Stephanie George


Professional Summary
Stephanie George is a hydroinformatics engineer specializing in community-scale flash flood early warning systems. By integrating high-resolution hydrologic modeling, IoT sensor networks, and social vulnerability mapping, she develops hyperlocal prediction tools that empower neighborhoods to act before floodwaters rise. Her systems bridge the gap between meteorological forecasts and street-level resilience, prioritizing at-risk populations through AI-driven risk equity analysis.
Core Innovations & Technical Leadership
1. Precision Flood Forecasting
Builds 1-meter resolution hydrodynamic models that:
Simulate surface runoff in urban terrain with 90% accuracy
Predict sewer backflow timing using real-time lidar drainage maps
Integrate live rainfall data from low-cost community rain gauges
2. Vulnerability-Aware Alerting
Designs social impact algorithms to:
Weight warnings by population density of elderly/disabled residents
Map evacuation routes avoiding known landslide zones
Prioritize alerts for basement apartments and mobile home parks
3. Citizen-Activated Defense
Pioneers participatory flood mitigation including:
Crowdsourced clogged drain reporting via chatbot
Dynamic sandbag deployment maps using volunteer GPS
AR overlays showing historical flood lines on smartphones
Career Milestones
Reduced flood response time by 68% in Houston's Sunnyside neighborhood pilot
Developed the FLood Alert Community Toolkit (FACT) adopted by 14 US cities
Patented a gutter flow sensor costing <$20 with 98% debris detection accuracy


TheresearchrequiresGPT-4fine-tuningduetothecomplexityandspecificityofflood
predictionandurbandata.GPT-4’sadvancedcapabilities,includingitslarger
parametersetandenhancedcontextualunderstanding,areessentialforanalyzing
intricatepatterns,simulatingfloodscenarios,andintegratingreal-timedata.
PubliclyavailableGPT-3.5fine-tuninglackstheprecisionanddepthneededtohandle
thenuancedanddynamicnatureofurbanfloodrisks.Fine-tuningGPT-4ensuresthemodel
canadapttodiversedatasets,processlargevolumesofinformation,andgenerate
actionableinsights,makingitindispensableforthisstudy.
Aspartofthesubmission,IrecommendreviewingmypastworkonAIapplicationsin
disastermanagement,particularlymypapertitled“AI-DrivenFloodPrediction:ACase
StudyofUrbanFloodRiskAssessment”.ThisstudyexploredtheuseofAItomodeland
predictfloodrisksinurbanareas,focusingonimprovingearlywarningsystemsand
publicsafety.Additionally,myresearchon“EthicalImplicationsofAIinDisaster
ManagementandUrbanResilience”providesafoundationforunderstandingthesocietal
impactofAI-drivensolutionsindisasterpreparednessandresponse.Theseworks
demonstratemyexpertiseinapplyingAItocomplexenvironmentalchallengesand
highlightmyabilitytoconductrigorous,interdisciplinaryresearch.