PRISM supports all faculty in recruiting postdocs. The faculty listed on this page have expressed special interest in the PRISM program and may be actively recruiting. This is one of many ways to identify potential postdoc mentors; also review the guidance and links in the PRISM Application Guide for other ways to explore Stanford faculty. As you look for potential postdoc mentors, consider how faculty research interests align with your own.
PRISM mentor | Research Interests |
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Nolan Williams Psyc: Behavioral Medicine, Psyc: Behavioral Medicine
Last Updated: August 15, 2023 |
The Stanford Brain Stimulation Lab, directed by Dr. Nolan Williams at Stanford School of Medicine, is looking for postdoctoral researcher candidates for an open postdoctoral position leading clinical trials and driving forward novel therapeutic strategies. The Brain Stimulation Lab (BSL) utilizes novel brain stimulation techniques to probe and modulate the neural networks underlying neuropsychiatric diseases/disorders in an effort to develop new models and novel therapeutics. Our lab is culturally diverse and interdisciplinary, consisting of basic neuroscientists, clinical researchers, data scientists, psychologists, residents, psychiatrists, and neurologists.
We are currently looking for a postdoctoral researcher with proven experience in clinical trials in psychiatry to take a leading role in trials conducted at the lab, drive forward novel therapeutic strategies, and/or develop novel analytical strategies and methodologies. The candidate will work closely with, and receive guidance from, a faculty member assigned to the trial and will lead a team of clinical research coordinators. The BSL includes dedicated teams for patient recruitment, neuroimaging data collection, data analysis, treatment, and regulatory affairs, which will support the candidate in carrying out their duties. The position is a unique opportunity to further develop a career in clinical/translational neuroscience and psychiatric research.
1. PhD in Neuroscience or related field; or M.D with training in psychiatry.
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Michael Frank Psychology
Last Updated: July 13, 2022 |
How do we learn to communicate using language? I study children's language learning and how it interacts with their developing understanding of the social world. I am interested in bringing larger datasets to bear on these questions and use a wide variety of methods including eye-tracking, tablet experiments, and computational models. Recent work in my lab has focused on data-oriented approaches to development, including the creation of large datasets like Wordbank and MetaLab. I also have a strong interest in replication, reproducibility, and open science; some of our research addresses these topics. |
Michael Frank Psychology
Last Updated: November 11, 2021 |
How do we learn to communicate using language? I study children's language learning and how it interacts with their developing understanding of the social world. I am interested in bringing larger datasets to bear on these questions and use a wide variety of methods including eye-tracking, tablet experiments, and computational models. Recent work in my lab has focused on data-oriented approaches to development, including the creation of large datasets like Wordbank and MetaLab. I also have a strong interest in replication, reproducibility, and open science; some of our research addresses these topics.
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Tobias Gerstenberg Psychology
Last Updated: August 17, 2020 |
The Causality in Cognition Lab at Stanford University studies the role of causality in our understanding of the world, and of each other. Some of the questions that guide our research:
In our research, we formalize people’s mental models as computational models that yield quantitative predictions about a wide range of situations. To test these predictions, we use a combination of large-scale online experiments, interactive experiments in the lab, and eye-tracking experiments. |
Kalanit Grill-Spector Psychology, Neuroscience Institute
Last Updated: November 11, 2021 |
My research utilizes multimodal imaging (fMRI, dMRI, qMRI), computational modeling, and behavioral measurements to investigate human visual cortex. We seek to understand how the underlying neural mechanisms and their anatomical implementation enable rapid and efficient visual perception and recognition. Critically, we examine how the human brain and visual perception change across development to understand how the interplay between anatomical constraints and experience shapes visual cortex and ultimately behavior. We strive to create a lab that reflects the diversity of our global community and is actively involved in solving scientific and societal problems that affect all of us. I am also involved in the Wu Tsai Neurosciences Institute and the Ophthalmology T32 training grant.
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Hyowon Gweon Psychology
Last Updated: April 24, 2023 |
We know far more than what we can directly experience. We learn about the world by drawing rich, abstract inductive inferences that go beyond what we can observe, and much of these observations come from behaviors of others around us. By engaging in social learning in diverse contexts, humans learn from others, share their knowledge with others, and even accumulate a body of cultural knowledge over generations. The Social Learning Lab (SLL) aims to understand the cognitive mechanisms that underlie the communicative interactions we experience in our lives. In particular, the ways in which young children learn from others provide a unique window to the interface between our ability to draw powerful inferences and to our understanding of others’ thoughts and actions (Theory of Mind). To better understand this process, we design and conduct behavioral experiments with young children and adults, often combined with computational models that help predict and explain behavioral results.
