Fifteen MIT faculty honored as “Committed to Caring” for 2021-23
Honor recognizes professors who went the extra mile advising during the pandemic’s disruptions.
Honor recognizes professors who went the extra mile advising during the pandemic’s disruptions.
In the Hoyt C. Hottel Lecture, Arnold tells the story of her pathbreaking research to engineer better enzymes for critical applications.
Professors Linda Griffith and Feng Zhang along with Guillermo Ameer ScD ’99, Darrell Gaskin SM ’87, William Hahn, and Vamsi Mootha recognized for contributions to medicine, health care, and public health.
Faculty members recognized for excellence via a diverse array of honors, grants, and prizes.
Co-chairs of the Ad Hoc Committee on Graduate Advising and Mentoring discuss the committee’s task of advising the Institute on policies and programs that support both students and faculty.
The head of MIT’s Department of Chemical Engineering will serve on the President’s Council of Advisors on Science and Technology.
Engineers have designed a relatively low-cost, energy-efficient approach to treating water contaminated with heavy metals.
Using nanoparticles that store and gradually release light, engineers create light-emitting plants that can be charged repeatedly.
Sachin Bhagchandani wins NCI Predoctoral to Postdoctoral Fellow Transition (F99/K00) Award.
SMART nanosensors are safer and less tedious than existing techniques for testing plants’ response to compounds such as herbicides.
How-to manual from MIT and the Fashion Institute of Technology codifies successful textiles partnership between designers, engineers.
Record number of honorees will engage in the life of the Institute through teaching, research, and other interactions with the MIT community.
This year’s projects address mobile evaporative vegetable preservation, portable water filtration, and dairy waste reduction.
For the past seven years, the MIT University Center for Exemplary Mentoring has created a robust infrastructure of resources, people, and support.
MIT researchers find a new way to quantify the uncertainty in molecular energies predicted by neural networks.