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.
Dr. Wesson uses tools from machine learning to improve the validity of population size estimation methods and overcome common biases. He is also exploring using geospatial data to investigate disparities in engagement in the HIV care cascade for vulnerable populations.
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 ArticleFacilitated by BCHSI, AWS connected with the laboratory of Charles Chiu to enable two COVID-19 projects.
Read ArticleDr. 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 ArticleButte 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 DetailsMarina Sirota uses a computational study to identify a dozen of other drugs to reduce inflammation.
Read ArticleDr. 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.
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.
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 ArticleSergio Baranzini and Sharat Israni will lead the NSF award & collaborate with Google, Lawrence Livermore Library, and Institute Systems for Biology.
Read ArticleFour 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 ArticleWe 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.
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.
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
Her research has focused on how psychological stress & diet both contribute to the risk for obesity, diabetes, & cardiovascular disease. She is interested in the question. Going forward, she is focused on applying these interests in the context of digital therapeutics & the Health eHeart study.
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 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.
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.
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 ArticleMajumdar will run the Center for Intelligent Imaging’s operations to accelerate the application of artificial intelligence (AI) technology to radiology.
Read ArticleCallcut 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 ArticleAlejandro Sweet-Cordero, explains how the UCSF500 test can identify inherited predispositions to cancer and help patients design prevention and surveillance strategies.
Read ArticleUCSF, Cornell Tech, Sage Bionetworks, Open mHealth and The Commons Project are collaborating to integrate EHR data to Android smartphones.
Read ArticleStudy suggests tau tangles, not amyloid plaques, drive daytime napping that precedes dementia.
Read ArticleBCHSI 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 ArticleDr. 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.
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 research
Read ArticleRamani 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.
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.
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.
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.
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 ArticleDr. 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.
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.
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?
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.
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.
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.
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.
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’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.
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.
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.
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.
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.
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.
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).
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.
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.
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.
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.
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.
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.
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 ArticleDr. 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.
The Jain lab focuses on computational chemistry and computational biology. The primary research areas are in structure-based drug discovery, rational approaches for predictive pharmacology, and applications involved in cancer. Researchers at academic and non-profit institutions are encouraged to download and make use of our software.
Dr. Sim’s group works to create an open software architecture that provides shared analysis, data presentation, and evaluation modules to support systematic and shared learning in mobile health. She also leads international efforts to build a single global portal for sharing individual participant-level data from clinical trials.
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.
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.
Learn more about Brian Shoichet’s efforts to engineer a new kind of painkiller.
Read ArticleStudents grab opportunity to start with R, Python, Unix commands, and Git and GitHub.
See DetailsDr. Pollard’s group aims to identify specific DNA alterations that are responsible for novel functionality, such as variation in gene expression.
The Shoichet lab seeks to bring chemical reagents to biology, combining computation and experiment. In a protein-centric approach, molecular docking, they discover new ligands that complement protein structures. Using a ligand-centric approach, they discover new targets for known drugs and reagents.
Recognizing that the elements of life are at angstrom-scale, the Douglas lab aims to create the computational building blocks for a new generation of therapies and devices.
The Sirota lab develops incremental computational methods in the context of disease diagnostics and therapeutics – especially leveraging ‘omics and clinical data to better understand the role of the immune system.
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
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.
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
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.
Key forum on the first 1000 days of life, especially for the underserved. Follow-up actions are being planned.
Read ArticleThe Butte lab builds tools in translational bioinformatics to make sense of big ‘omics and clinical data and solve new classes of problems in Oncology.
UC San Francisco Chancellor Sam Hawgood, MBBS, took the first official trip of his administration to Washington, D.C., last week,
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