PRISM Mentors

Dr. Jeremy Heit is a neurointerventional surgeon (neurointerventional radiologist) who specializes in treating stroke, brain aneurysms, brain arteriovenous malformations, brain and spinal dural arteriovenous fistulae, carotid artery stenosis, vertebral body compression fractures, and congenital vascular malformations. Dr. Heit treats all of these conditions using minimally-invasive, image-guided procedures and state-of-the-art technology.

The practice of Radiology is undergoing a radical transformation from one in which the primary result of an imaging examination is a written report addressing the reasons that the examination was ordered, to one in which the output is a (set of) quantitative measurement(s) with links to knowledge that could affect treatment.  For example, while a traditional report might have said “there is a mass in the right upper lobe of the lung,” the report of the future might say “The mass in the right upper lobe of the lung has grown by 25% since the last examination 3 months ago; it now measures 60 cc and has imaging features consistent with adenocarcinoma with an EGFR mutation that has has a favorable response to TK inhibitors. Click these links for similar cases and their clinical history. See references [1-4] for the latest articles of relevance.” Our lab, in collaboration with other IBIIS labs, radiologists, and other clinicians, and other collaborators from the School of Medicine, is involved in many aspects of creating that future, including advanced software for image visualization and quantitative analysis, image segmentation software that isolates regions within images for further analysis, software that extracts imaging features (e.g., shape, size, margin sharpness, pixel texture) within these regions, and algorithms for computing similarity between images and between patients as expressed by their images, demographic and clinical data.

The QIAI lab focuses on cutting‐edge research at the intersection of imaging science and biomedical informatics, developing and applying AI methods to large amounts of medical data for biomedical discovery, precision medicine, and precision health (early detection and prediction of future disease). The lab develops novel methods in text and image analysis and AI, including multi-modal and multi-task learning, weak supervision, knowledge representation, natural language processing, and decision theory to tackle the challenges of leveraging medical Big Data. Our exciting work is bridging a spectrum of biomedical domains with multidisciplinary collaborations with top scientists at Stanford as well as with other institutions internationally. The QIAI lab provides a unique multidisciplinary environment for conducing innovative AI-based healthcare research with a strong record of scholarly publication and achievement. Core research topics in the laboratory include: (1) automated image annotation using unsupervised methods of processing associated radiology reports using word embeddings and related methods; (2) developing methods of analyzing longitudinal EMR data to predict clinical outcomes and best treatments, (3) creating multi-modal deep learning models integrating multi-dimensional EMR and other data to discover electronic phenotypes of disease, (4) developing AI models with noisy or sparse labels (weak supervision), and cross-modal, multi-task learning, and observational AI approaches, and (5) developing and implementing algorithms for distributed computation for training deep learning models that leverage multi-institutional data while avoiding the barriers to data sharing.

The PIMed Laboratory has a multi-disciplinary direction and focuses on developing analytic methods for biomedical data integration, with a particular interest in radiology-pathology fusion to facilitate radiology image labeling . The radiology-pathology fusion allows the creation of detailed spatial labels, that later on can be used as input for advanced machine learning, such as deep learning. The recent focus of the lab has been on applying deep learning methods to detect and differentiate aggressive from indolent prostate cancers on MRI using the pathology information (both labels and the image content). Other applications include breast cancer and brain samples. 

Dr. Spielman’s research is in the field of MRI, spectroscopy (MRS), and PET, with a focus on the development of new methods of imaging in vivo metabolism. Current projects include 13C MRS of hyperpolarized substrates for the assessment of glycolysis and oxidative phosphorylation in cancer, 1H MRS measurements brain oxidative stress and neurotransmission, and combined PET/MRS studies.  He has focused on a novel array of both acquisition and analysis techniques for use in preclinical and clinical studies.

Associated T32s: Stanford Center for Imaging Training (SCIT), Stanford Molecular Imaging Scholars (SMIS), Stanford Training in Biomedical Imaging Instrumentation (TBI2)

We are seeking a talented individual for a research associate position in our multidisciplinary team. Our advanced pediatric MRI research program spans across novel developments in hardware, pulse sequences, machine learning algorithms, image reconstruction methods, and image analysis techniques, all with an integrated clinical translational component. Efforts bridge across multiple departments on the Stanford University campus and UC Berkeley, as well as with Silicon Valley companies. The position offers the opportunity to work with multiple faculty, post-doctoral scholars, graduate students, and undergraduates. Responsibilities include developing novel techniques, contributing to grant proposals, writing and submitting manuscripts, and developing intellectual property.

