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1 News Results matching all criteria

Barbara Liskov 2

Algorithms & Theory AI & ML Computational Biology Computer Architecture Graphics & Vision Human-Computer Interaction Programming Languages & Software Engineering Robotics Security & Cryptography Systems & Networking Cybersecurity Education Energy Entertainment Health Care Manufacturing Transportation Wireless

June 27 '19

CSAIL hosts first-ever TEDxMIT

Speakers — all women — discuss everything from gravitational waves to robot nurses

CSAIL hosts first-ever TEDxMIT

6 Group Results

Research Group

Clinical Decision-Making Group

We focus on furthering the application of technology and artificial intelligence in medicine and health-care.

+10

Peter Szolovits

Leads

Peter Szolovits

Research Areas

Computational Biology

Impact Areas

Lead

Peter Szolovits

Peter Szolovits

Research Group

Distributed Robotics Laboratory

We aim to develop the science of autonomy toward a future with robots and AI systems integrated into everyday life, supporting people with cognitive and physical tasks.

+13

Daniela Rus

Leads

Daniela Rus

Research Areas

Impact Areas

Lead

Daniela Rus

Daniela Rus

Research Group

Geometric Data Processing Group

Our group studies geometric problems in computer graphics, computer vision, machine learning, optimization, and other disciplines.

+6

Justin Solomon

Leads

Justin Solomon

Research Areas

Algorithms & Theory

Graphics & Vision

Impact Areas

Lead

Justin Solomon

Justin Solomon

Research Group

Imagination, Computation, and Expression Laboratory (ICE Lab)

We develop computing systems for creative expression, cultural analysis, and social change.

Peter Mawhorter

+1

D. Fox Harrell

Leads

D. Fox Harrell

Research Areas

Human-Computer Interaction

Impact Areas

Lead

D. Fox Harrell

Peter Mawhorter

D. Fox Harrell

Research Group

Learning and Intelligent Systems

We conduct interdisciplinary research aimed at discovering the principles underlying the design of artificially intelligent robots.

+5

Leslie Kaelbling

Tomas Lozano-Perez

Leads

Leslie Kaelbling

Tomas Lozano-Perez

Research Areas

Impact Areas

Lead

Leslie Kaelbling

Tomas Lozano-Perez

Leslie Kaelbling

Tomas Lozano-Perez

Research Group

Sussman Lab

We focus on understanding the problem-solving strategies used by scientists and engineers, with the goals of automating parts of the process and formalizing educational methods.

Gerald Sussman

Leads

Gerald Sussman

Research Areas

Programming Languages & Software Engineering

Systems & Networking

Impact Areas

Lead

Gerald Sussman

Gerald Sussman

5 Project Results

Project

3D Generative Adversarial Networks

Cognitive AI Community of Research

We study the problem of 3D object generation. We propose a novel framework, 3D Generative Adversarial Network (3D-GAN), leveraging recent advances in volumetric convolutional networks and generative adversarial nets.

Leads

Research Areas

Graphics & Vision

Impact Areas

Project

Apprenticeship Scheduling: Learning to Schedule From Human Experts

Interactive Robotics Group

Robot scheduling via learning from demonstration for tasks in medicine and more

Leads

Research Areas

Impact Areas

Project

PrivacyML - A Privacy Preserving Framework for Machine Learning

Decentralized Information Group

Cognitive AI Community of Research

Computing & Society Community of Research

We are developing a general framework that enforces privacy transparently enabling different kinds of machine learning to be developed that are automatically privacy preserving.

Lalana Kagal

Vaikkunth Mugunthan

Leads

Lalana Kagal

Vaikkunth Mugunthan

Research Areas

Security & Cryptography

Impact Areas

Internet of Things

Lalana Kagal

Vaikkunth Mugunthan

Project

Sensible Deep Learning for 3D Data

Geometric Data Processing Group

Machine Learning

Developing state-of-the-art deep learning algorithms for analyzing and modeling 3D geometry

Justin Solomon

Leads

Justin Solomon

Research Areas

Graphics & Vision

Impact Areas

Justin Solomon

Project

Single-Image 3D Interpreter Network

Cognitive AI Community of Research

We aim to understand 3D object structure from a single image. We propose an end-to-end framework which sequentially estimates 2D keypoint heatmaps and 3D object structure, by training it on both real 2D-annotated images and synthetic 3D data and by integrating a 3D-to-2D projection layer.

