Ushering in a new era of computing
Dan Huttenlocher is a professor of electrical engineering and computer science and the inaugural dean at MIT Schwarzman College of Computing.
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Dan Huttenlocher is a professor of electrical engineering and computer science and the inaugural dean at MIT Schwarzman College of Computing.
New technique significantly reduces training and inference time on extensive datasets to keep pace with fast-moving data in finance, social networks, and fraud detection in cryptocurrency.
New technique could diminish errors that hamper the performance of super-fast analog optical neural networks.
MIT undergraduate researchers Helena Merker, Harry Heiberger, and Linh Nguyen, and PhD student Tongtong Liu, exploit machine-learning techniques to determine the magnetic structure of materials.
Researchers have developed a programmable optical device for high-speed beam steering.
The MIT senior will pursue postgraduate studies in computer science in Ireland.
New research reveals a scalable technique that uses synthetic data to improve the accuracy of AI models that recognize images.
New system can teach a group of cooperative or competitive AI agents to find an optimal long-term solution.
A new technique that accurately measures how atom-thin materials expand when heated could help engineers develop faster, more powerful electronic devices.
Students reflect on their top performance in Dhaka, Bangladesh, which ended a 44-year drought for MIT.
An experimental platform that puts moderation in the hands of its users shows that people do evaluate posts effectively and share their assessments with others.
MIT CSAIL researchers solve a differential equation behind the interaction of two neurons through synapses to unlock a new type of speedy and efficient AI algorithm.
Students describe what it’s like to compete at the very top tiers of computing.
Jack Cook, Matthew Kearney, and Jupneet Singh will begin postgraduate studies at Oxford University next fall.
Researchers make headway in solving a longstanding problem of balancing curious “exploration” versus “exploitation” of known pathways in reinforcement learning.