Research

We develop a computational model that explains how people make causal judgments in physical scenes by mentally simulating counterfactual outcomes and comparing those to what actually happened.

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

In order to be able to design synthetic organs that function autonomously, we will need to engineer artificial tissue homeostasis. To control the size of these artificial tissues, two major mechanisms will have to be engineered.

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.

Adding domain knowledge to word embeddings.

Developing techniques to allow self-driving cars and other AI-driven systems to explain behaviors and failures.

We develop image analysis algorithms for whole-heart cardiac MRI in patients with severe congenital heart disease.

Integrating multi-modal clinical data and using recurrent and convolution neural networks to predict when patients will need important interventions.

We develop novel algorithms for neuroimage segmentation, registration, and joint modeling of brain structure with clinical and genetic data.

Our team uses accelerometers and machine learning to help detect vocal disorders. We capture data about the motions of patient's vocal folds to determine if their vocal behavior is normal or abnormal.

EQ-Radio can infer a person’s emotions using wireless signals. It transmits an RF signal and analyzes its reflections off a person’s body to recognize his emotional state (happy, sad, etc.).

Developing state-of-the-art tools that process 3D surfaces and volumes

Our goal is to help medical researchers and clincians understand the growing repositories of waveform and signal data collected from critically ill patients.

From audio recordings of clinician-subject interactions, we determine the spoken language bio-markers that are associated with health outcomes, such as dementia and depression.

A "precision medicine" approach for finding relevant cancer treatments in clinical literature and eligible trials. For a given patient with associated demographics (age, gender) and disease (cancer type, genetic variants), we query a database of all pubmed articles and clinicaltrials.gov trials using NLP techniques to find the most useful and relevant treatments for the patient. Our ensemble-based system performed very well in the TREC 2016 Precision Medicine challenge.

We develop algorithms for fetal MRI interpretation, to enable noninvasive fetal monitoring from MRI.

Our focus is using computational tools to summarize health-related data to help clinicians focus on decision-making rather than just keeping up with the data.

Transitioning machine learning models across electronic health record (EHR) versions can be improved by mapping different EHR encodings to a common vocabulary.

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

We develop machine learning algorithms to automatically and quantitatively assess the severity of pulmonary edema from chest x-ray images.

When discussing racial disparities in medical treatments, critics often cite social factors as confounders which explain away any differences. Comparing the health of whites to that of non-whites we do see that environmental and social factors conspire to yield higher rates of disease and shorter life spans in non-white populations. But does that really show that medical treatment itself is free from bias? We examine end-of-life care in the ICU, stratified by ethnicity, and controlled for acuity using severity assessment scores. Our analysis agrees with previous studies that nonwhites tend to receive more aggressive (high-risk, high reward) treatments, such as mechanical ventilation than non-whites, despite receiving comparable-or-moderately-less noninvasive treatments. Going further, we show that using treatment patterns and clinical notes, we are able to infer a patient's race. Finally, we show evidence suggesting nonwhite have a much greater distrust of the medical community among than whites do. We find that race, even in the great equalizer of end-of-life care, does continue to influence the treatments administered to a patient.

Many optimization problems in machine learning rely on noisy, estimated parameters. Neglecting this uncertainty can lead to great fluctuations in performance. We are developing algorithms for these already nonconvex problems that are robust to such errors.

Using adversarial signals and a cycle-consistency based regularization, we can supplement paired regression tasks with unpaired data to improve regression performance.

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.