News
Student insight on the “Future of Nuclear Energy” study
Amy Umaretiya, a master’s student in the Technology and Policy Program, joined other student researchers in Washington to lend their voices and ideas for exploring nuclear energy’s potential to address climate change.
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Model helps robots navigate more like humans do
In simulations, robots move through new environments by exploring, observing, and drawing from learned experiences. Researchers with CSAIL and the MIT Center for Brains, Minds, and Machines used an underlying motion planner that was developed at LIDS by Sertac Karaman and Emilio Frazzoli.
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MITx MicroMasters adds new pathways to access to master’s degrees
Learners from around the globe now have enhanced access to “blended” master’s programs at MIT and elsewhere.
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MIT and HUBweek showcase ideas and innovators making the future
IDSS will help kick off HUBweek with a student-run Policy Hackathon, bringing together students, data scientists, policy and urban planning researchers, and other domain specialists to tackle local, complex challenges.
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The cartographer of cells
Statistics and Data Science Center affiliate Aviv Regev helped pioneer single-cell genomics. Now she’s co-chairing a massive effort to map the trillions of cells in the human body.
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Advancing global retail operations through analytics and machine learning
David Simchi-Levi discusses insights from recent research in collaboration with some of the world's largest retailers.
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Report outlines keys to election security
IDSS affiliate and Political Science professor Charles Stewart III has co-authored a report calling for ballot paper trails and other resilient practices to avoid election hacking.
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Social Physics: Reinventing Analytics to Better Predict Human Behaviors
The research of IDSS and Media Lab professor Alex ' Sandy' Pentland shows that predicting human behavior requires the ability to frequently analyze relatively small data sets collected over short periods of time.
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A game changer takes on cricket’s statistical problem
Laboratory for Information and Decision Systems alumnus and CSAIL lecturer Jehangir Amjad uses statistical methods to predict likely outcomes with limited data.
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Reducing false positives in credit card fraud detection
Model developed by researchers at the Laboratory for Information and Decision Systems (LIDS) extracts granular behavioral patterns from transaction data to more accurately flag suspicious activity.
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