PRISM Mentors

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.

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.

Cancer Imaging, Nanoparticles, MRI, PET/MR, Cancer Immunotherapy Imaging, Tumor Associated Macrophages, Stem Cell Tracking

My research focuses on the neurobiological basis of language, reading, and cognition in children.  Functional imaging studies demonstrate that language and reading skills require the integrated activity of a network of distributed brain regions.  Diffusion magnetic resonance imaging (dMRI) documents that variations in the properties of long-range white matter pathways connecting these brain regions within the cerebrum and between the cerebrum and cerebellum are associated with variations in language and reading skills.  These white matter pathways may be disturbed in childhood illnesses, such as brain tumors. We have been collecting dMRI scans on children born preterm and full term at different ages, including infancy.  We also have been collecting clinical scans on children with brain tumors in the cerebellum and posterior fossa. We seek students who want to learn techniques for analyzing dMRI and related imaging methods in children and to link the neurobiological findings to clinical outcomes.  Selected studies include: (1) analyzing white matter pathways in preterm infants at near term age in relation to medical and environmental variables; (2) applying spherical deconvolution to scans of children age 6 to 8 years who are learning to read; (3) evaluating longitudinal change in children with mutism after resection of a posterior fossa brain tumor.

Malaria is one of the leading causes of childhood morbidity and mortality in the world. The etiologic agent of severe malaria, Plasmodium falciparum, exclusively infects red blood cells during the blood stage of its life cycle, when all of the symptoms of malaria occur. P. falciparum is an obligate intracellular parasite, suggesting that it critically depends on host factors for its biology and pathogenesis. This concept is also supported by population genetic studies, which indicate that humans have evolved certain red cell traits, such as hemoglobinopathies, to protect against malaria. The importance of these host-pathogen interactions raises the possibility that critical red cell factors could serve as targets for new, host-directed therapeutics for malaria. However, our understanding of host determinants for malaria is limited because red cells are enucleated and lack DNA, hindering genetic manipulation. In the Egan laboratory we have surmounted this hurdle by adapting advances from stem cell biology to the study of malaria host factors. Specifically, we have developed approaches to differentiate primary human CD34+ hematopoietic stem/progenitor cells down the erythroid lineage to enucleated red blood cells that can be infected by P. falciparum. This thus gives us access to the nucleated progenitor cells for genetic modification using RNAi and CRISPR-Cas9 genome editing. We are using these methods to develop forward genetic screens to identify novel host factors for malaria, as well as to perform mechanistic studies to understand the specific functions of critical host factors during the developmental cycle of malaria parasites. In addition, the lab has projects focused on understanding human adaptation to malaria using clinical samples. Our long term goal is to explore the possibility of host-directed therapeutics for malaria.

The Itakura Lab has an immediate opening for a creative and motivated postdoctoral scholar to conduct applied research in the areas of machine learning and pattern/feature detection with a focus on either computer vision/image or genomic/molecular data processing and analysis. The lab focuses on implementing machine learning frameworks and radiogenomic approaches on heterogeneous, multi-scale cancer data (e.g., clinical, imaging, histopathologic, genomic, transcriptomic, epigenomic, proteomic) to accelerate discoveries in cancer diagnostics and therapeutics. Projects include prediction modeling of survival and treatment responses, biomarker (feature) discovery, cancer subtype discovery, and identification of new therapeutic targets. Guided by critical and relevant problems in oncology, these projects have the potential to lead to clinically actionable or translatable findings.

The successful candidate will join the Department of Medicine, Division of Oncology and work. The job description:

• Build and implement algorithms in machine learning applied to either imaging data (computer vision) or genomic/molecular data (computational biology)
• Develop software tools for integrative analysis of heterogeneous, multi-omic cancer data using machine learning
• Publish and present research findings in journals and conferences

Required Qualifications:

• PhD (or MD/PhD) in Computer Science, Engineering, Informatics, Statistics, Applied Physics, or a related field with strong skills in data mining, machine learning, or statistics
• Experience in modeling, integrative analyses, parallel computing, and/or software development desirable
• Biomedical knowledge or research experience is not a requisite
• Demonstrated ability to work independently, problem-solve, author manuscripts, strive for innovation, and be highly self-motivated
• Strong interpersonal and communication skills, and ability to work as part of a multi-disciplinary team

Our lab is dedicated to discovering the underpinnings of immune-related diseases in the heart. Many cancer drugs may cause immune-related toxicity in the heart, including severe myocarditis, making it difficult for patients with cancer to get the life-saving treatments they need. We have previously discovered that several key types of immune cells may be involved in potentiating disease. We are currently performing experiments to pin down the underlying mechanisms of how immune cells may cause various inflammatory heart diseases. We use a combination of precision medicine-oriented techniques including single-cell RNA-seq, TCR-seq, and CyTOF as well as classical molecular biology, cell modeling and animal modeling to answer mechanistic questions about the pathogenesis of cardiac inflammatory diseases, with the goals of discovering therapeutic targets which can be brought to the patient bedside. 

