Reza Abbasi-Asl, PhD

Dr. Abbasi-Asl is an Assistant Professor in the Department of Neurology and Director of Data Analytics and Visualization at Weill Institute for Neuroscience at UCSF. He completed his PhD and MSc in Electrical Engineering & Computer Sciences at UC Berkeley in 2018. His lab focuses on understanding the brain functions and related disorders using interpretable machine learning and statistical tools.

Computational health science interests:

Biological Modeling, Computational Neuroscience, Deep Machine Learning and Data Visualization

Ahmed Alaa, PhD

Ahmed Alaa is an Assistant Professor of Computational Precision Health at UC Berkeley and UCSF, with affiliations in the EECS and Statistics departments at UC Berkeley. Previously, he was a postdoctoral associate at Massachusetts Institute of Technology and the Broad Institute of MIT and Harvard University. He obtained his Ph.D. in Electrical and Computer Engineering from UCLA, where he received the 2021 Edward K. Rice Outstanding Doctoral Student Award from the UCLA Samueli School of Engineering. His research interests include machine learning for healthcare, computer vision for medical imaging, clinical informatics, statistics, and causal inference.

Computational health science interests:

Clinical Research Informatics, Deep Machine Learning and Data Visualization

Rima Arnaout, MD

Dr. Arnaout is a physician-scientist with a background in genetics, clinical research and programming, and a practicing cardiologist board-certified in multi-modality cardiovascular imaging. She studied Biology and Bioengineering at MIT, and got her MD at Harvard. She did her Residency in Internal Medicine at MGH, and trained as a Cardiology Fellow at UCSF. Her research aims to improve the resolution and accuracy of cardiovascular phenotypes, thus leading to novel insights and therapies.

Computational health science interests:

Clinical Research Informatics, Deep Machine Learning and Data Visualization, Population Precision Medicine

Sourav Bandyopadhyay, PhD

Sourav did his PhD in Bioinformatics and Systems Biology with Trey Ideker at UC San Diego. In 2010, he obtained a prestigious faculty fellowship at UCSF designed to provide early independence to the world’s best scientists. His research focuses on the generation and network analysis of physical and genetic maps governing pathway deregulation in cancer. Sourav is committed to the development of systems biology techniques to understand pathway structure with a translational focus on the discovery of novel intervention points in cancer, using deep computational health sciences and pharmacogenomics approaches.

Computational health science interests:

Cancer and Precision Oncology, Deep Machine Learning and Data Visualization

Sergio Baranzini, PhD

Sergio E. Baranzini studied at the University of Buenos Aires in Argentina, earning his degree in clinical biochemistry and later obtaining a PhD with honors in human molecular genetics. Dr. Baranzini came to UCSF as a postdoctoral scholar, and joined as faculty in the Department of Neurology in 2003. He is a member of the American Association of Immunologists, the American Society for Human Genetics and an elected member of the American Neurological Association. His research focuses on the genetics and molecular mechanisms underlying complex diseases; and work in his lab involves human genetics, immunology, molecular biology, bioinformatics and systems biology approaches.

Computational health science interests:

Biological Modeling, Computational Neuroscience, Very Large Data Molecular Measurements

William Brown, III, PhD, DrPH

Dr. Brown is an Assistant Professor at the UCSF School of Medicine in the Division of Prevention Science. He implements innovative biomedical informatics methods, creates research tools, and follows the principals of community-based participatory research to address health disparities among underserved communities, and to create a Learning Health System that is responsive to vulnerable populations, particularly those at risk for acquiring HIV/AIDS.

Computational health science interests:

Clinical Research Informatics

Tony Capra, PhD

Dr. Capra received a PhD in computer science from Princeton University under the supervision of Mona Singh. He was a postdoc in the group of Katherine Pollard at the Gladstone Institutes and UCSF. As a postdoc, he developed comparative genomics algorithms that integrated genome-scale data to develop testable hypotheses about the function of non-coding regulatory elements and to study recent human evolution.

Computational health science interests:

Population Precision Medicine, Very Large Data Molecular Measurements

Jean Feng, PhD

Dr. Feng is an Assistant Professor in the Department of Epidemiology and Biostatistics. She obtained a BS in Computer Science from Stanford University and a PhD in Biostatistics from the University of Washington under Noah Simon and Erick Matsen. Her current research interests are in developing reliable and interpretable machine-learning algorithms that aid clinical decision-making.

Computational health science interests:

Clinical Research Informatics, Deep Machine Learning and Data Visualization, Population Precision Medicine

Valy Fontil, MD, MAS

Dr. Fontil is an implementation scientist and primary care physician at UCSF in the Division of General Internal Medicine at Zuckerberg San Francisco General Hospital. He is an expert in developing and adopting digital health technology for chronic disease management across multiple clinical settings, including safety-net healthcare institutions. His work has focused on developing and scaling technology-enabled healthcare interventions and innovations, such as clinical decision support technology platforms, in real-world settings targeted to reduce cardiovascular risk in high-risk populations vulnerable to health disparities.

