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  • 👋 Hi, I’m @pleiby
  • 👀 I’m interested in optimization modeling and data-science for energy-environmental policy analysis.
  • 🌱 I’m currently learning Julia/JuMP for optimization and R/rstan-Julia/Turing for Bayesian causal inference
  • 💞️ I’m looking to collaborate on applied policy modeling or data science studies relevant to energy security and climate policy, and advanced mobility technology (automated/shared/electrified vehicles).
  • 📫 You can reach me here or at leibypn at outlook.com

Paul Leiby's Projects

18s191 icon 18s191

Course 18.S191 at MIT, fall 2020 - Introduction to computational thinking with Julia:

complementarity.jl icon complementarity.jl

provides a modeling interface for mixed complementarity problems (MCP) and math programs with equilibrium problems (MPEC) via JuMP

dataviz icon dataviz

A book covering the fundamentals of data visualization.

eia_mer icon eia_mer

EIA Monthly Energy Review (MER) data: access and visualize/analyze

handson-ml icon handson-ml

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.

handson-ml2 icon handson-ml2

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

hilbert-huang-transform icon hilbert-huang-transform

The Hilbert–Huang transform (HHT) is a way to decompose a signal into so-called intrinsic mode functions (IMF) along with a trend, and obtain instantaneous frequency data. It is designed to work well for data that is nonstationary and nonlinear.

honeybeeventilationmodel icon honeybeeventilationmodel

This function contains a user friendly simulation of ventilation dynamics in honeybee nests. It is intended to accompany a manuscript submitted to Journal of the Royal Society Interface. The function allows for optional input so that the user can easily play with the parameters in the model and visualize the resulting dynamics.

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