Our lab is dedicated to discovering the underpinnings of immune-related diseases in the heart. Many cancer drugs may cause immune-related toxicity in the heart, including severe myocarditis, making it difficult for patients with cancer to get the life-saving treatments they need. We have previously discovered that several key types of immune cells may be involved in potentiating disease. We are currently performing experiments to pin down the underlying mechanisms of how immune cells may cause various inflammatory heart diseases. We use a combination of precision medicine-oriented techniques including single-cell RNA-seq, TCR-seq, and CyTOF as well as classical molecular biology, cell modeling and animal modeling to answer mechanistic questions about the pathogenesis of cardiac inflammatory diseases, with the goals of discovering therapeutic targets which can be brought to the patient bedside. 

Our lab’s research portfolio crosses multiple disciplines including computational neuropsychiatry, multimodal neuroimaging, cognitive neuroscience and neurocognitive rehabilitation. Our computational neuropsychiatry research mainly involves investigating alterations in the organization of connectome in various neurodevelopmental and neurocognitive disorders using state of the art neuroimaging techniques (fMRI, sMRI, DWI, functional NIRS) combined with novel computational methods (graph theoretical and multivariate pattern analyses). The ultimate research goal is to translate the findings from computational neuropsychiatry research toward developing personalized interventions. We have been developing personalized interventions that integrate computerized cognitive rehabilitation, real-time functional brain imaging and neurofeedback, as well as virtual reality (VR) tailored toward targeted rehabilitation of the affected brain networks in patients with neurocognitive disorders.

Ongoing studies in Dr. Hosseini’s lab include: .

• Multimodal data integration using multilayer networks for early detection of Alzheimer's disease   
• Real-time fNIRS neuromonitoring and neurofeedback for targeted enhancement of working memory in children with ADHD.
• Multimodal neuroimaging study to examine the effect of long-term, cognitive intervention on brain networks in older adults at risk of developing Alzheimer’s disease.
• Developing a low-cost, wireless, wearable, optical imaging system for personal and population-based functional neuroimaging and neuro-intervention.
• Noninvasive optical imaging for monitoring of functional stroke recovery and to direct and optimize stroke therapies.

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.

The Sherlock lab uses experimental approaches to understand the evolutionary process, specifically interested in i) what's the rate of beneficial mutation, ii) what is the distribution of fitness effects of beneficial mutations, iii) what are the identities of beneficial mutations (and are they gain or loss of function, are they recessive, dominant or overdominant, are the genic or regulatory?) and iv) how do each of these change as a function of genotype, ploidy and environment. We are also interested in how mutations that are beneficial in one environment fare in others, to explore the trade-offs that inevitably occur when fitness increases in a specific environment, and we are interested in exploring at what level experimental evolution can be deterministic, and at what level it is stochastic. We typically use short-term continuous (chemostat) and serial batch culture experiments in conjunction with lineage tracking and high throughput sequencing to understand the adaptive changes that occur in yeast in response to selective pressures as they evolve in vitro.

The Horns Lab creates and uses new technologies to understand and manipulate cells. We aim to discover the fundamental principles governing how cells and tissues operate, and to harness these insights to improve human health. Our work unites molecular engineering, synthetic biology, and genomics to answer questions and solve problems in immunology, neuroscience, cancer, and aging.

Our laboratory develops and applies state of the art genetic, genomic and stem cell technologies to its research programs. These methodologies are used to discover the mechanisms mediating disease susceptibility and drug response, and to develop new therapies. As one example, we developed a novel computational genetic analysis method, which has identified genetic factors affecting disease susceptibility and biomedical responses in mouse models. Over 25 genetic factors affecting susceptibility to drug addiction, chronic pain, infectious diseases, and others have already been identified. We recently developed a novel AI for mosue genetic discovery and have received two NIH grants for advancing AI-based genetic discovery. 

