Biomedical Informatics Faculty

Core Faculty (Executive Committee members)

  • Carlos D. Bustamante. (Program Director and Chair, Biomedical Data Science). Research focuses on analyzing genome wide patterns of variation within and between species to address fundamental questions in biology, anthropology, and medicine. My group works on a variety of organisms and model systems ranging from humans and other primates to domesticated plant and animals. Much of our research is at the interface of computational biology, mathematical genetics, and evolutionary genomics.
  • Russ B. Altman. Research focuses on the creation of computational tools and resources to solve problems in biology and medicine.  Current projects are focused on three areas:  1) creating a database for how genetic variation in humans is associated with differences in drug response (pharmacogenomics), with particular recent emphasis on the drug warfarin, (PharmGkb), 2) creating methods for identifying protein and RNA molecular function, in order to understand how we may engineer them to function differently (FEATURE) and 3) understanding how physics-based simulation of biological structures can be facilitated at scales ranging from molecules to intact humans (Simbios). Informatics methodologies include:  supervised and unsupervised machine learning, natural language processing, molecular dynamics simulations, database design, knowledge representation.
  • Steven Bagley. (Executive Director). I oversee the day-to-day operations of the BMI Graduate Training Program. I also conduct research in biomedical informatics. I am currently working on analysis of the FDA's Adverse Events database.
  • Manisha Desai. My research interests involve the application and development of biostatistical tools to study medicine. I am particularly interested in developing and evaluating methods used to handle missing data, correlated or longitudinal data, and issues that arise in observational studies. In addition, my group is involved with developing user-friendly software for implementing new methods and for doing simulations to evaluate methods. Our research has application to oncology, preventive medicine, cardiovascular disease, and nephrology to name a few.
  • Mark Musen. My group studies the use of semantic technology—software that includes symbolic models of the application areas on which in operates—to enhance the management and interpretation of biomedical data and to promote open science. From the Protégé system for building such models (ontologies), to BioPortal for disseminating such ontologies, to CEDAR for enabling scientists to use ontologies to describe their experiments and their experimental data, my team is exploring the complete life cycle of computer-encoded biomedical knowledge. Our goal is to make online scientific data FAIR (findable, accessible, interoperable, and reusable). That ambitious objective will be achieved only when biomedical investigators can describe their experiments in a machine-processable manner with the same scope and precision that we associate with prose journal articles. CEDAR provides the basis for much of this work to enhance scientific communication. Collaborations with a large group of colleagues provide opportunities to evaluate our ideas in practice.
  • Daniel Rubin. Research interests focus on biomedical and translational imaging informatics.  We develop computational methods to identify and to extract information and meaning from images ("imaging phenotype") and to integrate and relate the image information to biological and clinical data ("molecular/clinical phenotype").  Our goal is to exploit images on a massive scale for discovery, similar to the data-driven approaches in modern bioinformatics, enabling us to discover image biomarkers of disease and to build predictive disease models from image data.  We translate our methods into practice by creating computer applications (such as decision support) that will improve diagnostic accuracy and clinical effectiveness.
  • Nigam Shah. My overarching interest is to make biomedical information actionable. I am interested in: (1) Annotation Analytics: Using methods for automated annotation and over 200 biomedical ontologies, we have created over 16 billion annotations on 22 public data sources. We are developing methods to mine such large annotation corpora for detecting hidden associations. (2) Data driven medicine: We are currently analyzing data from the electronic health record data warehouses of Stanford hospital comprising of over one million patients (~9.5 million notes) to identify statistically significant patterns of off-label drug use and for drug safety surveillance (http://tinyurl.com/SIG-emr-mining). (3) Socially Conscious Informatics: Clinical decision support tools traditionally focus on supporting a high trained individual—the doctor. Let's turn decision support on its head to aid the patient and provide support on a cell phone. We are semi-finalists in the Data Design Diabetes challenge (www.datadesigndiabetes.com/)
  • Robert Tibshirani. Research is in applied statistics and biostatistics. Our lab specializes in computer-intensive methods for regression and classification, bootstrap, cross-validation and statistical inference, and signal and image analysis for medical diagnosis.
  • Dennis Wall. The Wall Lab uses machine learning and systems biology to develop clinical solutions for the detection and treatment of autism and other complex human diseases. The lab's research falls into three categories three general categories: (1) Translating the thinking of systems biology to the field of autism genetics with the intent to develop effective early-stage diagnostics and targets for therapeutic intervention. The work involves the generation and analysis of genomic and phenotypic databases using computational tools of systems biology, machine learning and network inference.(2) Efforts to understand and characterize the clinical significance and utility of human genetic variation. This work involves clinical-grade annotation of human genetic variation, estimating the rates of both true and false positives in present day genetic testing and their likely impacts on the practice of personalized care, the construction of an authoritative knowledgebase for clinical decision support, and efforts in educating present and future doctors on the potentials of genomics in individualized healthcare.(3) Redefining human diseases through computational and comparative network analysis. The work involves the integration and analysis of transcriptomic, genomic and bibliomic data to network all known human diseases. Deliverables include revealing disease connections, properly reshaping blurred boundaries of classification, and opportunities for drug treatment repositioning.

Advising Faculty

  • Euan Ashley.The Ashley lab is focused on the application of whole genome sequencing to the medical care of individuals and families. We lead the Stanford Center for Inherited Cardiovascular Disease, one of the few medical centers in the country where patient genome sequences can be readily incorporated into clinical care. In 2010, we led the team of BMI faculty that completed the first clinical interpretation of a human genome. We extended this to a pipeline that would handle families in 2011. We are also fascinated by network biology. Part of the Stanford heart transplant team, we are focused on understanding the heart’s response to disease or exercise stress. We are part of a team of three major transplant centers that was recently awarded $9m to explore the genetic control of cardiac transcriptional activity via RNA sequencing and network modeling. Finally, although many of our questions can be answered in silico, to establish causality, we turn to the wet lab to explore the biology of key genes and signaling modules.
