Kiana Ceasar

Professional Summary

Kiana Ceasar is an innovative urban climate engineer specializing in wind tunnel effect simulation and optimization for 3D vertical cities. Combining computational fluid dynamics (CFD), AI-driven urban planning, and sustainable design principles, Kiana develops solutions to mitigate hazardous wind conditions in high-density urban environments. Her work ensures that skyscrapers, skybridges, and elevated green spaces coexist harmoniously with natural airflow patterns—enhancing pedestrian comfort, reducing energy consumption, and preventing structural wind loads.

Core Innovations & Technical Leadership

1. AI-Augmented Wind Simulation

  • Develops multi-scale CFD models that:

    • Predict microclimate turbulence at street level with 1-meter resolution

    • Optimize building aerodynamics using generative design algorithms

    • Simulate extreme weather scenarios (typhoons, dust storms) for resilient cities

2. Vertical City Wind Mitigation

  • Designs architectural interventions to:

    • Dissipate vortex shedding around supertall towers

    • Calm wind acceleration through porous skybridge networks

    • Redirect airflow to enhance natural ventilation in urban canyons

3. Policy-Driven Urban Climate Tools

  • Creates digital twin platforms for:

    • Real-time wind hazard monitoring via IoT sensor grids

    • Automated compliance checking against LEED Wind Comfort Criteria

    • Climate-responsive zoning recommendations for municipalities

Career Milestones

  • Reduced pedestrian-level wind speeds by 52% in Dubai’s Vertical Downtown project

  • Pioneered the "Windprint" certification now adopted by 8 global cities for high-rise approvals

  • Developed the open-source UrbanCFD.ai toolkit used by 10,000+ planners

TheresearchrequiresGPT-4fine-tuningduetothecomplexityandspecificityofurban

windsimulationdata.GPT-4’sadvancedcapabilities,includingitslargerparameter

setandenhancedcontextualunderstanding,areessentialforanalyzingintricatewind

patternsandoptimizingbuildingdesigns.PubliclyavailableGPT-3.5fine-tuninglacks

theprecisionanddepthneededtohandlethenuancedanddynamicnatureofwindtunnel

effectsinverticalcities.Fine-tuningGPT-4ensuresthemodelcanadapttodiverse

urbangeometries,processlargedatasets,andgenerateactionableinsights,makingit

indispensableforthisstudy.

A line of yellow wind turbines with attached solar panels is situated in front of a cityscape. The background features dry, mountainous terrain under a clear blue sky with a few scattered clouds.
A line of yellow wind turbines with attached solar panels is situated in front of a cityscape. The background features dry, mountainous terrain under a clear blue sky with a few scattered clouds.

Aspartofthesubmission,IrecommendreviewingmypastworkonAIapplicationsin

urbanplanning,particularlymypapertitled“AI-DrivenUrbanSimulation:ACaseStudy

ofWindFlowOptimizationinHigh-RiseBuildings”.ThisstudyexploredtheuseofAI

tomodelandoptimizewindpatternsinurbanenvironments,focusingonimproving

pedestriancomfortandbuildingsafety.Additionally,myresearchon“Ethical

ImplicationsofAIinSmartCityDevelopment”providesafoundationforunderstanding

thesocietalimpactofAI-drivensolutionsinurbaninnovation.Theseworksdemonstrate

myexpertiseinapplyingAItocomplexurbanchallengesandhighlightmyabilityto

conductrigorous,interdisciplinaryresearch.