The Peltz laboratory develops and uses state of the art genetic, genomic and stem cell technologies in its research programs. These methodologies are used to discover the mechanisms mediating disease susceptibility and drug response, and to develop new therapies. As one example, we developed a novel computational genetic analysis method, which has identified genetic factors affecting disease susceptibility and biomedical responses in mouse models. One of the genetic findings is the basis for an ongoing clinical trial that tests a new therapy for preventing opiate withdrawal from occuring in babies born to mothers that take opiates. Over 25 genetic factors affecting susceptibility to drug addiction, chronic pain, infectious diseases, and others have been identified. An ongoing effort is now analyzing 10000 biomedical responses in panels of inbred mouse strains. Single-cell RNA sequencing and metabolic analysis are used to identify developmental and disease-causing pathways. Stem cell-based methods for liver engineering are also used. As examples of this, the Peltz lab has produced mice with humanized livers that are used to improve drug safety; developed methods to engineer human liver from adipocyte stem cells; and to produce human liver organoids from stem cells, which are used for studying the pathogenesis of human genetic liver diseases.

The FUNR Lab, lead by Flora Rutaganira uses choanoflagellates—the closest living single-celled relatives to animals—to study the origin of animal cell communication. We apply chemical, genetic, and cell biological tools to probe choanoflagellate cell-cell communication. We hope that our research has implications for understanding not only animal cell signaling, but also the origin of multicellularity in animals.

The FUNR Lab, lead by Flora Rutaganira uses choanoflagellates—the closest living single-celled relatives to animals—to study the origin of animal cell communication. We apply chemical, genetic, and cell biological tools to probe choanoflagellate cell-cell communication. We hope that our research has implications for understanding not only animal cell signaling, but also the origin of multicellularity in animals.

We are interested in fundamental aspects of cell-cell recognition, migration and development with the mammalian immune and vascular systems as  models. We use molecular, genetic and single cell transcriptomic and mass cytometric approaches to study  the development and trafficking of  lymphocytes, NK cells and dendritic cells and their role in immune function in health and diseases. 
 
The vascular endothelium controls immune cell recruitment from the blood,  and thus determines the nature and magnitude of immune and inflammatory responses.     In a major new effort, we are applying single cell approaches (scRNAseq and mass cytometry), and novel computational approaches to probe endothelial cell specialization and responses in models of immune and tumor angiogenesis and inflammation.  
 
Although our focus is on fundamental problems in biology, the work is intrinsically translational and the laboratory is interested in applying its  discoveries to models of infection and immune pathology: examples include genetic studies of GPCR's and assessment of novel therapeutics in models of inflammatory bowel disease, psoriasis, cancer, aging and infection.
 
We are actively recruiting fellows with experience in biocomputation and coding who can take advantage of the datasets we are generating;   or experience in vascular biology, immunology,  imaging and cytometry.

Environmental microbiology (e.g. diatoms, algae) and synthetic biology

Topics: Nitrogen fixation, lipid biosynthesis and transprot, cellular endosymbiosis, nonmodel organisms

Application areas: Fertilizers, Biofuels

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 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. 

The Pop Lab is a research group led by Prof. Eric Pop in Electrical Engineering (EE) and Materials Science & Engineering (MSE) at Stanford University. We are located in the Paul Allen Center for Integrated Systems (CIS), working in the Stanford Nanofabrication Facility (SNF) and the Stanford Nano Shared Facilities (SNSF). We are affiliated with the Stanford SystemX Alliance and the Non-Volatile Memory Technology Research Initiative (NMTRI).

Our research is at the intersection of nanoelectronics and nanoscale energy conversion, exploring topics such as:

• Energy-efficient transistors, data storage (memory), and thermoelectrics
• 2D materials (graphene, h-BN, MoS2, WSe2,...) and phase-change materials (GST, VO2)
• Fundamental physical limits of current and heat flow, e.g. ballistic electrons and phonons
• Applications of nanoscale energy transport, conversion and harvesting

Our work includes nanofabrication, characterization, and multiscale simulations. On-campus collaborations include Materials Science, Physics, Chemical and Mechanical Engineering, and off-campus they range from UIUC, UC Davis, Georgia Tech, UT Dallas, Univ. of Tokyo and Singapore (NUS), to TU Wien, Univ. Bologna and Poli Milano.

To learn more about us, please visit http://poplab.stanford.edu

The Pop Lab is a research group led by Prof. Eric Pop in Electrical Engineering (EE) and Materials Science & Engineering (MSE) at Stanford University. We are located in the Paul Allen Center for Integrated Systems (CIS), working in the Stanford Nanofabrication Facility (SNF) and the Stanford Nano Shared Facilities (SNSF). We are affiliated with the Stanford SystemX Alliance and the Non-Volatile Memory Technology Research Initiative (NMTRI).

