Enhanced IP Geolocation Model for Security and Verification
Designed a novel IP geolocation confidence model combining location observation data with network topography analysis to improve the accuracy of geographic...
What It Is
Pipelines train models on data to produce predictive or generative outputs and improve performance through iteration.
What This Domain Trains You To Notice
This domain is valuable because data and AI systems expose the full path from collection to action. They make it obvious that storage, transformation, meaning, trust, and incentives all shape the value of the output.
Why It Transfers
The transfer advantage is strong here. Learning to ask where data came from, how it changed, and who is rewarded by its use builds a habit that improves product, operational, and strategic thinking in other domains. This domain gets more useful when it is compared with adjacent systems instead of being treated as a silo. That is where reusable judgment starts to form.
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Used Tableau and sampled data to identify impossible or improbable movement patterns in location observation datasets, then designed a system to tag and filter...
Machine Learning and Data Science
Projects involving predictive modeling, ML pipelines, feature engineering, and data-driven decision systems.
Machine Learning Pipeline for Automated Ad Pricing
Before TensorFlow existed, built a continuously-learning SQL-based machine learning system that optimized real-time bid pricing for programmatic advertising —...
Neural Networks
Neural Networks is a computational models inspired by biological neural systems.
Scoring Algorithm for Customer Revisit Attribution
Designed, implemented, and tested 150 iterations of a scoring algorithm to determine the likelihood of a customer revisiting a physical store, based on mobile...
Voronoi Regions for International Geofencing
Created an algorithm to build an optimal international geofencing system for hundreds of millions of postal address regions, incorporating feedback from 32...