Computational health science interests:

Clinical Research Informatics, Population Precision Medicine

Gabriela K. Fragiadakis, PhD

Dr. Fragiadakis is an Assistant Professor in the Division of Rheumatology and Director of the Data Science CoLab. She obtained her PhD in Microbiology and Immunology and postdoctoral studies at Stanford University. Her lab focuses on systems-level immune organization using single-cell methods, including CyTOF and single-cell sequencing. She also leads the UCSF Data Library project, a web-based data sharing platform for projects with multimodal biological and clinical data.

Computational health science interests:

Biological Modeling, Deep Machine Learning and Data Visualization, Population Precision Medicine, Very Large Data Molecular Measurements

Jin Ge, MD, MBA

Dr. Ge is a gastroenterologist and transplant hepatologist who cares for patients with liver diseases. He received his MD/MBA at the University of Pennsylvania before completing his internal medicine residency, and gastroenterology and advanced/transplant hepatology fellowships at UCSF. While at UCSF, he also completed a certificate program in advanced clinical research training, with a focus on data science. His 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. He also currently serves as the GI/Liver Domain Team lead for the NIH sponsored National COVID Cohort Collaborative (N3C).

Computational health science interests:

Clinical Research Informatics, Deep Machine Learning and Data Visualization, Population Precision Medicine

Efstathios D. Gennatas, MBBS, PhD

Efstathios (Stathis) D. Gennatas, MBBS AICSM PhD, is an Assistant Professor in the Department of Epidemiology and Biostatistics, and a member of the Center for Intelligent Imaging at the University of California, San Francisco. He completed medical school at Imperial College London and doctoral training in Neuroscience at the University of Pennsylvania. His work includes applied data science in basic research, clinical predictive modeling, and the development of machine learning methods and software for biomedical data analysis. His overarching goal is to help make precision medicine a reality in a safe, fair, and efficient way through interdisciplinary collaboration and team science.

Computational health science interests:

Cancer and Precision Oncology, Clinical Research Informatics, Deep Machine Learning and Data Visualization, Population Precision Medicine

Maria Glymour, ScD, MS

Dr. Glymour serves as the Director for the UCSF PhD program in Epidemiology and Translational Science. She co-leads the UCSF T32 training grant on Aging and Chronic Disease, which offers financial support for pre- and post-doctoral researchers.

Computational health science interests:

Clinical Research Informatics, Deep Machine Learning and Data Visualization

Hani Goodarzi, PhD

Dr. Goodarzi obtained his PhD in quantitaive and computational biology from Princeton University. He later did his postdoctoral training in Cancer Systems Biology at Rockefeller University in New York. He came to UCSF in 2016 as an Assistant Professor in the Departments of Biochemistry and Biophysics, and Urology. He is a member of the New York Academy of Sciences and the American Association of Cancer Research. His research combines modern computational and experimental technologies to understand cancer progression at a molecular level, with the goal of developing novel strategies for studying, diagnosing, and ultimately treating cancer.

Computational health science interests:

Cancer and Precision Oncology, Very Large Data Molecular Measurements

Lea Grinberg, MD, PhD

Dr. Grinberg is a neuropathologist specializing in brain aging and associated neurodegenerative disorders. She received her medical and doctorate degrees in São Paulo, Brazil. Currently, Dr. Grinberg is an Associate Professor at the UCSF Memory and Aging Center and leads an international initiative to develop a pipeline, using high-resolution histology, to improve and validate multimodal neuroimaging tools. She is has served as Chair of the HUPO Brain Proteome Project, and was the recipient of the UNESCO-L’Oréal Award For Women in Science as well as the John Douglas French Alzheimer Foundation Distinguished Research Scholar Award.

Computational health science interests:

Clinical Research Informatics, Computational Neuroscience, Deep Machine Learning and Data Visualization

Ryan Hernandez, PhD

Ryan studies patterns of genetic variation in modern day populations to gain insight into their evolutionary history. His focus is on the development of population genetic models that can best explain observed data. Most recently, he has been evaluating models of background selection under parameters that are realistic for humans. Ryan’s scientific approach tends to be highly computational, often involving a thorough analysis of detailed simulations.

Computational health science interests:

Very Large Data Molecular Measurements

Christopher Hess, MD, PhD

Dr. Hess completed his PhD in Electrical Engineering and later his MD at the University of Illinois. He joined UCSF as a resident in Radiology, and during his training was one of the first class of NIH T-32 Radiology research fellows, chief resident and recipient of the department’s Elmer Ng Award for outstanding resident. He completed his Radiology residency in 2007 and went on to complete a Neuroradiology fellowship at UCSF in 2008. He joined the Neuroradiology Section faculty that same year. He became chief of Neuroradiology in 2015, until 2018 when he became Chair of the Department of Radiology and Biomedical Imaging.

Computational health science interests:

Computational Neuroscience, Deep Machine Learning and Data Visualization

A Jay Holmgren, PhD

Dr. Holmgren is an Assistant Professor in the Department of Medicine and the Center for Clinical Informatics and Improvement Research (CLIIR). He earned his BA in History and Masters of Health Informatics from the University of Michigan and his PhD in Health Policy from Harvard University. Dr. Holmgren is an expert on the use of digital tools in health care delivery, and his research focuses on the impact of information technology on patients, clinicians, and health care organizations.

