School of Medicine
Showing 1-50 of 145 Results
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Russ B. Altman
Kenneth Fong Professor and Professor of Bioengineering, of Genetics, of Medicine (General Medical Discipline), of Biomedical Data Science and, by courtesy, of Computer Science
Current Research and Scholarly InterestsI refer you to my web page for detailed list of interests, projects and publications. In addition to pressing the link here, you can search "Russ Altman" on http://www.google.com/
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Shaimaa Bakr
Postdoctoral Scholar, Biomedical Informatics
Masters Student in Biomedical Informatics, admitted Autumn 2020BioShaimaa is a graduate of the Ph.D. program, the Department of Electrical Engineering at Stanford. Shaimaa is a member of the Gevaert and RIIPL labs. Prior to Stanford, Shaimaa received her B.Sc. (Summa Cum Laude) from the American University in Cairo, where she studied Electronics Engineering and Computer Science. She obtained her MS degree in Electrical Engineering from Rensselaer Polytechnic Institute, working in the Cognitive and Immersive Systems lab, and advised by Professor Richard Radke. Shaimaa is interested in applying and developing machine learning methods for medical imaging and molecular data.
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Thomas Barba, MD, PhD
Postdoctoral Scholar, Biomedical Informatics
BioI am a postdoctoral scholar with a medical background in Internal Medicine and a degree in Immunology from the University of Lyon (France). As a practitioner in hospital medicine, I am mainly interested in rare autoimmune diseases such as systemic lupus erythematosus (SLE).
My postdoctoral project in Prof Olivier Gevaert's laboratory aims at developing deep learning tools that take advantage of data fusion procedures to assist clinical decision-making in the management of complex diseases. -
Alison Callahan
Instructor, Medicine - Biomedical Informatics Research
BioAlison Callahan is an Instructor in the Center for Biomedical Informatics and Clinical Data Scientist in the Stanford Health Care Data Science team led by Nigam Shah. Her current research uses informatics to expand and improve the data available about pregnancy and birth, and to develop and maintain and EHR-derived obstetric database. She is also the co-leader of the OHDSI Perinatal & Reproductive Health (PRHeG) working group. Her work in the SHC Data Science team focuses on developing and implementing methods to assess and identify high value applications of machine learning in healthcare settings.
Alison completed her PhD in the Department of Biology at Carleton University in Ottawa, Canada. Her doctoral research focused on developing HyQue, a framework for representing and evaluating scientific hypotheses, and applying this framework to discover genes related to aging. She was also a developer for Bio2RDF, an open-source project to build and provide the largest network of Linked Data for the life sciences. Her postdoctoral work at Stanford applied methodologies developed during her PhD to study spinal cord injury in model organisms and humans in a collaboration with scientists at the University of Miami. -
Jonathan H. Chen, MD, PhD
Assistant Professor of Medicine (Biomedical Informatics)
Current Research and Scholarly InterestsInformatics solutions ares the only credible approach to systematically address challenges of escalating complexity in healthcare. Tapping into real-world clinical data streams like electronic medical records will reveal the community's latent knowledge in a reproducible form. Delivering this back as clinical decision support will uniquely close the loop on a continuously learning health system.
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Henry Corbett Cousins
MD Student, expected graduation Spring 2024
Ph.D. Student in Biomedical Informatics, admitted Autumn 2021
MSTP StudentBioHenry is an MD-PhD candidate and Knight-Hennessy Scholar in the Medical Scientist Training Program and the Biomedical Informatics Program, where he is advised by Professor Russ Altman. He develops machine-learning methods to study the effects of complex genetic variation on human disease mechanisms, with focus on neurological and ophthalmic disorders. His goal is to translate genomic discoveries into disease-modifying therapies.
