Crowdsourcing big-data analysis
Web-based system automatically evaluates proposals from far-flung data scientists.
Laboratory for Information and Decisions Systems (LIDS)
LIDS Smart Urban Infrastructures Workshop highlights emerging research
Speakers from academia, industry, and government discuss the evolution of smart urban systems.
Multi-university effort will advance materials, define the future of mobility
With support from the Toyota Research Institute, MIT faculty will focus on next-generation energy storage.
“Virtual batteries” could lead to cheaper, cleaner power
Coordinating smart appliances and electric cars may help balance supply and demand in the power grid.
Making better decisions when outcomes are uncertain
A simple statistical trick could help make a ubiquitous model of decision processes more accurate.
John Tsitsiklis appointed director of the Laboratory for Information and Decision Systems
Professor of electrical engineering and computer science will also take on the new role of associate director of the Institute for Data, Systems, and Society.
Data diversity
Preserving variety in subsets of unmanageably large data sets should aid machine learning.
Meeting of the minds for machine intelligence
Industry leaders, computer scientists, and venture capitalists gather to discuss how smarter computers are remaking our world.
Asu Ozdaglar named associate department head of EECS
Expert in optimization theory to join the Department of Electrical Engineering and Computer Science leadership.
Automating big-data analysis
With new algorithms, data scientists could accomplish in days what has traditionally taken months.
One year on, Institute for Data, Systems, and Society looks ahead
Event and new IDSS projects will provide insights into some of society's most pressing data challenges.
Self-driving cars, meet rubber duckies
“Duckietown” class at Computer Science and Artificial Intelligence Lab teaches autonomy, aims to become global research platform.
Using new models and big data to better understand financial risk
Bringing together engineers, data theorists, mathematicians, economists, biologists, and policy experts, IDSS is looking at financial risk through a multidisciplinary lens.
Using data from social networks to understand and improve systems
Researchers in IDSS are learning how ideas evolve over networks, quantifying the influence of individuals in networks, and making better predictions.