PRISM Mentors

The STAAR Lab is a dynamic new neuroscience lab in Stanford’s Psychiatry Department, led by Neir Eshel, MD, PhD. We are looking to hire curious and ambitious postdocs to join our team. Lab projects focus on the neural circuitry of aggressive and compulsive behaviors, using optogenetics, in vivo imaging, electrophysiology, and sophisticated machine learning/artificial intelligence analyses of animal behavior. There are ample opportunities for career development and clinical exposure based on candidate interest. Compensation and benefits are highly competitive. The ideal postdoctoral candidate has an MD and/or PhD in neuroscience or related field and extensive experience with rodent neuroscience. Excellent analytical skills, e.g., Python & Matlab, are strongly preferred. An expert data analyst may be considered even without animal experience. We are strongly committed to diversity and inclusion.

Dr. Palesh's research is in the area of cancer control. She is primarily interested in investigating the impact of cancer treatments on sleep, neurocognitive impairment, cancer-related fatigue and quality of life. Her current projects include investigating the impact of behavioral interventions (e.g., behavioral, physical activity, CAM) on improving sleep, circadian function, autonomic nervous system functioning, neurocognitive functioning, fatigue and quality of life in cancer patients and survivors. She is also investigating the relationship between dysregulation of the neuroendocrine stress response system, circadian disruption, sleep problems, fatigue, and disease progression in cancer patients with primary and metastatic cancers. Dr. Palesh's current NIH-funded studies include Phase III RCT of Brief Behavioral Intervention on Sleep, Circadian and ANS function and etiology and long-term outcome of cancer related neurocognitive impairment in newly diagnosed patients with breast cancer. Other projects are focused on understanding cancer survivorship needs and experiences of women diagnozed with cancer.

Nirao Shah's lab is interested in understanding the molecular and neural networks that regulate sexually dimorphic social behaviors.

I am most passionate about improving the role of family and friends in the long-term self-management of patients with advanced chronic illnesses. We are spearheading the first ever Center of Excellence to support family caregivers of Veterans and are seeking fellows as collaborators.  I also co-direct the postdoctoral and post-residency fellowships in Health Services Research and Medical Informatics at the VA Palo Alto Health Care System.

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.

http://web.stanford.edu/~mcfrank

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:

• How does the mind learn to represent the causal structure of the world?
• What is the relationship between causal thinking and counterfactual simulation?
• How do we hold others responsible for the outcomes of their actions?

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.

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.

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.

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.

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:

• delineating how cognitive control and attention modulate learning and memory
• specifying the mnemonic computations and representations supported by the hippocampus and medial temporal cortex, and their interactions with frontoparietal networks
• examining how memory performance in healthy older adults relates to brain structure and brain function, and to molecular and genetic risks for Alzheimer's disease

More details about our work can be found on my lab's website under Research and Publications.

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.

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.

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.

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.

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.

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.

Micro nano scale technologies in medicine

Extracellular vesciles

Early Cancer Detection

Biomedical engineering

microrobotics

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.

Microfludics
Diagnostics
Early Cancer Detection
Exosomes
 
 

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, for the first time, have demonstrated magnetic levitation of living cells and its application to detect minute differences in densities at the single-cell level.  We apply this unique tool to perform ultra-sensitive density measurements, magnetic blueprinting, imaging, sorting and profiling of millions of cells and rare biological materials in seconds in real-time at a single-cell resolution.  For instance, magnetic levitation technology can sort rare circulating tumor markers and cells from patient whole blood  without relying on any markers, tags or antibodies, which cut cross multiple disciplines of magnetics, microfluidics and molecular biology.
Our lab's mission is to bridge the gap between biology, engineering and nanotechnology; to develop simple, inexpensive, easy-to-use, yet, broadly applicable platforms that will change the way in which medicine is practiced as well as how patients are monitored, diagnosed and treated for precision medicine. We apply key biological principles to engineering designs.  Interfacing our unique bioengineering platforms with next-generation sequencing technologies, we aim to understand and answer fundamental questions mainly in cancer biology, antibiotic resistance, and regenerative medicine.
Our focus is to develop new tools and technologies to investigate and fundamentally understand disease and wellness. Our research efforts are summarized as follows:

• Creating new tools and technologies to detect and isolate circulating biological signatures, materials and markers from biological fluids (i.e., circulating tumor cells, circulating tumor emboli, exosomes in blood, urine, and saliva).
• Enabling investigations of these rare biological materials to “decode” the molecular, genetic and proteomics characteristics to better understand the biology of disease, with a special focus on cancer biology and metastasis.
• Detecting antibiotic susceptibility using magnetic levitation. 
• Evolving these technologies into the next generation of applications in antibiotic resistance to eradicate biofilms and resistant microorganisms.
• Exploring self-assembly of single cells under microgravity conditions for bioprinting, tissue engineering and regenerative medicine. 

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.  

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.

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.

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.

Department URL:
https://canarycenter.stanford.edu/

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.