MIT spinout DataCebo helps companies bolster their datasets by creating synthetic data that mimic the real thing.

By breaking an intractable problem into smaller chunks, a deep-learning technique identifies the optimal areas for thinning out traffic in a warehouse.

An easy-to-use technique could assist everyone from economists to sports analysts.

A new, data-driven approach could lead to better solutions for tricky optimization problems like global package routing or power grid operation.

By focusing on causal relationships in genome regulation, a new AI method could help scientists identify new immunotherapy techniques or regenerative therapies.

A cross-departmental team is leading efforts to utilize machine learning for increased efficiency in heating and cooling MIT’s buildings.

With this new approach, a tailsitter aircraft, ideal for search-and-rescue missions, can plan and execute complex, high-speed acrobatic maneuvers.

The MIT Schwarzman College of Computing awards seed grants to seven interdisciplinary projects exploring AI-augmented management.

Assistant Professor Cathy Wu is addressing traffic control problems by leveraging deep reinforcement learning.

Researchers develop a machine-learning technique that can efficiently learn to control a robot, leading to better performance with fewer data.