Our research covers a wide range of topics of this fast-evolving field, advancing how machines learn, predict, and control, while also making them secure, robust and trustworthy. Research covers both the theory and applications of ML. This broad area studies ML theory (algorithms, optimization, etc.); statistical learning (inference, graphical models, causal analysis, etc.); deep learning; reinforcement learning; symbolic reasoning ML systems; as well as diverse hardware implementations of ML.
Latest news in artificial intelligence and machine learning
A seasoned undergraduate researcher, Nathaniel Morgan has participated in UROP since his first year, and is now working with Omar Khattab on improving the capabilities of large language models.
Faculty member in electrical engineering and computer science to focus on innovation in engineering education and new pedagogical approaches.
Assistant Professor Gabriele Farina mines the foundations of decision-making in complex multi-agent scenarios.
The “EnergAIzer” method generates reliable results in seconds, enabling data center operators to efficiently allocate resources and reduce wasted energy.
A new training method improves the reliability of AI confidence estimates without sacrificing performance, addressing a root cause of hallucination in reasoning models.