Summit features the latest research of women and other underrepresented genders in MIT EECS, along with an opportunity to network, share experiences, and learn.
New technique applied to small computer chips enables efficient vision and detection algorithms without internet connectivity.
New products presented by students at the annual event included a curb-climbing wheelchair attachment and seizure-preventing glasses.
A new “common-sense” approach to computer vision enables artificial intelligence that interprets scenes more accurately than other systems do.
Working directly with oyster farmers, MIT students are developing a robot that can flip heavy, floating bags of oysters, helping the shellfish to grow and stay healthy.
Marcos Berríos ’06, Christina Birch PhD ’15, and Christopher Williams PhD ’12 make up a third of the 2021 NASA astronaut candidate class.
MIT-IBM Watson AI Lab researchers aim to design concrete mixtures that use AI to shrink environmental footprint and cost, while recycling byproducts and increasing performance.
“Evolution Gym” is a large-scale benchmark for co-optimizing the design and control of soft robots that takes inspiration from nature and evolutionary processes.
The new machine-learning system can generate a 3D scene from an image about 15,000 times faster than other methods.
Graduate student Lucy Du designs novel prosthetics and seeks to inspire others to pursue engineering.
Chandrakasan honored for his “contributions to ultralow-power circuits and systems, and leadership in academia and advancing diversity in the profession.”
A new AI-powered, virtual platform uses real-world physics to simulate a rich and interactive audio-visual environment, enabling human and robotic learning, training, and experimental studies.
Electrical engineer and Stanford University professor discusses how computer software can support advanced designs and new functionalities.
Sihao Huang, William Kuhl, Tingyu Li, Giramnah Peña-Alcántara, Sreya Vangara, and Kelly Wu will pursue graduate studies in global affairs at Tsinghua University in Beijing.
A deep learning model rapidly predicts the 3D shapes of drug-like molecules, which could accelerate the process of discovering new medicines.