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Yau, Christina

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Christina Yau, PhD

Developing biomarker-informed patient-centered clinical trial designs to improve outcomes and minimize toxicity

My research focuses on identifying predictive biomarkers of response and refining clinical trial endpoints. The goal is to develop patient-centered, biomarker-informed clinical trial designs to identify novel regimens that improve outcomes while minimizing toxicity for early stage breast cancer patients.

Ntranos, Vasilis

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Vasilis Ntranos, PhD

Computational methods development at the intersection of information theory, genomics, and machine learning

Our research revolves around key algorithmic and statistical challenges that arise in computational biology, with a particular focus on variant effect prediction and single-cell genomics — and is highly collaborative, spanning multiple biological domains in immunology, human genetics, and cancer biology.

Ge, Jin

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Jin Ge, MD, MBA

Electronic Interventions to Improve the Care of Patients with Chronic Liver Diseases and Cirrhosis

Dr. Ge’s research focuses on using clinical informatics, data science and artificial intelligence to improve the quality of care for patients with advanced liver diseases and awaiting transplant.

Gennatas, Efstathios

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Efstathios D. Gennatas, MBBS, PhD

Advanced and Accessible Health Data Science for All

Dr. Gennatas has developed rtemis, a comprehensive data science platform, which supports advanced visualization, statistical analyses, and machine learning. It provides both a highly flexible and efficient API as well as a no-code web application to bring advanced data science tools to biomedical researchers and clinicians regardless of technical expertise.

Big Little Lives

Source: UCSF Magazine

Bakar faculty Marina Sirota et al microbiome work highlighted in the context of pregnancy outcomes.

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Alaa, Ahmed

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Ahmed Alaa, PhD

Machine learning for Cardiology

Developing machine learning models for multi-modal data to predict cardiovascular disease risk and response to therapy.

Yala, Adam

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Adam Yala, PhD

Machine Learning for Precision Oncology: Algorithms for Multi-modal Inference and Policy Design

The Yala lab develops machine learning models for personalized care and translates them to clinical practice. It focuses on designing modeling approaches that are robust to data-generation biases, offer mechanisms for clinical deployment and can adapt to diverse clinical requirements.

Yang, Yang

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Yang Yang, PhD

“Push and go CMR”: comprehensive and free-breathing AI-powered cardiac magnetic resonance imaging

This project aims to improve the throughput by automated rapid scanning with minimal input from technicians and improve patient comfortableness with an ECG-free and free-breathing scan procedure with self-navigation powered by an inline AI framework.

Rauschecker, Andreas

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Andreas M. Rauschecker, MD, PhD

Digital and Computational Health Science

Dr. Rauschecker’s lab leverages modern AI methodologies to quantify, describe, and understand clinical brain MRIs. This work includes new imaging biomarker discovery, using AI for diagnostics and prognostics, and using AI for better understanding variation in the normal brain.

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.

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