My research focuses on advanced MRI and PET/MRI techniques and their application to alleviate neurological disease.  I lead an inter-disciplinary team of physicians, graduate and post-doctoral students, and research associates with technical expertise in all the required realms to perform successful advanced imaging studies.  As an active clinical neuroradiologist, I have a strong track record of integrating advanced imaging methods to clinical patients and have published extensively on its value in a wide range of diseases.  During the past several years, I have become convinced that AI generally and deep learning in particular will transform medicine.  Radiology will be fundamentally affected.  In the area of deep learning, I have demonstrated its use to improve MR reconstruction, reduce MR contrast dose and radiation dose, segmentation of brain metastases, and to predict the future.

Dr. Michael Zeineh received a B.S. in Biology at Caltech in 1995 and obtained his M.D.-Ph.D. from UCLA in 2003. After internship also at UCLA, he went on to radiology residency and neuroradiology fellowship both at Stanford. He has been faculty in Stanford Neuroradiology since 2010. He spearheads many initiatives in advanced clinical imaging at Stanford, including clinical fMRI and DTI. Simultaneously, he runs a lab with the goal of discovering new imaging abnormalities in neurodegenerative disorders, with a focus on detailed microcircuitry in regions such as the hippocampal formation using advanced, multi-modal in vivo and ex vivo methods, with applications to neurodegenerative disorders such as Alzheimer’s disease and mild traumatic brain injury.

Specific projects:

Ex vivo MRI of iron in Alzheimer’s disease
MR-histopathology correlation (both traditional histology and clearing methods)
MR-PET of AD
7T MR in AD
Analysis of iron-changes in exosomes from AD
Multi-modal MRI (DTI, ASL, QSM, rsfMRI) in mild traumatic brain injury
7T MR in Epilepsy
Ultra-high resolution 7T MRI
X-ray imaging of iron
X-ray imaging of myelin and myelin orientation
Scattered light imaging
Hippocampal microanatomy

We combine biophysical methods with in vivo approaches to understand how viruses such as HIV and SARS-CoV-2 infect host cells and elicit specific humoral immune responses. Our research will translate knowledge of the structural correlates of antibody-mediated neutralization of viruses into the rational development of highly protective antibodies. A related goal is the structure-based design of potent and stable immunogens for vaccination.

I am a Professor in the Department of Surgery at Stanford University. I trained as a dentist and have a certificate in Periodontics and a PhD. My lab works in the field of Regenerative Medicine and Dental Medicine, with a focus on the biological and mechanical regulation of tissue repair and regeneration. Our objective has remained unchanged for the last two decades: to make new discoveries that improve patient outcomes.

While conducting clinically relevant research has been my main objective, it has always gone hand-in-hand with another goal: I believe that education is one of the most important tools to improving human health, and I am committed to diversifying our profession for the good of our communities and society. I use every avenue available to transform the way people think about science and medicine and emphasize its contribution to their daily lives. I also invest my time in supporting initiatives that promote inclusion, equity, and diversity. I am the Vice Chair of Diversity and Inclusion in our Department of Surgery at Stanford, and in this role I oversee diversity/equity/inclusion initiatives that impact students ranging from high school and college, through to trainees and junior faculty. In my lab I have assembled a team of individuals from different racial, ethnic, gender, age, and socioeconomic backgrounds, and I believe our science is stronger because of this diversity.

Health economics, implementation science, access to care, use and effects of consumer health information.  Co-director of the NCI/VA Big Data Fellowship.  https://www.herc.research.va.gov/include/page.asp?id=bd-step

Our interest is in developing diagnostic and prognostic markers for urological diseases. Our work spans discovery, measurement methodologies, and clinical validation of candidate biomarkers. We have primarily used genomic and proteomic approaches for biomarker discovery. While our primary focus has been in prostate cancer, we have also worked in kidney cancer and other malignancies. We are also working to characterize the functional roles of several of the candidate biomarkers in cancer. In the past several years our work has expanded into benign urologic diseases including benign prostatic hyperplasia, obstructive nephropathy, and androgen insensitivity syndrome. In collaboration with bioengineers and radiologists, we have active research in molecular imaging, and protein and nucleotide detection on biological samples. We also participate in several large clinical trials for development, validation and implementation of clinical biomarkers in prostate cancer.

