
New system cleans messy data tables automatically
MIT researchers, including SDSC affiliate David Sontag, have created a new program that, with just 50 lines of code, spots and fixes likely errors.
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Sam Brinton saves the world
A champion for LGBTQ rights, Brinton got dual masters in nuclear engineering and TPP so he could “save the world from nuclear waste.”
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2021 Women in Data Science conference addresses healthcare and bias
One of many Women in Data Science conferences around the world, WiDS Cambridge explored machine learning applications in healthcare and equity in models and algorithms.
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Collaborators in climate action
Symposium highlights ambitious goals of MIT–industry research targeting technologies to reduce greenhouse gas emissions, including work from LIDS principal research scientist Kalyan Veeramachaneni.
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How to replace everything in the industrialized world
Jessika Trancik, core faculty in IDSS, speaks about the many aspects involved with transforming our world in response to climate change and shifting to clean energy technologies.
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Technology innovation gives government leverage to drive down emissions fast
IDSS core faculty Jessika Trancik's research on innovation shows that government policies can kick off fast progress in moving toward a carbon-free future.
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Job connectivity improves resiliency in US cities, study finds
A new study from researchers including IDSS core faculty Alex "Sandy" Pentland offers insight for city planning and workforce development.
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One-stop machine learning platform turns health care data into insights
With the Cardea software system, hospitals may eventually be able to solve "hundreds of different types of machine learning problems," says Kalyan Veeramanchaneni, a principal research scientist with LIDS.
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Post-pandemic, supply chains retool for a new (ab)normal
COVID-19 didn’t break the supply chain, it simply accelerated changes already underway, says IDSS affiliate Yossi Sheffi. His new book details what’s coming next.
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Seeking the cellular mechanisms of disease, with help from machine learning
Caroline Uhler blends machine learning, statistics, and biology to understand how our bodies respond to illness.
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