  • Sanjay Basu. Research interests include machine learning and microsimulation modeling of relevance to prevention and treatment of cardiovascular diseases and type 2 diabetes. Current projects include estimation of risk and benefit from alternative primary and secondary prevention strategies for high blood pressure, dyslipidemia, and elevated glucose; use of machine learning methods on cohort and randomized trial data to estimate heterogeneous treatment effects; and analysis of social determinants of health including global health using machine learning and econometric methods.
  • Serafim Batzoglou. Our research has focused on the development of algorithms and systems for genomics. Topics include: sequence alignment algorithms, hidden Markov models, whole-genome comparison, annotation of biological features in genomes, microarray analysis, gene regulation, and DNA sequencing.
  • Mohsen Bayati. I have two main research interests: large-scale statistical data-mining, and applications of information technology in healthcare. In particular, I use tools from graph theory, machine learning, probability, and statistical physics in data-driven healthcare (predictive models, optimization, and decisions), high dimensional statistics, and networks.
  • Gill Bejerano. Our lab seeks to understand the human genome through vertebrate comparative, functional, and paleo-genomics, including such topics as the functional landscape of the human genome and its evolution, with particular focus on vertebrate gene regulation and its contributions to morphological diversity, development, and human disease; functions, origins, and evolution of proximal and distal cis-acting regulatory elements; the paradoxical existence of ultraconserved elements; co-option of mobile DNA elements (repeats) as a driving force in vertebrate evolution; and the interpretation of ancestral genomes.
  • Jayanta Bhattacharya. I have published empirical economics and health services research on the elderly, adolescents, HIV/AIDS and managed care. Most recently, I have been working on the labor market consequences of the obesity epidemic. I have researched the regulation of the viatical-settlements market (a secondary life-insurance market that often targets HIV patients) and summer/winter differences in nutritional outcomes for low-income American families. I am working on a project examining the labor-market conditions that help determine why some U.S. employers do not provide health insurance.
  • Kwabena Boahen. Our group has two synergistic goals: To understand how brains work; this will advance treatment of neurological diseases. And to build computers that work like brains; this will increase computational power a million fold. To these two ends, we are building large-scale neural models to link cellular-level biophysical processes with the system-level functions that they enable (e.g., cognition), through an interdisciplinary effort that brings electronics and computer science in contact with neurobiology and medicine.
  • Margaret Brandeau. Research focus is the application of mathematical and economic models to evaluate disease prevention and treatment programs. Current research focuses on HIV and drug abuse interventions, hepatitis B screening and vaccination, pandemic influenza preparedness, and bioterror response planning.
  • Douglas Brutlag. Professor Emeritus of Biochemistry and Medicine (by courtesy), teaches courses in Genomics and Medicine (Biochem 118) and Your Genes and your Health (Bio 84).
  • Jonathan H. Chen. In the face of ever escalating complexity in medicine, integrating informatics solutions is the only credible approach to systematically address challenges in healthcare. Tapping into real-world clinical data streams like electronic medical records with machine learning and data analytics will reveal the community's latent knowledge in a reproducible form. Delivering this back to clinicians, patients, and healthcare systems as clinical decision support will uniquely close the loop on a continuously learning health system. My group seeks to empower individuals with the collective experience of the many, combining human and artificial intelligence approaches to medicine that will deliver better care than what either can do alone.
  • J. Michael Cherry. Lab develops and maintains the Saccharomyces Genome Database (SGD). The SGD provides information and tools on budding yeast genome, its products and their interactions. Several computational tools have been developed to provide to allow the research community to explore the collected data sets. Tools for querying >50,000 full-text papers are also provided. SGD has become an essential research tool used daily by thousands of researchers around the globe. Dr. Cherry's second area of research is in the creation of ontologies to aid communication between biologists as well as biological database projects. His group is a founding member of the Gene Ontology (GO) Collaboration.
  • Markus Covert. Research focus is on building computational models of complex biological processes and using these models to guide an experimental program. Such an approach leads to a relatively rapid identification and validation of previously unknown components and interactions. Biological systems of interest include metabolic, regulatory, and signaling networks as well as intercellular interactions. Current research involves the dynamic behavior of NF-kappa B, an important family of transcription factors whose aberrant activity has been linked to oncogenesis, tumor progression, and resistance to chemotherapy.
  • Christina Curtis. Our laboratory couples innovative experimental approaches, high-throughput omic technologies, statistical inference and computational modeling to interrogate the evolutionary dynamics of tumor progression and therapeutic resistance. To this end, I and my team have developed an integrated experimental and computational framework to measure clinically relevant patient-specific parameters and to measure clonal dynamics. My research also aims to develop a systematic interpretation of genotype/phenotype associations in cancer by leveraging state-of-the-art technologies and robust data integration techniques. For example, using integrative statistical approaches to mine multiple data types I lead a seminal study that redefined the molecular map of breast cancer, revealing novel subgroups with distinct clinical outcomes and subtype-specific drivers.
  • Rhiju Das. The Das group strives to predict how sequence codes for structure in proteins, nucleic acids, and heteropolymers whose folds have yet to be explored. We use new computational and experimental tools to tackle the de novo modeling of protein and RNA folds, the high-throughput structure mapping of riboswitches and random RNAs, and the design of self-knotting and self-crystallizing nucleic acids.
  • Scott Delp. Experimental and computational approaches to study human movement. Development of biomechanical models to analyze muscle function, study movement abnormalities, design new medical products, and guide surgery. Imaging technology development including MRI and microendoscopy. Optogenetic manipulation of peripheral neural circuits. Biomedical technology development.
  • David Dill. Our lab is interested in Boolean modeling to gaining insight into cellular processes at a systems level.  Our work includes analysis of Boolean circuit models using methods based on logic and automata theory, applied to understanding of the cell cycle, signal transduction networks, etc., and Boolean analysis of relationships in multiple large data sets, to understand regulation and global differences in gene expression among cell types.
