Regina Barzilay, other MIT community members elected to the National Academy of Engineering for 2023
Seven researchers, along with 14 additional MIT alumni, are honored for significant contributions to engineering research, practice, and education.
Connor Coley, Dylan Hadfield-Menell named AI2050 Early Career Fellows
Department of EECS Assistant Professors Connor Coley and Dylan Hadfield-Menell have been named to the inaugural cohort of AI2050 Early Career Fellows by Schmidt Futures, a philanthropic initiative from Eric and Wendy Schmidt aimed at helping to solve hard problems in AI.
Six With Ties to MIT Honored as ACM Fellows
Six distinguished scientists with ties to MIT were recognized “for significant contributions in areas including cybersecurity, human-computer interaction, mobile computing, and recommender systems among many other areas.”
Big Data, Bigger Ideas: the four coauthors of “Data Science In Context” share their perspectives on a growing field.
As the pioneers of a developing field, data scientists often have to deal with a frustratingly slippery question: what is data science, precisely, and what is it good…
Three from MIT named 2023 Rhodes Scholars
Jack Cook, Matthew Kearney, and Jupneet Singh will begin postgraduate studies at Oxford University next fall.
Expanding the MIT-IBM Watson AI Lab’s network of neurons
On October 6, nearly 50 undergraduate and graduate students and postdocs, primarily from MIT, attended the MIT-IBM Watson AI Lab’s networking event. The goal was to connect young…
Recent chair announcements within EECS
The Department of Electrical Engineering and Computer Science (EECS) recently announced the following crop of chair appointments, all effective July 1, 2022. Karl Berggren has been named the…
Four from MIT receive NIH New Innovator Awards for 2022
Awards support high-risk, high-impact research from early-career investigators.
In-home wireless device tracks disease progression in Parkinson’s patients
By continuously monitoring a patient’s gait speed, the system can assess the condition’s severity between visits to the doctor’s office.
A geometric deep-learning model is faster and more accurate than state-of-the-art computational models, reducing the chances and costs of drug trial failures.