Technology Executive · Artificial Intelligence · Space Systems · Public Safety · Emerging Technology

LinkedIn · GitHub

I build and lead technology organizations tackling problems where software, AI, and infrastructure meet the physical world — from space-domain awareness and tactical-edge computing to wildfire intelligence, geospatial analysis, and global-scale cloud platforms.

That work spans an unusual arc: the U.S. Army Signal Corps; academia (philosophy and advanced logic); technology and innovation leadership at the Michigan State Police; public-sector CTO and VP roles at Information Builders and TIBCO/Citrix; senior global infrastructure leadership at General Motors and GM Financial; defense and space R&D; and today, serving as Chief Technology Officer of the U.S. Department of the Interior.

Most of my career's output lives inside production government, defense, and public-safety environments and cannot be published. What is here is real, running work: publicly releasable research, autonomous data pipelines, prototypes, reference architectures, and engineering experiments — maintained personally, because I believe technology executives should stay close to the systems they are accountable for.

An AI-enabled wildfire intelligence and situational-awareness system fusing satellite fire detection, weather, lightning, aircraft, cameras, fire-spread modeling, and incident reporting into a single operational picture for public-safety use. System overview → firestorm-platform

The publicly releasable layer is a fleet of autonomous data pipelines — each one harvests an open federal or scientific source on a schedule, quality-controls and reshapes it, and publishes slim JSON for operational frontends. No keys, no secrets, public-domain data:

flowchart LR
    A["Open federal + scientific sources<br/>GOES · FIRMS · NGFS · HRRR · GLM · ADS-B · NWS · EPA"] --> B["Autonomous pipelines<br/>scheduled harvest · QC · reshape"]
    B --> C["Slim JSON on CDN<br/>keyless · public-domain"]
    C --> D["Operational frontends<br/>FIRESTORM · PROMETHEUS"]

Geospatial and operational intelligence work supporting Department of the Interior mission environments — hazards, cyber, and kinetic situational awareness intersected with federal land boundaries.

System overview → prometheus-platform · Public components: hazard × DOI-lands intersections · cyber + kinetic intel feeds · DOI land-boundary masks

Advanced research and engineering in Space Domain Awareness and orbital intelligence: real-time orbital analytics, conjunction assessment, satellite anomaly detection, maneuver and intent analysis, space weather, and decision support. This line of work traces back to Deep Sight, a Space Domain Awareness platform I built and scaled from a Defense Innovation Unit accelerator prototype into a deployed production system supporting U.S. Air Force and U.S. Space Force space-situational-awareness missions.

Implementation is intentionally not public — the capability overview, lineage, and research index live at deepsight. Related research includes orbital-data enrichment — augmenting two-line element sets into higher-fidelity state estimates to improve conjunction assessment and risk modeling.

A consumer-facing space experience for iOS — live orbital awareness, sky observation, and exploration — currently in private development ahead of public release.

Ongoing engineering experiments in agentic AI and orchestration, voice-first operational copilots, persistent-memory architectures for operational AI, computer vision, and edge/on-device inference. This work feeds directly into the systems above; components are published as they become releasable.

• Work supporting the Defense Innovation Unit space portfolio — commercial Space Domain Awareness, featured in DIU's Commercial Space Industry Providing Tools to Advance and Augment Space Superiority
• Systems developed for U.S. Air Force / U.S. Space Force space-situational-awareness mission sets
• Research conducted with NASA — satellite anomaly detection for collision avoidance (Goddard), Earth-science emissions analysis, and space-weather data modernization (CME prediction, electromagnetic-variance analysis)
• Satellite anomaly-detection research recognized in DIU portfolio contests for space superiority (2021, 2023, 2025); patent work in detecting orbital variance associated with intersection events and GNSS spoofing

A few markers of scale from that career: a $427M CTO portfolio and global technology strategy at GM Financial; executive lead for migration and modernization of 80,000+ Kubernetes clusters across multi-region, multi-cloud environments; executive owner of 18+ PB global data platforms at 99.99% uptime; statewide law-enforcement systems serving 3,000+ sworn officers and 570+ agencies; tactical-edge communications in multiple areas of conflict.

U.S. Army veteran. PhD, Philosophy — Advanced Logic (Wayne State University); M.A., Philosophy — Logic & Ethics, and B.A., Philosophy (Western Michigan University).

• Commercial Space Industry Providing Tools to Advance and Augment Space Superiority — Defense Innovation Unit, 2022
• Augmenting GEOINT with Synthetic Aperture Radar and Hyper-Spectral Analysis for Underwater Artifact Identification — NRO–NSA GEMCUTTER summary request, 2024
• Hyper-Spectral Imagery Enhancement with SAR Derivatives for Target Identification — DIU Phase II SSA summary, 2024
• Two-Line Element (TLE) Smoothing for Increased Accuracy in Covariance Calculation — U.S. Space Force Unified Data Library presentation, 2022
• Reverse-Ephemeris Extra-Terram Navigation for Space Exploration — DARPA position summary on route guidance, 2024

Much of Deepinder Uppal's work involves government, defense, public-safety, operational, or security-sensitive environments. Production repositories, infrastructure configurations, protected datasets, and non-public implementation details are intentionally not published here.

This GitHub contains publicly releasable research, reference architectures, open datasets, prototypes, technical demonstrations, and selected engineering work.

_{Deepinder Uppal · Chief Technology Officer, U.S. Department of the Interior · AI, space systems, and public-safety technology · linkedin.com/in/deepsinghuppal}