Leads

Research Areas

Graphics & Vision

Impact Areas

6 People Results

Mustafa Doga Dogan

Graduate Student

E doga@mit.edu

RM

Member

HCI Engineering Group

Gregory Falco

Research Affiliate

Dr. Falco is a hacker and expert in IoT and IIoT cybersecurity.

E gfalco@mit.edu

Dr. Falco is a hacker and expert in IoT and IIoT cybersecurity.

Alexandre Kaspar

Graduate Student

Graphics, crowdsourcing and hybrid fabrication.

E akaspar@mit.edu

RM

Lead

Crowdsourcing in Graphics and Vision

Computational Knitting: Patterning and Shaping for Everyone

Graphics, crowdsourcing and hybrid fabrication.

Dmitriy Smirnov

Graduate Student

https://people.csail.mit.edu/smirnov/

E smirnov@mit.edu

RM 32-D475B

Member

Geometric Data Processing Group

Sensible Deep Learning for 3D Data

https://people.csail.mit.edu/smirnov/

Yu Wang

Graduate Student

E wangyu9@mit.edu

RM 32-D475A

Member

Geometric Data Processing Group

Geometry and topology for scientific computing and shape analysis

Junyi Zhu

Graduate Student

E junyizhu@mit.edu

RM

Member

HCI Engineering Group

18 News Results

m-blocks

AI & ML Robotics Manufacturing Transportation

October 30 '19

Self-transforming robot blocks jump, spin, flip, and identify each other

Developed at MIT’s Computer Science and Artificial Intelligence Laboratory, a team of robots can self-assemble to form different structures with applications in inspection, disaster response, and manufacturing

Self-transforming robot blocks jump, spin, flip, and identify each other

knitting

Algorithms & Theory AI & ML Graphics & Vision Programming Languages & Software Engineering Systems & Networking Entertainment Manufacturing

August 05 '19

Computer-aided knitting

New CSAIL research uses machine learning to customize clothing designs

Computer-aided knitting

Barbara Liskov 2

Algorithms & Theory AI & ML Computational Biology Computer Architecture Graphics & Vision Human-Computer Interaction Programming Languages & Software Engineering Robotics Security & Cryptography Systems & Networking Cybersecurity Education Energy Entertainment Health Care Manufacturing Transportation Wireless

June 27 '19

CSAIL hosts first-ever TEDxMIT

Speakers — all women — discuss everything from gravitational waves to robot nurses

CSAIL hosts first-ever TEDxMIT

Yunzhu Li 2

AI & ML Graphics & Vision Systems & Networking Health Care Manufacturing

June 18 '19

Teaching artificial intelligence to connect senses like vision and touch

MIT CSAIL system can learn to see by touching and feel by seeing, suggesting future where robots can more easily grasp and recognize objects.

Teaching artificial intelligence to connect senses like vision and touch

human robot collab 1

AI & ML Robotics Manufacturing

May 22 '19

This robot helps you lift objects — by looking at your biceps

CSAIL system can mirror a user's motions and follow nonverbal commands by monitoring arm muscles.

This robot helps you lift objects — by looking at your biceps

origami gripper

AI & ML Robotics Health Care Manufacturing

March 14 '19

New robot hand is soft and strong

Gripper inspired by “origami magic ball” can grasp wide array of delicate and heavy objects

New robot hand is soft and strong

Trustworthy AI 2

Algorithms & Theory AI & ML Cybersecurity Health Care Internet of Things Manufacturing Transportation Wireless

February 12 '19

MIT CSAIL holds trustworthy AI event with Microsoft

Workshop explores technical directions for making AI safe, fair, and understandable

MIT CSAIL holds trustworthy AI event with Microsoft

CSAIL1

Algorithms & Theory Computer Architecture Big Data Cybersecurity Health Care Internet of Things Manufacturing

January 31 '19

MIT hosts workshop on theoretical foundations of deep learning

Last week MIT’s Institute for Foundations of Data Science (MIFODS) held an interdisciplinary workshop aimed at tackling the underlying theory behind deep learning. Led by MIT professor Aleksander Madry, the event focused on a number of research discussions at the intersection of math, statistics, and theoretical computer science.

