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.

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

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

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

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

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

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.

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

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

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

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

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

AI/ML epidemiology portfolio for infectious disease outbreak forecasting, surveillance modeling, and reproducible public health analytics.

Tiny, transparent experiments in Active Inference using pymdp

Active Inference Pocket Lab — portable simulation and learning environment for Active Inference experiments

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.

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