Building better batteries, faster
PhD student Pablo Leon uses machine learning to expedite research on novel battery materials, while helping newer students navigate graduate school.
PhD student Pablo Leon uses machine learning to expedite research on novel battery materials, while helping newer students navigate graduate school.
An MIT-developed device with the appearance of a Wi-Fi router uses a neural network to discern the presence and severity of one of the fastest-growing neurological diseases in the world.
Engineers 3D print materials with networks of sensors directly incorporated.
The MIT researcher and former professor discusses how Covid-19 and the influx of virtual technologies created a new medical ecosystem that needs more synchronized oversight.
The faculty members will work together to advance the cross-cutting initiative of the MIT Schwarzman College of Computing.
Graduate student Jana Saadi works on making the product design process more creative and inclusive.
Hailing from a small town in Italy, Matteo Bucci is determined to address some of the unknowns plaguing fundamental science.
Researchers use machine learning to automatically solve, explain, and generate university-level math problems at a human level.
New research ties inaccuracies in pulse oximeter readings to racial disparities in treatment and outcomes.
Researchers train a machine-learning model to monitor and adjust the 3D printing process to correct errors in real-time.
Engineers working on “analog deep learning” have found a way to propel protons through solids at unprecedented speeds.
“Interpretability methods” seek to shed light on how machine-learning models make predictions, but researchers say to proceed with caution.
Methods that make a machine-learning model’s predictions more accurate overall can reduce accuracy for underrepresented subgroups. A new approach can help.
Researchers have made strides toward machine-learning models that can help doctors more efficiently find information in a patient’s health record.
A geometric deep-learning model is faster and more accurate than state-of-the-art computational models, reducing the chances and costs of drug trial failures.