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Russ Poldrack Psychology
Last Updated: July 13, 2022 |
Our lab uses the tools of cognitive neuroscience to understand the brain systems involved in decision making, executive function, and behavioral change. We also develop tools to improve the reproducibility and transparency of neuroimaging research, including data sharing and data analysis. |
Russell Poldrack Psychology
Last Updated: January 13, 2022 |
Our lab uses the tools of cognitive neuroscience to understand how decision making, executive control, and learning and memory are implemented in the human brain. We also develop neuroinformatics tools and resources to help researchers make better sense of data and to do research that is more transparent and reproducible. |
Russell Poldrack Psychology
Last Updated: June 27, 2022 |
My lab's research uses neuroimaging to understand the brain systems underlying decision making and executive function. We are also engaged in the development of neuroinformatics tools to help improve the reproducibility and transparency of neuroscience, including the Openneuro.org and Neurovault.org data sharing projects and the Cognitive Atlas ontology. |
Nilam Ram Psychology, Communication
Last Updated: February 08, 2022 |
The Stanford Screenomics Lab is a multidisciplinary group that uses newly available data streams to understand what people actually do on their smartphones, and how the content of their screen experiences relate to health and well-being. We use a variety of computer vision and text analysis tools to extract information from long sequences of screenshots, develop new descriptors of smartphone behavior and smartphone content, and examine how those behavior and content are related to users' emotions, sleep, and mental health. Our lab is committed to global diversity and fostering minority representation in social science, and we collaborate widely with schools and departments across Stanford and other universities. |
Nilam Ram Communication, Psychology
Last Updated: February 08, 2022 |
The Stanford Media & Psychology Lab is a multidisciplinary group focused on design and data analysis techniques for study of media and human behavior, integrating established and new disciplines to accelerate research innovations that foster innovations in psychological theory and social policy. Current research directions include emotional regulation, media and technology use, lifespan development, and new methods for analysis of intensive longitudinal analysis–including analysis of ecological momentary assessment and smartphone sensor data. Our lab is committed to global diversity and fostering minority representation in social science, and we collaborate widely with schools and departments across Stanford and other universities. |
Anthony Wagner Psychology
Last Updated: January 12, 2022 |
Memory is central to who we are and how we behave, with knowledge about the past informing thoughts and decisions in the present. Learning and memory provide critical knowledge that guides everyday activities, from remembering to take medications or recognizing previously encountered people, places, and things, to representing our goals and navigating our worlds. The research objectives of the Stanford Memory Laboratory are to understand the psychological and neural mechanisms that build memories and enable their expression, as well as how these mechanisms change with age and disease. Current research directions – which combine behavior, brain imaging, virtual reality, and computational approaches – include:
More details about our work can be found on my lab's website under Research and Publications. |
Laura Attardi Radiation Oncology, Genetics
Last Updated: December 01, 2021 |
The gene encoding the p53 transcription factor is the most commonly mutated gene in human cancer, yet we lack a complete understanding of how its loss promotes cancer and how to target this pathway therapeutically. My lab studies p53 in the context of two very deadly and common cancer, pancreatic cancer and lung cancer, to understand how p53 loss promotes tumor initiation and progression. We are investigating not only how p53 mutation changes tumor cells themselves but also how these changes in tumor cells alter the cells of the tumor microenvironment to promote cancer development. We strive to understand p53 function using varied approaches, including mass spectrometry, CRISPR screening, ATAC-sequencing, spatial transcriptomics and in vivo mouse analyses. Using these combined approaches, we are gaining key new insights into the fundamental functions of p53 in vivo, which will ultimately inform us on how to target this critical pathway therapeutically.
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Max Diehn Radiation Oncology, Stanford Cancer Center, Stem Cell Bio Regenerative Med
Last Updated: August 28, 2020 |
The overarching research goal of the Diehn lab is to develop and translate novel diagnostic assays and therapies to improve personalized treatment of cancer patients. We have a major focus on the development and application of liquid biopsy technologies for human cancers, with a particular emphasis on lung cancers and circulating tumor DNA (ctDNA). We also investigate mechanisms of treatment resistance to radiotherapy, immunotherapy, and targeted agents. Most of our research projects start by identifying an unmet need in the clinical management of cancer patients that we then try to solve via development or application of novel technologies. We use genomics, bioinformatics, stem cell biology, genome editing, mouse genetics, and preclinical cancer models in our work. Discoveries from our group are currently being tested in multiple clinical trials at Stanford and elsewhere in order to translate them into the clinic.