Computational health science interests:

Clinical Research Informatics

Julian Hong, MD, MS

Dr. Hong is a radiation oncologist and informatician. He serves as Director of Clinical Informatics in Radiation Oncology. His lab focuses on combining clinical domain knowledge with data science to analyze real world multi-modal data, develop actionable computational tools, and evaluate these advances in the clinic for personalized cancer care.

Computational health science interests:

Cancer and Precision Oncology, Clinical Research Informatics, Deep Machine Learning and Data Visualization, Population Precision Medicine

Yulin Hswen, ScD

Dr. Hswen is an Assistant Professor in the Department of Epidemiology and Biostatistics and the Bakar Computational Health Institute at UCSF. Dr. Hswen graduated with a Doctoral Degree in social and computational epidemiology at Harvard University. Through the collection of unconventional and underground data from online social networks, Dr. Hswen seeks to captures unfiltered conversations to uncover the connections between social experiences and health. Dr. Hswen is the founder and Principal Investigator of Covidseeker.com a platform that captures geospatial temporal data from Smartphones to better study the risks between human mobility and infectious disease.

Computational health science interests:

Clinical Research Informatics, Deep Machine Learning and Data Visualization, Population Precision Medicine

Ajay Jain, PhD

Following a long period of applied research in defense applications and in speech understanding, Prof. Jain began a research career exclusively focused on issues in computational chemistry and computational biology. He has done foundational work in computer-aided drug design, and currently develops models for multitudes of human targets in order to help guide the design and selection of compounds during preclinical research. The Jain lab has active collaborations with both academic and industry partners; and are particularly interested in applications involving cancer.

Computational health science interests:

Biological Modeling, Computational Pharmacology

Fei Jiang, PhD

Dr. Jiang is an assistant professor in biostatistics in the University of California, San Francisco. Her 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.

Computational health science interests:

Computational Neuroscience, Deep Machine Learning and Data Visualization

Elsbeth Kalenderian, DDS, MPH, PhD

Elisabeth (Elsbeth) Kalenderian received her DDS from Rijks Universiteit Groningen, The Netherlands. She came to Boston University as a Fulbright Scholar and completed an internship and residency in oral and maxillofacial surgery. She spent several years at Harvard University, receiving a Masters degree in Public Health and serving at the Harvard School of Dental Medicine as assistant dean for clinical affairs, associate professor and Chair of the Department of Oral Health Policy and Epidemiology. She obtained her PhD from the University of Amsterdam by leading a project to develop foundational diagnostic terminology for use in electronic dental health records; and she continues to lead efforts to improve these systems both for enhanced patient care and meaningful use.

Computational health science interests:

Clinical Research Informatics

Kord Kober, PhD

Dr. Kober received his doctoral degree in molecular evolutionary biology from the University of California, Santa Cruz; and worked on the UCSC Cancer Genome Browser. He Co-Directs the WIHS Genomics group as well as the UCSF School of Nursing Genomics Lab, which operates a large biobank supporting biospecimens for research.

Computational health science interests:

Clinical Research Informatics, Very Large Data Molecular Measurements

Aaron Kornblith, MD

Dr. Kornblith is a physician-scientist and Associate Professor of Emergency Medicine and Pediatrics. He completed his training at UCSF and is a practicing general and pediatric emergency physician at Zuckerberg San Francisco General and UCSF Benioff Children’s Hospitals. Dr. Kornblith’s research program is focused on using reliable, reproducible, and trustworthy data science practices to improve the diagnostic evaluation of sick and injured child through advanced data analytics and novel hardware design.

Computational health science interests:

Deep Machine Learning and Data Visualization

Peder Larson, PhD

Peder Larson is an Associate Professor in Residence and a Principal Investigator in the Department of Radiology and Biomedical Imaging at UCSF. Dr. Larson’s research program is primarily centered around developing new MRI scanning and reconstruction technology for improved clinical outcomes. Dr. Larson is an active member of International Society for Magnetic Resonance in Medicine, the Institute for Electrical and Electronics Engineering, the UC Berkeley and UCSF Graduate Group in Bioengineering, and the California Institute for Quantitative Biosciences.

Computational health science interests:

Deep Machine Learning and Data Visualization

Ann Lazar, PhD

Dr. Lazar received her PhD in Biostatistics from the University of Colorado Denver School of Public Health, Masters of Science in Biostatistics from the University of Michigan School of Public Health and Bachelors of Science from the University of California Berkeley. She was a recipient of the Ruth L. Kirschstein National Research Fellowship Award while she was post-doctoral fellow in Biostatistics and Data Science at the Harvard School of Public Health and Dana Farber Cancer Institute. She received training in oral health disparities via a NIH P30 grant after joining the faculty at UCSF.

Computational health science interests:

Clinical Research Informatics, Population Precision Medicine

Jingjing Li, PhD

Dr. Li’s predoctoral training was in engineering and machine learning, and later received his PhD in Molecular Genetics from the University of Toronto. He did his postdoc research at Stanford Genetics as a Banting Fellow, and later received training in clinical research at Stanford Pediatrics. As a faculty member at UCSF, his research aims to innovate our analytical frameworks for large-scale disease genome analysis.

