Researchers launched an in-house Data Science and Artificial Intelligence (DSAI) challenge to beat MIT’s machine-learning models for predicting clinical trial outcomes. The results are now available.

Addressing climate change means matching data with targeted action. Tracking the right metrics, and using them the right way, is essential.

MIT researchers have developed a methodology for systematically tracking the outcomes of academic licensing transactions as a means of increasing funding to bridging the “valley of death.”

A new study led by MIT Sloan Prof. Andrew W. Lo finds that borrowing ideas and tools from the gaming community can improve online teaching techniques and improve learning outcomes for students.

Labor, health, and environmental concerns threaten the might of meat. But alternative foods face major challenges.

A free, updated simulator allows users to visualize environmental impacts and better guide climate decision-making in their organizations.

The studies describe new methods for accelerating drug approvals during pandemics and for providing more accurate measures of the probabilities of success for clinical trials of vaccines.

Online interface simulates 100 years of energy, land and climate data in less than one second to identify solutions to limit warming to within 2 degrees Celsius by 2100

MIT Sloan and CSAIL researchers apply artificial intelligence techniques to one of the largest datasets of clinical trial outcomes to handicap the drug and device approval process

MIT study explores the key factors behind patient outcomes in clinical trials evaluating new treatments for non-small-cell lung cancer.