  • Ron Dror. My primary research interests are in computational biology, with an emphasis on spatial structure and dynamics at the molecular and cellular levels. My work, usually carried out in close collaboration with experimentalists, spans fields ranging from biochemistry and cell biology to parallel computing, image processing, and machine learning.
  • James Ferrell. Research lab has two main goals: to understand the regulation of entry into and progression through mitosis and meiosis, and to understand the basic logic of signaling cascades and loops.
  • Hunter Fraser. We study the regulation and evolution of gene expression using a combination of experimental and computational approaches. Our work brings together quantitative genetics, genomics, epigenetics, and evolutionary biology to achieve a deeper understanding of how genetic variation within and between species affects genome-wide gene expression and ultimately shapes the phenotypic diversity of life. Some of our long-term goals are to better understand: 1) How new mutations affect gene expression, 2) What selective pressures act on these mutations, 3) How (and how often) changes in gene expression affect other phenotypes, including human disease
  • Sam (Sanjiv) Gambhir. Research focuses on merging advances in molecular biology with those in biomedical imaging to advance the new field of molecular imaging. Strategies for imaging cellular/molecular events in small animals and humans are in development. These include studying gene expression, signal transduction, enzyme levels and receptor levels in vivo. Use of these technologies for better management of cancer patients are emphasized. Mathematical modeling of these processes to better quantitate imaging data are also being pursued.
  • Andrew Gentles. Research focuses on using genomic datasets from next-generation sequencing and array technologies in combination with clinical data to identify processes driving disease.
  • Margot Gerritsen. My work is about understanding and simulating complicated fluid flow problems. My research focuses on the design of highly accurate and efficient parallel computational methods to predict the performance of enhanced oil recovery methods. I'm particularly interested in gas injection and in-situ combustion processes. These recovery methods are extremely challenging to simulate because of the very strong nonlinearities in the governing equations. Outside petroleum engineering, I'm active in coastal ocean simulation with colleagues from the Department of Civil and Environmental Engineering, yacht research and pterosaur flight mechanics with colleagues from the Department of Mechanical and Aeronautical Engineering, and the design of search algorithms in collaboration with the Library of Congress and colleagues from the Institute of Computational and Mathematical Engineering.
  • Olivier Gevaert. My lab focuses on biomedical data fusion: the development of machine learning methods for biomedical decision support using multi-scale biomedical data. Previously we pioneered data fusion work using Bayesian and kernel methods studying breast and ovarian cancer. Additionally, we developed computational algorithms for the identification of driver genes using multi-omics data. Furthermore, we are working on multi-scale biomedical data fusion methods, bridging the molecular using omics data, cellular using pathology data and tissue using medical imaging data.
  • Will Greenleaf. My lab is interested in leveraging the power of high-throughput sequencing methods to 1) understand rare genomic and epi-genomic heterogeneity at the level of cellular subpopulations and even single cells 2) investigate chromatin structure at the level of the 30-nm fiber, and 3) probe the relationship between primary sequence and functionality of macromolecules (RNA and protein). All of these research endeavors lie at the intersection of physics, engineering, and biology, and require the analysis of large, novel data sets.
  • Leonidas Guibas. Heads the Geometric Computation group in the Computer Science Department of Stanford University and is a member of the Computer Graphics and Artificial Intelligence Laboratories. He works on algorithms for sensing, modeling, reasoning, rendering, and acting on the physical world. His interests span computational geometry, geometric modeling, computer graphics, computer vision, sensor networks, robotics, and discrete algorithms --- all areas in which he has published and lectured extensively.
  • Summer Han. My research areas include statistical genetics, risk prediction modeling, cancer screening, and health policy modeling. I have been developing various statistical methods to analyze large-scale genetic data to understand the interplay between genes and the environment for various complex disease including cancer and neurological diseases. My recent methodological papers were published in high-profile statistical journals including the Journal of the American Statistical Association and Biometrics. In addition to statistical genetics, I have worked on several cancer screening and health policy modeling projects, by developing stochastic simulation models utilizing/merging various data sources including cancer registry data, epidemiologic case-control or cohort data, and nationally representative data such as NHANES (National Health and Nutrition Examination Survey). I have a wide range of methodological projects that BMI students may be interested in learning and working on. Currently, I am advising several medical students and Neurosurgery residents in conducting research, which includes: SEER-Medicare data-based surgery outcome analysis, mutation profiling for lung cancer using Stanford EMR database, and meta-analysis of
    substance use disorders.
  • Trevor Hastie. Specializes in applied nonparametric regression and classification. His current research focuses on applied problems in biology and genomics, medicine and industry, in particular data mining, prediction and classification problems.
  • Tina Hernandez-Boussard. My background and expertise is in the field of biomedical informatics and 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 the quality of healthcare delivered. This involves managing and manipulating large clinical datasets, which range from administrative claims data to electronic health records to genomic data and applying novel methods to assess clinical and policy related research questions.
  • Susan Holmes. Applications to Biology, in particular phylogenetic trees. Computational statistics, in particular, nonparametric computer intensive methods such as the bootstrap. Teaching using simulations and web-based tools. Image analysis. Immunology.
  • Purvesh Khatri. Translational medicine, biomarker discovery, role of immunity in disease, bioinformatics methods development
  • Teri E. Klein. Research interests extend over the broad spectrum of pharmacogenetics, computational biology and bioinformatics. Applications include the development of a pharmacogenetics knowledge base, structure-function relationships, de novo modeling and the structural basis of disease.
