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Peterson, Thomas

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Thomas A. Peterson, PhD

Digital and Computational Health Science

Application of statistical and computational methodologies to prospective and retrospective clinical datasets for finding meaningful statistical associations and creating state-of-the-art tools for precision medicine

Olshen, Adam

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Adam B. Olshen, PhD

Developing tools for the analysis of genomic data and identifying biomarkers in cancer

Dr. Olshen has helped develop tools in such area as DNA copy number, mutation hotspot detection, and integration of data from multiple genomic assays. He is currently developing biomarkers to predict cancer outcomes in pediatric cancers.

Rudrapatna, Vivek

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Vivek Rudrapatna, MD, PhD

The Real-World Evidence Laboratory

Our group uses a variety of machine learning and biostatistical methods, such as natural language processing, deep learning, and causal inference, to transform electronic health records data into real-world evidence on treatment effects.

Lazar, Ann

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Ann A. Lazar, PhD

Biostatistics, Data Science and Precision Health

Dr. Lazar’s research focuses on biostatistics, data science and precision health. She has secured grant funding as PI from NIH, foundations and the University of California Office of the President, including grants for the HBCU initiative.

Operational Excellence

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Optimize and sustain delivery of BCHSI programs and services through implementation of best practices that promote efficiency, effectiveness, and quality.

Policy & Advocacy

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Champion the benefits and potential impact of computational health sciences; advocate priorities to advance the field; and promote data-driven solutions to key challenges faced by UCSF, UC Health and the […]

Training

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Train and educate innovators and thought leaders in computational health sciences at UCSF.

People

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Attract, nourish and retain the best talent, reflecting the rich diversity of our global community, to enable research in UCSF departments while providing a home to data-driven thought leaders and […]

Knowledge Assets

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Develop and enable safe and respectful access to the latest AI tools and relevant data assets of interest to all researchers in UCSF and beyond, while remaining attentive to social […]

Research

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Build and apply tools and methods in computational science to advance the field and support research in biomedical disciplines, with a focus on advancing equitable precision medicine.

AIMBE honors two Bakar Faculty

Source: Bakar Institute

Katie Pollard and Duan Xu are inducted into the prestigious American Institute for Medical and Biological Engineering College of Fellows.

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Kornblith, Aaron

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Aaron Kornblith, MD

Accurate and Consistent Advanced Diagnostic Strategies for Injured Children

Dr. Kornblith is focused on novel diagnostic strategies to enhance the care of injured children. He uses
a modern data science framework to develop accurate, consistent, and interpretable advanced analytic
models for rapid detection of intra-abdominal bleeding using clinical decision rules, computer vision,
and device design.

Fragiadakis, Gabriela

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Gabriela K. Fragiadakis, PhD

Characterizing immune organization and patient immune state using single-cell methods

Dr. Fragiadakis’s research focuses on analyzing immune state in diverse sets of patient cohorts using high-dimensional single-cell technologies, including single-cell sequencing and CyTOF. She uses multi-modal data integration methods to evaluate patient differences and infer broader principles of immune organization.

Semere, Wagahta

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Wagahta Semere, MD, MHS

Promoting Quality and Safety by Engaging Diverse Diabetes Patients and their Caregivers in Secure Messaging

Dr. Semere is using machine learning and novel computational linguistics techniques to characterize secure message communication between racially/ethnically diverse patients with diabetes, their caregivers, and providers. She is applying this information to develop family-centered technology based communication strategies that promote effective diabetes management.

Spitzer, Matthew

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Matthew H. Spitzer, PhD

Systems approaches to understand immune responses, particularly to cancer

We focus on understanding how the immune system coordinates responses with an emphasis on tumor immunology. We combine experimental and computational methods to understand how the immune system responds to tumors and to rationally initiate curative immune responses against cancer.

