By Karen
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.
By Karen
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.
By Karen
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.
By Karen
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.
By Karen
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.
By Karen
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
By Karen
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.
By Karen
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.
By Karen
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.
By Karen
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.
By Karen
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.
By Karen
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.
By Karen
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.
By Karen
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.
By Karen
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.
By Karen
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.
By Karen
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.
By Karen
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.
By Karen
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.
By Karen
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.
By Karen
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.
By Karen
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.
By Karen
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.
By Karen
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