  • Anshul Kundaje. Our research focusses on development of statistical and machine learning methods for integrative analysis of diverse functional genomic and genetic data to learn models of gene regulation. We have led the analysis efforts of the Encyclopedia of DNA Elements (ENCODE) and The Roadmap Epigenomics Projects with the development of novel methods for (1) adaptive thresholding and normalization of massive collections of ChIP-seq and DNase-seq data, (2) dissecting combinatorial transcription factor co-occupancy within and across cell-types, (3) predicting cell-type specific enhancers from chromatin state profiles, (4) exploiting expression and chromatin co-dynamics with to predict enhancer-target gene links, (5) jointly modeling sequence grammars at regulatory elements and their chromatin state dynamics, expression changes of regulators and functional interaction data to learn unified multi-scale gene regulation programs, (6) elucidating the heterogeneity of chromatin architecture at regulatory elements and (7) improving the detection and interpretation of potentially causal disease-associated variants from GWAS studies. More recently, we have been developing methods to (1) infer causal regulatory mechansims by integrating diverse functional genomic data from temporal (e.g. differentiation/reprogramming) and perturbation (e.g. drug response, knockdown, genome-editing) experiments; (2) model the complex relationships between genetic variation, regulatory chromatin variation and expression variation in healthy and diseased individuals
  • Ruijiang Li. Our lab is focused on the development and validation of diagnostic, prognostic, predictive biomarkers based on imaging and molecular characteristics in cancer patients. The ultimate goal is to translate these biomarkers into clinical practice to improve early detection, diagnosis, and enable more accurate prognostication and prediction of therapy response for precision cancer medicine. We are conducting systematic cancer biomarker discovery and validation studies through: (1) curation and integration of high-throughput molecular (genomic, transcriptomic) and imaging (radiologic or histopathologic) data with clinical annotation from large patient populations and (2) development and application of novel statistical methods and machine earning/deep learning techniques.
  • Matthew Lundgren. Deep Learning in medical imaging (diagnosis, prediction) and clinical imaging outcomes prediction,  clinical decision support, imaging utilization and appropriateness, cohort feature engineering with structured and unstructured EMR data for modeling applications
  • Parag Mallick. Research in the Mallick lab centers on developing and applying multi-scale systems approaches to enable personalized, predictive medicine in cancer. Specifically, we are developing computational methods and experimental techniques to identify diagnostic and prognostic circulating biomarkers. Biomarker-based approaches to detect cancers as early as possible and to personalize treatment are envisioned to radically improve patient outcomes and reduce healthcare costs. Within our multi-scale framework, one can consider biomarkers to be host-scale variables that inform tumor and cell-scale phenomena. Our approach to marker discovery begins with the development of molecular/cellular-scale models that attempt to describe how cells are likely to behave in response to endogenous (mutation) or exogenous perturbation (therapeutics). At the tumor-scale, we are investigating tumor heterogeneity and evolution. Recently, we have been interrogating the role of tumor-microenvironment in directing tumor evolution. At the host-scale, we are attempting to model the relationship between the tumor and the circulating proteomes to help inform biomarker candidate selection. Together, these inquiries will enable us to better understand cancer and to enable rational, model-driven approaches to biomarker discovery.
  • Vinod Menon. Experimental and theoretical systems neuroscience: Cognitive neuroscience; Cognitive development; Psychiatric neuroscience; Functional brain imaging; Dynamical basis of brain function; Nonlinear dynamics of neural systems.
  • Stephen Montgomery. Identifying the molecular causes of phenotypic diversity will be enhanced by our ability to decipher individual genomes.  However, the complexity of life, our resolution and subsequent ability to define individual traits and the vast information encoded within each genome has made the direct translation of genotype to phenotype elusive.  The Montgomery Lab aims to uncover and define how a specific class of genetic variation, namely those variants which effect the expression of genes, first impact cellular state and behavior and then ultimately play a role in defining human traits and disease.  For more information, visit http://montgomerylab.stanford.edu/
  • Sandy Napel. Research focuses on CT and other medical imaging modalities. Our lab is currently interested in efficient and reproducible methods of extracting and visualizing medical information from the thousands of images typically generated by one or more radiological exams performed for each patient.
  • Richard Olshen. Dr. Olshen's interests regarding research are in statistics and mathematics and their applications to medicine and biology. Many efforts have concerned binary tree-structured algorithms for classification, regression, survival analysis, and clustering. Those for classification and survival analysis have been used with success in computer-aided diagnosis and prognosis, especially in cardiology, oncology, and toxicology. With the late Leo Breiman, Jerome Friedman (of Stanford), and Charles Stone (of the University of California, Berkeley) he coauthored the book Classification and Regression Trees, that gives motivation, algorithms, various examples, and mathematical theory for what have come to be known as CART algorithms. The approaches to tree-structured clustering have been applied to lossy data compression, especially in digital radiography (with Robert Gray of the Department of Electrical Engineering at Stanford and others), and also to HIV genetics. Recent research concerns applying information on SNPs (single nucleotide polymorphisms) and other features as together they predispose to hypertension in a population of white women. Much of his work concerns analyses of longitudinal data. Some that was of interest concerned the pharmacokinetics of intracavitary chemotherapy with systemic rescue. Related efforts have also been to the development of mature walking, longitudinal studies of cholesterol, and many aspects of glomerular filtration in patients with nephrotic disorders. With the late David Sutherland, Edmund Biden, and Marilynn Wyatt I coauthored The Development of Mature Walking. He was one of the founders of the NCI-designated UCSD Clinical Cancer Center, which is now the University of California, San Diego Medical Center Moores Cancer Center. He was a Statistical Editor of the Journal of the National Cancer Institute. Some research has involved more mathematical problems, including those that arise concerning exchangeable probabilities, conditional levels of particular test statistics, topological category regarding CART-like estimators in regression, and successive standardization of rectangular arrays of numbers. Dr. Olshen is a Professor of Health Research and Policy (Biostatistics) and (by Courtesy) Professor of Electrical Engineering and of Statistics and  Chief, Division of Biostatistics.