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Richard Frock Radiation Oncology
Last Updated: July 13, 2022 |
The Frock laboratory is interested in elucidating mechanisms of DNA double-stranded break (DSB) repair and chromosome translocations. We employ a high-throughput sequencing technology that identifies and maps cellular DSBs. We are interested in further developing this technology to more fully quantify the DSB repair fates from targeted DSBs. Our research disciplines are broad and cover aspects of molecular and cancer biology, bioinformatics. immunology, genome editing, and radiation biology. |
Ted Graves Radiation Oncology
Last Updated: July 13, 2022 |
My laboratory is focused on development and application of molecular imaging techniques towards understanding radiation and cancer biology and improving treatment of human disease. Using modalities including positron emission tomography (PET), computed tomography (CT), fluorescence imaging, bioluminescence imaging, and small animal conformal radiotherapy, we are investigating the molecular and physiologic factors that determine tumor response to therapy. In addition, we are applying this knowledge towards the development of combination therapies that improve tumor response and minimize normal tissue toxicity. We are a multi-disciplinary group with expertise in engineering, biology, chemistry, medicine, and computer science.
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Anusha Kalbasi Radiation Oncology
Last Updated: May 11, 2023 |
The Kalbasi laboratory tackles questions at the intersection of immunology and cancer biology, with an emphasis on therapeutic development. Here are some selected areas of interest: Cytokine-based rewiring of T cells: Advances in gene therapy and synthetic biology have ushered in a new era in T cell therapy. Engineered T cells can now be dynamically modulated to perform context-specific functions. To leverage these technologies, the lab is studying how external cytokine signals, especially common gamma chain family, shape T cell function (Kalbasi, et al. Nature 2022). https://clinicaltrials.gov/ct2/show/NCT04119024?cond=il13ra2&draw=2&rank=1
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Ruijiang Li Radiation Oncology
Last Updated: August 11, 2020 |
My lab is focused on the development of imaging and molecular biomarkers for precision cancer medicine. We are interested in a broad range of clinical applications, including early cancer detection, diagnosis, prognostication, and prediction of treatment response. To achieve this goal, we integrate and analyze large-scale patient data sets with clinical annotations, including both imaging (radiologic, histopathologic) and molecular (genomic, epigenomic, transcriptomic) data. In addition, we develop and apply novel statistical and machine learning methods. We are a multidisciplinary team with a diverse background and yet converging theme. Our ultimate goal is to clinically translate novel biomarkers to guide selection of optimal therapy and improve outcomes for cancer patients.
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Wu Liu Radiation Oncology
Last Updated: December 11, 2021 |
Use artificial intelligence in image and biology guided radiotherapy and medical image analysis (PET/CT). Theranostic nanoparticles for radiosensitization and medical imaging. Novel treatment technique for ocular disease radiotherapy. Radio-neuromodulation using focused kV x-rays. Ultrasound parametric imaging.
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Bill Loo Radiation Oncology
Last Updated: December 01, 2020 |
My lab is an interdisciplinary group spanning medical physics and technology development, basic cancer and radiation biology, and preclinical and clinical imaging.
The two main programs are: 1. Development of next-generation medical linear accelerator technology for delivery of ultra-rapid FLASH radiation therapy for cancer, working closely with collaborators at SLAC National Accelerator Laboratory. We are currently designing and building a system for preclinical FLASH research in small animal models. Using the same platform technologies, we are also laying the groundwork for a clinical treatment system (PHASER) for FLASH radiation therapy for general cancer therapy in human patients, with a focus on compact, economical, and clinically efficient design. 2. Fundamental radiation biology research in ultra-rapid FLASH radiation therapy in small animal and in vitro models. We are investigating the biological mechanisms underlying the observed therapeutic index of FLASH, producing less normal tissue radiation injury and simultaneously equal or increased tumor killing compared to conventional dose rate irradiation. We are investigating physical, radiochemical, immunological, vascular, and other microenvironmental aspects in multiple model systems.
