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

A warning sign stands in a natural, grassy area, cautioning about dangerous rip currents. The sign has a red border with the words 'Warning' in white and 'Dangerous Rip Currents' below. The background features clear blue skies and a horizon line where the water meets the land.
A warning sign stands in a natural, grassy area, cautioning about dangerous rip currents. The sign has a red border with the words 'Warning' in white and 'Dangerous Rip Currents' below. The background features clear blue skies and a horizon line where the water meets the land.

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.

Floodwaters cover a residential street, partially submerging garages and houses on either side. Trees without leaves are visible, and the sky is overcast, adding to the somber atmosphere.
Floodwaters cover a residential street, partially submerging garages and houses on either side. Trees without leaves are visible, and the sky is overcast, adding to the somber atmosphere.

Aspartofthesubmission,IrecommendreviewingmypastworkonAIapplicationsin

disastermanagement,particularlymypapertitled“AI-DrivenFloodPrediction:ACase

StudyofUrbanFloodRiskAssessment”.ThisstudyexploredtheuseofAItomodeland

predictfloodrisksinurbanareas,focusingonimprovingearlywarningsystemsand

publicsafety.Additionally,myresearchon“EthicalImplicationsofAIinDisaster

ManagementandUrbanResilience”providesafoundationforunderstandingthesocietal

impactofAI-drivensolutionsindisasterpreparednessandresponse.Theseworks

demonstratemyexpertiseinapplyingAItocomplexenvironmentalchallengesand

highlightmyabilitytoconductrigorous,interdisciplinaryresearch.