Our investigative focus is the design, conduct, analysis, and interpretation of human molecular epidemiology studies of complex cardiovascular disease (CVD) related traits including coronary atherosclerosis and risk factors for coronary atherosclerosis. In addition to performing discovery and validation population genomic studies, we use contemporary genetic studies to gain important insight on the causal and mechanistic nature of associations between purported risk factors and adverse cardiovascular related health outcomes through instrumental variable analyses and genetic risk score association studies of intermediate phenotypes.  Successful applicants will be immersed in cutting-edge molecular epidemiology studies of traits related to cardiovascular disease using large scale population biobanks including the Million Veteran Program, the Women‚Äôs Health Initiative, and the UK Biobank, with the goal of improving biological understanding, refining risk prediction, and discovering new therapeutic targets.

Our heart transplant research group focuses on clinical and translational research in the field of heart transplantation. Our major projects currently focus on (1) donor heart evaluation and selection for heart transplantation, (2) evidence-based strategies to expand the heart transplant donor pool, (3) incidence, etiology, and mechanisms of primary graft dysfunction, (4) non-invasive biomarkers of acute rejection, (5) drug therapy to prevent and treat cardiac allograft vasculopathy--the leading cause of long-term graft failure after heart transplantation, and (6) developing genomic tools to monitor for early development of post-transplant malignancies. We are funded by the NIH and transplant-related foundations, and our work involves collaborations with other research groups across campus in Oncology, Bioengineering, Infectious Disease, and Biostatistics. 

"The fundamental theme of work in the Knowles lab is the application of genetics to improve human health. We view this as a continuum from Discovery to the development of Model Systems to Clinical Translation to larger Public Health efforts.

Currently, discovery and basic translational efforts center on understanding the genetic basis of insulin resistance and related cardiovascular traits using GWAS studies coupled with exploration in model systems both in vitro (including classic cell lines as well as induced pluripotent stem cells) and in vivo (primarily mouse models). Clinical-translational research efforts in the lab are at the intersection of genetics, insulin resistance and hypercholesterolemia. We are asking if we can improve an individual’s risk by giving them information (i.e. genetic risk score) about their inherited risk of heart disease. We are also performing a clinical trial to determine the mechanism of statin-associated diabetes (which predominantly occurs in those with insulin resistance). Finally, Familial Hypercholesterolemia (FH) is a major focus given its morbidity and mortality and public health impact. As the Chief Research Advisor for The FH Foundation (FHF), a patient-led non-profit research and advocacy organization, we are attempting to raise the profile of familial hypercholesterolemia (FH), an inherited disease that causes extremely elevated LDL cholesterol levels and risk of coronary disease. We helped lead the FHF efforts to establish a national patient registry (CASCADE FH), apply for an ICD10 code for FH, advocate for genetic testing to be offered to FH patients and are now using cutting-edge “big-data” approaches to identify previously undiagnosed FH patients in electronic medical records (FIND FH). We collaborate with the CDC, AHA and ACC on these efforts."

I am a physician scientist with interests in cardiomyopathies, rare and undiagnosed diseases, therapeutics and genomics. I have research training in myocardial and skeletal muscle biology and genetics, genomics, and multi-scale networks. In addition to my research training, I am a physician with interest and experience treating patients with hypertrophic cardiomyopathy, neuromuscular disease associated cardiomyopathies, and inherited dilated cardiomyopathies. I have clinical training in medicine, cardiology, cardiovascular genetics, and advanced heart failure and transplant cardiology. I have extensive translational science efforts, as site PI for ongoing clinical trials for hypertrophic cardiomyopathy and dilated cardiomyopathy and for cardiomyopathy consortia including NONCOMPACT, PPCM and the Precision Medicine Study/DCM Consortium. I am Co-PI of Stanford’s NIH-funded Center for Undiagnosed Diseases, a clinical site of the Undiagnosed Diseases Network. I am also Co-PI of the NIH-funded Bioinformatics Center of the Molecular Transducers of Physical Activity Consortium. I pursue projects and collaborations at the intersection of striated muscle genetics, genomics, therapeutics and clinical investigation.

