Machine Learning and Decision Making Lab

The Machine Learning and Decision Making Lab (ML&DM Lab) conducts research on the foundations of machine learning and decision making, as well as their real-world applications.

Our goal is to advance the foundational principles of machine learning and decision making, while also tackling practical challenges across different domains such as transport, energy, life sciences, autonomous systems, recommendation systems, and decision support systems.

Our current research focuses on the following topics:

Interactive Machine Learning and Sequential Decision Making: including topics around active learning, multi-armed bandits (online learning), reinforcement learning, human-in-the-loop machine learning, and multi-agent/federated learning
Efficient Deep Learning: including computation-efficient, data-efficient and uncertainty-aware deep learning
Unsupervised Learning: including generative AI & Large Language Models, unsupervised representation learning, cluster modeling and learning with graphs and networks

The Machine Learning and Decision Making Lab is led by Professor Morteza Haghir Chehreghani. More information about the lab can be found on the external research group page.