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You are here: Home / Archives for Deep Machine Learning and Data Visualization

Hess, Christopher

March 10, 2018 By Karen

Christopher Hess, MD, PhD

Developing and translating biomedical imaging to diagnose and treat neurological disease

Dr. Hess’s research interests lie in the development and translational application of magnetic resonance imaging techniques for diagnosis and treatment monitoring in neurologic disease. His scientific background is in MRI acquisition, reconstruction and image analysis, focusing on diffusion and high-field MRI. His primary clinical interests are in neurovascular disease, dementia, brain development, and epilepsy.

Arnaout, Rima

February 20, 2018 By Karen

Rima Arnaout, MD

Improving the resolution and accuracy of cardiovascular phenotypes to develop novel insights and therapies

Dr. Arnaout’s lab is currently developing computational methods to bring precision phenotyping to echocardiography, and also using the zebrafish animal model to study cardiovascular developmental gene function and to model human cardiovascular disease.

Raj, Ashish

February 20, 2018 By Karen

Ashish Raj, PhD

Mathematical modeling and data science in neurology and radiology

Ashish’s team develops novel image processing and analysis algorithms for MRI. His lab also works to model brain connectivity networks using graph theory, and investigates how these networks are disrupted with disease and trauma.

Grinberg, Lea

July 14, 2017 By Karen

Lea Grinberg, MD, PhD

Computational approaches to imaging the human brain at the macro and micro level

The Grinberg Lab processes whole human brains for state-of-the-art quantitative histological analysis, digitize all of the results, and precisely registers to MRI. They are developing advanced tools for analysis of microscopic images that enable more comprehensive and higher-throughput studies of human brain tissue.

Tosun, Duygu

February 27, 2017 By Karen

Duygu Tosun, PhD

Developing algorithmic approaches for multi-modal data analysis

Dr. Tosun develops new algorithmic approaches for processing and analysis of multi-disciplinary/modal data including neuroimages, genetics, proteomics, as well as cognitive functioning measures in a unified framework. The primary aim is to identify multi-disciplinary/modality biomarkers for detecting the changes associated with disease specific neuropathology, improving understanding of pathophysiological progression and potentially providing a means of monitoring the efficacy and regional specificity of drug therapy for neurodegenerative diseases.

Majumdar, Sharmila

February 27, 2017 By Karen

Sharmila Majumdar, PhD

Developing image processing and analytics for musculoskeletal research

Dr. Majumdar’s research work on imaging, particularly magnetic resonance and micro computed tomography, and development of image processing and analysis tools, has been focused in the areas of osteoporosis, osteo-arthritis and lower back pain. Her research is diverse, ranging from technical development to clinical trials.

Nagarajan, Srikantan

January 12, 2017 By Karen

Srikantan Nagarajan, PhD

Brain imaging analysis and brain computer interfaces for diagnosis and assessment in various patient populations

Dr. Nagarajan has multiple research interests, including understanding human brain plasticity associated with learning and disease, and determining neural mechanisms of controlling speech. He focuses on the development and refinement of multimodal structural and functional brain imaging and brain computer interfaces, for diagnosis and assessment in various patient populations. His current translational research program includes conducting multimodal brain imaging studies in people with Autism, Dementia, Tinnitus, Brain Tumors, Epilepsy, Traumatic Brain Injury, Stroke and Voice Disorders.

Lupo, Janine

January 12, 2017 By Karen

Janine Lupo, PhD

Developing novel methods for MRI data collection and analysis in neurological research

Dr. Lupo is focused on developing novel methods for acquisition, reconstruction, post-processing, and quantitative analysis of magnetic resonance brain images. Using a combination of multiparametric structural, physiological, and metabolic MRI techniques, her goal is to quantitatively characterize heterogeneity within malignant brain tumors, monitor response to novel treatment regimens, and investigate the long-term effects of therapy on healthy brain tissue structure and cognitive function. Many of the methodologies we develop initially to evaluate patients with brain tumors are also being applied to other neurological diseases.

