PRISM Mentors

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)

Postdoctoral Research Fellow – Cell biology & microfluidics, UCSF & Stanford

A joint postdoc position between the labs of Wallace Marshall (UCSF) and Sindy Tang (Stanford) is immediately available in the area of single-cell wound healing. The broad question we aim to answer is how the single-celled ciliate Stentor can heal drastic wounds. We are looking for a candidate with a background in cell biology or related fields. This position will allow ample opportunities to learn new techniques including microfluidics for single-cell manipulation and mathematical modeling.

Application
For questions or applications (see below), please feel free to reach out to Prof. Wallace Marshall (wallace.ucsf@gmail.com) or Prof. Sindy Tang (sindy@stanford.edu). To apply, please include a CV (with a publication list) and some detail about your background and interest in the project.

Project description:
Biomechanical mechanisms conferring wound resilience in single-celled organisms
Stentor coeruleus is a large, single-celled ciliate that has remarkable wound healing and regenerative capacity (see Slabodnick et al., Current Bio, 2014 https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.10...). It is found in environments that can be subject to high mechanical stresses due to natural flows or predation. The overall goal of this project is to investigate how this organism employs both mechanical and biochemical mechanisms upstream of wounding for wound prevention, as well as downstream of wounding for robust healing from wounds (https://bmcbiol.biomedcentral.com/articles/10.1186/s12915-021-00970-0).

We have sequenced the Stentor genome, and developed tools for molecular manipulation of Stentor gene expression to pave the way to a molecular understanding of Stentor wound response.   This project involves conceptualization of a novel chemical screen to test the role of the cytoskeleton in conferring wound resistance to the cell, and the role of large-scale mechanical force generation in complementing biochemical healing modes to close wounds of increasing severity.

Some questions we ask are: how does Stentor cell mechanics give rise to wound resistance? How do cells respond to shear or other types of stresses? What molecular pathways are important in Stentor wound healing, and are they the same as in other eukaryotes?
The project combines cell biology, microfluidics for precise wounding (see Blauch et al., PNAS 2017 https://www.pnas.org/doi/abs/10.1073/pnas.1705059114), and mechanobiology modeling.

Required Qualifications:
Desired skills for this project include:
• Cell biology
• Chemical screen
• RNAi and genetic transformation
• Past experience with Stentor, other ciliates, or manipulation (e.g., microinjection) of large cells or embryos such as Drosophila
• Mathematical modelling of cellular processes
Candidates proficient in certain skills outlined above will have opportunities to receive training in complementary skill sets.

Required Application Materials:
Your CV with a publication list, and some detail about your background and interest in the project.

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.

My lab focuses on translating advanced MRI into clinical practice. In Alzheimer's disease, we are investigating the nature of iron deposition to understand how iron interacts with inflammation, amyloid, and tau in the progression of AD. We bring to this disease the full arsenal of imaging: ultra-high resolution MRI of human AD specimens coupled with novel histological methods including x-ray microscopy and electron microscopy. We bring this armamentarium full circle to living human imaging with 7.0T MR and multi-tracer PET-MR. In mild traumatic brain injury, we are studying the imaging signatures of brain insult that occur in high-contact sports using advanced MRI combined with mouthguard accelerometer measurements of impacts. In chronic fatigue syndrome, we are identifying microstructural changes that accompany fatigue and correlate with systemic circulating cytokines that together may form a biomarker for this disorder.

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.

Specificity and efficacy in intracellular signal transduction can be conferred by the anchoring and co-localization of key enzymes and their upstream activators and substrate effectors by scaffold proteins. The Kapiloff lab investigates “signalosomes” formed by scaffold proteins, asking fundamental questions such as: 1) how are signalosomes constituted; 2) how are upstream signals integrated by signalosomes to regulate in a concerted manner downstream effectors; 3) what is the physiologic relevance of these signalosomes; and 4) can signalosomes be targeted in a clinically relevant manner so as to constitute new therapeutic strategies. In particular, the Kapiloff lab studies signaling within the myocardium and retina. Using a comprehensive approach that includes biochemistry, cell biology, and in vivo physiology, ongoing projects address the regulation of pathological cardiac remodeling and the effects of disease on retinal neurons.

The lifetime risk of developing cardiovascular disease (CVD) is determined by the genetic makeup and exposure to modifiable risk factors. The Cardiovascular Link to Environmental ActioN (CLEAN) Lab is interested in understanding how various environmental pollutants (eg. tobacco, e-cigarettes, air pollution and wildfire) interact with genes to affect the transcriptome, epigenome, and eventually disease phenotype of CVD. The current focus is to investigate how different toxic exposures can adversely remodel the vascular wall leading to increased cardiac events. We intersect human genomic discoveries with animal models of disease, in-vitro and in-vivo systems of exposure, single-cell sequencing technologies to solve these questions. Additionally, we collaborate with various members of the Stanford community to develop biomarkers that will aid with detection and prognosis of CVD. We are passionate about the need to reduce the environmental effects on health through advocacy and outreach. We strongly believe that the mechanistic understanding of the adverse health effects of harmful exposures will help to devise a targeted approach towards reduction of environmental toxins as well as to identify areas in need of improving environmental equity.