Our research is at the intersection of nanoelectronics and nanoscale energy conversion, exploring topics such as:

• Energy-efficient transistors, data storage (memory), and thermoelectrics
• 2D materials (graphene, h-BN, MoS2, WSe2,...) and phase-change materials (GST, VO2)
• Fundamental physical limits of current and heat flow, e.g. ballistic electrons and phonons
• Applications of nanoscale energy transport, conversion and harvesting

Our work includes nanofabrication, characterization, and multiscale simulations. On-campus collaborations include Materials Science, Physics, Chemical and Mechanical Engineering, and off-campus they range from UIUC, UC Davis, Georgia Tech, UT Dallas, Univ. of Tokyo and Singapore (NUS), to TU Wien, Univ. Bologna and Poli Milano.

To learn more about us, please visit http://poplab.stanford.edu

We are an interdisciplinary team focusing on generating new biomaterials to tackle healthcare challenges of critical importance to society. We are using these new biomaterials as sustained delivery technologies that can act as tools to better understand fundamental biological processes and to engineer next-generation healthcare solutions.

The Alsentzer Lab at Stanford is seeking a postdoctoral fellow to advance trustworthy, deployable AI methods for healthcare. 

The Alsentzer Lab is an interdisciplinary research group in the Department of Biomedical Data Science at Stanford University. Our mission is to leverage machine learning (ML) and natural language processing (NLP) to augment clinical decision-making and expand access to high-quality healthcare. Our lab develops new methods to improve model trustworthiness and leverages heterogeneous clinical data, such as electronic health records and genomic data, to provide actionable insights to clinicians, researchers, and patients. The lab bridges computer science and medicine through affiliations with Stanford’s Department of Computer Science and the Data Science team at Stanford Health Care.

Our research spans both core methodological advancements (e.g., developing novel ML architectures and evaluation metrics) and translational applications (e.g., deploying AI tools into clinical workflows). Candidates with experience in either—or both—are encouraged to apply. 

The postdoctoral fellow will work closely with Dr. Alsentzer to shape a research agenda that aligns with their interests while addressing critical challenges in AI for healthcare. Potential research directions include:

• How can we efficiently adapt foundation models to local clinical contexts?
• How can we design multimodal foundation models to better model and predict disease progression?
• How can we generate faithful and verifiable summaries of longitudinal EHR data?
• How can we better measure and mitigate the impact of biased training data for downstream clinical uses?
• Can we improve the factuality and reasoning of foundation models by integrating external biomedical knowledge? 
• Can we design models that leverage clinically useful information without relying on “shortcut” features that capture the processes of medicine? 
• How can we develop few-shot learning approaches for diagnosing and treating patients with rare diseases?
• Can we design clinically-useful metrics for evaluation and continuous monitoring after deployment?

This position is designed to equip postdocs with the skills and experience to lead interdisciplinary research at the intersection of AI and healthcare. Fellows will have access to critical resources for interdisciplinary research in ML for Health, including HIPAA-compliant compute infrastructure with high memory GPUs and access to Stanford Healthcare data, which includes EHRs for over 5M patients and 100M clinical notes.  These resources will enable the development of impactful methods that can be translated into real-world clinical applications.

The coastal margin is a complex socio-ecological landscape that is experiencing more frequent and stronger hazards from the coasts due to global climate change. Saltwater intrusion and Sea level rise (SWISLR) are placing coastal ecosystems under increasing threat, while humans in the coastal margin are pressured to make critical decisions regarding livelihood and well-being. Assessing, predicting, and mitigating the myriad challenges to the coastal margin requires a holistic approach that can integrate knowledge from different disciplines and work at multiple scales.

The Yeh Lab studies the apicoplast, a unique plastid organelle in Plasmodium falciparum parasites that cause malaria. We are particularly focused on unbiased chemical and genetic screens to discover new cell biology and therapeutic targets for this important global health disease. Our work highlights the untapped opportunities in exploring divergent biology in non-model organisms, a theme we plan to expand in the lab by studying ocean algae (malaria's cousins!) and their role in the global ecosystem.

