BAyesian Model-Building Interface (Bambi) in Python.

The official implementation of "Joint Modeling of Image and Label Statistics for Enhancing Model Generalizability of Medical Image Segmentation" via Pytorch

A professional, research-grade comparison of Gaussian Copula and Variational Autoencoder (VAE) methods for synthetic tabular data generation. Includes full evaluation pipeline with distribution overlap, correlation analysis, PCA projections, pairplots, metrics, and automated visual reports.

A collection of scripts used for modeling global daily maximum surges

Implementation is to use gradient descent to find the optimal values of θ that minimize the cost function.

Python package for conducting power analysis for experiments using regression and/or clustered data.

Fitness estimates of SARS-CoV-2 variants

Pure-Python library of heavy-tailed probability distributions (Pareto, Burr, LogNormal, etc.) built from first principles.

Data science projects created using actual data, using Python and R.

Health and Economic Data Dashboard with Forecasting

Details the data modeling techniques used, the functionality of the output, and an in-depth idea of how a plan finder works based off of user inputs.

A Machine Learning modeling pipeline that predicts customer churn of an organization based on customer historical behavior

AI-powered system that converts offline fantasy football draft board photos to complete digital results. Universal manufacturer compatibility through color detection + intelligent player matching with direct ESPN upload.

Entity prioritization and escalation detection using GLMM statistical models

Pi estimator based on the Monte Carlo method

Graduate-level course on Scientific Computing (CIMAT, Spring 2025). Includes assignments and simulations on Monte Carlo methods, MCMC algorithms (e.g., Metropolis-Hastings, t-walk), inverse problems, and Bayesian inference using Python.

Automatically determine trends, correlations, and feature selections given dataset(s)

Predict Fantasy Premier League (FPL) points using two models: a Random Forest regression (ML_xP.py) and a custom statistical model (xP_FPL.py). This project explores different approaches to predicting player performance, with a detailed comparison for Gameweek 5 of the 2024/25 EPL season.

📁 Read files from IPFS trustless gateways with an async API using the rs-car wrapper for fast and efficient data retrieval.

The interface library for probabilistic modeling in HEP