  • Douglas K. Owens. Research concerns health policy, clinical policy, and the development of analytic methods for evaluating policy questions. Particular interest in technology assessment and the application of decision theory to clinical/health policy problems. Special interest in questions related to disease caused by the human immunodeficiency virus (HIV) and cardiovascular disease.
  • Art Owen. My research interests include analysis of high throughput biological data, for instance finding age-related genes in multiple species and tissues with the Kim lab.  I am generally interested in settings where both rows and columns of the data matrix correspond to entities of interest, that is, neither are IID.  Special interests include adjusting for the effects of latent variables, finding ways to bootstrap and cross-validate non-IID data, and making extensions to three-way and higher order data arrays. I also work on Monte Carlo methods.
  • Julia Palacios. I seek to provide statistically rigorous answers to concrete, data driven questions in evolutionary genetics. My research involves probabilistic modeling of evolutionary forces and the development of computationally tractable methods that are applicable to big data problems. Past and current research relies heavily on the theory of stochastic processes, Bayesian nonparametrics and recent developments in machine learning and statistical theory.
  • Vijay Pande. My research interests lie at the intersection of machine learning, Bayesian statistics, atomistic simulation, bioinformatics, and cheminformatics methods and its application to problems of linking drug efficacy and side effects to geneomics and systems biology. My group also has expertise in related synergistic areas, such as theoretical physical chemistry, structural biology, computer science, and large-scale distributed computing. By combining our methods with the Folding@home distributed computing project (currently the most powerful supercomputer in the world, with almost 10 petaflops of performace), we have a unique opportunity to push the state of the art in these and related areas. Finally, via collaborations with biotechs, pharmaceutical companies, and experimental groups interested in drug design, we can directly test our predictions, thus strengthening our methods as well as the direct impact of our results.
  • Dmitri Petrov. Three main topics are studied in the lab: 1) mutation and evolution of global genomic properties, 2) evolution and population dynamic of transposable elements in eukaryotes, and 3) evolution and population dynamic of transposable elements in eukaryotes.
  • Sylvia Plevritis. Research program focuses on computational modeling of cancer biology and cancer outcomes. We develop stochastic models of the natural history of cancer based on clinical research data. We predict population-level cancer outcomes under different screening and treatment interventions. We also analyze genomic and proteomic data in order to identify molecular networks that are perturbed in cancer initiation and progression and relate these perturbations to patient outcomes.
  • Russell Poldrack. My lab's research uses neuroimaging to understand how neural systems give rise to complex cognitive functions and how these systems break down in neuropsychiatric disorders. We use machine learning techniques to decode behavior from neuroimaging data and to characterize the multidimensional structure of neural representations. We are also heavily involved in the development of neuroinformatics tools, including ontologies of mental function (through the Cognitive Atlas project), data sharing (through the OpenFMRI and Neurovault projects), and automated meta-analysis (through the Neurosynth project).
  • Jonathan Pritchard. Our group uses statistical and computational methods to study questions in genomics and evolutionary biology. Much of our work focuses on questions relating to genetic variation and evolution. An important part of our work is in developing appropriate statistical and computational approaches that can yield new insights into biological data.
  • Ingmar H. Riedel-Kruse. Our lab is focused at two topics: (1) Engineering (and programming) biological games and proving their utility for education and large scale science. (2) Quantitative and modeling approaches to decipher the biophysics and genetic network dynamics underlying vertebrate development and pattern formation - with a longer term interest in tissue engineering. We have a variety of rotation projects; and based on your specific interest you can use and learn a number of techniques, such as zebrafish, micro-fluidics, programming, theory, molecular and cell biology, imaging - or any combination thereof.
  • Manuel Rivas. The Rivas lab is based in the Department of Biomedical Data Science. We are a new lab with a focus on population analytics using genomic and phenotype data. We will develop statistical models, algorithms, and computational tools for the analysis of millions of samples. Scientific themes that the lab will focus on: 1) Generating effective therapeutic and preventative hypotheses for human diseases from human genetic, imaging, wearable sensor, and environmental data; 2) developing technologies for integrated learning healthcare systems with a particular focus on underserved communities and developing regions of the world; 3) genetic epidemiology where the aim is to understand the global distribution of common and rare disease predisposition genes; and 4) high dimensional methods development and optimization.
  • Chiara Sabatti. My research focus is on developing  statistical methods for the analysis of high throughput genomics data. Areas of particular interests  at the moment are: association mapping of multiple related phenotypes, DNA copy number variant detection, analysis of rare variants in population isolates and  reconstruction of gene regulatory networks.
  • Manish Saggar. We are a computational neuropsychiatry lab, dedicated to developing methods for a better understanding of individual differences in brain functioning in healthy and psychiatric populations. We primarily work with noninvasive human neuroimaging data (fMRI, EEG, and fNIRS). We are currently working in these three areas - [a] Modeling spatiotemporal dynamics in brain activity to develop person- and disorder-centric biomarkers; [b] Understanding the role of brain dynamics for optimized learning and performance (e.g., creativity) in individual and team settings; and [c] Developing methods that use network science, machine learning, and signal processing for better understanding of brain dynamics.
  • Julia Salzman. Our goal is to use experimental and statistical tools to construct a high dimensional picture of gene regulation, including cis and trans control of the full repertoire of RNAs expressed by cells.  Currently, we are focusing on the function and biogenesis of circular RNA, which we recently discovered to be a ubiquitous and uncharacterized component of eukaryotic gene expression.  A second major goal is to study gene expression variation in human cancer.  Here, we combine mining massive public datasets, and experimental study of primary tumors and cell lines with bioinformatic and statistical methods.  We use the cancer genome as window into functional roles played by RNA, and are attempting to characterize potential biomarkers.
  • Molly Schumer. Hybridization between species is a common process in the evolutionary history of many species, including our own. Despite this, the evolutionary consequences of hybridization are still relatively poorly understood. We focus on using genomic, computational and experimental approaches to understand how hybridization shapes the evolution of genomes and species. Current projects in the lab include developing new approaches to detect selection after hybridization, time-transect monitoring of hybrid genome evolution, and understanding the genetic architecture of hybrid incompatibilities.