Xu, Duan

January 12, 2017 By Karen

Duan Xu, PhD

Developing new MRI techniques

Dr. Xu’s research focuses on investigating new MRI techniques with primary applications in pediatric neuroradiology. Another research focus is the development of new techniques on ultrahigh field MR scanners for small animal imaging, both in vivo and ex vivo. Techniques include high resolution MR anatomic, diffusion, and spectroscopy are being developed in collaboration with various colleagues in Neurodevelopment Biology, Neurology, Pediatrics, Neonatology, and Physiology.

Segal, Mark

September 7, 2016 By Karen

Mark Segal, PhD

Development and application of statistical methods to address problems in computational biology and genomics

Dr. Segal has devised methods for addressing several aspects of analyzing data deriving from high-throughput biotechnologies, straddling low-level (e.g., pre-processing) to high-level (e.g., linked survival phenotypes, regulatory module elicitation) approaches. He is currently engaged in developing and comparing methods for inferring 3D genome architecture utilizing data from chromatin conformation capture assays.

Seeley, William

September 7, 2016 By Karen

William Seeley, MD

Selective vulnerability in neurodegenerative disease

The Seeley Lab uses advanced neuroimaging techniques to map the specific neural networks and regions targeted early in each neurodegenerative disease. The patterns of network- and region-level vulnerability serve as maps for exploring cellular and molecular pathogenesis with quantitative neuropathological approaches. The lab’s research relies on the visualization and analysis of very large datasets using increasingly sophisticated modeling approaches. Overall, the lab seeks to clarify mechanisms of selective vulnerability and disease progression in order to develop novel therapeutic strategies and tools for monitoring change in patients during life.

Ye, Jimmie

June 16, 2016 By Karen

Jimmie Ye, PhD

Building new experimental and computational approaches to generate and interpret human biological data

This collaborative team of data scientists, computational biologists and genome detectives, have a shared vision —a fundamental understanding of human biology with an eye to improving human health. See website

Keiser, Michael

June 16, 2016 By Karen

Michael Keiser, PhD

Small molecule therapeutics with protein network perturbations

In classical pharmacology, drugs struck single notes, where one drug would hit one target to treat one disease. But drugs frequently modulate entire target “chords” at once, and this can be essential to their action. The Keiser lab is decoding this molecular music, both in terms of new and useful chords for the treatment of complex diseases, and also to identify the jarring notes that existing drugs unintentionally hit when they induce side effects. Michael is also uncovering the biological roots of Alzheimer’s disease. See more about his Distinguished Investigator Grant.

Hu, Xiao

June 16, 2016 By Karen

Xiao Hu, PhD

Intelligent Informatics with Big Clinical Data to Predict Patient State Changes

The Hu Lab uses signal modeling expertise and machine learning models to tackle neurocritical care problems, and more. See Dr. Hu’s work on making sense of the body’s complex signals, indicating everything from intracranial pressure to alarm fatigue.

Bandyopadhyay, Sourav

June 16, 2016 By Karen

Sourav Bandyopadhyay, PhD

Biological Networks in Cancer

The Bandyopadhyay lab focuses on methods to map pathway networks in cancer, understanding at a systems level how networks differ between cancer and normal cells. These will become the platform for the rational application of precision medicine in cancer therapies. See website

Altschuler, Steven and Wu, Lani

June 16, 2016 By Karen

Steven Altschuler, PhD and Lani Wu, PhD

Fundamentals in Cellular Heterogeneity Using Quantitative Techniques

The Altschuler-Wu lab investigates fundamental questions about the origins and impact of cellular heterogeneity in collective cellular decision making, tissue development and homeostasis. Results from our studies are applied to investigate mechanisms of drug resistance, cancer evolution and new therapeutic strategies. A common theme is the combined use of single-cell perturbation assays, quantitative imaging, data-driven modeling and theory.

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