Computational health science interests:

Biological Modeling, Cancer and Precision Oncology, Computational Neuroscience, Deep Machine Learning and Data Visualization, Population Precision Medicine, Very Large Data Molecular Measurements

Janine Lupo, PhD

Dr. Lupo received her Bachelor’s degree in Engineering in 2001 from the University of Pennsylvania, and PhD in Bioengineering from UCSF and UC Berkeley in 2006. She is an Associate Professor in Radiology and Biomedical Imaging, and her research is carried out at the Surbeck Laboratory for Advanced Imaging.

Computational health science interests:

Clinical Research Informatics, Deep Machine Learning and Data Visualization

Courtney Lyles, PhD

Dr. Lyles is a health services researcher who focuses on digital health interventions conducted in under-resourced healthcare settings. This includes developing new technologies, such as machine learning algorithms and mobile apps, as well as broader efforts to spread and sustain evidence-based innovation solutions at a population level.

Computational health science interests:

Clinical Research Informatics, Population Precision Medicine

Sara Murray, MD

Dr. Murray is an Associate Professor of Medicine in the Division of Hospital Medicine at the University of California, San Francisco (UCSF), and serves as the Medical Director of Clinical Informatics for UCSF Health. She works with the clinical systems teams at UCSF to optimize the current electronic health record (EHR) infrastructure and design novel informatics solutions for the medical center.

Computational health science interests:

Clinical Research Informatics

Srikantan Nagarajan, PhD

Dr. Nagarajan is a Professor in Residence, Director of the Biomagnetic Imaging Laboratory, in the Department of Radiology and Biomedical Imaging at the University of California, San Francisco. He has joint appointments in the Department of Bioengineering and Therapeutic Sciences and in the Department of Otolaryngology, Head and Neck Surgery at UCSF. He is an active member in the UCB-UCSF Joint Graduate Program in Bioengineering and has served on the Executive Committee.

Computational health science interests:

Clinical Research Informatics, Computational Neuroscience, Deep Machine Learning and Data Visualization

Vasilis Ntranos, PhD

Vasilis Ntranos obtained his PhD in Electrical Engineering from USC in 2015. After his graduate studies, Vasilis completed his postdoctoral training with Prof. Lior Pachter and Prof. David Tse in the Electrical Engineering & Computer Science Dept. at UC Berkeley (2015-2018), the Electrical Engineering Dept. at Stanford University (2015-2019), and the Biology and Biological Engineering Department at Caltech (2018-2019). Vasilis joined UCSF in November 2019, where he is currently an Assistant Professor in the Dept. of Epidemiology & Biostatistics, and the Diabetes Center. Vasilis’ main research interests are in computational methods development at the intersection of information theory, genomics, and machine learning, with a primary focus on single cell biology.

Computational health science interests:

Deep Machine Learning and Data Visualization, Population Precision Medicine, Very Large Data Molecular Measurements

Adam B. Olshen, PhD

Dr. Olshen is a professor in the Bioinformatics Division of the Department of Epidemiology and Biostatistics and Director of the Computation Biology and Informatics (CBI) Core in the Helen Diller Family Comprehensive Cancer Center at UCSF. His main research interest is statistical genomics in the context of cancer. His work includes developing tools for the analysis of genomic data and identifying biomarkers based on genomic and other types of data. He received a PhD in Biostatistics from the University of Washington and was previously faculty at the Memorial Sloan-Kettering Cancer Center.

Computational health science interests:

Cancer and Precision Oncology, Deep Machine Learning and Data Visualization, Very Large Data Molecular Measurements

Valentina Pedoia, PhD

Dr. Pedoia obtained her doctoral degree in computer science working on features extraction from functional and structural brain MRI in subjects with glial tumors. After graduating in 2013, she joined the Musculoskeletal and Imaging Research Group at UCSF as post-doctoral fellow, and later joined the UCSF faculty as an Assistant Professor. She is a data scientist with a primary interest in developing algorithms for advanced computer vision and machine learning for improving the usage of non-invasive imaging as diagnostic and prognostic tools.

Computational health science interests:

Clinical Research Informatics, Deep Machine Learning and Data Visualization

Thomas A. Peterson, PhD

Dr. Peterson works in close coordination with clinical experts to advance computational health sciences by leveraging advanced statistical, machine learning, and AI techniques on both prospective (RCT) and retrospective (EHR) datasets. As the director of the Analytics Core for UCSF REACH and the director for the Laboratory for Digital and Computational Health Science, Dr. Peterson specializes in techniques for studying and creating tools from a datasets of varying sizes, data modalities, and complexity.

Computational health science interests:

Clinical Research Informatics, Deep Machine Learning and Data Visualization, Population Precision Medicine

Kathryn Phillips, PhD

Kathryn A. Phillips is an expert on the economic and reimbursement issues relevant to the translation of new technologies – particularly personalized/precision medicine – into clinical care and health policy.

