Research

Printable Hydraulics allows fluid-actuated robots to be automatically fabricated using 3D printers.

A system that can help you to design our own drone!

A polyhedral compiler for expressing image processing, DNN, and linear/tensor algebra applications

Our system for generating ad hoc “cache hierarchies” increases processing speed while reducing energy consumption

Multi-robot path planning for robot swarms that can both fly and drive

Traffic is not just a nuisance for drivers: It’s also a public health hazard and bad news for the economy.

Clinical concept extraction (CCE) of named entities - such as problems, tests, and treatments - aids in forming an understanding of notes and provides a foundation for many downstream clinical decision-making tasks. Historically, this task has been posed as a standard named entity recognition (NER) sequence tagging problem, and solved with feature-based methods using hand-engineered domain knowledge. Recent advances, however, have demonstrated the efficacy of LSTM-based models for NER tasks, including CCE. This work presents CliNER 2.0, a simple-to-install, open-source tool for extracting concepts from clinical text. CliNER 2.0 uses a word- and character- level LSTM model, and achieves state-of-the-art performance. For ease of use, the tool also includes pre-trained models available for public use.

The creation of low-power circuits capable of speech recognition and speaker verification will enable spoken interaction on a wide variety of devices in the era of Internet of Things.

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

What if we could control robots more intuitively, using just hand gestures and brainwaves?