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.
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.
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.
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.
Robert Dick is an Associate Professor in the Department of Electrical Engineering and Computer Science, in the Electrical and Computer Engineering division. He also co-founded and served as CEO of Stryd, Inc., which produces wearable electronics for athletes. He received his Ph.D. degree from Princeton University in 2002 and his B.S. degree from Clarkson University in 1996. He worked as a Visiting Professor at Tsinghua University’s Department of Electronic Engineering in 2002, as a Visiting Researcher at NEC Labs America in 1999, and was on the faculty of Northwestern University from 2003-2008.
Prof. Dick has published in the areas of embedded operating systems, data compression, embedded system synthesis, dynamic power management, low-power and temperature-aware integrated circuit design, wireless sensor networks, human perception aware computer design, reliability, embedded system security, and behavioral synthesis. He especially likes projects in which a deep understanding of a particular application leads to a new fundamental concept or technology with broader application. He is a principal investigator in MICDE’s catalyst grant titled “Embedded Machine Learning Systems To Sense and Understand Pollinator Behavior”.
He received an NSF CAREER award and won his department’s Best Teacher of the Year award in 2004. In 2007, his technology won a Computerworld Horizon Award and his paper was selected as one of the 30 in a special collection of DATE papers appearing during the past 10 years. His 2010 work won a Best Paper Award at DATE.
Fernanda Valdovinos is an Assistant Professor in the department of Ecology and Evolutionary Biology and Complex Systems. She received her Ph.D. in Ecology and Evolutionary Biology from the Faculty of Science, University of Chile in 2008. Before joining the University of Michigan, she was a researcher in the Estación Biológica de Doñana, Spain, at the Pacific Ecoinformatics and Computational Lab in Berkeley, CA and at the department of Ecology and Evolutionary Biology at the University of Arizona.
Her lab studies the structure and dynamics of ecological networks at ecological and evolutionary scales; including their resilience to biodiversity loss, biological invasions, climate change, and exploitation by humans. She is a principal investigator in MICDE Catalyst Grant: “Embedded Machine Learning Systems To Sense and Understand Pollinator Behavior”.
Jianzhi (George) Zhang is a Professor of Ecology and Evolutionary Biology interested in the relative roles of chance and necessity in evolution. He got his B. S. from Fudan University in Shanghai, China, and his Ph. D. in Genetics from Pennsylvania State University. He was a Fogarty postdoctoral fellow at the National Institute of Allergy and Infectious Diseases before moving to the University of Michigan.
Professor Zhang’s research focuses on two main research areas: (1) yeast as an experimental system for studying evolution, where his research group uses the budding yeast Saccharomyces cerevisiae and its relatives as model organisms to understand a variety of evolutionary processes such as the genetic basis of phenotypic variations among strains and species, or molecular and genomic bases of heterosis; and (2) computational evolutionary genomics where they use evolutionary, genomic, and/or systemic approaches to analyze publicly available data to characterize and understand pleiotropy, robustness, epistasis, gene-environment interaction, gene expression noise, translational regulation, RNA editing, convergent evolution, adaptation, origin of new genes, among-protein evolutionary rate variation, and other important genetic and evolutionary phenomena. Projects may also involve modeling and simulation, including the MICDE catalyst grant project where the team is using deep neural networks to infer molecular phylogenies and extract phylogenetically useful patterns from amino acid or nucleotide sequences, which will help understand evolutionary mechanisms and build evolutionary models for a variety of analyses.
Yuanfang Guan is an associate professor of Computational Medicine and Bioinformatics. She got her B.S. from the University of Hong Kong, and her Ph.D. in Molecular Biology from Princeton University.
Prof. Guan is interested in machine learning in biology and medicine. Her team has written the majority of the best-performing algorithms in DREAM challenges, the largest systems biology benchmark study. Prof. Guan was awarded the ‘Consistent Best Technical Performer’ for her groups achievements in the DREAM challenges and in recognition of the open source software that they have contributed to the bioinformatics field. She is one of the very few people globally who own multiple gold medals in the annual Data Science Bowl by Kaggle.
Her team has written many award-winning deep learning methods. In traditional machine learning, she is the inventor of GuanRank, adaptive GPR and several other algorithms that are often used as the reference algorithms in benchmark studies/challenges.