tolerant GPU training and telemetry systems for aerospace and satellite workloads.
- Target Roles: ML Engineer, ML Infrastructure / MLOps, Security-focused Cloud Engineer.
- Core Expertise: GPU reliability, mission-control style ML infrastructure, and secure deployment pipelines.
- Technical Stack: PyTorch (profiling & optimization), Python, Docker, Kubernetes, AWS, and Cloud Security.
- GPU Memory Defragmenter: Built a Transformer-driven predictor to reduce OOM failures and improve utilization on RTX-class hardware.
- PulseNet: Developed a secure predictive-maintenance pipeline using NASA C-MAPSS data with integrated encryption and audit logging.
- CommandX: Designed mission-control stacks for satellite telemetry, autonomous GNC, and real-time health monitoring.
- Education: M.S. in Information Technology Security, Arizona State University.
- Fellowship: Technology Innovation Fellow with Honeywell Aerospace Labs.
- Publications: IEEE INDICON (Adaptive EV charging) and IEEE (GPU memory optimization).
| Area | Focus |
|---|---|
| Machine Intelligence | PyTorch (custom memory profiling), PINNs, Reinforcement Learning, Time-Series Modeling. |
| High-Performance Compute | Python, SQL, C++; GPU-aware training loops; Latency-sensitive model serving. |
| Cloud & Security | AWS (VPC, IAM, CloudWatch), Docker, Kubernetes, Applied Cryptography, Zero-Trust. |
| Tooling & Observability | Git, GitHub Actions, Streamlit, TLE Tooling, Security Auditing. |
Transformer-based predictor that models GPU memory fragmentation and proactively defragments training workloads.
- Impact: Reduced OOM failures on NVIDIA RTX 4060 testbeds by 43.6% and improved GPU utilization by 6.4%.
- Tech: Custom PyTorch memory profiling, time-series modeling, targeted compaction.
Physics-Informed Neural Network (PINN) engine simulating multi-physics trajectories under strict safety bounds.
- Impact: Sub-millisecond latency for trajectory states with risk-aware constraints.
- Tech: PINNs, RK4 Integrator, ISA Atmosphere modeling.
End-to-end stack unifying orbital physics, autonomous GNC, and telemetry visualization.
- Impact: Real-time streaming of orbital elements and anomaly flags for mission-critical decision making.
- Tech: Python, SGPL/TLE Tooling, Real-time Dashboards.
Repositories: PulseNet · CommandX · GPU Defragmenter