  • Gavin Sherlock. The Sherlock lab uses genomic approaches to shed light on biological systems, particularly employing high throughput sequencing and rigorous analyses of the resulting data. We are characterizing adaptive evolution at the molecular level to understand the adaptive landscape that yeast populations traverse when evolving under a selective pressure.  Specifically, we want to know what are the mutational and fitness trajectories taken as a population explores the landscape.  We have sequenced the genomes of many adaptive clones and identified the genes and pathways that are the targets of adaptive mutation under a particular selective pressure.  We are currently sequencing DNA from entire populations, developing rigorous statistical models to identify low frequency mutations and distinguish them from sequence errors. In a second project, we are using high throughput sequencing to sequence the transcriptome of the human fungal pathogen, Candida albicans.  Candida albicans is an obligate diploid, and in many ways, different alleles of the same gene can be thought of a paralogs, as they never go through a haploid phase where deleterious alleles would be exposed.  This should result in a relaxed evolutionary constraint, and possible lead to allele specific transcription.  We are focusing our efforts on understanding the RNA sequencing data to look for allele specific events, which requires significant bioinformatics expertise.  We have recently phased SNPs in the diploid genome, which gives us greater power to detect such events. Finally, we are also looking at changes that occur in cancer.  We have profiled DNA methylation changes in prostate cancer, and found that there are large scale genome wide difference between normal and tumor prostate tissue.  We are currently working to understand the origins of these changes.
  • Hua Tang. Genetic variation does not only underlie phenotypic diversity among individuals, but also documents the evolutionary history of a species. Research in our laboratory aims to uncover the evolutionary forces that have shaped the patterns of genetic variation in humans, to elucidate the genetics basis of complex traits, and to shed light on the mechanisms that lead to diverse phenotypes and disparate disease risk among populations. We approach these questions by developing statistical and computational approaches, by analyzing large-scale genomic data, and by collaborating with experts in a variety of fields.
  • Terry Winograd. Research is on human-computer interaction design, with a focus on the theoretical background and conceptual models. He directs the teaching programs and HCI research in the Stanford Human-Computer Interaction Group. He is also a founding faculty member of the Hasso Plattner Institute of Design at Stanford (the "d.school") and on the faculty of the Center on Democracy, Development, and the Rule of Law (CDDRL)
  • Wing Hung Wong. Current interest centers on the application of statistics to problems arsing from biology. We are particularly interested in questions concerning gene regulation and signal transduction.
  • Lei Xing. Medical imaging informatics, image reconstruction, Image-guided intervention, CT, MRI and radionuclide imaging (PET/CT, SPECT/CT), intensity modulated radiation therapy (IMRT), treatment planning and plan optimization, image segmentation and deformable registration, tele-radiology/treatment planning, radiobiology modeling, biologically conformable radiation therapy (BCR), application of molecular imaging to radiation oncology.
  • James Zou. We develop a wide range of machine learning algorithms and are especially interested in extracting disease insights from population genomics and epigenomics. On the methodology side, we are investigating new approaches to adaptive data analysis, representation learning for bio-medical data, new probabilistic models that encourage diversity, and multi-view learning. Application topics include: whole-genome and exome sequence analysis, risk prediction, synthetic biology, chromatin dynamics and transcription regulation.

Collaborating Faculty

  • Michael Bassik. We study how endocytic pathogens such as bacterial toxins, viruses, and protein aggregates enter the cell, disrupt homeostasis, and cause apoptosis. More broadly, we would like to understand how diverse stresses induced by biological, chemical, and therapeutic agents signal to the cell death machinery. 
    To do this, we use basic cell biology and biochemistry, as well as novel ultra-complex shRNA libraries we have developed, which have allowed the first systematic genetic interaction maps in mammalian cells. A complementary interest is the development of technologies for screening and measuring genetic interactions, with the ultimate goal of finding synergistic drug targets for endocytic pathogens and other diseases such as cancer and Alzheimer’s.
  • Catherine Blish. The Blish lab is focused on using a systems immunology approach to develop new methods to prevent and control infectious diseases. Our studies are highly translational in nature, bringing comprehensive immune profiling techniques such as mass cytometry to clinical and epidemiologic studies of HIV transmission and influenza vaccination. We are particularly interested in the role of NK cells in viral immunity, the etiology behind the susceptibility of pregnant women to viruses, and the impact of viral diversity and escape in the interplay between the virus and the host immune response.
  • Stanley N. Cohen. The collection and interpretation of large amounts of data obtained from DNA and protein microarrays has become an important approach toward understanding the biological regulatory circuits that control gene expression. In the prevailing paradigm, clusters of genes that show common patterns of expression on microarrays are identified computationally and relationships among these genes are inferred by the experimenter in part by using his/her prior knowledge.
  • Ronald Davis. Our lab is using Saccharomyces cerevisiae and Human to conduct whole genome analysis projects. The yeast genome sequence has approximately 6,000 genes. We have made a set of haploid and diploid strains (21,000) containing a complete deletion of each gene. In order to facilitate whole genome analysis each deletion is molecularly tagged with a unique 20-mer DNA sequence. This sequence acts as a molecular bar code and makes it easy to identify the presence of each deletion.
  • Joshua E. Elias. Developing new mass spectrometry-based experimental and computational tools that advance the field of proteomics, and applying them to a variety of important biomedical paradigms, including cancer, aging, and stem cell biology.
  • Mary Goldstein. Health services research in primary care and geriatrics. Ongoing work includes evaluation of methods of implementing clinical practice guidelines, for which she leads a multisite hypertension guidelines project using the ATHENA decision support system. Another research focus is evaluation of newly developed tools for automated guidelines, particularly for quality assessment.
