Marc Baldo, Jacopo Buongiorno, and Hsiao-hua Burke, along with 13 additional MIT alumni, are honored for significant contributions to engineering research, practice, and education.
The MIT Edgerton Center technical instructor’s expertise and dedication enriches the student experience.
Two professors and three additional alumni recognized for “dreaming up solutions to global challenges — advancing health, sustainability, and human connection.”
The sticky, wearable sensor could help identify early signs of acute liver failure.
In a Q&A, the reigning Classic Tetris World Champion describes his recent game crash — and the behind-the-scenes race that led him and fellow player Willis “BlueScuti” Gibson to record achievements.
Autonomous helicopters made by Rotor Technologies, a startup led by MIT alumni, take the human out of risky commercial missions.
Richard Lester describes an emerging new initiative that will back climate efforts at the Institute and find outside partnerships to drive actionable innovation.
At the ASM Materials Education Foundation’s 2023 Undergraduate Design Competition, seniors Louise Anderfaas and Darsh Grewal design a super-strong aluminum plate for applications such as planes and cars.
The method lets researchers identify and control larger numbers of atomic-scale defects, to build a bigger system of qubits.
A plastic microfluidic chip can remove some risky cells that could potentially become tumors before they are implanted in a patient.
MIT delegates share observations and insights from the largest-ever UN climate conference.
The graduate students will aim to commercialize innovations in AI, machine learning, and data science.
Me-Shirts, winner of the annual MIT materials science competition, has developed a biodegradable material than can be easily added and removed from shirts.
Awarded $65.67 million from ARPA-H, the researchers will work to develop ingestible capsules that deliver mRNA and electric stimuli to treat metabolic disorders such as diabetes.
Exploiting the symmetry within datasets, MIT researchers show, can decrease the amount of data needed for training neural networks.