Feng, Jean

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Jean Feng, PhD

Developing reliable and interpretable machine learning algorithms for clinical decision making

Dr. Feng has developed machine learning and deep learning algorithms that accurately quantify their uncertainty and are appropriate for high dimensional datasets. Her current research is in developing safe machine learning algorithms that learn and recalibrate using streaming Electronic Health Records data.

Wesson, Paul Douglas

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Paul Douglas Wesson, PhD

The science for the last mile: Enhanced epidemiologic surveillance to accelerate HIV elimination

The objective of this NIAID-funded K01 award is to identify the residual drivers of HIV infection in Fast Track cities. Methods from semi-parametric
statistical modeling, HIV phylogenetics, and minority stress theory will be used to augment surveillance data.

Spetz, Joanne

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Joanne Spetz, PhD

Health workforce and the organization and delivery of healthcare services

Dr. Spetz uses econometric and mixed methods to study the organization of the health workforce and delivery of healthcare services, primarily with large secondary and survey-based datasets.

Sarkar, Urmimala

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Urmimala Sarkar, MD, MPH

Innovating for Health Equity

Dr. Sarkar’s research focuses on: (1) Ambulatory patient safety, (2) Digital health innovations to improve the safety and quality of outpatient care, (3) Social media research for behavior change, (4) Safety-net implementation of evidence-based digital health in real-world settings.

Hswen, Yulin

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Yulin Hswen, ScD

Social and Computational Epidemiology

Dr. Hswen current research seeks to identify authentic attitudes, feelings and beliefs that influence behaviors and drive populations. Through the collection of unconventional and underground online social networks, Dr. Hswen captures unfiltered conversations to further understand the connections between social experiences and health.

Yao, Keluo

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Keluo Yao, MD

Improving Research and Operational Software for Laboratory Medicine

Pathology and laboratory medicine provide 70% of the information in most patient care settings. Therefore, using the best software is the best way to deliver that information to the clinicians on the front lines. I am interested in developing these software solutions by utilizing the best practices from the cutting edge artificial intelligence technologies to traditional methods.

Ushizima, Daniela

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Daniela Ushizima, PhD

Algorithms for Multimodal and N-Dimensional Imaging Experiments

Dr. Ushizima research focuses on computer vision algorithms for monitoring diseases progression while exploring information from multiple imaging techniques. Together with Grinberg’s lab, developed quantitative histological analysis of whole human brains using multiple instruments.

Jiang, Fei

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Fei Jiang, PhD, MS

High quality statistical and computational publications focusing on addressing practical problems in the medical domain

Dr. Jiang’s research interest lies in machine learning methods, high dimensional models, functional data analysis and their applications in analyzing neurological, image, genetics data, and in designing adaptive randomization clinical trials. Her efforts yield high quality statistical and computational publications, which focus on addressing practical problems in the medical domain.

Scheffler, Aaron

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Aaron Scheffler, PhD, MS

Analysis of complex datasets to better understand biological systems and inform meaningful clinical decisions

Dr. Scheffler develops statistical methods for high-dimensional signals produced by his collaborators in neurology and orthopedics. The proper interrogation of this data will lead to improved health outcomes at both the patient and population level.

Abbasi-Asl, Reza

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Reza Abbasi-Asl, PhD

Interpretable machine learning to understand brain functions and related disorders

The Abbasi Lab aims to understand functions of the brain and related disorders by leveraging principles in machine learning and statistics. The lab studies large-scale single-cell level neurophysiological datasets as well as medical images and structured and unstructured healthcare data.

Lyles, Courtney

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Courtney Lyles, PhD

Reinforcement Learning Algorithm for Motivating Physical Activity among Patients with Diabetes and Depression

We are developing & testing smartphone apps that use reinforcement learning models to personalize text-messages to encourage physical activity. The apps are designed for English & Spanish speaking individuals diagnosed with depression and diabetes, being treated at the ZSFGH.

