The MOOC Learner Project provides learning scientists, instructional designers and online education specialists with open source software that enables them to efficiently extract teaching and learning insights from the data collected when students learn on the edX or open edX platform.
SensiCut augments standard laser cutters with a speckle sensing add-on that can identify common workshop materials, including visually similar ones.
Using adversarial signals and a cycle-consistency based regularization, we can supplement paired regression tasks with unpaired data to improve regression performance.
We develop machine learning algorithms to automatically and quantitatively assess the severity of pulmonary edema from chest x-ray images.
Our goal is to explore language representations in computational models. We develop new models for representing natural language and investigate how existing models learn language, focusing on neural network models in key tasks like machine translation and speech recognition.
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.
Automatic speech recognition (ASR) has been a grand challenge machine learning problem for decades. Our ongoing research in this area examines the use of deep learning models for distant and noisy recording conditions, multilingual, and low-resource scenarios.
Clinical notes often describe the most important aspects of a patient's physiology and are therefore critical to medical research. However, these notes are typically inaccessible to researchers without prior removal of sensitive protected health information (PHI), a natural language processing (NLP) task referred to as de-identification. In order to build tools that perform deid, one typically needs the very same data that is private, thus creating a chicken-and-the-egg problem. In this work, we generate "fake" clinical notes where the deidentified information is replaced with real-seeming values (e.g. "Tim Lywood" instead of "George Beveridge") that still respect reasonable distributional semantics. We evaluate models trained on this synthetic data and show that they perform just as well as models trained on the sensitive PHI-bearing notes.
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.
We work towards a principled understanding of the current machine learning toolkit and making this toolkit be robust and reliable.