Code for UTexas Structural Econometrics and IO
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Updated
Apr 13, 2021 - Jupyter Notebook
Code for UTexas Structural Econometrics and IO
TRE'25: Joint Estimation and Prediction of City-wide Delivery Demand: A Large Language Model Empowered Graph-based Learning Approach
Public charging station utilization dataset for the city of Hamburg. Dataset is described in the respective paper: Placing BEV Charging Infrastructure: Influencing Factors, Metrics, and their Influence on Observed Charger Utilization
Retail pricing and promotion decision engine with demand estimation, counterfactual optimization, IV sensitivity, A/B test design, and Streamlit deployment.
Tool to quickly quantify potential for Park & Drive locations based on MATSim traffic models
A collection of PM guides, prompt templates, and use case documentation for AI product managers
Applied Newsvendor model for optimal inventory ordering in print media wholesale distribution. Demand estimation from censored sales + lost sales, empirical & kernel density distributions, critical ratio optimization, full-network & per-wholesaler calculations, and before/after evaluation of a real supply test (Bezugstest).
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