Computational health science interests:

Cancer and Precision Oncology, Population Precision Medicine

Romain Pirrachio, MD, PhD

Dr. Pirrachio obtained his MD from Université Paris Diderot with a specialization in Anesthesiology and Critical Care. He received an MS, MPH, and PhD in Medical Research Methodology and Biostatistics. He was a postdoctoral fellow in the School of Public Health at UC Berkeley. He served as Chair for the Department of Anesthesia and Critical Care at European Hospital Geroges Pompidou and a researcher in Biostatistics at the INSERM U-1153 unit. He is currently a UCSF Professor, Chief of Anesthesia and Perioperative Care at ZSFGH, Vice Chair for the Department of Anesthesia and Perioperative Care at UCSF and Affiliate to the Division of Biostatistics at UC Berkeley. He is the co-founder of the ACTERREA research group.

Computational health science interests:

Clinical Research Informatics

Ashish Raj, PhD

Dr. Raj earned his Bachelor’s in Electrical Engineering from the University of Auckland, and later his PhD in Electrical and Computer Engineering from Cornell. He has extensive experience in computer vision, signal processing, graph theory, medical imaging and informatics. He works with teams of basic and clinical scientists to find innovative ways to apply computation and algorithms to biomedical applications.

Computational health science interests:

Computational Neuroscience, Deep Machine Learning and Data Visualization

Vijay Ramani, PhD

Dr. Ramani earned a BSE in Chemical Engineering at Princeton University, with minors in Quantitative & Computational Biology, and Engineering Biology. Studying an Integrated Science Curriculum, he was motivated to pursue research in quantitative molecular biology. He spent time at Sangamo Therapeutics as a Computational Biologist developing high-throughput sequencing assays. He completed a PhD in Genome Sciences at the University of Washington, where he developed new molecular technologies to study biomolecular phenomena at scale and learned to apply novel bulk- and single-cell sequencing technologies. As a Sandler Fellow, he is developing an independent research program at UCSF.

Computational health science interests:

Biological Modeling, Very Large Data Molecular Measurements

Kate Rankin, PhD

Dr. Rankin is a Professor in Residence in the Memory and Aging division of the UCSF Department of Neurology. She trained at Yale, Fuller, and UCSF, obtaining a PhD in Clinical Psychology with a specialization in Neuropsychology. At the MAC she investigates the quantitative neurology of human socioemotional behavior, and develops informatics tools to accelerate clinical research and patient care approaches.

Computational health science interests:

Clinical Research Informatics, Computational Neuroscience, Deep Machine Learning and Data Visualization

Andreas M. Rauschecker, MD, PhD

Andreas Rauschecker earned his MD and PhD at Stanford with a PhD in Neuroscience studying the organization of the human brain’s visual system using fMRI and DTI. He completed his Radiology Residency at the University of Pennsylvania, where he translated methods used in his PhD to clinical brain imaging. Dr. Rauschecker also completed an Informatics Fellowship at Penn. At UCSF he completed a T-32 sponsored research fellowship and his clinical neuroradiology fellowship. Dr. Rauschecker joined the UCSF faculty as a neuroradiologist in 2020. Dr. Rauschecker’s work focuses on developing and applying algorithms to analyze and quantify clinical brain MRI in the context of a variety of pathologies.

Computational health science interests:

Clinical Research Informatics, Computational Neuroscience, Deep Machine Learning and Data Visualization

Benjamin Rosner, MD, PhD

Dr. Rosner holds a PhD in engineering and an MD from the University of Minnesota. He is a digital health expert, researcher, entrepreneur, and practicing hospital-based physician. He publishes and speaks extensively on the use of digital health technologies in improving the four pillars of the quadruple aim. Prior to CLIIR, Dr. Rosner was the founding Chief Medical Information Officer of HealthLoop, a prominent digital patient engagement company. He has worked closely with top leadership at CMS, and was the lead author of an Improvement Activity in the Merit Based Incentive Payment System (MIPS) of CMS’ Quality Payment Program.

Computational health science interests:

Deep Machine Learning and Data Visualization, Population Precision Medicine

Vivek Rudrapatna, MD, PhD

Vivek Rudrapatna obtained his MD and PhD at the Icahn School of Medicine at Mount Sinai, followed by a residency in internal medicine at Baylor College of Medicine and a fellowship in gastroenterology at UCSF. He joined the UCSF faculty in 2020 as a physician scientist. He spends part of his time as a gastroenterologist and inflammatory bowel disease specialist, and spends the remainder as a clinical data science investigator at the Bakar Computational Health Sciences Institute. His research team develops methods for using clinical data to uncover real-world evidence on treatment effects and advance the goals of precision medicine across use cases.

