School of Engineering welcomes new faculty in 2026
The newest MIT engineering faculty are conducting research across a diverse range of subject areas.
The newest MIT engineering faculty are conducting research across a diverse range of subject areas.
With up to $53 million from ARPA-H, the researchers will use AI to generate safer herbicides that reduce dependence on imports and improve the security of the U.S. food supply.
Documentary shines a powerful light on a painful disease that affects 265 million women, highlights efforts at MIT’s Center for Gynepathology Research to study the disease and find treatments.
The MIT professors collaborated to identify the first AI-discovered antibiotic, a scientific breakthrough that is widely recognized as a milestone in AI-driven drug discovery.
The particles carry a customized dose of cancer antigens that can provoke a strong immune response against tumors.
Professor Michael Yaffe and six MIT alumni are honored for their outstanding professional achievement and commitment to service.
In six talks, researchers from across MIT described projects aimed at advancing the understanding of and interventions against neurodegenerative diseases such as Alzheimer’s, Parkinson’s, and ALS.
MIT researchers developed a handheld system that gently collects living cells from patient samples to aid cancer testing and development of personalized medicines.
Tracking down senescent cells, which accumulate as we age, could help diagnose age-related disorders and guide researchers working on new treatments.
Novel research expands scientific understanding of how RNA shapes the reading of genetic information.
Faculty members and researchers were honored in recognition of their scholarship, service, and overall excellence.
In the new method, cells package and export their RNA, enabling researchers to sequence and analyze the RNA without killing the cells.
Led by Andee Wallace PhD ’20, Robigo uses cutting-edge biotechnology to engineer microbes to fight pests, reducing the need for harmful chemical pesticides.
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
By wiring together colonies of these bacteria, the researchers built circuits that can perform complicated calculations.