Official Pytorch repository for "Diffusion Posterior Sampling for Linear Inverse Problem Solving: A Filtering Perspective", where FPS (Filtering Posterior Sampling) as well as its extension FPS-SMC are proposed.

An Intuitive tutorial on Bayesian filtering

Code supplement for "The Discriminative Kalman Filter for Bayesian Filtering with Nonlinear and Nongaussian Observation Models"

Variational Joint Filtering

Python library for adaptive Gaussian mixture state estimation. Useful for navigation and tracking in nonlinear non-Gaussian systems. Capable of incorporating negative information and other imprecise evidence.

A Julia package that provides high-level abstractions for simulating and deploying stochastic filters

Code supplement for "Discriminative Bayesian Filtering Lends Momentum to the Stochastic Newton Method for Minimizing Log-Convex Functions"

XeLaTeX for "A Discriminative Approach to Bayesian Filtering with Applications to Human Neural Decoding"

This is a comprehensive project focused on implementing popular algorithms for state estimation, robot localization, 2D mapping, and 2D & 3D SLAM. It utilizes various types of filters, including the Kalman Filter, Extended Kalman Filter, Unscented Kalman Filter, and Particle Filter.

These slides were presented at my dissertation defense (Division of Applied Mathematics, Brown University, 23 May 2018).

Expected free energy minimization with approximations to nonlinear observation functions

This repository contains code implementations for the Discriminative Kalman Filter.

code for neural filtering project

A comprehensive deep dive into Kalman Filtering — from linear basics to advanced nonlinear methods. Based on my thesis research, this site collects derivations, reviews, and resources for students, engineers, and researchers.

# Casimir Nanopositioning Platform (Research Prototype)

End-to-End Python econometric pipeline for modeling geopolitical risk in international trade using advanced Bayesian filtering, high-dimensional fixed effects, and split-panel jackknife inference. Replicates Hardwick's (2025) methodology with full robustness testing and automated reporting.

The workspace for our computational robotics class. I explored different techniques such as Bayesian filtering in a Gridworld environment, Graph Based Motion Planning on a chess board, Kalman Filtering... Work in progress !

A Kalman filter and Particle Filter implementation for Gaussian object tracking

Some notes on algorithms for time series and sequential data

🌍 Model political distance and trade dynamics using a quantitative framework to enhance understanding of international trade relationships.