Computational health science interests:

Clinical Research Informatics, Deep Machine Learning and Data Visualization, Population Precision Medicine

Andrej Sali, PhD

Andrej Sali develops computational methods for determining and modulating structures and functions of protein assemblies. He has a BSc in Chemistry from the University of Ljubljana, Slovenia, and a PhD from University of London, where he developed the MODELLER program for comparative modeling of protein structures. He was a Jane Coffin Childs Memorial Fund fellow at Harvard, studying lattice Monte Carlo models of protein folding. From 1995 to 2002, he was at Rockefeller University. He then moved to UCSF as a Professor of Computational Biology, in the Departments of Bioengineering, Therapeutic Sciences, and Pharm. Chem.; and in California Institute for Quantitative Biosciences (QB3). Andrej has been Sinsheimer Scholar, Sloan Research Fellow, Hirschl Trust Career Scientist, Zois Awarded Science Ambassador of Slovenia, and elected Fellow of ISCB. He has been editor of Structure since 2002; and a founder of Prospect Genomix and Global Blood Therapeutics.

Stephan Sanders, PhD

Dr. Sanders trained as a pediatric physician in the UK before undertaking a PhD and postdoctoral research position at Yale. As a PI, Dr. Sanders co-leads the whole-genome sequencing working group of the ASC. This group developed the Category-Wide Association Study approach to WGS analysis, which provides a framework to define and account for the multiple comparisons inherent to these analyses.

Computational health science interests:

Clinical Research Informatics

Urmimala Sarkar, MD, MPH

Urmimala Sarkar MD, MPH is Associate Chair for Faculty Experience for the Department of Medicine, Professor of Medicine at UCSF in the Division of General Internal Medicine, Associate Director of the UCSF Center for Vulnerable Populations, and a primary care physician at Zuckerberg San Francisco General Hospital’s Richard H. Fine People’s Clinic. Dr. Sarkar’s work centers on innovating for health equity and improving safety and quality of outpatient care for everyone, especially low-income and diverse populations. Her expertise spans topics including medical errors and patient safety, diabetes, and cancer prevention and survivorship. Dr. Sarkar’s research is collaborative and intersects with methods found in human centered design, human factors engineering, data science, health services research, and implementation science.

Computational health science interests:

Cancer and Precision Oncology, Clinical Research Informatics, Deep Machine Learning and Data Visualization

Aaron Scheffler, PhD, MS

Dr. Scheffler received his PhD in biostatistics from the University of California, Los Angeles and is an Assistant Professor in the Department of Epidemiology and Biostatistics. His research addresses the statistical challenges posed by highly structured data collected in applications from imaging to wearable technologies.

Computational health science interests:

Clinical Research Informatics, Computational Neuroscience, Deep Machine Learning and Data Visualization, Population Precision Medicine

William Seeley, MD

Dr. Seeley is a behavioral neurologist and neuroscientist who studies neurodegenerative disease. He received his MD from the UCSF School of Medicine before completing neurology residency at the Massachusetts General and Brigham and Women’s Hospitals. He is currently a Professor of Neurology and Pathology and a member of the UCSF Memory and Aging Center. He is also Director of the UCSF Neurodegenerative Disease Brain Bank. His laboratory uses advanced neural network analysis to model and predict the anatomical spread of neurodegeneration in living patients and high-dimensional neuropathological approaches to clarify disease pathogenesis. Dr. Seeley’s work was recognized in 2011 with a fellowship from the John D. and Catherine T. MacArthur foundation. He is also a Fellow of the American Academy for the Advancement of Science.

Computational health science interests:

Computational Neuroscience, Deep Machine Learning and Data Visualization

Mark Segal, PhD

Dr. Segal obtained a BSc (Mathematics) from the University of Western Australia and a PhD (Statistics) from Stanford. He was on the faculty at Harvard before coming to UCSF in 1989. His primary research interests are in bioinformatics and modern regression techniques. His focus is on the development and application of statistical methods to address problems in computational biology and genomics.

Computational health science interests:

Cancer and Precision Oncology, Deep Machine Learning and Data Visualization, Very Large Data Molecular Measurements

Wagahta Semere, MD, MHS

Dr. Semere is a health services researcher and primary care physician at UCSF in the Division of General Internal Medicine at Zuckerberg San Francisco General Hospital. Her work focuses on leveraging technology-based platforms and clinical informatics methods to develop communication tools that promote better engagement of racially/ethnically diverse and low-income patients with providers in chronic disease management.

Computational health science interests:

Clinical Research Informatics

Youngho Seo, PhD

Dr. Seo received his bachelor’s degree in Physics from Korea Advanced Institute of Science and Technology. He completed a master’s degree in Physics at the University of Alabama in Huntsville, followed by a second master’s degree and then PhD in Physics from UCLA. Following postdoctoral training at UCLA in experimental neutrino physics, he joined the UCSF Physics Research Laboratory (PRL) in 2003, and was trained in medical imaging physics before joining the faculty in 2006. Dr. Seo now leads a group of physicists and engineers working in the field of radionuclide and x-ray imaging instrumentation and physics, and directs the UCSF PRL, and preclinical PET/SPECT/CT/Optical imaging core facility at the UCSF Center for Molecular and Functional Imaging.

