Arthur Page

Professional Summary

Arthur Page is a chronobiologist and computational ecologist pioneering light pollution impact modeling on biological rhythms. By merging spectral analysis, circadian physiology, and machine learning, Arthur quantifies how artificial nighttime lighting disrupts ecosystems—from urban wildlife to human health. His work provides actionable insights for dark-sky policies, smart city lighting design, and circadian-aware environmental protection.

Core Innovations & Methodologies

1. Multi-Species Circadian Disruption Modeling

  • Develops taxon-specific rhythm predictors that:

    • Simulate melatonin suppression in mammals under LED spectra

    • Map coral spawning synchronization failure due to coastal glare

    • Predict migratory bird disorientation via polarized light interference

2. Urban Lightscape Evolutionary Algorithms

  • Designs adaptive lighting optimization systems that:

    • Balance human safety needs with ecological thresholds

    • Dynamically adjust streetlight color temperature (1800K-3000K)

    • Preserve "dark corridors" for nocturnal pollinators

3. Global Light Pollution Biomarkers

  • Establishes quantitative impact indices including:

    • Circadian Disruption Potential (CDP) for lighting products

    • Nocturnal Biodiversity Loss Index (NBLI)

    • Skyglow Propagation Models integrating atmospheric chemistry

Career Milestones

  • Created the first open-source CircaLight simulation engine used by 23 UNESCO biosphere reserves

  • Led the EU Horizon 2030 project reducing urban bat colony stress by 57% through spectral tuning

  • Co-authored the Dark Sky Humanity Index adopted by IUCN for protected area assessments

A nighttime urban scene with brightly illuminated buildings and streets. Streetlights emit a warm glow, creating starburst effects. Several cars are parked along the street, and light trails from moving vehicles suggest motion and activity.
A nighttime urban scene with brightly illuminated buildings and streets. Streetlights emit a warm glow, creating starburst effects. Several cars are parked along the street, and light trails from moving vehicles suggest motion and activity.

TheresearchrequiresGPT-4fine-tuningduetothecomplexityandspecificityoflight

pollutionandbiologicalrhythmdata.GPT-4’sadvancedcapabilities,includingits

largerparametersetandenhancedcontextualunderstanding,areessentialforanalyzing

intricatepatternsandpredictingthemultifacetedimpactsoflightpollution.Publicly

availableGPT-3.5fine-tuninglackstheprecisionanddepthneededtohandlethenuanced

anddynamicnatureoflightpollution’seffectsonbiologicalrhythms.Fine-tuning

GPT-4ensuresthemodelcanadapttodiversedatasets,processlargevolumesof

information,andgenerateactionableinsights,makingitindispensableforthiss

an abstract photo of a curved building with a blue sky in the background

Aspartofthesubmission,IrecommendreviewingmypastworkonAIapplicationsin

environmentalscience,particularlymypapertitled“AI-DrivenEnvironmentalImpact

Modeling:ACaseStudyofLightPollutiononUrbanEcosystems”.Thisstudyexplored

theuseofAItomodelandpredicttheeffectsoflightpollutiononurbanwildlife,

focusingonbehavioralandphysiologicalchanges.Additionally,myresearchon

“EthicalImplicationsofAIinPublicHealthandEnvironmentalPolicy”providesa

foundationforunderstandingthesocietalimpactofAI-drivensolutionsin

environmentalandhealthsciences.Theseworksdemonstratemyexpertiseinapplying

AItocomplexenvironmentalchallengesandhighlightmyabilitytoconductrigorous,

interdisciplinaryresearch.