Across the spectrum of medical research — basic, translational, clinical, population, prevention — the emerging power of parallel computing and data science hold fresh promise.
AREAS OF INTEREST
Using computational simulation and visualization to understand the behavior of complex biological systems and networks
Abbasi-Asl, Reza
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.
Altschuler, Steven and Wu, Lani
Steven Altschuler, PhD and Lani Wu, PhD
Fundamentals in Cellular Heterogeneity Using Quantitative Techniques
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.
Baranzini, Sergio
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.
Douglas, Shawn
Shawn Douglas, PhD
Novel Tools and Devices at Nanoscale and Finer
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.
Fragiadakis, Gabriela
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.
Jain, Ajay
Ajay Jain, PhD
Predictive computational modeling focused on algorithmic approaches for drug discovery
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.
Li, Jingjing
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.
Ramani, Vijay
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.
Shoichet, Brian
Brian Shoichet, PhD
Discovering reagents to modulate G-Protein Coupled Receptors (GPCRs)
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.
Sirota, Marina
Marina Sirota, PhD
Data Science in Disease Diagnosis and Treatment
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.
Spitzer, Matthew
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.
Sweet-Cordero, Alejandro
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.
Ushizima, Daniela
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.
Developing medical models that propose the customized care of each cancer patient
Butte, Atul
Atul Butte, MD, PhD
A New Frontier of Problems Relevant to Genomic Medicine
The Butte lab builds tools in translational bioinformatics to make sense of big ‘omics and clinical data and solve new classes of problems in Oncology.
Gennatas, Efstathios
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.
Goldstein, Ted
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?
Goodarzi, Hani
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.
Hong, Julian
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.
Huang, Franklin
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.
Lazar, Ann
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.
Li, Jingjing
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.
Lupo, Janine
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.
Olshen, Adam
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.
Phillips, Kathryn
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
Sarkar, Urmimala
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.
Segal, Mark
Mark Segal, PhD
Development and application of statistical methods to address problems in computational biology and genomics
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.
Seo, Youngho
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.
Spitzer, Matthew
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.
Sweet-Cordero, Alejandro
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.
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.
Yala, Adam
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.
Yao, Keluo
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.
Using the wealth of clinical and phenotypic data to uncover new knowledge relating to health and disease
Alaa, Ahmed
Ahmed Alaa, PhD
Machine learning for Cardiology
Developing machine learning models for multi-modal data to predict cardiovascular disease risk and response to therapy.
Arnaout, Rima
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.
Blum, Michael
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.
Brown, William
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.
Butte, Atul
Atul Butte, MD, PhD
A New Frontier of Problems Relevant to Genomic Medicine
The Butte lab builds tools in translational bioinformatics to make sense of big ‘omics and clinical data and solve new classes of problems in Oncology.
Feng, Jean
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.
Gansky, Stuart
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.
Gennatas, Efstathios
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.
Glymour, Maria
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.
Goldstein, Ted
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?
Grinberg, Lea
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.
Hong, Julian
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.
Hswen, Yulin
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.
Kalendarian, Elsbeth
Elsbeth Kalendarian, DDS, MPH, PhD
Developing electronic dental health records for the information age
Elsbeth works on development and implementation of the Dental Diagnostic System (DDS) in EHR, and is providing leadership in the creation of international standards in dental health records.
Kober, Kord
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).
Lazar, Ann
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.
Lupo, Janine
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.
Lyles, Courtney
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.
Majumdar, Sharmila
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.
Murray, Sara
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.
Nagarajan, Srikantan
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.
Pedoia, Valentina
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.
Peterson, Thomas
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
Pirrachio, Romain
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.
Rankin, Kate
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.
Rauschecker, Andreas
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.
Rudrapatna, Vivek
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.
Sanders, Stephan
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.
Sarkar, Urmimala
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.
Scheffler, Aaron
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.
Semere, Wagahta
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.
Seo, Youngho
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.
Sim, Ida
Ida Sim, PhD, MD
Developing infrastructure to enable the translation of clinical and mobile data into knowledge to improve health
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.
Sirota, Marina
Marina Sirota, PhD
Data Science in Disease Diagnosis and Treatment
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.
Spetz, Joanne
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.
Sweet-Cordero, Alejandro
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.
Tosun, Duygu
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.
Whooley, Mary
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.
Xu, Duan
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.
Yang, Yang
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.
Yao, Keluo
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.
Zaitlen, Noah
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.
Using computational methodologies to understand the organization and information processing of neural systems
Abbasi-Asl, Reza
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.
Baranzini, Sergio
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.
Grinberg, Lea
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.
Hess, Christopher
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.
Li, Jingjing
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.
Raj, Ashish
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.
Rankin, Kate
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.
Rauschecker, Andreas
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.
Scheffler, Aaron
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.
Seeley, William
William Seeley, MD
Selective vulnerability in neurodegenerative disease
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.
Seo, Youngho
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.
Tosun, Duygu
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.
Ushizima, Daniela
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.
Xu, Duan
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.
