BCHSI faculty Marina Sirota et al will focus on data sharing, computational drug discovery, leveraging real world data to help prevent preterm birth.Read Article
BCHSI faculty Laura van’t Veer & team findings will guide treatment prioritization.Read Article
BCHSI, CTSI and UC Health award two AI/Machine Learning Demo Pilot projects.Read Article
Jean Feng, Romain Pirracchio, et al published on Quality Improvement for Clinical AI.Read Article
BCHSI Faculty, Andrej Sali, Brian Shoichet, Michael Keiser, are investigators for this QBI Coronavirus Research Group.Read Article
As researchers who depend on data, join us on Monday, May 9 to learn how to deepen your research inquiry.See Details
Ashish Raj co-led the development of a computational pipeline, Matrix Inversion and Subset Selection.Read Article
Jimmie Ye, et al, published in Science on how they deployed mux-seq to profile 1.2M immune cells for lupus.Read Article
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.
Sharat Israni, et al and collaborators publish on knowledge networks identified by NSC as a core component of AI frameworks.Read Article
Butte articulated priorities to Energy & Commerce Subcommittee on Health “Future of Biomedicine” Hearing.Read Article
UCSF BCHSI data scientist’s research draws attention to an entirely new approach to drug discovery for Alzheimer’s.Read Article
Franklin Huang: Providing precision oncology solutions for veterans using data science and focusing on disparities.Read Article
A UCSF-Stanford data analysis shows a strong association between taking SSRIs and surviving the virus.Read Article
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.
Ida Sim, MD, PhD and new UCSF program faculty will be affiliated with the BCHSI.Read Article
Stuart Gansky, MS, DrPh, co-director, and William Brown III, PhD, DrPH as associate unit director.Read Article
Marina Sirota, PhD (co-senior author) published a study using computation to pinpoint an existing drug that may prevent Alzheimer’s Disease.Read Article
Learn about UCSF’s knowledge network project and other innovative work supported by NSF Convergence Accelerator.Watch Video
Katie Pollard and Duan Xu are inducted into the prestigious American Institute for Medical and Biological Engineering College of Fellows.Read Article
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.
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.
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.
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.
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.
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.
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.
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.
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.
Dani Ushizima explores algorithms and a data analysis pipeline to help accurately distinguish COVID-19 abnormalities in CT scans and chest X-rays.Read Article
Facilitated by BCHSI, AWS connected with the laboratory of Charles Chiu to enable two COVID-19 projects.Read Article
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.
UC Health will distribute daily updates about SARS-CoV-2 testing volume, the # of positive tests & age distribution of confirmed cases gathered from its 5 medical centers.Read Article
Butte lab developed a COVID-19 County Tracker app to track cases nationwide. It features plots of total cases by states and county, with interactive exploration.See Details
Marina Sirota uses a computational study to identify a dozen of other drugs to reduce inflammation.Read Article
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.
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.
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.
Vijay Ramani has been named to Forbes magazine’s annual 30 Under 30 list of rising stars in healthcare.Read Article
Sergio Baranzini and Sharat Israni will lead the NSF award & collaborate with Google, Lawrence Livermore Library, and Institute Systems for Biology.Read Article
BCHSI and BIDS welcome the 2019 Data Science Health Innovation Fellows.See Details
Four UCSF faculty members are among the 100 new members elected to the National Academy of Medicine this year, including BCHSI affiliate faculty Julia Adler-Milstein.Read Article
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.
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.
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
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.
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.
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.
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.
Seeley and colleagues used maps of brain connections to predict how brain atrophy would spread in individual patients with frontotemporal dementia.Read Article
Majumdar will run the Center for Intelligent Imaging’s operations to accelerate the application of artificial intelligence (AI) technology to radiology.Read Article
Callcut led the product development of the new AI screening tool, known as Critical Care Suite, which is being licensed by UCSF Innovation Venture to GE Healthcare.Read Article
Alejandro Sweet-Cordero, explains how the UCSF500 test can identify inherited predispositions to cancer and help patients design prevention and surveillance strategies.Read Article
UCSF, Cornell Tech, Sage Bionetworks, Open mHealth and The Commons Project are collaborating to integrate EHR data to Android smartphones.Read Article
Study suggests tau tangles, not amyloid plaques, drive daytime napping that precedes dementia.Read Article
BCHSI faculty Marina Sirota led the first cohort of the AI4All program, focused on promoting greater diversity and inclusion in the field of AI in biomedicine.Read Article
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.
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.
New fellowship program from UCSF’s BCHSI, UC Berkeley’s BIDS, and Janssen Research & Development to recruit data scientists for innovative, high-impact, data-driven healthcare researchRead Article
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.
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.
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.
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.
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.
Julia Adler-Milstein will lead the new partnership between UCSF and The Doctor’s Company to make substantive advances in patient safety and digital health.Read Article
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.
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.
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?
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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).
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.
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.
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.
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.
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.
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.
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.
ICHS teamed up with the UCSF Library Data Science Initiative to offer a series of Software Carpentry workshops on campus.Read Article
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.