Focal Domains

Where AI Edge Computing meets Ecological Intelligence

cat ecological_ai_manifesto.txt

AI systems should not simply extract value from the environment but actively contribute to its regeneration and resilience. By applying biomimetic principles and ecological understanding to AI architecture, we can create technology that operates within planetary boundaries while enhancing human capabilities.

My work operates at the convergence of three distinct but interconnected domains, creating a unique interdisciplinary approach to AI Edge Computing that integrates biological systems thinking with technological innovation.

AI Edge Computing Architecture

Building decentralized, resilient computing infrastructure that operates at the edge - closer to data sources and with reduced dependency on centralized cloud systems.

Designing computational frameworks optimized for resource-constrained environments
Implementing FOSS principles to create accessible, transparent, and community-oriented systems
Developing model compression and optimization techniques for efficient local deployment
Creating NixOS-based reproducible computing environments for future-proof deployment

Model Optimization Embedded Systems Distributed Computing NixOS Containerization Python Systems Architecture

Bioinformatics & Multi-modal Data Integration

Leveraging extensive experience in processing and analyzing complex biological data sets to develop advanced data integration frameworks for AI systems.

Creating multi-modal frameworks that integrate text, image, and structured biological data
Developing interfaces between protein language models and biological ontologies
Applying genomics and transcriptomics analysis techniques to complex data problems
Building data pipelines for processing terabytes of heterogeneous data

Genomics Transcriptomics Large Dataset Analysis Statistical Modeling Data Visualization Protein Language Models Biological Ontologies

Planetary Intelligence Systems

Integrating ecological principles and systems thinking into AI architecture to create technology that operates in harmony with planetary boundaries.

Developing ecological intelligence frameworks that anticipate planetary adaptation needs
Designing technology that mimics natural systems' resilience and adaptability
Creating computational models inspired by complex biological networks
Implementing resource-aware computing that minimizes ecological footprint

Systems Thinking Biomimicry Resilience Engineering Ecological Modeling Sustainable Computing Complex Systems Analysis

grep "synergy" interdisciplinary_approach.md

The true power emerges at the intersection of these domains, where biological understanding informs technological design, and ecological thinking creates more resilient computing systems. This interdisciplinary approach enables solutions that are technically sophisticated while remaining grounded in planetary awareness.

Current Applications

I'm currently focused on applying this interdisciplinary approach to several key projects:

BioDOG (Biological Database-Oriented Generation) - A framework interfacing protein language models with biological ontologies to enable enhanced analysis of complex biological systems
Mahakala - A lightweight, edge-deployable AI system designed for resource-constrained environments with ecological awareness built into its architecture
Localized LLM Deployment - Creating accessible infrastructure for community-oriented AI technology that respects privacy and operates independently of centralized cloud systems

Interdisciplinary Advantage

My background in oceanography and bioinformatics provides unique insights that directly translate to AI Edge Computing challenges:

Large Dataset Analysis → Handling sensor networks Complex Systems Modeling → AI architecture design Environmental Adaptation → Resilient computing Ecological Networks → Distributed systems Resource Optimization → Efficient computing