Srikantan Nagarajan, UCSF BCHSI faculty / neuroscientist, provided data to young composers.Read Article
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.
UCSF, Stanford researchers predict newborn health outcomes using AI, EHR data
Marina Sirota, UCSF BCHSI and Stanford collaborators publish in Science Translational Medicine.Read Article
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.
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.
High Honors for Outstanding Science Contributions
Atul Butte is named 2022 fellow by the American Association for the Advancement of Science.Read Article
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
Bakar faculty Marina Sirota et al microbiome work highlighted in the context of pregnancy outcomes.Read Article
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, 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.
Travel time to abortion facilities grew significantly after Supreme Court overturned Roe v. Wade
Bakar faculty, senior author Yulin Hswen publishes on abortion access post: Dobbs v Jackson Women’s Health Decision.Read Article
Win NIH High-Risk, High-Reward Research Grant
Bakar faculty Hani Goodarzi receives the NIH Director’s Transformative Research Award.Read Article
2 BCHSI Faculty Elected to the National Academy of Medicine for 2022
Two Bakar Faculty – Ida Sim and Katie Pollard are among the 3 elected.Read Article
Scientists show how EMR may be used to learn more about Alzheimer’s disease
The NIA NIH highlighted work by BCHSI faculty lead Alice Tang, Sirota Lab and Information Commons team.Read Article
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.
Joint Computational Health PhD Program Opens Applications Today
Program is administered in partnership with the Division of Computing, Data Science, and Society at Berkeley and UCSF BCHSI.Read Article
UCSF AI4ALL Summer 2022 Wraps Up with Final Symposium
29 talented high school students learned about AI applications in biomedicine.Read Article
De-ID Clinical Notes Scale Project Wins Golden Sautter Award
BCHSI faculty gain highest UC Tech award honor for Deidentifying Clinical Notes at Scale.Read Article
March of Dimes Launches New Data-Focused Prematurity Research Center at UCSF
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
New Biomarker Classifications May Improve Treatment for High-Risk Breast Cancer Patients
BCHSI faculty Laura van’t Veer & team findings will guide treatment prioritization.Read Article
Two Artificial Intelligence / Machine Learning Demonstration Projects Awarded
BCHSI, CTSI and UC Health award two AI/Machine Learning Demo Pilot projects.Read Article
Towards Continual Monitoring & Updating AI algorithms in Healthcare
Jean Feng, Romain Pirracchio, et al published on Quality Improvement for Clinical AI.Read Article
UCSF Awarded $67.5 Million to Develop New Antiviral Therapies
BCHSI Faculty, Andrej Sali, Brian Shoichet, Michael Keiser, are investigators for this QBI Coronavirus Research Group.Read Article
Informations Commons Day at UCSF
As researchers who depend on data, join us on Monday, May 9 to learn how to deepen your research inquiry.See Details
UCSF Researchers Use Gene Expression Data to Map Cell Types in the CNS
Ashish Raj co-led the development of a computational pipeline, Matrix Inversion and Subset Selection.Read Article
Single-Cell Seq Study Uncovers Blood Cell Type Features for Lupus
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.
A Biomedical Open Knowledge Network Harnesses the Power of AI
Sharat Israni, et al and collaborators publish on knowledge networks identified by NSC as a core component of AI frameworks.Read Article
Atul Butte Testifies before Congress
Butte articulated priorities to Energy & Commerce Subcommittee on Health “Future of Biomedicine” Hearing.Read Article
An Entirely New Approach to Drug Discovery for AD
UCSF BCHSI data scientist’s research draws attention to an entirely new approach to drug discovery for Alzheimer’s.Read Article
Precision Medicine to Address Prostate Cancer in Veterans
Franklin Huang: Providing precision oncology solutions for veterans using data science and focusing on disparities.Read Article
Could an antidepressant prevent more COVID deaths?
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.
Computational Precision Health Program Funded by $50M Gift Launches
Ida Sim, MD, PhD and new UCSF program faculty will be affiliated with the BCHSI.Read Article
Disparities Research in Chronic Diseases with $22.5M Grant
Stuart Gansky, MS, DrPh, co-director, and William Brown III, PhD, DrPH as associate unit director.Read Article
Can an Already Approved Drug Treat Alzheimer’s Disease?
Marina Sirota, PhD (co-senior author) published a study using computation to pinpoint an existing drug that may prevent Alzheimer’s Disease.Read Article
Policy & Advocacy
UCSF SPOKE featured at NSF Convergence Accelerator Expo
Learn about UCSF’s knowledge network project and other innovative work supported by NSF Convergence Accelerator.Watch Video
AIMBE honors two Bakar Faculty
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.
Wesson, Paul Douglas
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.
Ushizima develops image recognition algorithms to diagnose COVID-19
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
UCSF and AWS Collaboration
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 Data Initiative Launches Daily Updates on COVID-19 Tests
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
Bakar Institute Develops COVID-19 County Tracker App
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
Acid Reflux Drug Is a Surprising Candidate to Curb Preterm Birth
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.
BCHSI Faculty Named on Forbes’ 30 under 30
Vijay Ramani has been named to Forbes magazine’s annual 30 Under 30 list of rising stars in healthcare.Read Article
Unprecedented Partnership to Advance Data in Biomedicine
Sergio Baranzini and Sharat Israni will lead the NSF award & collaborate with Google, Lawrence Livermore Library, and Institute Systems for Biology.Read Article
Data Science Health Innovation Fellows announced
BCHSI and BIDS welcome the 2019 Data Science Health Innovation Fellows.See Details
Julia Adler-Milstein Elected to the National Academy of Medicine for 2019
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.
Brain Maps Allow Individualized Predictions of FTD Progression
Seeley and colleagues used maps of brain connections to predict how brain atrophy would spread in individual patients with frontotemporal dementia.Read Article
UCSF Launches Artificial Intelligence Center to Advance Medical Imaging
Majumdar will run the Center for Intelligent Imaging’s operations to accelerate the application of artificial intelligence (AI) technology to radiology.Read Article
FDA approves Artificial Intelligence Algorithm That Reads Chest X-Rays
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
Genetic Test Found a Life-Saving Therapy for an Infant’s Rare Cancer
Alejandro Sweet-Cordero, explains how the UCSF500 test can identify inherited predispositions to cancer and help patients design prevention and surveillance strategies.Read Article
Mobile Devices and Health
UCSF, Cornell Tech, Sage Bionetworks, Open mHealth and The Commons Project are collaborating to integrate EHR data to Android smartphones.Read Article
Alzheimer’s Disease Destroys Neurons that Keep Us Awake
Study suggests tau tangles, not amyloid plaques, drive daytime napping that precedes dementia.Read Article
AI For All — Data-Driven Summer Program Energizes a New Generation
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.
University of California announces data science collaboration with Janssen
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.
New Collaboration To Advance Patient Safety In The Digital Era
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.
Van t’Veer, Laura
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.