"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.

Our lab studies the molecular mechanisms of and develops therapies to treat autoimmune and rheumatic diseases, with a focus on rheumatoid arthritis, osteoarthritis, multiple sclerosis, and systemic lupus erythematosus.

The overriding objectives of our laboratory are:

1) To investigate the mechanisms underlying autoimmune diseases.

2) To develop novel diagnostics and therapeutics for autoimmune and rheumatic diseases.

3) To investigate the role of innate immune inflammation in osteoarthritis.

We perform translational research, with the goal of rapidly converting discoveries made at the bench into practical patient care tools and therapies.

The Climate and Earth System Dynamics Group is led by Prof. Noah S. Diffenbaugh. Our research takes an integrated approach to understanding climate dynamics and climate impacts by probing the interface between physical processes and natural and human vulnerabilities. This interface spans a range of spatial and temporal scales, and a number of climate system processes. Much of the group's work has focused on the role of fine-scale processes in shaping climate change impacts, including studies of extreme weather, water resources, agriculture, human health, and poverty vulnerability.

The mission of the Water & Energy Efficiency for the Environment Lab (WE3Lab) is to reduce the cost and carbon intensity of water desalination and reuse. Ongoing research efforts include:

1) developing automated, precise, robust, intensified, modular, and electrified (A-PRIME) water desalination technologies to support a circular water economy;

2) optimizing the coordinated operation of decarbonized water and energy systems; and

3) supporting the design and enforcement of water-energy-food policies (e.g., Effluent Limitation Guidelines, the Clean Power Plan, CA Sustainable Groundwater Management Act, etc.).

Stanford Solutions Science Lab.

The Stanford Solutions Science Lab designs solutions to improve health and well-being of children, families, and the planet.  Dr. Robinson originated the solution-oriented research paradigm. He is known for his pioneering obesity prevention and treatment research, including the concept of stealth interventions. His research applies social cognitive models of behavior change to behavioral, social, environmental and policy interventions for children and families in real world settings, making the results relevant for informing clinical and public health practice and policy. His research is largely experimental, conducting rigorous school-, family- and community-based randomized controlled trials. He studies obesity and disordered eating, nutrition, physical activity/inactivity and sedentary behavior, the effects of television and other screen time, adolescent smoking, aggressive behavior, consumerism, and behaviors to promote environmental sustainability. Rich longitudinal datasets of physical, physiological, psychological, behavioral, social, behavioral, and multi-omics measures are available from our many community-based obesity prevention and treatment trials in low-income and racial/ethnic minority populations of children and adolescents and their parents.

Stanford Screenomics Lab - Human Screenome Project.

People increasingly live their lives through smartphones. Our Stanford Screenomics app captures everything that people see and do on their smartphone screens – a record of digital life – by taking a screenshot every 5 seconds. The resulting sequence of screenshots, make up an individual’s screenome, an unique and dynamic sequence of exposures, thoughts, feelings, and actions. To date, we have collected more than 350 million screenshots from 6-12 months of phone use from national samples of about 500 hundred adults and adolescents and their parents. Opportunities available to study the screenome to understand digital media use and its impacts on health and behavior,  develop novel diagnostics and prognostics from the screenome, and deliver precision interventions to improve health and well being. An opportunity to help build this paradigm-disrupting new science.

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  COVID in Children.
Exosomes and microRNAs in the regulation of  Immune Responses
NK Cell Diversity and Responses to viral and allo antigens
Novel T regulatory populations

Molecular and Cellular Immunobiology/CyTOF/bioinformatics

Our research focus is in computational systems biology, primarily in cancer and more recently in neurodegenerative diseases.  We develop and apply methods to understand biological processes underlying disease, using high-throughput genomic and proteomic datasets and integrating them with phenotypes and clinical outcomes. A key interest is dissecting how the cellular composition and organization of tissues affects their behaviour in disease; and how these things might be targeted for therapy or diagnostic purposes. We collaborate with many wet lab and clinical groups at Stanford, including in the areas of cancer, immunology, and neuroscience.

Multi-omics, multi-modal, multi-scale data fusion for precision medicine

Vast amounts of biomedical data are now routinely available for patients ranging from sequencing of tissues to liquid biopsies. In addition, new computational tools for quantitatively analyzing radiographic images are now available. Multi-scale data is now available for complex diseases at molecular, cellular and tissue scale to establish a more comprehensive view of key biological processes. Intra and inter individual heterogeneities are often quoted as the main challenge for studying complex diseases. These heterogeneities exist at all scales, from microscopic to macroscopic. We develop multi-scale modeling approach to counter heterogeneity and uncover potentially untapped synergies between different data modalities by integrating information across spatial scales. Multi-scale modeling involves linking information from molecules, cells, tissues, and organs all the way to the organism and the population. We propose to use high dimensional molecular data with tissue scale image data to develop a statistical multi-scale modeling approach in the context of multi-modal & multi-scale modeling. Such modeling can contribute toward predicting diagnosis and treatment by revealing synergies and previously unappreciated relationships. Multi-scale modeling also can contribute to a more fundamental understanding of disease development and can reveal novel insights in how data at different scales are linked to each other.

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.