He received an AB summa cum laude from Harvard University in 2017, where he studied genetic mechanisms of retinal development with Professor Joshua Sanes. He then graduated with an MPhil with distinction from the University of Cambridge as a Gates Cambridge Scholar. He previously worked at Leaps by Bayer and the Massachusetts Eye and Ear Infirmary and has received several awards related to research and teaching. -
Norman Downing
Clinical Assistant Professor, Medicine - Biomedical Informatics Research
BioI am a faculty member in Biomedical Informatics Research at Stanford and board-certified internal medicine and clinical informatics. I split my time between clinical practice, hospital medical informatics and applications of artificial intelligence in healthcare. I work with the Clinical Excellence Research Center – a research group dedicated to reducing the cost of high-quality care – directing the Partnership in AI collaboration with the Stanford Artificial Intelligence Lab. Recognizing that the complexity of medicine has grown beyond the abilities of even the most expert clinician, we focus applications of computer vision to address some of the greatest challenges in healthcare: perfecting intended care for frail patients in settings ranging from the intensive care unit to the home. I have published work in the New England Journal of Medicine, Health Affairs, Annals of Internal Medicine, and the Journal of the American Medical Informatics Association. My interests include a design-based approach to understand how technology has impacted the work of clinicians and implications for new care models, workflow, and technology integration.
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Matthew A. Eisenberg
Clinical Assistant Professor (Affiliated), Med/BMIR
BioDr. Matthew A. Eisenberg joined Stanford Health Care in early 2013 and is the Medical Informatics Director for Analytics & Innovation with a focus on interoperability and health information exchange, regulatory reporting, health care analytics, patient reported outcomes and other uses of technology to meet our strategic initiatives.
Dr. Eisenberg is board certified in Pediatrics and Clinical Informatics. He is a Clinical Assistant Professor (Affiliated) in the Stanford Center for Biomedical Informatics Research at the Stanford University School of Medicine and he serves as the Stanford Health Care site director for the Stanford Clinical Informatics Fellowship Program. He previously held the position of Clinical Assistant Professor in Pediatrics at the University of Washington School of Medicine. He is a current member of the eHealth Exchange Coordinating Committee, a Sequoia Project Board member and serves as the current chair of the Epic Care Everywhere Network Governing Council. He is a member of the Carequality Advisory Council (past co-chair) and a member of IHE USA Implementation Committee. He is a Fellow of the American Academy of Pediatrics and a member of the American Medical Informatics Association and their Clinical Informatics Community. -
Ahmet Görkem Er
Graduate Visiting Researcher Student, Biomedical Informatics
BioAhmet Görkem Er is a visiting student researcher as a Fulbright Ph.D. Dissertation Research Grantee at Stanford. He holds an M.D. degree with a double specialty of internal medicine and infectious diseases and clinical microbiology and is pursuing a Ph.D. in medical informatics at Middle East Technical University (Turkey). He is interested in machine learning approaches in healthcare and working on multi-scale data fusion and radiogenomics in Gevaert's Lab.
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Scott Fleming
Ph.D. Student in Biomedical Informatics, admitted Autumn 2018
BioScott Fleming is a Ph.D. Student in Stanford's Biomedical Informatics Training Program, Department of Biomedical Data Science. He completed his B.S. in Mathematical and Computational Science at Stanford University. During that time, he worked with Dr. Leanne Williams to build pipelines for analyzing heterogeneous, high-dimensional datasets in order to discover patterns of brain activity that contribute to anxiety and depression. His most recent work has focused on developing machine learning methods to make accurate and effective crowd-powered diagnoses for children with autism and other developmental disorders.
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Sajjad Fouladvand, PhD, MSc
Postdoctoral Scholar, Biomedical Informatics
BioSajjad Fouladvand, PhD, MSc is a postdoctoral scholar at Stanford Center for Biomedical Informatics Research. Dr. Fouladvand's research career thus far has been focused on developing and applying artificial intelligence (AI) algorithms to solve real-world healthcare problems. Prior to Stanford, he worked at the Institute for Biomedical Informatics at the University of Kentucky (UK) while completing his PhD in Computer Science. During this time, he also received training at Mayo Clinic’s Department of Artificial Intelligence and Informatics as an intern.
