Biomedical computer science

Brain

How brains respond to stimulation therapies

Matthew Johnson’s research lab aims to understand how the nervous system responds and adapts to stimulation-based therapies, such as deep brain stimulation. His team's studies are improving these therapies to help people with Parkinson's disease and Essential Tremor reclaim control over their motor function.

arrhythmia in the heart as viewed through electrical activity

Prediction and prevention of cardiac arrhythmias

Alena Talkachova’s group visualizes electrical activity in the heart and small patches of cardiac tissue. They use nonlinear dynamics approaches to predict transition from normal to abnormal cardiac rhythms, and to prevent arrhythmias in the heart. They also develop novel tools to guide mapping-specific ablation in patients with atrial fibrillation.

Robotic arm

Implantable brain chips

Zhi Yang’s lab studies the emerging area of implantable brain devices that can understand thoughts, such as to help amputees control robotic limbs or enable new electroceuticals. They’re developing neural recording, processing, and stimulation chips, and have devices in clinical trials.

Research from our graduate faculty

Examples of quantitative parameter estimation with convolutional neural networks

Quantitative MR imaging methods and translation

Patrick Bolan’s group focuses on developing computational methods for quantitative magnetic resonance imaging, and integration of such methods in clinical trials of cancer and obesity.

A dendritic cell

Geometry for cells

Meghan Driscoll’s lab aims to understand the functions of cell geometry and dynamics for cancer and immune cells. To do so, the lab combines advanced microscopy with the development of machine learning and computer graphics algorithms.

Data-driven approach to neuroscience

Algorithms to improve the quality of neuroimaging data

Kendrick Kay's lab aims to integrate broad interdisciplinary insights to understand brain function. In particular, the lab specializes in analysis methods for fMRI data, including advanced statistical and analysis methods as well as methods for improving the quality of fMRI data.

Major white matter pathways reconstructed using 7 Tesla MRI.

Brain connectomics: From algorithms to applications

Diffusion magnetic resonance imaging is revolutionizing brain connectivity mapping, rapidly advancing our understanding of neurological and psychiatric illnesses. Christophe Lenglet's lab aims to create imaging and computational methods to uncover the mechanisms underlying these conditions and discover biomarkers that will accelerate clinical trials.

Rendering showing cognition

Neural foundations of complex cognition

Jan Zimmermann’s lab explores the neural foundation of decision-making. The interdisciplinary team uses approaches from neuroscience, economics, psychology, math and physics to figure out how organisms adaptively use their finite neural coding capacity to make choices.