Using computational methodologies to analyze large genomic and proteomic data sets, design molecular tools to probe cellular function, integrate data, and predict complex cellular processes – all in order to achieve a systems-level understanding of human disease
Butte, Atul
Atul Butte, MD, PhD
A New Frontier of Problems Relevant to Genomic Medicine
The Butte lab builds tools in translational bioinformatics to make sense of big ‘omics and clinical data and solve new classes of problems in Oncology.
Douglas, Shawn
Shawn Douglas, PhD
Novel Tools and Devices at Nanoscale and Finer
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.
Jain, Ajay
Ajay Jain, PhD
Predictive computational modeling focused on algorithmic approaches for drug discovery
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.
Keiser, Michael
Michael Keiser, PhD
Small molecule therapeutics with protein network perturbations
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.
Shoichet, Brian
Brian Shoichet, PhD
Discovering reagents to modulate G-Protein Coupled Receptors (GPCRs)
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.
Sirota, Marina
Marina Sirota, PhD
Data Science in Disease Diagnosis and Treatment
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.
Applying powerful computational algorithms that emulate the human mind – driving innovation in research and medical practice
Abbasi-Asl, Reza
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.
Alaa, Ahmed
Ahmed Alaa, PhD
Machine learning for Cardiology
Developing machine learning models for multi-modal data to predict cardiovascular disease risk and response to therapy.
Altschuler, Steven and Wu, Lani
Steven Altschuler, PhD and Lani Wu, PhD
Fundamentals in Cellular Heterogeneity Using Quantitative Techniques
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.
Arnaout, Rima
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.
Feng, Jean
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.
Fragiadakis, Gabriela
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.
Gennatas, Efstathios
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.
Glymour, Maria
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.
Grinberg, Lea
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.
Hess, Christopher
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.
Hong, Julian
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.
Hswen, Yulin
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.
Jiang, Fei
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.
Keiser, Michael
Michael Keiser, PhD
Small molecule therapeutics with protein network perturbations
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.
Kornblith, Aaron
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.
Larson, Peder
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.
Li, Jingjing
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.
Lupo, Janine
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.
Majumdar, Sharmila
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.
Nagarajan, Srikantan
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.
Olshen, Adam
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.
Pedoia, Valentina
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.
Peterson, Thomas
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
Raj, Ashish
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.
Rankin, Kate
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.
Rauschecker, Andreas
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.
Rosner, Benjamin
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.
Rudrapatna, Vivek
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.
Sarkar, Urmimala
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.
Scheffler, Aaron
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.
Seeley, William
William Seeley, MD
Selective vulnerability in neurodegenerative disease
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.
Segal, Mark
Mark Segal, PhD
Development and application of statistical methods to address problems in computational biology and genomics
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.
Seo, Youngho
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.
Spitzer, Matthew
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.
Tison, Geoff
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.
Tosun, Duygu
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.
Ushizima, Daniela
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.
Whooley, Mary
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.
Xu, Duan
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.
Yala, Adam
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
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.
Yao, Keluo
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.
Ye, Jimmie
Jimmie Ye, PhD
Building new experimental and computational approaches to generate and interpret human biological data
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
Establishing connections between molecular discoveries and population health
See here for more about Precision Public Health initiatives at UCSF
Arnaout, Rima
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.
Feng, Jean
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.
Fragiadakis, Gabriela
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.
Gansky, Stuart
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.
Gennatas, Efstathios
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.
Hong, Julian
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.
Hswen, Yulin
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.
Lazar, Ann
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.
Li, Jingjing
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.
Lyles, Courtney
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.
Peterson, Thomas
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
Rosner, Benjamin
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.
Rudrapatna, Vivek
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.
Scheffler, Aaron
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.
Tison, Geoff
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.
Tosun, Duygu
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.
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.
Whooley, Mary
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.
Yala, Adam
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
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.
Ye, Jimmie
Jimmie Ye, PhD
Building new experimental and computational approaches to generate and interpret human biological data
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
Zaitlen, Noah
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.
Measuring and studying sets of biomolecules – from genomes to proteomes to microbiomes – of value in health monitoring, preventive measures and precision medicine
Baranzini, Sergio
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.
Butte, Atul
Atul Butte, MD, PhD
A New Frontier of Problems Relevant to Genomic Medicine
The Butte lab builds tools in translational bioinformatics to make sense of big ‘omics and clinical data and solve new classes of problems in Oncology.
Fragiadakis, Gabriela
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.
Goldstein, Ted
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?
Goodarzi, Hani
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.
Kober, Kord
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).
Li, Jingjing
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.
Olshen, Adam
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.
Pollard, Katherine
Katherine Pollard, PhD
Developing statistical and computational methods for the analysis of massive genomic datasets
Dr. Pollard’s group aims to identify specific DNA alterations that are responsible for novel functionality, such as variation in gene expression.
Ramani, Vijay
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.
Segal, Mark
Mark Segal, PhD
Development and application of statistical methods to address problems in computational biology and genomics
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.
Sweet-Cordero, Alejandro
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.
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.
Ye, Jimmie
Jimmie Ye, PhD
Building new experimental and computational approaches to generate and interpret human biological data
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
Zaitlen, Noah
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.