While at UK as a PhD candidate, he developed a deep learning model based on transformer and Long Short-Term Memory (LSTM) models to analyze multi-stream healthcare data for prediction of opioid use disorder (OUD). While at Mayo Clinic as an intern, he created a LSTM based framework to predict progression from cognitively unimpaired to mild cognitive impairment in an aging population. In his new role at Stanford, Dr. Fouladvand is involved in conducting AI and healthcare data science research in close collaboration with clinicians, scientists, and healthcare systems with access to deep clinical data warehouses and broad population health data sources. -
Jason Fries
Research Engineer, Med/BMIR
Current Role at StanfordI'm currently working as a staff research scientist in the Shah Lab and research scientist at Snorkel AI. My interests fall in the intersection of computer science and medical informatics. My research interests include:
• Machine learning with limited labeled data, e.g., weak supervision, self-supervision, and few-shot learning.
• Multimodal learning, e.g., combining text, imaging, video and electronic health record data for improving clinical outcome prediction
• Human-in-the-loop machine learning systems.
• Knowledge graphs and their use in improving representation learning -
Andrew Gentles
Assistant Professor (Research) of Pathology, of Medicine (BMIR) and, by courtesy, of Biomedical Data Science
Current Research and Scholarly InterestsComputational systems biology of human disease. Particular focus on integration of high-throughput datasets with each other, and with phenotypic information and clinical outcomes.
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Olivier Gevaert
Associate Professor of Medicine (Biomedical Informatics) and of Biomedical Data Science
Current Research and Scholarly InterestsMy lab focuses on biomedical data fusion: the development of machine learning methods for biomedical decision support using multi-scale biomedical data. We primarily use methods based on regularized linear regression to accomplish this. We primarily focus on applications in oncology and neuroscience.
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John Graybeal
Rsch Technical Mgr 2, Med/BMIR
Current Role at StanfordJohn is a Technical Program Manager at Stanford University's School of Medicine. He leads the Center for Enhanced Data Annotation and Retrieval (CEDAR), and the NCBO BioPortal Repository, .
John's work encompasses whatever is needed: project management, product management, systems architecture, dev ops, and administration, to name a few fun roles. -
Summer Han
Associate Professor (Research) of Neurosurgery, of Medicine (Biomedical Informatics) and, by courtesy, of Epidemiology and Population Health
Current Research and Scholarly InterestsMy current research focuses on understanding the genetic and environmental etiology of complex disease and developing and evaluating efficient screening strategies based on etiological understanding. The areas of my research interests include statistical genetics, molecular epidemiology, cancer screening, health policy modeling, and risk prediction modeling. I have developed various statistical methods to analyze high-dimensional data to identify genetic and environmental risk factors and their interactions for complex disease.
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Josef Hardi
Software Developer 3, Med/BMIR
BioI'm a software engineer with a keen interest in data science. I have over 10 years’ experience in software development and 5 years in the data processing. Currently, I work as a backend developer for the Stanford Center of Biomedical Informatics Research; tackling issues in data and metadata management and interoperability. I also actively engage in the work of converting health and claim records to the OMOP common data model as part of my collaboration with the Stanford Population Health Sciences. I have experience with Java, Python, R, RDF, OWL, OBDA, Schema.org and Elasticsearch.
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Zihuai He
Assistant Professor (Research) of Neurology and of Medicine (BMIR)
Current Research and Scholarly InterestsStatistical genetics and other omics to study Alzheimer's disease.
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Tina Hernandez-Boussard
Professor of Medicine (Biomedical Informatics), of Biomedical Data Science, of Surgery and, by courtesy, of Epidemiology and Population Health
Current Research and Scholarly InterestsMy background and expertise is in the field of computational biology, with concentration in health services research. A key focus of my research is to apply novel methods and tools to large clinical datasets for hypothesis generation, comparative effectiveness research, and the evaluation of quality healthcare delivery. My research involves managing and manipulating big data, which range from administrative claims data to electronic health records, and applying novel biostatistical techniques to innovatively assess clinical and policy related research questions at the population level. This research enables us to create formal, statistically rigid, evaluations of healthcare data using unique combinations of large datasets.