Rankin, Kate

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Kate Rankin, PhD

Building Strategic Clinical Informatics Tools to Bridge Precision Medicine Research and Patient Care

Dr. Rankin investigates the underpinnings of human socioemotional behavior in aging and neurodegenerative disease with quantitative brain imaging, and develops tools for harmonizing cross-disciplinary data and analytic processes to facilitate scientific collaboration and clinical care.

Phillips, Kathryn

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Kathryn Phillips, PhD

Examining Health Services and Health Economics, focusing on new technologies to improve healthcare

Dr. Phillips focuses on the value of new technologies and how to effectively and efficiently implement them into health care. Her core specialty is precision medicine. Her work spans multiple disciplines, including basic, clinical & social sciences

Glymour, Maria

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Maria Glymour, ScD, MS

Causal Inference, Artificial Intelligence and Health Research

Dr. Glymour is exploring approaches of causal inference methods for research on health inequalities, stroke, and Alzheimer’s disease and how Artificial Intelligence can accelerate health research.

Murray, Sara

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Sara Murray, MD

Using EHR data for quality and value improvements

Dr. Murray is leading the Advanced Analytics & Innovation team, she is involved in large analytic projects using EHR data to inform quality and value improvement efforts at the medical center. She is interested in predictive analytics and has done research in EHR phenotyping.

Sanders, Stephan

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Stephan Sanders, PhD

Using genomics and bioinformatics to understand the etiology of developmental disorders

Sanders Lab aims to identify the etiology of developmental disorders through the discovery of genetic risk factors. They aim to continue progress, leverage findings to build a complete understanding of ASD, & extend this approach to other human disorders, including congenital malformations.

Hong, Julian

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Julian Hong, MD, MS

Developing and implementing computational tools in oncology to improve patient care

Dr. Hong’s research program focuses on combining clinical domain knowledge with data science to generate insights from real world data, develop actionable computational tools, and evaluate the benefit of these advances in personalized cancer care.

Li, Jingjing

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Jingjing Li, PhD

From Big Data to Big Mind: Building Data-Driven Frameworks to Solve Complex Diseases

The main theme of our research is large-scale analysis of disease genomes by integrating multi-omics data, evolutionary insights, electronic health records, as well as digitized clinical traits from imaging and wearable sensor readouts. The ultimate goal is to build data-driven frameworks to detect diseases before symptoms emerge and to achieve precision health management.

Mobile Devices and Health

Source: New England Journal of Medicine

UCSF, Cornell Tech, Sage Bionetworks, Open mHealth and The Commons Project are collaborating to integrate EHR data to Android smartphones.

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Tison, Geoff

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Geoff Tison, MD, MPH

Applying machine learning and deep-learning techniques to large-scale electronic health data

Dr. Tison applies machine learning and deep-learning techniques to large-scale electronic health data from heterogeneous sources in order to achieve the goal of personalized cardiovascular prognosis and disease prevention.

Rosner, Benjamin

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Benjamin Rosner, MD, PhD

Understand use of CLIIR to improve quality and value of healthcare

Audit-log data captures moment to moment clinical care processes within EHR. It makes these labor-intensive approaches obsolete, providing more comprehensive data faster with more accuracy. Taking this audit-log data and combining it with clinical data enables us to research the impact of provider workflows, behaviors and interactions with the EHR on patient outcomes.

Ramani, Vijay

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Vijay Ramani, PhD

Novel molecular technologies to study gene regulation

Ramani Lab invents molecular tools to study biology, e.g. devising ways to molecularly tag nucleic acids and proteins with unique genomic or proteomic identifiers, then using these to quantify biological phenomena at the level of single cells and single molecules, using high-throughput sequencing and cutting-edge mass spectrometric techniques.

Huang, Franklin

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Franklin Huang, MD, PhD

Understanding how to use digital tools to improve quality and value of healthcare

Dr. Huang studies biological processes relating to cancer disparities with a focus in prostate cancer. His lab uses cancer genomics including single-cell approaches to understand mechanisms that drive lethal, aggressive disease. A major focus is to identify and uncover roles for cancer genes and identify new cancer vulnerabilities.

