A college for the computing age
With the initial organizational structure in place, the MIT Schwarzman College of Computing moves forward with implementation.
Department
Electrical Engineering and Computer Science
A smart surface for smart devices
External system improves phones’ signal strength 1,000 percent, without requiring extra antennas.
Giving cryptocurrency users more bang for their buck
Routing scheme boosts efficiency in networks that help speed up blockchain transactions.
At halfway point, SuperUROP scholars share their research results
In a lively poster session, more than 100 undergraduates discuss their yearlong research projects on everything from machine learning to political geography.
Demystifying artificial intelligence
Doctoral candidate Natalie Lao wants to show that anyone can learn to use AI to make a better world.
Accelerating the pace of engineering
The 2019-20 School of Engineering MathWorks Fellows are using MATLAB and Simulink to advance discovery and innovation across disciplines.
Technique reveals whether models of patient risk are accurate
Computer scientists’ new method could help doctors avoid ineffective or unnecessarily risky treatments.
Using artificial intelligence to enrich digital maps
Model tags road features based on satellite images, to improve GPS navigation in places with limited map data.
MIT graduate students lead conference on microsystems and nanotechnology
Student committee puts together research showcase while balancing coursework, qualifying exams, and extracurriculars.
How to verify that quantum chips are computing correctly
A new method determines whether circuits are accurately executing complex operations that classical computers can’t tackle.
Sending clearer signals
Associate Professor Yury Polyanskiy is working to keep data flowing as the “internet of things” becomes a reality.
How well can computers connect symptoms to diseases?
Models that map these relationships based on patient data require fine-tuning for certain conditions, study shows.
Three from MIT are named 2020 fellows of the IEEE
Two staff members from Lincoln Laboratory and a professor in the School of Engineering are recognized for their influential research.
Finding the true potential of algorithms
Using mathematical theory, Virginia Williams coaxes algorithms to run faster or proves they’ve hit their maximum speed.
Tool predicts how fast code will run on a chip
Machine-learning system should enable developers to improve computing efficiency in a range of applications.