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Everett Moding Radiation Oncology
Last Updated: March 14, 2022 |
We perform translational cancer research by analyzing human tissue and blood samples with next-generation sequencing to understand the genetic underpinnings and expression signatures that determine treatment response and resistance. We use genetically engineered mouse models to validate our findings, perform mechanistic experiments, and test new therapies. Our ultimate goal is to translate our findings to the clinic to improve outcomes for patients with cancer. |
Guillem Pratx Radiation Oncology
Last Updated: July 13, 2022 |
The Physical Oncology Lab develops instruments and algorithms at the interface between medical physics and biophysics, for applications in cancer research and cancer care. We use unconventional physical mechanisms to non-invasively interrogate biological processes in living organisms and physically enhance the efficacy of radiation treatments.
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Jiangbin Ye Radiation Oncology
Last Updated: July 14, 2022 |
An emerging hallmark of cancer is the modulation of metabolic pathways by malignant cells to promote cancer development. Dr. Jiangbin Ye’s professional interest is to investigate the causes and consequences of the abnormal metabolic phenotypes of tumor cells, with the prospect that therapeutic approaches might be developed to target these metabolic pathways to improve cancer treatment. The lab’s current goal is to explore the complex role of metabolic reprogramming in epigenetic regulations, and how cell fate and differentiation process are controlled by these epigenetic regulations. Ye’s lab is located in the Stanford University School of Medicine, with state-of-art research facilities. The multidiscipline research environment provides unique and outstanding training and collaborating opportunities. The candidate will have direct access to modern metabolomics research tools, including YSI biochemical analyzer, Seahorse XF Analyzer, hypoxia chamber and Agilent Q-TOF LC-MS. The lab is specialized in both untargeted and targeted metabolomics analysis, particularly isotope tracing technique for metabolic flux analysis. Dr. Ye is committed to mentoring and training for the candidate, providing all the support the candidate needs to reach the career goal.
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Heike Daldrup-Link Radiology
Last Updated: July 14, 2022 |
CAR (chimeric antigen receptor) T-cell therapy has shown promising results in patients with leukemia and lymphoma. However, therapy response in patients with solid tumors is highly variable. An imaging test, which could directly visualize CAR T-cells in patients would greatly improve our understanding of factors that lead to successful treatment outcomes. Immune cells can be labeled with clinically translatable iron oxide nanoparticles, which can be detected with magnetic resonance imaging (MRI). However, thus far, it was required to use transfection agents to shuttle iron labels into CAR T-cells. Most transfection agents are not approved for use in humans and demonstrate low efficiency for cell labeling with nanoparticles. We developed new cell labeling techniques, which do not require transfections. This project will test the efficacy of transfection-agent free cell labeling techniques for time-efficient labeling of CAR T-cells with iron oxide nanoparticles for subsequent in vivo tracking in mouse models of cancer. Tracking nanoparticle-labeled CAR T-cells in vivo will enable us to understand and optimize the tumor accumulation of CAR T-cells, prescribe tailored dosing regimen and develop appropriate combination therapies.
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Heike Daldrup-Link Radiology, Ped: Hematology-Oncology
Last Updated: July 13, 2022 |
Cancer Imaging, Nanoparticles, MRI, PET/MR, Cancer Immunotherapy Imaging, Tumor Associated Macrophages, Stem Cell Tracking
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Utkan Demirci Radiology
Last Updated: July 23, 2021 |
Micro nano scale technologies in medicine Extracellular vesciles Early Cancer Detection Biomedical engineering microrobotics
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Utkan Demirci Radiology
Last Updated: January 12, 2022 |
Micro nano scale technologies in medicine Extracellular vesciles Early Cancer Detection Biomedical engineering microrobotics
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Utkan Demirci Radiology
Last Updated: August 11, 2020 |
Microfludics
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UTKAN DEMIRCI Radiology
Last Updated: June 30, 2022 |
Utkan Demirci is a professor at Stanford University School of Medicine and serves as the interim division chief and co-director of the Canary Center for Cancer Early Detection in the Department of Radiology. His group focuses on developing innovative microfluidic biomedical technology platforms with broad applications to multiple diseases. Some of his inventions have already been translated into Food and Drug Administration-approved products serving patients. He has mentored and trained many successful scientists, entrepreneurs, and academicians. Currently the group has a strong core focused on bio fabrication, Extracellular vesicles enrichment and isolation, small scale robotics for biomedicine and development of point of care metamaterial based optical sensors.
