The Algorithms & Complexity research group is committed to advancing the theoretical foundations of computer science through the study of algorithms and computational complexity. The group's research focuses on:

• Algorithmic Game Theory & Resource Allocation: Developing mathematical frameworks and algorithms to address issues in resource distribution and strategic interactions, with applications in economics and multi-agent systems.

• Sublinear and Streaming Algorithms: Designing efficient algorithms that process massive datasets while using minimal computational resources, addressing challenges in data streaming and big data environments.

• Computational Learning Theory: Investigating models of machine learning with an emphasis on the computational aspects of learning, including PAC learning and robust learning algorithms.

• Distributed and Randomized Algorithms: Creating algorithms optimized for decentralized systems, with a focus on improving efficiency in networks and distributed computing.

• Optimization and Big Data: Applying algorithmic insights to solve complex optimization problems that arise in big data analysis, ensuring that solutions are scalable and efficient.