Whooley, Mary

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Mary Whooley, MD

Learning Health Systems & Cardiovascular Outcomes Research

Dr. Whooley’s work focuses on applying the methods of health services research (including data science, implementation science, and program evaluation) to accelerate the adoption of evidence-based practices within the context of a national learning healthcare system. One of her specific interest areas is in the domain of cardiovascular health outcomes.

Larson, Peder

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Peder Larson, PhD

Developing new MRI scanning and reconstruction technology for improved clinical outcomes

Dr. Larson’s research program focuses on developments aimed at several applications: Metabolic imaging methods using hyperpolarized carbon-13 MRI; Semi-solid tissue MRI, for imaging of tendons, cortical bone, myelin, and lung tissue; and PET/MRI systems that combine the exceptional soft-tissue contrast of MRI with the functional contrast of PET.

Pirrachio, Romain

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Romain Pirrachio, MD, PhD

Applied biostatistical research, machine Learning and predictive analytics in critical care

Dr. Pirracchio focuses on three clinical research areas including evaluation and optimization of daily cares in the intensive care unit. He is also broadly interested in problems of causal inference and prediction, particularly developing novel methodologies for addressing scientific questions using complex observational data subject to sampling biases.

Seo, Youngho

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Youngho Seo, PhD

Using quantitative SPECT/CT, PET/CT, and PET/MR molecular imaging tools for a broad range of research areas

Dr. Seo applies his expertise in radionuclide and x-ray imaging physics and instrumentation to develop quantitative imaging techniques for everything from small animal research to analysis of clinical research data.

Pedoia, Valentina

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Valentina Pedoia, PhD

Using machine learning to extract features from MRI to study degenerative joint disease

Dr. Pedoia develops analytics to model the complex interactions between morphological, biochemical and biomechanical aspects of the knee joint as a whole; deep learning convolutional neural network for musculoskeletal tissue segmentation and for the extraction of silent features from quantitative relaxation maps for a comprehensive study of the biochemical articular cartilage composition; with ultimate goal of developing a completely data-driven model that is able to extract imaging features and use them to identify risk factors and predict outcomes for Osteoarthritis.

Goldstein, Ted

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Ted Goldstien, PhD

Applying Bioinformatics to Precision Medicine

Dr. Goldstien uses the tools of big data, statistics and machine learning to answer questions related to Precision Medicine such as: How can we learn from the inventory of genomic test and knowledge in the EMR about patient outcomes and the data associated with individual patients to better direct their care? How can we better use repeatable animal models to translate knowledge to human patients? How can we integrate existing knowledge and high throughput data? Can we use genomic data to bring new therapies to bear?

Zaitlen, Noah

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Noah Zaitlen, PhD

Understanding genetic and environmental underpinnings of common disease

The Zaitlen lab develops statistical and computational tools to discover the genetic basis of complex phenotypes, with particular interest in human disease, variation in drug/treatment response, and disease outcomes. Current projects primarily focus on incorporating environmental context into medical genetics.

Van t’Veer, Laura

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Laura Van t’Veer, PhD

Characterizing biomolecular signatures for precision cancer treatments

Dr. van ‘t Veer’s research focuses on personalized medicine, to advance patient management based on knowledge of the genetic make-up of the tumor as well as the genetic make-up of the patient. This allows clinicians to optimally assign systemic therapy for those patients in need of such treatment, and to ensure the selection of the therapy that is most effective.

Hess, Christopher

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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.

Goodarzi, Hani

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Hani Goodarzi, PhD

Identification and characterization of key regulatory programs that underlie cancer progression

The Goodarzi laboratory employs a systems biological and multidisciplinary approach that integrates computational and experimental strategies to identify and characterize key regulatory programs that underlie cancer progression.

Arnaout, Rima

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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

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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.