Malaria is one of the leading causes of childhood morbidity and mortality in the world. The etiologic agent of severe malaria, Plasmodium falciparum, exclusively infects red blood cells during the blood stage of its life cycle, when all of the symptoms of malaria occur. P. falciparum is an obligate intracellular parasite, suggesting that it critically depends on host factors for its biology and pathogenesis. This concept is also supported by population genetic studies, which indicate that humans have evolved certain red cell traits, such as hemoglobinopathies, to protect against malaria. The importance of these host-pathogen interactions raises the possibility that critical red cell factors could serve as targets for new, host-directed therapeutics for malaria. However, our understanding of host determinants for malaria is limited because red cells are enucleated and lack DNA, hindering genetic manipulation. In the Egan laboratory we have surmounted this hurdle by adapting advances from stem cell biology to the study of malaria host factors. Specifically, we have developed approaches to differentiate primary human CD34+ hematopoietic stem/progenitor cells down the erythroid lineage to enucleated red blood cells that can be infected by P. falciparum. This thus gives us access to the nucleated progenitor cells for genetic modification using RNAi and CRISPR-Cas9 genome editing. We are using these methods to develop forward genetic screens to identify novel host factors for malaria, as well as to perform mechanistic studies to understand the specific functions of critical host factors during the developmental cycle of malaria parasites. In addition, the lab has projects focused on understanding human adaptation to malaria using clinical samples. Our long term goal is to explore the possibility of host-directed therapeutics for malaria.

My laboratory is focused broadly on plant chemistry and is deeply invested in pathway discovery. Despite the important roles of plant natural products in plant and human health, very few complete plant biosynthetic pathways are known. This lack of knowledge limits our understanding of natural product mode of action in plants and prevents access to engineered pathways. New plant genome sequences and synthetic biology tools have enabled three research areas in my lab: 1) methods for accelerating pathway discovery in plants (especially for clinically used therapeutics), and 2) discovering new molecules from plants that are important for plant fitness, and 3) using metabolic engineering in plants as a tool to systematically and quantitatively determine the impact of plant molecules on human and plant health and ultimately optimize plant fitness and crop nutrient load. I am looking for postdocs who are interested in joining an interdisciplinary team of scientists and engineers to discover how plant natural products are made and their mode of action, and develop new tools for engineering biosynthetic pathways. Our vision is to use engineered biosynthesis to reveal mechanisms by which natural products from plants contribute to plant fitness and human health.

Alzheimer's disease pathology begins decades before clinical symptoms of dementia are present, providing an important opportunity to understand early disease and the impact of this disease on cognitive aging.  We combine multimodal neuroimaging and genetics to determine how AD changes and risk factors influence subtle cognitive decline in older individuals. We have a particular focus on PET imaging of Amyloid and Tau proteins, but also work with structural and functional MRI data. The ultimate goals of our work are to improve our ability to predict who is most at risk for dementia, and to understand the time course of brain changes that occur decades before clinical symptoms are present.  We are specifically recruiting trainees with expertise in genetics, neuroimaging, or neuropsychology, to work on large scale multimodal imaging-genetic studies.

Our team’s research spans the fields of dermatology, technology and public health. One of our main projects is centered on developing innovative skin cancer prevention interventions using social media. Another project area is the use of shared decision-making, mobile app technology for monitoring and optimal care of low risk skin cancers. We collaborate closely with colleagues in bioinformatics and computer science on use of visual Artificial intelligence methods to skin image monitoring. Additionally, we advocate for diversity and gender equity in medicine by writing both original data articles and perspective pieces on these topics. We collaborate with epidemiologists, clinicians, biostatisticians, basic, computer and social scientists at Stanford University as well as other institutions.

Our team’s research spans the fields of dermatology, technology and public health. One of our main projects is centered on developing innovative skin cancer prevention interventions using social media. Another project area is the use of shared decision-making, mobile app technology for monitoring and optimal care of low risk skin cancers. We collaborate closely with colleagues in bioinformatics and computer science on use of visual Artificial intelligence methods to skin image monitoring. Additionally, we advocate for diversity and gender equity in medicine by writing both original data articles and perspective pieces on these topics. We collaborate with epidemiologists, clinicians, biostatisticians, basic, computer and social scientists at Stanford University as well as other institutions.

The Endocrine Oncology Research Laboratory is engaged in cutting-edge endocrine and neuroendocrine clinical, translational and basic research. Our research is focused on:

• Identifying the molecular basis of endocrine cancers that could impact patient care.
• Creating new and improved methods, strategies, technologies, and algorithms for the diagnosis of endocrine neoplasms.
• Defining genetic testing criteria, and optimal screening and surveillance strategies for inherited endocrine and neuroendocrine syndromes.
• Discovering new molecular, genetic, proteomic, and metabolomic markers for developing better diagnosis and novel targets for treatment of metastatic and advance endocrine and neuroendocrine cancers or biomarkers which could predict prognosis/response to surgical therapy.
• Advanced imaging and genetics that will allow for precision endocrine surgery.