Department URL:
https://med.stanford.edu/cvmedicine.html

Dr. Yang is a physician-scientist whose research focuses on cardiovascular regeneration and restoration. His laboratory combines stem cell biology with novel imaging technology to advance clinical implementation of induced pluripotent stem cells and their derivatives. Induced pluripotent stem cells and their secretes will trigger a paradigm shift. His research provides a requisite validation with emphasis on clinical translation. Dr. Yang is a Principal Investigator of the National Institute of Health (NIH) funded Cardiovascular Cell Therapy Research Network designed to conduct multi-center clinical trial on novel stem cell therapy. In addition, he leads multiple NIH, foundation, and pharmaceutical research grants along with five clinical trials. He has received several prestigious awards, including the NIH Career Development Award, NIH Career Enhancement Award in Stem Cell Biology, NIH Mid-career Award, and multiple awards from both the American Heart Association and American College of Cardiology. He is a frequent guest speaker and session chair at national and international meetings.

Dr. Lin's active NIH-funded research portfolio includes developing a novel patient-reported outcome measure for emergency asthma care; evaluating post-acute transitions and outcomes for high-risk populations; and enhancing gender equity in the health professions workforce. Her prior funded projects have evaluated the impact of value-based care on emergency care delivery and payment; drivers of ED admission rates; and changes in the intensity of emergency care.

The Utz Lab focus is on the normal immune system and how it differs from the immune system of patients with immunodeficiency disorders, infections, and autoimmune diseases. Autoimmune diseases being studied include systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), systemic sclerosis (scleroderma), myositis, primary biliary cirrhosis (PBC), Sjögren's disease, insulin dependent diabetes (type I diabetes or IDDM), multiple sclerosis (MS), inflammatory bowel disease (IBD), and mixed connective tissue disease (MCTD).

We work to advance water resources management to promote resilient and equitable responses to an uncertain future. We develop computational modeling approaches that bridge the natural, built, and social environments. Our approach improves understanding of the water and climate risks that threaten people and the environment, while developing systems-based engineering and policy solutions.

Our research investigates how environmental changes like climate and land use change are affecting infectious diseases in humans and wildlife. We use tools from disease ecology, including mathematical and statistical models, health surveillance data, remotely sensed data, laboratory experiments, and field surveys to better understand the mechanisms by which changes in temperature and habitat affect vectors and disease transmission. 

Our research focuses on the control of immune responses to alloantigen and viruses (EBV, SARS-CoV-2) using both experimental models and human immunology. Current studies ongoing in the lab are:

• Insight into Development and Progression of Multi-System Inflammatory Syndrome and Long Haul COVID in Children.
• Exosomes and microRNAs in the regulation of  Immune Responses
• NK Cell Responses to EBV
• Development of strategies to control alloimmune responses

My laboratory investigates the immune response to viruses and allogeneic tissues. We are interested in characterizing the human immune response to EBV, CMV, and SARS-CoV-2 to distinguish features that are associated with control of the virus or result in pathologies including COVID-19, MIS-C, and post-transplant viral disease. Projects that are available include 1) analysis of the diversity of TCR usage in the response to EBV, CMV, and SARS-CoV-2 through the use of next generation sequencing and single cell approaches to evaluate T cell phenotype and function; 2) characterization of the natural killer (NK) cell populations that participate in the response to viruses; 3) determining the role of the viral protein LMP1 in activation of the PI3K/Akt/mTOR pathway and the effect of targeting this pathway in EBV-associated  B cell lymphoma development. 4) identification of novel host gene targets and pathways of oncogenesis utilized by EBV.  Human immunology projects utilize cell lines as well as existing extensive repositories of  human blood and tissue samples. Animal models of transplant immunology and tumor immunology are also established in the lab.

Vast amounts of molecular data characterizing the genome, epi-genome and transcriptome are becoming available for a wide range of complex disease such as cancer and neurodegenerative diseases. In addition, new computational tools for quantitatively analyzing medical and pathological images are creating new types of phenotypic data.  Now we have the opportunity to integrate the data at molecular, cellular and tissue scale to create a more comprehensive view of key biological processes underlying complex diseases. Moreover, this integration can have profound contributions toward predicting diagnosis and treatment. The Gevaert lab focuses on achieving progress in multi-scale modeling by tackling challenges in biomedical multi-scale data fusion. Applications are in the area of complex diseases with most projects in the lab focused on oncology, and possible new directions studying neuro-degenerative & cardiovascular diseases.