Computational health science interests:

Cancer and Precision Oncology, Clinical Research Informatics, Computational Neuroscience, Deep Machine Learning and Data Visualization

Marina Sirota, PhD

Prior to this position, Marina was the Lead Research Scientist in Systems Medicine at Stanford. She has worked as a Senior Research Scientist at Pfizer where she focused on developing Precision Medicine strategies in drug discovery. Marina has a PhD in Biomedical Informatics from Stanford University, where her graduate work focused on predicting drug-disease relationships based on gene expression to identify novel therapeutic indications for known drugs.

Computational health science interests:

Biological Modeling, Clinical Research Informatics, Computational Pharmacology

Joanne Spetz, PhD

Joanne Spetz is Director and Brenda and Jeffrey L. Kang Presidential Chair in Healthcare Finance at the Philip R. Lee Institute for Health Policy Studies (IHPS) at UCSF. She also is Associate Director for Research at the Institute and at Healthforce Center at UCSF. Her research focuses on the health care workforce and organization of health care services, including how these influence adoption of new technologies and processes of care, and the resulting quality of care. With training in economics, she has expertise in the analysis of large secondary datasets, mixed methods evaluation research, survey research, and econometric analysis. She teaches health economics and finance in the Master’s in Translational Medicine program.

Computational health science interests:

Clinical Research Informatics

Matthew H. Spitzer, PhD

Dr. Spitzer completed his graduate training in Immunology at Stanford University in the laboratories of Garry Nolan and Edgar Engleman. There, he developed experimental and analytical methods to model the state of the immune system and immune responses to cancer using high dimensional single-cell data. Dr. Spitzer moved to UCSF as a UCSF Parker Fellow and a Sandler Faculty Fellow and is now an Assistant Professor in the Departments of Otolaryngology-Head and Neck Surgery and Microbiology & Immunology and an investigator of the Parker Institute for Cancer Immunotherapy and the Chan Zuckerberg Biohub.

Computational health science interests:

Biological Modeling, Cancer and Precision Oncology, Deep Machine Learning and Data Visualization

Alejandro Sweet-Cordero, MD

Dr. Sweet-Cordero studied Biology and Anthropology at Stanford, and earned his medical degree from UCSF in 1995. Further medical training included a residency in Pediatrics at UCSF, and subspecialty training in Pediatric Hematology/Oncology at Dana Farber Cancer Institute/Boston Children’s Hospital. He was a post-doctoral fellow in the laboratory of Tyler Jacks at the MIT Center for Cancer Research. As a post-doctoral fellow he also collaborated closely with Todd Golub and other members of the Broad Institute. Dr. Sweet-Cordero was an Associate Professor at Stanford up until 2016, when he joined UCSF as Associate Professor in Pediatrics and Benioff Chair of Child Health.

Computational health science interests:

Biological Modeling, Cancer and Precision Oncology, Clinical Research Informatics, Very Large Data Molecular Measurements

Geoff Tison, MD, MPH

Geoff Tison, MD, MPH is a Cardiologist and Assistant Professor at the University of California, San Francisco (UCSF). His research focuses on cardiovascular prevention, using statistical and machine learning methods to analyze large-scale health data for disease prevention and phenotyping. He obtained formal training in machine learning, statistics, epidemiology and clinical research during his tenure at Johns Hopkins and as a National Institutes of Health T32 scholar. Dr. Tison received MD and MPH degrees from the Johns Hopkins Schools of Medicine and Public Health, completed internal medicine training at the Johns Hopkins Hospital, and fellowships in cardiology, advanced echocardiography and preventive cardiology at UCSF.

Computational health science interests:

Deep Machine Learning and Data Visualization, Population Precision Medicine

Duygu Tosun, PhD

Dr. Tosun is an Assistant Professor of Radiology at UCSF and Co-Director of the Center for Imaging of Neurodegenerative Diseases (CIND) at the San Francisco Veterans Affairs Medical Center. Dr. Tosun obtained her BSc in Electrical and Electronic Engineering from Bilkent University, Turkey in 1999, and she received her MSE in Electrical and Computer Engineering from The Johns Hopkins University, Maryland in 2001. In 2003, she completed her MA in Mathematics, and later a PhD in Electrical and Computer Engineering from The Johns Hopkins University in 2005, followed by a postdoctoral fellowship in Neurology from UCLA in 2008.

Computational health science interests:

Clinical Research Informatics, Computational Neuroscience, Deep Machine Learning and Data Visualization, Population Precision Medicine

Daniela Ushizima, PhD

Dr. Ushizima is a Computer Scientist focused on Computer Vision. In 2015, she received the DOE Early Career award to develop AI/ML algorithms applied to scientific images across domains. She joined UCSF as Affiliate Faculty at BCHSI, UCSF, targeting biomedical data analyses. As Staff Scientist, Berkeley Lab, she leads the Image Processing team for CAMERA. She co-founded ImageXD as a Data Scientist at BIDS, UCB.

Computational health science interests:

Biological Modeling, Computational Neuroscience, Deep Machine Learning and Data Visualization

Laura van 't Veer, PhD

Dr. van ‘t Veer received undergraduate training in biology and a MS in molecular oncology at the University of Amsterdam. She earned her PhD in Medicine at the University of Leiden and completed two postdoctoral fellowships, first at Harvard Medical School and later at the Netherlands Cancer Institute. Dr. van ‘t Veer has become a world renowned Molecular Biologist and inventor of MammaPrint®. In addition to her Professorship, she is the Program Leader of the UCSF Helen Diller Family Comprehensive Cancer Center Breast Oncology Program, Director of Applied Genomics with the HDFCCC, and UCSF-Site Principal Investigator of the Athena Breast Health Network.