  • Mark A. Hlatky. Main research work is in "outcomes research", especially examining the field of cardiovascular medicine. Particular areas of interest are the integration of economic and quality of life data into randomized clinical trials, evidence-based medicine, decision models, and cost-effectiveness analysis. I am also interested in the application of novel genetic, biomarker, and imaging tests to assess risk and guide clinical management of coronary artery disease.
  • Hanlee Ji. Research group is focused on the genomic analysis of cancer with several general goals. 1) Developing novel, cost-effective genomic technologies involving next generation DNA sequencing for application in cancer mutation characterization and genomic biomarker discovery. My group is focused on integrating genomic analysis with clinical issues in oncology and integration into oncology clinical trials. 2) Developing computational analytical methods for next generation sequencing and application in genomic diagnostic technologies. 3) Applying functional comparative genomic approaches to identify genes involved in genomic instability in colorectal carcinoma and understanding these genes role in biological networks
  • Paul Khavari. The Khavari Lab uses epithelial tissue as a model system to study stem cell biology, cancer and new molecular therapeutics. Epithelia cover external and internal body surfaces and undergo constant self-renewal while responding to diverse environmental stimuli. Epithelial homeostasis precisely balances stem cell-sustained proliferation and differentiation-associated cell death, a balance which is lost in many human diseases, including cancer.
  • Karla Kirkegaard. Lab investigates the cell biology, genetics and biochemistry of RNA viral propagation, using poliovirus as a model system. For many subcellular viruses and parasites, RNA, not DNA, is the carrier of genetic information. Poliovirus serves as a model to increase our understanding of positive-strand RNA viruses for which no vaccine is available and which remain a significant health hazard: examples include other picornaviruses, such as rhinoviruses, coxsackieviruses and the deadly enterovirus 71, as well as more distantly related positive-strand RNA viruses such as hepatitis C and Dengue fever.
  • Thomas Krummel. Surgical Innovation, Simulation and Virtual Reality in Surgical Education, Fetal Healing-Cellular and Biochemical Mechanisms.
  • Lianne Kurina. Our research program is focused on the physical and mental health of active-duty U.S. Army soldiers. We have developed the Stanford Military Data Repository (SMDR) in collaboration with colleagues at the Army's Office of the Surgeon General to enable large-scale cohort studies addressing disability, heat injury, musculoskeletal injuries, pregnancy outcomes, and behavioral health outcomes among soldiers.
  • Curt Langlotz. I am interested in making imaging information more useful for clinical care and research by optimizing the accuracy and completeness of imaging results.  My research focuses primarily on information produced by radiologists, rather than automated analysis of clinical images themselves.  I have a longstanding interest in the development of systems and standards that facilitate structured data capture during routine image interpretation.  I am also exploring natural language processing technology to extract knowledge from the narrative text that remains a part of the radiology report.  Together with automated reasoning techniques these approaches can provide real-time decision support for radiologists to improve accuracy and reduce errors.  I have a background in technology assessment and experience with health IT software businesses, and believe that successful informatics projects ultimately must translate from conception in the lab through assessment in the clinical reading room to dissemination as open source or commercial software.
  • Michael Levitt. Research asks if it is possible to understand the molecular structure and function of proteins and nucleic acids in enough detail to make accurate predictions about structure and function. We are mounting a two-pronged attack on this problem using both molecular dynamics simulation and molecular modeling.
  • Jin Billy Li. The landscape of RNA editing in the transcriptomes The main interest of Jin Billy Li's lab is to identify and interpret the RNA editing sites using a variety of approaches including genomics, technology development, and computational biology. RNA editing is a phenomenon where genomically encoded information is changed in the RNA. Adenosine-to-Inosine (A-to-I) editing is the most common type of editing, and is achieved by enzymes called Adenosine deaminase acting on RNA (ADAR). RNA editing is critical because ADAR knockout mice die before or shortly after birth. Despite the fact that RNA editing was first discovered over twenty years ago, it has been surprisingly under appreciated and under explored. Very few RNA editing sites had been discovered in humans, mainly due to technological barriers. We recently expanded the RNA "editome" to about 400 sites by computational prediction followed by targeted next generation sequencing (Li et al., Science 2009, 324:1210-1213). This, however, is probably just tip of the iceberg. Our lab will continue the discovery of the RNA editing sites in the transcriptomes of human and may model organisms, as well as various disorders such as autism and cancers. Our main approach is next generation sequencing and computational data analysis. Bioinformatics skills are also needed in a genome-wide association study to link genetic variations with the RNA editing level of a nearby editing site. In a longer term, we aim to perform functional genomic screening of these newly identified RNA editing sites.
  • Henry Lowe. Primary research interests are in the areas of clinical and research information systems design, development and evaluation including multimedia clinical systems, integration of data to support patient care and clinical research, biomedical terminologies, automated indexing of biomedical documents, cancer information systems and biomedical data security.
  • Garry Nolan. Dr. Nolan’s laboratory focuses on the analysis of biological events at the single cell level using novel genetic and FACS-based approaches at the intersection of immunology, autoimmunity, biochemistry, and cancer. The laboratory studies phospho-protein immune cell and cancer signaling, and other metabolic parameters by analysis of biochemical functions at the single cell level in primary cell populations.  This includes interrogation of cancer (Cell, 2004) and immune signaling networks in complex cell populations (Science, 2005), drug screening approaches (Nature Methods, 2005, (cover article), Nature Chemistry and Biology, 2007a,  Nature Chemistry and Biology, 2007b (cover article)) and using multiparameter data to stratify signaling maps from patient samples, (Cancer Cell, 2008, cover article).  Other major interest areas of the laboratory include mapping of signaling networks within complex populations of immune cells, developing systems biology approaches to develop an atlas of immune cell differentiation (Nature Biotechnology, In Press, 2011), the development of mechanism-based diagnostics for use in clinical trial studies. The data generated at the single cell level ranges from 10-15 parameters per cell (hundreds of thousands of cells per sample, and dozens of samples per experiment) to up to 50-100 parameters per cell using a new mass spectrometer flow cytometer we have co-developing and recently published upon (Bendall et al, Science, 2011).  To analyze these datasets and infer signaling networks within each cell subpopulation, we have developed novel hardware (Field Programmable Gate Arrays and GPUs tethered to standard CPUs with novel compiler/distributor architecture) to implement the more computationally intensive algorithms we are using for our Bayesian inference and other bioinformatics approaches.  The combination of hardware/software/biology applied in the laboratory to clinical samples sits at the edge of the translational arena in that we focus on developing techniques that can provide mechanistically relevant answers to clinicians while simultaneously helping biologist answer questions of basic importance to biology.
