News

Mingman Zhao, a PhD student in EECS, spoke to the inaugural 6.883/6.S083 class about common issues in using machine learning tools to address problems.

Machine learning for everyone
MIT News Office | July 10, 2019

IDSS affiliate Tommi Jaakkola co-designed 'Modeling with Machine Learning: From Algorithms to Applications,' a new course on applications of machine learning for students from a variety of disciplines.

READ MORE
Daron Acemoglu

Daron Acemoglu named Institute Professor
MIT News Office | July 10, 2019

Economist and IDSS faculty member Daron Acemoglu, whose far-ranging research agenda has produced influential studies about government, innovation, labor, and globalization, has been named Institute Professor, MIT’s highest faculty honor.

READ MORE
Omer Tanovic says that his engineering background has taught him never to lose sight of the intended applications of his work, or the practical parameters for implementation.

Cool wireless
LIDS | July 8, 2019

LIDS student Omer Tanovic is working to make wireless communication more energy efficient.

READ MORE
Sirui Li, Manon Revel, Arnab Sarker, Erin Walk, Weizi Li, Janelle Schlossberger

Welcoming the 2019 Hammer Fellows
July 3, 2019

Meet the four incoming students in the IDSS Social and Engineering Systems doctoral program -- and the two postdocs -- who have joined the ranks of the Michael Hammer Society of Fellows.

READ MORE
Ali Jadbabaie addresses a full room at the IDSS hosted conference L4DC.

IDSS hosts inaugural ‘Learning for Dynamics and Control’ conference
July 3, 2019

L4DC explored an emerging scientific area at the intersection of real-time physical data, machine learning, control theory, and optimization.

READ MORE
Robots currently attempt to identify objects in a point cloud by comparing a template object — a 3-D dot representation of an object, such as a rabbit — with a point cloud representation of the real world that may contain that object.

Spotting objects amid clutter
MIT News Office | June 21, 2019

Robots can quickly find objects hidden in 3D data thanks to a new technique developed by researchers from the Laboratory for Information and Decision Systems (LIDS) -- including Aero/Astro and IDSS/LIDS professor Luca Carlone and Heng Yang, a grad student in LIDS and MechE.

READ MORE
Commuters wait for a Red Line subway train, part of Boston’s public transit system. A new study by MIT researchers shows that low-income people use mass transit significantly more often when they have access to fare reductions.

An experiment illuminates the value of public transportation
MIT News Office | June 21, 2019

IDSS affiliate and DUSP professor Jinhua Zhao is co-author on a paper, led by DUSP grad student Jeffrey Rosenblum, showing how fare discounts for low-income people could increase public transit ridership.

READ MORE
l-r: Dr. Florian Metzler, Dr. Maimuna Majumder, Dr. Christopher Saulnier, Dr. Lita Das, Dr. Paul Natsuo Kishimoto, Dr. Adam Williams; photo: Marco Miotti

IDSS congratulates 2019 graduates
June 7, 2019

IDSS and IDSS-affiliated graduates were awarded 25 doctoral degrees, 52 Master of Science degrees, 7 Master of Engineering degrees, and 14 bachelor’s degrees with a Minor in Statistics and Data Science.

READ MORE
Using their “Cybersafety” methodology, Professor Stuart Madnick (left), graduate student Shaharyar Khan (right), and Professor James Kirtley Jr. (not pictured) identified several cyber vulnerabilities in a small power plant, including a system that poses a risk because it relies on software rather than mechanical safety devices to keep turbines from spinning out of control. Photo: Stuart Darsch

Protecting our energy infrastructure from cyberattack
MIT News Office | June 6, 2019

Researchers including IDSS affiliate and MIT Sloan professor Stuart Madnick are addressing cyber vulnerabilities in energy systems using a methodology that examines the interaction of people, equipment, and policies.

READ MORE
Tamara Broderick

Tamara Broderick receives Notable Paper Award from AISTATS for ‘A Swiss Army Infinitesimal Jackknife’
June 5, 2019

The annual international conference on Artificial Intelligence and Statistics recognized the paper for demonstrating how a linear approximation, known as the "infinitesimal jackknife" in statistics literature, can be used to address a variety of challenges in machine learning.

READ MORE

© MIT Institute for Data, Systems, and Society | 77 Massachusetts Avenue | Cambridge, MA 02139-4307 | 617-253-1764 | Design by Opus