A carefully curated collection of high-quality tools, libraries, research papers, projects, and tutorials centered around Model Context Protocol (MCP) — a novel paradigm designed to enable modular, adaptive coordination between large language models (LLMs) and external tools or data contexts.

A carefully curated collection of high-quality tools, libraries, research papers, projects, and tutorials centered around Joint Embedding Predictive Architecture (JEPA).

A carefully curated collection of high-quality libraries, projects, tutorials, research papers, and other essential resources focused on Structured State Space Models (SSMs), with a special emphasis on S4, Mamba, and their variants.

A carefully curated collection of high-quality libraries, projects, tutorials, research papers, and other essential resources focused on Physics-Informed Machine Learning (PIML) and Physics-Informed Neural Networks (PINNs).

A carefully curated collection of high-quality libraries, projects, tutorials, research papers, and other essential resources focused on TinyML — the intersection of machine learning and ultra-low-power embedded systems.

A carefully curated collection of high-quality libraries, projects, tutorials, research papers, and other essential resources focused on Mechanistic Interpretability, a growing subfield in machine learning interpretability research that aims to reverse-engineer neural networks into understandable computational components.

A carefully curated collection of tools, datasets, papers, and open-source implementations focused on Synthetic Data Generation (SDG) and its realism-driven extension, Realistic Artificial Data (RAD) — a paradigm that moves beyond superficial resemblance toward causally grounded, physically consistent, and privacy-safe data generation.

A carefully curated collection of high-quality resources, tools, research papers, implementations, and tutorials dedicated to Post-Quantum Cryptography (PQC) — the emerging frontier of cryptographic security designed to withstand quantum adversaries.

Official website of AIT Lab, Texas State University

Building Trustworthy Mental Health Benchmarks on Bluesky: A Validation-Aware Weak-Supervision Framework