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Utkan Demirci Radiology
Last Updated: July 13, 2022 |
The Demirci Bio-Acoustic MEMS in Medicine Lab (BAMM) specializes in creating technologies to manipulate cells in nanoliter volumes to enable solutions for real world problems in medicine including applications in infectious disease diagnostics and monitoring for global health, cancer early detection, cell encapsulation in nanoliter droplets for cryobiology, and bottom-up tissue engineering. |
Gozde Durmus Radiology
Last Updated: August 10, 2020 |
Our lab's research lies at the interface of biology, engineering, nanotechnology, and medicine. We develop and apply translational micro/nanotechnologies to study cellular heterogeneity and complex biological systems for single cell analysis and precision medicine. At this unique nexus, we apply key biological principles to design engineering platforms. Our research philosophy is to apply these platforms to fundamentally understand and address the mechanisms of disease (i.e., cancer, infections).
We are seeking open and honest, creative, dedicated, and team-oriented individuals to join our research team. Our lab prioritizes inclusion and diversity to achieve excellence in research and to foster an intellectual climate that is welcoming and nurturing. Two positions are available for energetic, self-driven and passionate postdoctoral fellow candidates. Applicants are expected to be technically competent in a discipline relevant to our mission and vision. |
Daniel Bruce Ennis Radiology
Last Updated: July 13, 2022 |
Daniel Ennis (Ph.D.) is an Associate Professor in the Department of Radiology. As an MRI scientist for nearly twenty years, he has worked to develop advanced translational cardiovascular MRI methods for quantitatively assessing structure, function, flow, and remodeling in both adult and pediatric populations. He began his research career as a Ph.D. student in the Department of Biomedical Engineering at Johns Hopkins University during which time he formed an active collaboration with investigators in the Laboratory of Cardiac Energetics at the National Heart, Lung, and Blood Institute (NIH/NHLBI). Thereafter, he joined the Departments of Radiological Sciences and Cardiothoracic Surgery at Stanford University as a post doc and began to establish an independent research program with an NIH K99/R00 award focused on “Myocardial Structure, Function, and Remodeling in Mitral Regurgitation.” For ten years he led a group of clinicians and scientists at UCLA working to develop and evaluate advanced cardiovascular MRI exams as PI of several NIH funded studies. In 2018 he returned to Stanford Radiology and the Radiological Sciences Lab to bolster programs in cardiovascular MRI. He is also the Director of Radiology Research for the Veterans Administration Palo Alto Health Care System where he oversees a growing radiology research program. |
Katherine Ferrara Radiology
Last Updated: June 06, 2022 |
Katherine Whittaker Ferrara is a Professor of Radiology and the Division Chief for the Molecular Imaging Program at Stanford. She is a member of the National Academy of Engineering and a fellow of the IEEE, American Association for the Advancement of Science, the Biomedical Engineering Society, the World Molecular Imaging Society, the Acoustical Society of America and the American Institute of Medical and Biological Engineering. Dr. Ferrara received her Ph.D. in 1989 from the University of California, Davis. Prior to her PhD, Dr. Ferrara was a project engineer for General Electric Medical Systems, involved in the development of early magnetic resonance imaging and ultrasound systems. Following an appointment as an Associate Professor in the Department of Biomedical Engineering at the University of Virginia, Charlottesville, Dr. Ferrara served as the founding chair of the Department of Biomedical Engineering at UC Davis. Her laboratory is known for work in molecular imaging and drug delivery.
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Jeremy Heit Radiology
Last Updated: July 13, 2022 |
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. |
Craig Levin Radiology, Physics, Electrical Engineering, Bioengineering, Radiology-MIPS, Stanford Cancer Center, Cardiovascular Med Institute, Neuroscience Institute
Last Updated: March 16, 2022 |
The research interests of the molecular imaging instrumentation lab are to create novel instrumentation and software algorithms for in vivo imaging of molecular signatures of disease in humans and small laboratory animals. These new cameras efficiently image radiation emissions in the form of positrons, annihilation photons, gamma rays, and/or light emitted from molecular contrast agents that were introduced into the body and distributed in the subject tissues. These contrast agents are designed to target molecular pathways of disease biology and enable imaging of these biological signatures in tissues residing deep within the body using measurements made from outside the body. The goals of the instrumentation projects are to advance the sensitivity and spatial, spectral, and/or temporal resolutions, and to create new camera geometries for special biomedical applications. The computational modeling and algorithm goals are to understand the physical system comprising the subject tissues, radiation transport, and imaging system, and to provide the best available image quality and quantitative accuracy. The work involves designing and building instrumentation, including arrays of position sensitive sensors, readout electronics, and data acquisition electronics, signal processing research, including creation of computer models, and image reconstruction, image processing, and data/image analysis algorithms, and incorporating these innovations into practical imaging devices. The ultimate goal is to introduce these new imaging tools into studies of molecular mechanisms and treatments of disease within living subjects.