Baranzini, Sergio

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Sergio Baranzini, PhD

Genetics and molecular mechanisms underlying complex neurological disease

Dr Baranzini’s current research involves immunological studies using the EAE model, sequencing of whole genomes and transcriptomes from patients with multiple sclerosis and developing bioinformatics tools to integrate this information with that coming from other high throughput technologies. Dr Baranzini uses a combination of “wet lab” methods including DNA microarrays, proteomics, and laser capture microdissection, in combination with “dry lab” analytical approaches encompassing bioinformatics, complexity theory, and mathematical modeling.

Adler-Milstein, Julia

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Julia Adler-Milstein, PhD

Examining the use of IT in healthcare delivery

Dr. Adler-Milstein’s research assesses the progress of health IT adoption; the impact of such adoption on healthcare costs and quality; and the relationships between market, organizational, and team structure and health IT use. A core focus of her work is on health information exchange and interoperability.

Grinberg, Lea

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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.

Brown, William

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William Brown, III, PhD, DrPH

Using informatics, mHealth, and New Media-based technologies to promote health among vulnerable populations and in underserved communities

Dr. Brown uses knowledge engineering, health informatics, comparative-effectiveness research, semantic harmonization, and integration of datasets (including EHR) to examine health disparities and develop patient-centered health information tools.

Sweet-Cordero, Alejandro

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Alejandro Sweet-Cordero, MD

Functional genomics to identify novel cancer therapeutics

The lab seeks to discover new therapeutic approaches to target the genetic mutations and altered signaling networks that are specific to cancer cells. Using functional genomics applied to mouse and human systems, we work to understand the transcriptional networks that regulate the outcome of specific oncogenic mutations and to understand how cancers become resistant to chemotherapy. This work relies heavily on computational genomic analysis, generating and using high-throughput datasets and next-generation sequencing for gene and network discovery. Our primary disease focus is lung cancer and pediatric sarcomas.

Kober, Kord

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Kord Kober, PhD

‘Omics data to understand mechanisms underlying common symptoms in chronic conditions

Dr. Kober uses ‘omics data (i.e., genotype and expression arrays, DNAseq — genome, exome, RNAseq, methylation arrays) to improve our understanding of the molecular mechanisms underlying common symptoms (e.g., fatigue, pain) or treatment failure experienced by patients with chronic medical conditions (e.g., cancer, HIV infection).

Tosun, Duygu

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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

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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

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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

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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

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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.

Kalendarian, Elsbeth

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Elsbeth Kalendarian, DDS, MPH, PhD

Developing electronic dental health records for the information age

Elsbeth works on development and implementation of the Dental Diagnostic System (DDS) in EHR, and is providing leadership in the creation of international standards in dental health records.

Lazar, Ann

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Ann Lazar, MS, PhD

A Tailored Approach to Reducing Oral Health Disparities

Dr. Lazar is working to develop an analysis framework and software tools to help understand how patient characteristics interact with dental treatments in order to improve treatment decisions for individual patients and develop targeted treatments to reduce oral health disparities.

Blum, Michael

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Michael Blum, MD

Cardiology and Digital Health Technology

Dr. Blum is a cardiologist who specializes in the care of patients with congestive heart failure, valvular heart disease and preventative cardiology. He is dedicated to the early detection of heart disease and prevention through a heart-healthy lifestyle that includes diet, exercise and stress reduction. He has a special research interest in clinical decision support technology, social media and collaborative workspaces and their impact on the quality, effectiveness, and cost of care delivery.

Gansky, Stuart

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Stuart Gansky, MS, DrPH

Oral health and health disparities

Dr. Gansky’s research concentrates on oral health, health disparities, applied statistical analyses and related method­ological issues. Balancing these components is essential to successful and practical population health research. Methodological examination helps ground health research and build convincing argu­ments, while collaborative health research generates opportunities for innovative statistical practice and provides challenges for developing ways to solve real world problems.

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