Research Spotlight

The Red Sea Robotics Exploratorium

About The Red Sea Robotics Research Exploratorium was created in April 2012 through a generous research award from the King Abdullah University of Science and Technology (KAUST). As a part of the KAUST Global Collaborative Research Program, Stanford University is part of a team of universities working to build a major science and technology university along a marshy peninsula on Saudi Arabia’s western coast. Meka Robotics joined the collaboration and provides the hardware for the development of dexterous underwater robot arms.

ChucK: A Music Programming Language

ChucK is a programming language for audio and music creation. The language is designed around a unique time-based, concurrent programming model that's precise and expressive (we call this strongly-timed), and the ability to add and modify code on-the-fly. It offers composers, researchers, and performers a powerful programming tool for building and experimenting with complex audio synthesis/analysis programs, and real-time interactive music.

Ocarina: Designing the iPhone's Magic Flute

Ocarina, created in 2008 for the iPhone, is one of the first musical artifacts in the age of pervasive, app-based mobile computing. It presents a flute-like physical interaction using microphone input, multitouch, and accelerometers – and a social dimension that allows users to listen-in on each other around the world. To date, Ocarina has over 10 millions users worldwide, and was a first-class inductee into Apple's Hall of Fame Apps.

SLOrk: Stanford Laptop Orchestra

The Stanford Laptop Orchestra (SLOrk) is a large-scale, computer-mediated ensemble and classroom that explores cutting-edge technology in combination with conventional musical contexts - while radically transforming both. Founded in 2008 by director Ge Wang and students, faculty, and staff at Stanford University's Center for Computer Research in Music and Acoustics (CCRMA), this unique ensemble comprises more than 20 laptops, human performers, controllers, and custom multi-channel speaker arrays designed to provide each computer meta-instrument with its own...

Neuromorphics: Compiling Code by Configuring Connections

Energy-efficient computing platforms are sorely needed to control autonomous robots and to decode neural signals in brain-machine interfaces. Inspired by the brain’s energy efficiency, we are exploring a hybrid analog-digital approach that uses subthreshold analog circuits to emulate graded dendritic activity and asynchronous digital circuits to emulate all-or-none axonal activity. We have used this approach to build Neurogrid, a sixteen-chip neuromorphic system that can simulate biophysically-detailed cortical models with up to a million neurons and six...

Brain-Inspired Computing

We are developing nanoscale electronic devices and circuits to emulate the functions of the synapses and neurons of the brain. The goal is to use nanoscale electronic devices to do information processing using algorithms and methods inspired by how the brain works. Currently, we are using phase change memory and metal oxide RRAM to perform gray-scale analog programming of the resistance values. These electronic emulations of the synapse are then connected in a neural network to process information and achieve simple learning behavior. In the past few years...

3X: A Workbench for eXecuting eXploratory eXperiments

3X is an open-source software tool to ease the burden of conducting computational experiments and managing data analytics. 3X provides a standard yet configurable structure to execute a wide variety of experiments in a systematic way, avoiding repeated creation of ad-hoc scripts and directory hierarchies. 3X organizes the code, inputs, and outputs for an experiment. The tool submits arbitrary numbers of computational runs to a variety of different compute platforms, and supervises their execution. It records the returning results, and lets the experimenter...

Deep Learning

Deep learning is a rapidly growing area of machine learning, that is becoming widely adopted within academia and industry. Whereas machine learning is a very successful technology, applying it today still often requires spending substantial effort hand-designing features to feed to the algorithm. This is true for applications in vision, audio, and text/NLP. To address this, Ng's group and others are working on "deep learning" algorithms, which can automatically learn feature representations (often from unlabeled data), thus bypassing most of this time-...

Salisbury BioRobotics Laboratory

The Salisbury Lab conducts research in the areas of robotics, medical robotics, haptic devices and haptic rendering algorithms. One project is developing a virtual environment that enables surgeons to plan and practice surgical procedures by interacting visually and haptically with patient-specific data derived from CAT and MRI scans. Our lab developed the first version of the personal robot (PR-1), which eventually was licensed to Willow Garage and was the genesis of the PR-2 personal robot. We continue to develop robot hands, addressing design, control...

Upcoming Events

Today

  1. 10:00am - University Oral Examination: A Momentum-Conserving Framework for the Simulation of Reduced
  2. 11:00am - Software Research Seminar: Machine Learning for Programming - Martin Vechev, Computer Science,

Wednesday, June 24th

  1. 11:00am - Seminar at Technicolor: Automated Experiments on Ad Privacy Settings - Amit Datta, ECE, CMU
    • Start time: 11:00am
    • End time: 11:30am
    • Where: Technicolor, 175 S San Antonio Road, Los Altos, CA 94022
    • Link: View details of this event

Thursday, June 25th

  1. 05:00pm - PARC Conferences & Talks: Ford's Future: An Automaker & Mobility Company - Dr. Ken
    • Start time: 05:00pm
    • End time: 06:00pm
    • Where: PARC - Pake Auditorium, 3333 Coyote Hill Road, Palo Alto, CA 94304
    • Link: View details of this event

Friday, June 26th

  1. 04:15pm - Stanford Security Seminar: A study of Pair Encodings: Predicate Encryption in prime order

Department Overview

Founded in 1965, the Stanford Computer Science (CS) Department continues to lead the world in computer science research and education. Throughout the past four decades, the Stanford CS Department has influenced society at levels that remain without parallel among academic institutions. Its spin-offs are among the most successful corporate ventures in the world, and many of the leaders in the academic and corporate research world are graduates of the Stanford CS Department.