MIT hosts workshop on theoretical foundations of deep learning

3-d shapes 1

Algorithms & Theory AI & ML Graphics & Vision Energy Entertainment Manufacturing

December 10 '18

Teaching machines to see in 3-D

CSAIL’s approach uses algorithms and “2.5-D” sketches to let computers visualize images from any perspective

Teaching machines to see in 3-D

Jeff Dean_Google

AI & ML Education Health Care Manufacturing Transportation Wireless

October 03 '18

Google AI’s Jeff Dean discusses using deep learning to solve challenging problems

Google AI’s Jeff Dean has a seemingly straightforward objective: he wants to use a collection of trainable mathematical units organized in layers to solve complicated tasks that will ultimately benefit many parts of society.

Google AI’s Jeff Dean discusses using deep learning to solve challenging problems

Jeff Dean

AI & ML Education Health Care Manufacturing Transportation Wireless

October 01 '18

Deep Learning to Solve Challenging Problems

October 1, 2018 - Jeff Dean of Google gave a CSAIL seminar titled "Deep Learning to Solve Challenging Problems."

Deep Learning to Solve Challenging Problems

Auxetics

AI & ML Robotics Manufacturing

May 14 '18

Materials that can change size and shape by twisting

Approach opens up new possibilities for robots, spacecraft, and more

Materials that can change size and shape by twisting

Robotic fish

AI & ML Robotics Manufacturing Transportation

March 21 '18

Soft robotic fish swims alongside real ones in coral reefs

Made of silicone rubber, CSAIL’s “SoFi” could enable a closer study of aquatic life.

Soft robotic fish swims alongside real ones in coral reefs

bbva1

Algorithms & Theory Security & Cryptography Big Data Cybersecurity Education Entertainment Health Care Internet of Things Manufacturing

January 16 '18

Goldwasser, Micali, and Rivest win BBVA Foundation Frontiers of Knowledge Awards

This week it was announced that MIT professors and CSAIL principal investigators Shafi Goldwasser, Silvio Micali, Ronald Rivest, and former MIT professor Adi Shamir won this year’s BBVA Foundation Frontiers of Knowledge Awards in the Information and Communication Technologies category for their work in cryptography.

Goldwasser, Micali, and Rivest win BBVA Foundation Frontiers of Knowledge Awards

materials 1

AI & ML Big Data Manufacturing

December 27 '17

Can computers help us synthesize new materials?

Last month, three MIT materials scientists and their colleagues published a paper describing a new artificial-intelligence system that can pore through scientific papers and extract “recipes” for producing particular types of materials.

Can computers help us synthesize new materials?

ACM Fellows

Algorithms & Theory Robotics Security & Cryptography Cybersecurity Internet of Things Manufacturing

December 11 '17

Four from MIT named 2017 Association for Computer Machinery Fellows

Today four MIT faculty were named among the Association for Computer Machinery's 2017 Fellows for making “landmark contributions to computing.”

Four from MIT named 2017 Association for Computer Machinery Fellows

MIT doctoral candidate Claudia Pérez-D'Arpino discusses her work teaching the Optimus robot to perform various tasks, including picking up a bottle.

AI & ML Human-Computer Interaction Robotics Manufacturing

May 10 '17

Teaching robots to teach other robots

Most robots are programmed using one of two methods: learning from demonstration, in which they watch a task being done and then replicate it, or via motion-planning techniques such as optimization or sampling, which require a programmer to explicitly specify a task’s goals and constraints.

Teaching robots to teach other robots

Researchers at MIT’s Microsystems Technology Laboratories have built a low-power chip specialized for automatic speech recognition. With power savings of 90 to 99 percent, it could make voice control practical for relatively simple electronic devices.

AI & ML Human-Computer Interaction Energy Internet of Things Manufacturing

February 13 '17

Voice control everywhere

The butt of jokes as little as 10 years ago, automatic speech recognition is now on the verge of becoming people’s chief means of interacting with their principal computing devices. In anticipation of the age of voice-controlled electronics, MIT researchers have built a low-power chip specialized for automatic speech recognition. Whereas a cellphone running speech-recognition software might require about 1 watt of power, the new chip requires between 0.2 and 10 milliwatts, depending on the number of words it has to recognize.

Voice control everywhere