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Sandy Napel Radiology
Last Updated: June 06, 2022 |
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.
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Sharon Pitteri Radiology
Last Updated: November 11, 2021 |
The Pitteri laboratory uses mass spectrometry to identify, quantify, and characterize proteins in complex biological and clinical samples. We are focused on using proteins and their post-translational modifications to better understand biology and to answer clinical problems in health and disease states. Currently, a main focus of the lab is developing and implementing new methods to study protein glycosylation in cancer. |
Daniel Rubin Biomedical Data Sciences, Radiology, Biomedical Informatics
Last Updated: August 17, 2020 |
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.
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Mirabela Rusu Radiology, HumanCentered Artificial Inte
Last Updated: January 12, 2022 |
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.
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Mirabela Rusu Radiology
Last Updated: August 11, 2020 |
Dr. Mirabela Rusu directs the Laboratory for Integrative Personalized Medicine (PIMed), which is part of the School of Medicine, Department of Radiology, Division of Integrative Biomedical Imaging Informatics. PIMed focuses on developing deep learning methods for radiology-pathology integration and to characterize the appearance of diseases on radiology images using the pathology information. Such integrative methods may be applied to create comprehensive multi-scale representations of biomedical processes and pathological conditions, thus enabling their in-depth characterization and the identification of imaging signatures of pathologic conditions. Our team extensively studies the appearance of prostate cancer on MRI, but also works on breast cancers as well as non-oncologic applications. |
Mirabela Rusu Radiology
Last Updated: November 29, 2021 |
The Laboratory for Integrative Personalized Medicine (PIMed) is directed by Dr. Mirabela Rusu, PhD, and is part of the School of Medicine, Department of Radiology, Division of Integrative Biomedical Imaging Informatics at Stanford University. 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 . Such integrative methods may be applied to create comprehensive multi-scale representations of biomedical processes and pathological conditions, thus enabling their in-depth characterization. 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. PIMed closely collaborates with the Urologic Cancer Innovation Lab at Stanford for the prostate cancer work.
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Dan Spielman Radiology
Last Updated: July 14, 2022 |
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.
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Tanya Stoyanova Radiology
Last Updated: July 13, 2022 |
Stoyanova lab is interested in understanding fundamental molecular mechanisms underlying the development of epithelial cancers and their utility as biomarkers and therapeutic targets. Currently, the major focus of our group is in prostate cancer. We are also interested in breast and neuroendocrine cancers. The ultimate goals of the laboratory are to: 1) improve the stratification of indolent from aggressive prostate cancer and 2) guide the development of novel and effective therapeutic strategies for metastatic cancers. |
Sindy Tang Mechanical Engineering, Bioengineering, Radiology
Last Updated: August 24, 2023 |
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. 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? Required Qualifications: Required Application Materials: |
Shreyas Vasanawala Radiology
Last Updated: July 13, 2022 |
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. |
Adam Wang Radiology, Electrical Engineering
Last Updated: July 14, 2022 |
My research interests revolve around the following areas: - Novel systems and methods for x-ray and CT imaging - Applications of x-ray/CT to image-guided interventions and therapy and diagnostic imaging - Dual energy / spectral imaging, including photon counting detectors - Applications of artificial intelligence / machine learning / deep learning to medical imaging - Monte Carlo and Deterministic methods for x-ray imaging and radiation dose - Model-based image reconstruction
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Greg Zaharchuk Radiology
Last Updated: January 12, 2022 |
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. |
Michael Zeineh Radiology
Last Updated: July 14, 2022 |
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. |
Michael Zeineh Radiology
Last Updated: July 14, 2022 |
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. |
Michael Zeineh Radiology, Radiology-RSL, Neuroscience Institute
Last Updated: January 29, 2023 |
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 |