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
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
By rethinking how large cloud computing systems operate, Associate Professor Christina Delimitrou seeks to make data centers more energy efficient.
The Institute’s undergraduate engineering, computer science, and economics programs are No. 1; its business program is No. 2.
In “Hack Yourself”, students apply computer science strategies to personal growth goals.
The recipients of the 2026 Ruth and Joel Spira Awards for Excellence in Teaching are Cullen Buie, Gabriele Farina, Ericmoore Jossou, and Farnaz Niroui.