BRITTNEYNORTON

I am BRITTNEY NORTON, an interdisciplinary computational ecologist and AI researcher pioneering the integration of biogeographic principles into machine learning model transfer strategies. With a Ph.D. in Spatial Ecology and Machine Learning (University of Cambridge, 2021) and a Postdoctoral Fellowship at the Smithsonian Institution’s Biodiversity Futures Lab (2022–2024), I bridge the gap between ecological dispersal theories and AI robustness. As the Founding Director of the BiogeoML Initiative and Lead Scientist of the NSF-funded EcoTransfer Project, I design adaptive model migration frameworks that mimic species colonization, adaptation, and extinction dynamics across heterogeneous data landscapes. My work on "island biogeography-inspired neural architecture search" won the 2023 ACM SIGSPATIAL Innovation Award and has been integrated into Google’s Earth Engine for wildfire prediction.

Research Motivation

Model transfer—the adaptation of pre-trained AI systems to new domains—faces challenges akin to species invading novel ecosystems: catastrophic forgetting, domain shift, and data scarcity. Yet, biogeography’s 150-year-old theories on biodiversity patterns remain untapped in AI. My research addresses three critical gaps:

1. Colonization Bottlenecks: Models fail to prioritize transferable features, akin to invasive species lacking pre-adaptations.

2. Adaptive Radiation Limits: Fine-tuning cannot replicate the rapid niche diversification seen in island radiations (e.g., Darwin’s finches).

3. Extinction Debt: Models degrade unpredictably in production, mirroring extinction cascades in fragmented habitats.

By reimagining model parameters as "digital species" and data domains as "landscape patches," I aim to establish evolutionarily stable transfer strategies that balance plasticity and stability.

Methodological Framework

My approach synergizes metacommunity ecology, graph neural networks (GNNs), and federated learning:

1. Species-Area-Curve Regularization (SACR)

• Developed BioTransfer, a framework translating biogeographic laws into AI constraints:

  • Island Biogeography Loss Function: Penalizes feature redundancy based on MacArthur-Wilson’s species equilibrium theory, improving few-shot transfer accuracy by 35%.

  • Adaptive Radiation Blocks: Modular neural components that diversify via competitive exclusion (simulating character displacement) in multi-task learning.

  • Validated on IUCN Red List data to predict invasive species ranges under climate change (AUC = 0.93).

• Deployed by Microsoft’s Planetary Computer to optimize crop disease models across 50+ agroecological zones.

2. Dispersal-Corridor Optimization

• Created DispersalNet, a GNN-based pathway for model migration:

  • Least-Cost Path Learning: Maps domain shifts as "digital landscapes," routing model updates through low-resistance feature corridors (analogous to wildlife corridors).

  • Stochastic Extinction Pruning: Dynamically removes redundant neurons mimicking background extinction rates, slashing model size by 40% without performance loss.

  • Enabled seamless transfer of COVID-19 lung CT models to TB detection in low-resource hospitals (WHO collaboration).

3. Latent Niche Embedding

• Pioneered NicheSpace, a contrastive learning system:

  • Hutchinsonian Hypervolume Encoding: Represents data domains as n-dimensional ecological niches, aligning model embeddings via competitive exclusion principles.

  • Founder Effect Simulation: Bootstraps model training with stochastically sampled "pioneer features" to avoid local optima.

  • Reduced catastrophic forgetting by 70% in lifelong learning robots for coral reef monitoring (Great Barrier Reef Foundation).

Ethical and Technical Innovations

1. Conservation-Inspired AI

   • Launched EcoML Commons, an open repository of 20,000+ pre-trained models annotated with biogeographic metadata (e.g., "data island" connectivity scores).

   • Authored the AI Biodiversity Impact Assessment Guidelines to audit model transfer’s carbon and computational "extinction footprints."

2. Equitable Knowledge Dispersal

   • Designed SeedBankML, a federated learning platform distributing model "propagules" to underserved regions via satellite-internet edge nodes.

   • Partnered with UNESCO to localize maternal health diagnostics in 15 Indigenous languages using ethnobiogeographic priors.

3. Resilience-by-Design

   • Developed Rewilding Regularization, recovering lost model plasticity via synthetic noise injections mimicking disturbance regimes.

   • Advocated for Digital CITES Agreements to regulate cross-border model transfers of endangered cultural data.

Global Impact and Future Visions

• 2023–2025 Milestones:

  • Enabled rapid adaptation of flood prediction models to Southeast Asian monsoon shifts using mangrove biogeography principles.

  • Trained 900+ practitioners via the BiogeoML Global Summit, fostering AI-ecology collaborations.

  • Reduced AI bias in biodiversity surveys by aligning model priors with Indigenous ecological knowledge (NatureServe Alliance).

• Vision 2026–2030:

  • Digital Pleistocene Rewilding: Restoring "extinct" model capabilities for ancient climate pattern analysis.

  • Exoplanetary Transfer Learning: Adapting biogeoML frameworks to interpret alien biosignatures (NASA Astrobiology Institute).

  • Bio-Digital Twin Ecosystems: Co-evolving AI models and real-world conservation strategies via coupled niche dynamics.

By treating AI not as a tool but as a synthetic ecosystem, I strive to create models that evolve, adapt, and thrive across the data universe—just as life has across Earth’s biomes.