Data

A new system by MIT researchers improves the efficiency of high-speed operations in data centers by better assigning time-sensitive data processing across CPU cores and ensuring hardware runs productively.

Achieving greater efficiency for fast data center operations

System better allocates time-sensitive data processing across cores to maintain quick user-response times.

February 21, 2019

Students participating in the Undergraduate Research Opportunities Program include: (clockwise from top left) Veronica Boyce, Nicholas Bonaker, Alexandra Dima, Neeraj Prasad, Elizabeth Weeks, and Alexander Denmark.

Exploring the nature of intelligence

Undergraduate research projects show how students are advancing research in human and artificial intelligence, and applying intelligence tools to other disciplines.

February 21, 2019

MIT and Harvard University researchers have developed a platform, called Riverbed, that ensures that web services adhere to users’ preferences on how their data are stored and shared in the cloud.

Putting data privacy in the hands of users

New platform forces data center servers to only use data in ways that users explicitly approve.

February 20, 2019

The binding interface between a peptide and its Bcl-2 protein target is composed of common structural motifs known as TERMs.

Predicting sequence from structure

Researchers have devised a faster, more efficient way to design custom peptides and perturb protein-protein interactions.

February 15, 2019

Using a new cooperative learning technique, MIT-IBM Watson AI Lab researchers cut by half the time it took a pair of robot agents to learn to maneuver to opposite sides of a virtual room.

Learning to teach to speed up learning

An algorithm that teaches robot agents how to exchange advice to complete a task helps them learn faster.

January 29, 2019

New research describes a single neural network that can collect missing data from a patient and add it to their chart.

Filling the gaps in a patient’s medical data

Neural network assimilates multiple types of health data to help doctors make decisions with incomplete information.

January 25, 2019

Members of the Health Analytics Collective: (left to right) Mohamad Zahreddine, Michelle Gayari, Duane Robinson, Uma Sharma, Vijay Ivaturi, Kelly Hill, Joga Gobburu, Alan Edelman, and Prasad Koppolu, gather at MMS Holdings in Canton, Michigan.

Julia Lab joins team to speed up drug approval process

Health Analytics Collective uses real-world evidence to modernize health and drug development decisions.

January 25, 2019

A model by MIT and Microsoft researchers identifies instances where autonomous systems have “learned” from training examples that don’t match what’s actually happening in the real world, which can be used to identify which learned actions could cause costly errors.

Identifying artificial intelligence “blind spots”

Model identifies instances when autonomous systems have learned from examples that may cause dangerous errors in the real world.

January 24, 2019

Professor Antonio Torralba, MIT director of the MIT—IBM Watson AI Lab and the inaugural director of the MIT Quest for Intelligence, addresses the audience at the MIT AI Policy Congress on Jan. 15.

AI, the law, and our future

MIT “Policy Congress” examines the complex terrain of artificial intelligence regulation.

January 18, 2019

Leo Celi, a researcher at MIT and a physician at Beth Israel Deaconess Medical Center, leads a global series of health care hackathons, or datathons, to bring doctors and data scientists together and encourage hospitals to make use of their electronic medical records.

Democratizing artificial intelligence in health care

Hackathons promote doctor-data scientist collaboration and expanded access to electronic medical-records to improve patient care.

January 18, 2019

MIT researchers are hoping to advance the democratization of data science with a new tool for nonprogrammers that automatically generates models for analyzing raw data.

Democratizing data science

Tool for nonstatisticians automatically generates models that glean insights from complex datasets.

January 15, 2019

A new doctoral field offering a computational specialty within MIT's Department of Earth, Atmospheric and Planetary Sciences resonates with the Institute’s growing awareness of the advantages provided by education based in computer science and engineering.

MIT adds computational Earth, atmospheric, and planetary sciences to its PhD offerings

New EAPS thesis field is the most recent to join the computational science and engineering doctoral program within the Center for Computational Engineering.

January 9, 2019

First row, left to right: Suvrit Sra, Frances Ross, Pulkit Agrawal, Kevin O’Brien, Richard Linares, and Cathy Wu. Second row, left to right: Negar Reiskarimian, Jacob Andreas, Giovanni Traverso, Manya Ghobadi, and Ashwin Gopinath.

School of Engineering welcomes new faculty

Eleven new professors join the MIT community.

January 9, 2019

Paul Michelman (left) and Ben Shields in the studio

MIT Sloan launches sports analytics podcast

“Counterpoints,” produced by MIT Sloan Management Review, aims to reveal fascinating insights about sports analytics.

December 12, 2018

MIT researchers find that the growing practice of compiling massive datasets about people’s movement patterns for urban planning and development research may, in fact, put people’s private data at risk — even if that data is anonymized.

The privacy risks of compiling mobility data

Merging different types of location-stamped data can make it easier to discern users’ identities, even when the data is anonymized.

December 7, 2018

MIT News | Massachusetts Institute of Technology

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