Team Leadership & Agile Management:

• Led a team of 6 satellite software developers, overseeing task delegation, sprint planning, and Agile workflows.
• Managed daily Scrum meetings, drove software architecture decisions, and delivered recurring progress updates to stakeholders, ensuring alignment with project timelines and customer expectations.

GRITT Software Development:

• Spearheaded the design and development of GRITT (GOES-R Radiometric and INR Trending Tool) for NASA, a Python-based data analysis platform deployed across NOAA's AWS cloud and satellite ground systems. GRITT enables engineers to detect radiometric anomalies (ABI sensors) and geolocation inaccuracies (L1b imagery), directly supporting mission-critical satellite data integrity.
• Backend Architecture: Engineered a distributed job scheduler (Python) to manage cron-based data ingestion, real-time processing, and report generation. Workers asynchronously processed jobs without Docker/Kubernetes dependencies, enabling lightweight deployment across multiple servers.
• Data Visualization & Customization: Integrated Plotly to empower users with dynamic, customizable plots and automated reports. Implemented user-defined logic for color-coded tables and dashboards, streamlining anomaly detection for operational teams.
• Scalable Data Pipeline: Built a backend leveraging InfluxDB for time-series data storage, automating ingestion from heterogeneous satellite data sources. Supported seamless Kubernetes integration for cloud-native scalability.

DevOps & Lab Infrastructure:

• Setup a Linux based software development lab for the company, deploying 6 Dell PowerEdge servers, 2 Synology NAS units, and ESXi hypervisors.
• Configured OpenNebula for Terraform-based AWS environment testing and GitLab CI/CD pipelines for Kubernetes/containerized deployments.
• Optimized 6 Linux workstations with NVIDIA GPUs for LLM fine-tuning R&D.

High-Performance Point-Cloud Processing:

• Engineered a C++11 data processing software stack converting raw FPGA-acquired Lidar data into geospatial 3D point-clouds.
• Leveraged SIMD vectorization, and lock-free concurrency to reduce processing time from 52 minutes to 1.8 seconds for 1 second of flight data (1700x acceleration), enabling near-real-time analysis.

Noise Reduction Algorithms:

• Developed statistical filtering modules to remove atmospheric/ sensor noise from point-clouds, improving data accuracy.

Calibration & Bias Correction:

• Built a C++ framework to automate sensor calibration, identifying timing anomalies in raw data streams.
• Prototyped a brute-force numerical solver to compute Lidar bias offsets, reducing geolocation errors.

Automated Workflows:

• Designed Python scripts to orchestrate end-to-end data processing, reducing manual intervention for batch operations.

Built software for hyperspectral imaging systems detecting IEDs and terrain anomalies for military reconnaissance applications.

IED Detection Algorithms:

• Engineered a Matlab-based road boundary tracking and disturbed earth detection system for aerial hyperspectral imagery.
• Developed software for processing airborne imagery data, performing spectral signature analysis to identify disturbed soil patterns indicative of buried threats.

Wearable Sensor Analytics:

• Developed a real-time disturbed-earth detection algorithm for a soldier-mounted hyperspectral camera system.
• Deployed on a heads-up display (HUD), the tool marked high-risk zones, leveraging a proprietary soil texture/reflectance model to reduce false positives.

GIS Visualization Tools:

• Developed a python tool to overlay hyperspectral road surveillance data on 2D google maps, enabling analysts to geo-locate IED risks using PostGIS.

Field Validation:

• Led 12+ field tests with wearable multispectral sensors, demonstrating algorithm reliability to DoD stakeholders in simulated combat environments.

Developed star-based navigation algorithms for the GOES-R Advanced Baseline Imager (ABI), ensuring sub-pixel geolocation accuracy in Earth observation imagery.

Star Navigation Algorithms:

• Engineered C++ software to detect star centroids in ABI instrument imagery, compute residuals against the star catalog, and feed corrections into a Kalman filter.
• This reduces geolocation errors, enabling precise alignment of Earth imagery pixels for weather forecasting and climate modeling.

Star Catalog Management:

• Built a C++ tool to generate 7-day schedules of viewable stars for the ABI imager, optimizing scans during 15-minute observation windows.
• Automated star selection based on magnitude, angular separation, and instrument field-of-view constraints.

Project Leadership:

• Directed two Critical Software Components (CSCs) for the GOES-R Ground Segment, adhering to CMMI Level 3 processes.
• Authored 50+ system/software requirements in DOORS and managed schedules via IBM ClearQuest/MS Project.

HAARP Signal Detection & Analysis:

• Deployed and maintained magnetometer arrays across 3+ remote Alaskan sites to capture ULF (0.1–5 Hz) signals generated by HAARP's ionospheric heating experiments.
• Developed Matlab software to correlate magnetic field data across sites, isolating propagated magnetospheric signals from terrestrial noise (SNR improvement).

Magnetospheric Tomography:

• Built an interactive 3D model of trapped proton fluxes in low-Earth orbit (LEO) using satellite magnetometer data, identifying radiation hotspots impacting spacecraft longevity (SAA).
• Utilized COMSOL for modeling aspects.