Cognitive science research workspace — Active Inference, Bayesian modeling, ant behavior, and computational neuroscience

Hierarchical Bayesian modeling toolkit for interoceptive psychophysics (HRDT & RRST). Includes Stan models, power analysis tools, and educational resources for researchers.

Football forecasting framework to simulate the FIFA World Cup using team strength modeling and probabilistic match prediction.

Agent-based political election simulator using predictive coding

An R package for modeling asymmetric spatial associations between cell types in tissue images using a multilevel Bayesian framework.

Inflation forecasting during crisis periods using Bayesian Dynamic Linear Models, traditional econometrics, and machine learning. Includes data, code, and comprehensive analysis report.

R code and Stan models for "Decoupling Decision and Intensity in Visual Attention" .

Stan implementation of Lee & Sarnecka's (2010, 2011) knower-level model

Portfolio of applied data science projects in Bayesian modeling, causal inference, hierarchical models, and agent-based simulation.

Probabilistic Graphical Models Project

Bayesian Marketing Mix Modeling project using PyMC to estimate marketing channel effectiveness through probabilistic inference, adstock transformation, uncertainty-aware ROI analysis, and seasonal trend modelling across multiple advertising channels.

A repository for the R code and data used in Iwasaki et al. (2023)

Three‑level hierarchical enactive inference model of mental action (focused‑attention meditation with expert–novice profiles) with reproducible simulations and figures.

Comprehensive analysis of differential gene expression using Bayesian statistics and advanced statistical modeling techniques. The project includes scripts, data, figures, and analysis results.

Adaptive learning classifier using a Beta–Binomial model to estimate student proficiency

Bayesian media mix modeling (MMX) framework with latent state decomposition and SKAN bias corrections for historical & causal attribution

The project focuses on the analysis of experimental data regarding the continuous biomass production of a cyanobacterium of the genus Nostoc. The primary objective is to understand how different cultivation factors influence biological growth over time, monitored through optical density (OD).

Contextual Bayesian ETF allocation with macro features and no-look-ahead backtesting

Scripts and processed data for modeling lowland tapir diel activity patterns using Bayesian circular mixed-effects models, including code, visualizations, and reproducible workflows.

RL was cheaper. The heuristic was safer. Neither was correct. POLARIS stress-tests operational policies under chaos, demand spikes, and black swan events asking one question: which policy survives when everything goes wrong? Built with constrained RL, Bayesian modeling, CVaR risk metrics, and a human-in-the-loop governor.