Computational health science interests:

Cancer and Precision Oncology, Very Large Data Molecular Measurements

Paul Douglas Wesson, PhD

Dr. Wesson is an epidemiologist focused on quantifying health disparities among socially marginalized populations. As an HIV researcher, he works on the unique challenges of sampling hard-to-reach populations for HIV surveillance and leverage information from the sampling process to generate estimates of the population size. Valid population size estimates provide a denominator for the population at risk and inform the allocation of limited public health resources. He also uses advanced epidemiologic and statistical techniques to study the social determinants of infectious disease risk. Working at the intersection of infectious disease, data science, and social epidemiology, he incorporates theories and principles from social epidemiology to inform his study designs and analyses.

Computational health science interests:

Population Precision Medicine

Mary Whooley, MD

Dr. Whooley is a primary care physician and implementation scientist. She is currently Director of the VA Measurement Science QUERI and the Center for Healthcare Improvement and Medical Effectiveness (CHIME), PI of the 20-year Heart and Soul Study, and Site PI for the Million Veterans Program at the San Francisco VA. She also serves as San Francisco VA Site PI for the UCSF Clinical Informatics Fellowship, has mentored numerous junior investigators, and has coauthored more than 250 publications.

Computational health science interests:

Clinical Research Informatics, Deep Machine Learning and Data Visualization, Population Precision Medicine

Duan Xu, PhD

Duan Xu is an Associate Professor who leads the Imaging Research for Neurodevelopment Laboratory at UCSF in the Department of Radiology and Biomedical Imaging. He obtained his BA in Integrated Science and BS in Biomedical Engineering from Northwestern University in 1999, followed by his PhD in Bioengineering from UCSF and UC Berkeley in 2005. He is a member of the Advanced Imaging Technologies Resource Group and a member of the Pediatric and Fetal Research Interest Group specializing in the baby brain, and is an active member of the International Society of Magnetic Resonance in Medicine, the American Society of Neuroradiology, the Society of Molecular Imaging, and the Medical Image Computing and Computer Assisted Intervention Society.

Computational health science interests:

Clinical Research Informatics, Computational Neuroscience, Deep Machine Learning and Data Visualization

Adam Yala, PhD

Adam Yala is an Assistant Professor of Computational Precision Health and EECS at UC Berkeley and UCSF. His research focuses on developing machine learning methods for personalized medicine and translating them to clinical care. His previous research has contributed to areas of: 1) predicting future cancer risk, 2) designing personalized screening policies. Adam’s tools underly multiple prospective trails and his research has been featured in the Washington Post, New York Times, Boston Globe and Wired. Prof Yala obtained his BS, MEng and PhD in Computer Science from MIT and he was a member of MIT Jameel Clinic and MIT CSAIL.

Yang Yang, PhD

Dr. Yang is an Associate Professor and Director of Mid-Field of MRI in the Department of Radiology and Biomedical Imaging at UCSF. He obtained his Ph.D. in Biomedical Engineering from the University of Virginia in 2016. He was an Assistant Professor in the Department of Radiology at Icahn School of Medicine at Mount Sinai from 2018 to 2022. Dr. Yang’s research focuses on developing and evaluating novel imaging techniques and incorporating AI to improve the clinical utility of MRI.

Computational health science interests:

Clinical Research Informatics, Deep Machine Learning and Data Visualization, Population Precision Medicine

Christina Yau, PhD

Dr. Yau is an Associate Professor in the Department of Surgery with extensive experience developing biomarkers of response and prognosis in the translational setting. Dr Yau developed the standardized analysis approach for biomarker evaluation in the I-SPY 2 trial for high-risk early stage breast cancer. She co-led the Bioinformatics and Statistics core that evolved I-SPY 2 into I-SPY 2.2 to enable response-guided treatment adaption using a sequential multiple assignment randomized trial design. In addition to her work on developing biomarker-informed, patient-centered clinical trial designs, she is an active member of the NCI’s Center for Cancer Genomics Genome Data Analysis Network and serve on the statistics team of the WISDOM trial.

Computational health science interests:

Cancer and Precision Oncology

Jimmie Ye, PhD

The Ye lab is interested in how the interaction between genetics and environment affect human variation at the level of molecular phenotypes. To study these interactions, the lab couples high-throughput sequencing approaches that measure cellular response under environmental challenges with population genetics where such measurements are collected and analyzed across large patient cohorts. The lab develops novel experimental approaches that enable the large-scale collection of functional genomic data en masse and computational approaches that translate the data into novel biological insights. This approach is used to initially study primary human immune cells in both healthy and diseased patients to understand host pathogen interactions and its role in autoimmunity.

Computational health science interests:

Deep Machine Learning and Data Visualization, Population Precision Medicine, Very Large Data Molecular Measurements