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!

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

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.