Algorithmic Intelligence Lab (AIL) focuses on the theoretical foundations and practical impact of intelligent systems. Our research centers on optimization, algorithms, and mathematical modeling, and on how these core principles drive advances in artificial intelligence, machine learning, social network analysis, and data science.

At AIL, we view intelligence through an algorithmic lens. We develop new algorithmic frameworks, optimization methods, and theoretical guarantees that address fundamental challenges such as scalability, robustness, efficiency, and interpretability. These foundations are tightly coupled with applications in modern data-driven systems, including learning on graphs and networks, large-scale inference, decision-making under uncertainty, and socially informed AI.

A defining feature of AIL is the bidirectional flow between theory and practice. Real-world problems motivate new theoretical questions, while rigorous analysis informs the design of reliable and principled algorithms. By bridging abstract theory with applied domains, we aim to advance both the understanding and deployment of intelligent systems.