  • Jon Palma. Administrative role in Clinical Informatics/Analytics at Lucile Packard Children's Hospital. Research focus in clinical informatics includes optimization of commercial EMRs to support complex clinical workflows in newborn intensive care; clinical decision support; real-time clinical dashboards; electronic sign-out tools; and IT-supported patient/family communication. Analytics roles and research interests include support of LPCH's enterprise data warehouse; enabling reporting of business and clinical metrics; and generating new knowledge from clinical data.
  • Natalie Pageler. In my administrative role as Associate Chief Medical Information Officer at Stanford Children’s Health, I oversee the development and maintenance of clinical decision support tools within the electronic medical record. These clinical decision support tools are designed to enhance patient safety, efficiency, and quality of care. My research focuses on rigorously evaluating--1) how these tools affect clinician knowledge, attitudes, and behaviors; and 2) how these tools affect clinical outcomes and efficiency of health care delivery.  As a pediatric intensivist, I have a specific interest in tools that facilitate improved situational awareness and information processing in the ICU as well as early warning systems that can detect clinical deterioration.
  • David Relman. Primary research focus is the human indigenous microbiota (microbiome), and in particular, the nature and mechanisms of variation in patterns of microbial diversity within the human body as a function of time (microbial succession), space (biogeography within the host landscape), and in response to perturbation, e.g., antibiotics (community robustness and resilience). One of the goals of this work is to define the role of the human microbiome in health and disease. We are particularly interested in measuring and understanding resilience in the human microbial ecosystem. Our work includes the human oral cavity, gut, and female reproductive tract, as well as an analysis of microbial diversity in marine mammals. This research integrates theory and methods from ecology, population biology, environmental microbiology, genomics and clinical medicine.
  • Ross D. Shachter. Dr. Shachter's early work developed a method for purchasing an expert's forecast that encourages accurate revelation of the expert's beliefs as probabilities. His interest in medical decision analysis led to joint work on scheduling patients for follow-up bladder cancer therapy. In recent years, his research has focused on the representation, manipulation, and analysis of uncertainty and probabilistic reasoning in decision systems. As part of this work, he developed the DAVID influence diagram processing system for the Macintosh. He has worked closely with many students in Bioinformatics, where he holds a courtesy appointment.
  • Robert Shafer. Research is on the mechanisms and consequences of HIV evolution with an emphasis on HIV drug resistance. Maintains an online database (HIV Drug Resistance Database ) designed to provide a publicly available resource for those performing HIV drug resistance surveillance, interpreting HIV drug resistance tests, and developing new antiretroviral drugs.
  • Arend Sidow. Current projects are in developmental genomics (mouse), gene regulation and chromatin function (mouse and human), cancer genomics (human), and inherited rare disorders (human).
  • Michael P. Snyder. Snyder laboratory the first to perform a large-scale functional genomics project in any organism, and currently carries out a variety of projects in the areas of genomics and proteomics both in yeast and humans. These include the large-scale analysis of proteins using protein microarrays and the global mapping of the binding sites of chromosomal proteins. His laboratory built the first proteome chip for any organism and the first high resolution tiling array for the entire human genome.
  • Julie Theriot. Research concentrates on interactions between infectious bacteria and the human host cell actin cytoskeleton. Listeria monocytogenes and Shigella flexneri are unrelated food-borne bacterial pathogens that share a common mechanism of invasion and actin-dependent intercellular spread in epithelial cells. Our studies fall into three broad areas: the biochemical basis of actin-based motility by these bacteria, the biophysical mechanism of force generation, and the evolutionary origin of pathogenesis.
  • Samson Tu. Modeling of biomedical ontologies and clinical guidelines and protocols, development of knowledge-based systems, knowledge representation, databases, temporal database and temporal reasoning, protocol-based health care.
  • Paul J (PJ) Utz. My lab works closely with the Khatri lab to validate and extend bioinformatics discoveries using MetaSignature and other algorithms developed by the Khatri lab. The focus of the lab is on (i) development and implementation of multiplexed assays, including protein and peptide arrays, CyTOF, transcript profiles, and more recently EpiCyTOF; and (ii) discovery of biomarkers and druggable targets for many autoimmune, inflammatory, immunodeficiency, and infectious diseases.

Adjunct Faculty

  • Carol Cain. Research Interests: Integration of health IT into clinical workflow, care delivery performance improvement, clinical decision support at the point of care, clinical education and maintenance of certification, health IT innovation, informatics in support of care delivery operations, clinical pathways, practice guidelines, and evidence-based medicine development and implementation.
  • Mike Higgins. Research Interests: Clinical decision support, decision theory, electronic health records, intelligent medical devices.
  • Chris Longhurst. Professor of Biomedical Informatics and Chief Information Officer at UC San Diego Health Sciences. (Formerly Chief Medical Information Officer, Stanford Children's Health)
  • David Paik. Research interests lie at the intersection of radiology, molecular biology and informatics. We focus on developing and validating computational methodologies for extracting useful information content from anatomic, functional and molecular images, drawing upon image processing, computer vision, computer graphics, computational geometry, machine learning, biostatistics, modeling and simulation.