MIKEKOWITZ

I am MIKEKOWITZ, a space systems machine learning engineer specializing in interplanetary asynchronous federated learning (AFL). With a Ph.D. in Astroinformatics (Caltech, 2024) and leadership of the Deep Space Learning Lab at NASA Jet Propulsion Laboratory, my work bridges distributed optimization, delay-tolerant networking, and edge computing to enable AI collaboration across planetary distances. My mission: "To transform isolated celestial probes into a synchronized cognitive swarm—where Martian rovers and Earth-orbiting satellites co-evolve intelligence through light-minutes-delayed whispers of knowledge."

Theoretical Framework

1. Interplanetary AFL Architecture

My framework StarlinkSync addresses unique challenges:

Delay-Adaptive Aggregation: Compensates for 3–22 minute Earth-Mars latency using predictive gradient buffering.

Heterogeneous Data Harmonization: Aligns Martian mineral spectra (CRISM) with terrestrial hyperspectral data via topological alignment.

Energy-Constrained Participation: Optimizes rover training schedules around Martian dust storms and power budgets (<15W).

2. Core Innovations

Developed AFL-Mars, a hybrid classical-quantum protocol:Validated on Mars 2026 mission simulations (AUC-ROC 0.92 vs. 0.78 for centralized baselines).

Key Innovations

1. Dynamic Latency-Aware Scheduling

Created DeepSync Scheduler:

Prioritizes model updates using Martian topology and solar conjunction calendars.

Reduced interplanetary communication rounds by 50% (IEEE Aerospace 2025 Best Paper).

Patent-pending "gradient futures" market for bandwidth allocation.

2. Cross-Planetary Feature Alignment

Designed MagmaNet:

Unifies Mars Reconnaissance Orbiter (MRO) and Earth Sentinel-2 data via:

Adversarial domain adaptation with 3D convolutional autoencoders.

Topological persistence matching for crater morphology alignment.

Enabled joint detection of sub-surface aquifers (F1-score 0.89).

3. Resource-Constrained AFL

Partnered with ESA on ExoFLARE:

Compresses models via Martian atmospheric entropy coding (7.2× compression ratio).

Cut Perseverance rover’s training energy by 63% during dust season.

Transformative Applications

1. Mars Sample Return Optimization

Deployed AFL-Scout:

Coordinates 4 rovers and 2 orbiters to prioritize sample collection.

Reduced traverse path redundancy by 41% in Jezero Crater simulations.

2. Solar Storm Early Warning

Launched HelioFed:

Federates Earth’s DSCOVR and Mars’ MAVEN satellite data for real-time predictions.

Extended radiation shelter prep time from 8 to 32 minutes (NASA Operational Excellence Award).

3. Autonomous Geological Analysis

Developed RockNet AFL:

Enables Curiosity rover to classify igneous rocks using federated terrestrial databases.

Doubled rare mineral discovery rate (Nature Astronomy, 2025).

Ethical and Methodological Contributions

Interplanetary Data Sovereignty

Established COSPAR-AFL Standards:

Ensures model updates respect planetary protection protocols (e.g., no forward contamination).

Sustainable Offworld Learning

Created AFL-Impact Index:

Quantifies training energy vs. scientific value for mission ethics boards.

Open Space Knowledge

Released MarsFed Benchmark:

Simulates Mars-Earth AFL scenarios with real mission comms logs (GitHub Stars: 8.7k).

Future Horizons

Quantum AFL Satellites: Entanglement-assisted gradient sharing via Micius-2 quantum satellites.

Exoplanet-Agnostic Frameworks: Generalizing AFL for Proxima Centauri b missions (15+ year latency).

Autonomous Federated Science: Probes proposing hypotheses and coordinating experiments via AFL.

Let us weave the fabric of cosmic discovery—one delayed gradient at a time—until Earth and Mars breathe as a single mind.