Q&A: The path to a PhD in computational science and engineering at MIT
MIT doctoral candidate Emily Williams reflects on her time at the Center for Computational Science and Engineering as she becomes the program’s first graduate.
MIT doctoral candidate Emily Williams reflects on her time at the Center for Computational Science and Engineering as she becomes the program’s first graduate.
Connor Coley works at the interface of chemistry and machine learning, to discover and design new drug compounds.
MIT faculty member in electrical engineering and computer science to focus on innovation in engineering education and new pedagogical approaches.
By rapidly generating a smooth path plan that cuts travel time and avoids obstacles, the open-source “MIGHTY” system could streamline disaster recovery and parcel delivery.
When it comes to emissions, individual driving patterns matter as much as how “green” the regional electricity mix is, MIT researchers report.
The Udall Foundation identifies and rewards future leaders in tribal public policy, Indigenous health policy, and the environment.
With a novel design, MIT researchers overcame a stubborn problem that has limited the effectiveness of chip-based systems for lidar.
The “MetaEase” technique provides a heads-up to potential scenarios that could cause long wait-times or outages.
Assistant Professor Gabriele Farina mines the foundations of decision-making in complex multi-agent scenarios.
An old patent from MIT Professor Bill Freeman inspired the new “Y-zipper,” a three-sided fastener that snaps gear, robots, and art into shape at the push of a button.
Afreen Siddiqi, Kathleen Thelen, and Vinod Vaikuntanathan, along with alumna Kate Manne, are appointed to the 2026 class of “trail-blazing fellows.”
A new debiasing technique called WRING avoids creating or amplifying biases that can occur with existing debiasing approaches.
Building on a long-standing MIT–IBM collaboration, the new lab will chart the convergence of AI, algorithms, and quantum computing.
A new method could bring more accurate and efficient AI models to high-stakes applications like health care and finance, even in under-resourced settings.
MIT researchers leveraged a surprise discovery to devise a faster and more precise biomedical imaging technique.