Dr. Nordsletten is an Associate Professor in the Departments of Biomedical Engineering and Cardiac Surgery. He is also a Reader in cardiovascular biomechanics at King’s College London, and is the recipient of the EPSRC HTCA leadership fellowship. His research focuses on the novel application of biomechanics integrated with magnetic resonance imaging (MRI) for the advancement of human cardiovascular health. This broad focus encompasses a range of projects spanning from numerical methods development through to direct analysis of medical imaging data for diagnostics in cardiovascular disease.
Kamran Diba is an Associate Professor in the Department of Anesthesiology in the School of Medicine. His research group is interested in how the brain computes, coordinates, stores and transfers information. Neuronal networks generate an assortment of neuronal oscillations that vary depending on the behavior and state of an animal, from active exploration to resting and different stages of sleep and anesthesia. Accordingly, in their recordings of large populations of spiking neurons in rodents, they observe state-dependent temporal relationships at multiple timescales. What role do these unique spike patterns play and what do they tell us about the function and limitations of each brain state? To answer these and related questions, they combine behavioral studies of freely moving, learning and exploring rats, multi-channel recordings of the simultaneous electrical (spiking) activity from hundreds of neurons during behavior and sleep, neural network models of this behavior, statistical and machine learning tools to uncover deep structure within high-dimensional spike trains and chemogenetics and optogenetics to manipulate protein signaling and action potentials in specific neural populations in precise time windows.
Yongsheng Bai is an Assistant Research Professor in the Department of Internal Medicine in the School of Medicine. His research interests lie in development and refinement of bioinformatics algorithms/software and databases on next-generation sequencing (NGS data), development of statistical model for solving biological problems, bioinformatics analysis of clinical data, as well as other topics including, but not limited to, uncovering disease genes and variants using informatics approaches, computational analysis of cis-regulation and comparative motif finding, large-scale genome annotation, comparative “omics”, and evolutionary genomics.
Arvind Rao is an Associate Professor in the Department of Computational Medicine and Bioinformatics, and Radiation Oncology in the School of Medicine.
His research is in:
1. Transcriptional Genomics: A bioinformatics framework that identifies tissue‐specific enhancers by integrating multi‐modal genomic data has been developed previously [Rao2010]. There is interest to integrate other sources of information (like epigenomic and ChIP datasets) to improve the efficacy of enhancer prediction. We have also participated in the TCGA Glioma groups’ work [Brat2015, Ceccarelli2016] on identifying transcriptional regulators underlying gliomagenesis.
2. Image Informatics: In order to quantify the phenotypic aspects of disease and their relationships with outcome and their genetic context, we have developed methods for the analysis of histopathology [ Mousavi2015, Vu2016] and radiology [Yang2015] images, focusing on tumor heterogeneity. One direction of our group is to develop image analysis tools to delineate tumor image features from radiology data and to develop predictive models to relate them along with underlying genomic measurements to outcomes in low grade gliomas. Further, we have also investigated methodologies to link tumor imaging, genetics and immune status in gliomas. More recently, my group has been studying the relationship between image-derived features, genetics and cognitive status in glioblastoma patients. Further, we have also developed methods for the analysis of multiparametric MR datasets in Radiation Oncology.
3. Heterogeneous Data Integration: Integrative decision making in the clinical domain involves the need for principled formalisms that can integrate pathology, imaging and genomic data sets to drive hypothesis generation and clinical action. We have focused on developing high throughput measurement pipelines from this diverse array of data sources and methods for their integration. Simultaneously, methods for visualization are also under investigation. A more recent interest of our group is to integrate genomics, imaging and (online) behavioral data from patient to assess their evolving response to treatment, in the context of learning healthcare platforms. This could also enable the development of hybrid diagnostics.
4. Informatics for Combinatorial Drug Screens: the availability of multimodal data sources (cell line genomics, drug assays) coupled with high throughput, high content imaging platforms have created the need for informatics frameworks to identify rational drug combinations capable of modulating disease-associated phenotype. In this context, we have worked with the Gulf Coast Consortium to create analysis platforms that jointly mine imaging and genomics data for combinatorial drug discovery.
The overall goal is to link different data sources, such as imaging-derived phenotypes with genomic alteration for clinical predictive models. This has prompted work in AI/ML models for image processing &computer vision, data integration and genomic analysis.
Shasha Zou is an Associate Professor of Climate and Space Science and Engineering. Her general research interest is about studying the dynamic interaction between the Sun’s extended atmosphere, i.e., solar wind, and the near-Earth space environment. In particular, she is interested in the physical processes of formation and evolution of ionospheric structures and their impact on technology, such as global navigation and communication satellite system (GNSS), during space weather disturbances using multi-instrument observations and numerical models. Numerical models often used include magnetohydrodynamic (MHD) model of the global magnetosphere, and physics-based global ionosphere and thermosphere model.
The consolidation of recent experiences into long-term memories is a fundamental function of the brain and critical for survival. Consolidation is linked to plastic changes at synapses between neurons. However, very little is known about how this plasticity is brought about by ongoing activity in neuronal networks, and how different brain states (e.g. sleep and waking) contribute to the consolidation process.
We study how neuronal and network activity in sleeping and awake brain states contributes to plasticity following novel sensory experiences. By combining behavioral, biochemical, electrophysiological, and optogenetic techniques, we study the effects of waking experiences and sleep on neural circuits in the rodent brain.
Professor Deegan’s research focuses on the dynamics of non-equilibrium systems. As a system, such as a fluid or a solid, is driven from equilibrium, it undergoes a series of transitions to progressively more organized dynamics. Everyday examples of this phenomenon are the bands of Jupiter, the Giant’s Causeway, and the crumpled edges of lettuce leaves.
Professor Deegan studies dynamical transitions though table-top experiments with the aim of understanding the origin of this behavior in each specific case and in general. His research covers a broad range of phenomena from drying drops to bursting balloons to vibrated slurries.
The Ahmed lab studies behavioral neural circuits and attempts to repair them when they go awry in neuropsychiatric disorders. Working with patients and with transgenic rodent models, we focus on how space, time and speed are encoded by the spatial navigation and memory circuits of the brain. We also focus on how these same circuits go wrong in addiction, epilepsy and traumatic brain injury.
In addition to electrophysiology in rodents and humans, we use imaging and photoactivation techniques to record and alter neuronal activity as rodents navigate custom-designed virtual reality environments. We also work on novel computational techniques to model and analyze our immensely large electrophysiology and imaging datasets to better understand how specific behaviors are encoded by neural circuits.
Dr. Ahmed received both his undergraduate and Ph.D. degrees in Neuroscience from Brown University. He then worked with epilepsy patients at Massachusetts General Hospital during his postdoctoral work, before joining the faculty at the University of Michigan as an Assistant Professor.
Necmiye Ozay is an Assistant Professor of Electrical Engineering and Computer Science, in the Electrical and Computer Engineering Division. Her research interests lie at the broad interface of dynamical systems, control, optimization and formal methods with applications in system identification and validation, autonomy and vision. She is particularly interested in developing novel event detection/information extraction algorithms from sensory data and designing robust cyber-physical systems that can autonomously react to these events and perform complex tasks in dynamic environments.