Postdoctoral Fellows and Visiting Scholars

Ruoyi joined the Ashley Lab in May 2025 as a Postdoctoral Scholar. Her research focuses on leveraging multimodal data and machine learning to improve cardiovascular risk prediction and enhance clinical trial design through event rate enrichment. She earned her Ph.D. in Biostatistics from the University of Washington, where she developed computational and statistical methods to study the genetic structure of human populations and uncover the genetic architecture of complex traits. Ruoyi is passionate about translating genomic discoveries into clinical applications that advance precision medicine.

Marina is a postdoctoral scientist in the Department of Cardiovascular Medicine at Stanford and conducts research in the Ashley Lab. She is also a NIH T32 fellow in Myocardial Biology.
Marina obtained her PhD in Biomedical Engineering from UC Davis in 2023 with a research focus on cardiovascular and cancer research (cardio-oncology). Her PhD research addressed the clinical need of cancer patients facing cardiovascular diseases after rigorous chemotherapy treatment. During her PhD, she discovered novel chemotherapy combinations for cancer cell growth inhibition. Additionally, she investigated the effects of the novel chemotherapy combinations on the cardiac EC-coupling system. For her postdoctoral work in the Ashley Lab, she conducts research on identifying novel recombinant AAVs using gene therapy, and investigates their ability to transduce cardiac tissue without causing off-target effects in the liver and other organs.

Zaniar is currently a fellow in cardiac electrophysiology program at Stanford. His research interest lies in the development of in vitro and in vivo platforms for studying heart regeneration and precision medicine. Zaniar’s work in Dr. Ashley’s lab is focused on identifying the mechanisms of cardiomyopathies and cardiac arrhythmias using several experimental systems ranging from genetically engineered animal models to pluripotent stem cell derived cardiac cell types. His ultimate goal as a clinician-scientist is to utilize this framework for drug discovery and identifying new therapeutic strategies that can prevent or reverse specific arrhythmias.

Josh is a Canadian biomedical data scientist with experience in bioinformatics, data science, and immunology. After completing a BSc and a MSc in Experimental Medicine at McGill university (Montreal, Canada), he relocated to the Netherlands for his PhD at Radboud University. During his PhD, he analyzed complex immunological data (bulk and single-cell transcriptomics, high-dimensional cytometry, high-throughput proteomics) derived from human observational studies or interventional studies (vaccination and experimental human infection). This work uncovered molecular and cellular correlates of clinically important endpoints such as disease severity, symptom progression, and antibody responses. In 2022, Josh relocated to Stanford to join Brice Gaudilliere lab to develop and apply multi-omic data integration and machine learning techniques, establishing that early gestational immune dysregulation can predict preterm birth. Since 2024, in the Ashley lab, Josh is focused on the application of deep learning to analyze the genetics of cardiovascular disease.

Bruna is a postdoctoral scholar in Cardiovascular Medicine. She completed her Doctor of Medicine in 2018 from Ruprecht Karl Universitat Heidelberg. She is a cardiologist Department of Cardiology, Pneumology and Angiology, Heidelberg University Hospital, Germany.

Edward is a postdoctoral fellow in the Ashley Lab of Stanford University, where he is designing new technologies for non-viral nucleic acid delivery to cardiomyocytes. He earned his Ph.D. from Harvard University in collaboration with the Massachusetts Institute of Technology (MIT), focusing on RNA delivery to endothelial cells of non-hepatic tissues. His research has advanced the development of novel cardiovascular therapies and deepened our understanding of endothelial cell biology under diseased conditions.

Eric is a biomedical and exercise physiologist, and currently a postdoctoral fellow in the Ashley Lab working with the MoTrPAC Consortium’s Bioinformatics Center. He has a PhD in physical activity, health, and exercise sciences from the University of New Mexico and further immunology and data science research experience at the University of California San Diego and the San Diego Supercomputer Center. Highlights of Eric’s prior research include studying the effects of maternal exercise related to offspring high-altitude pulmonary hypertension, human and data analysis studies to quantify the effects of heat stress on running performance, and studying the effects of the gene PGAP3 related to asthma development and exacerbations. Eric’s research interests involve integrating several types of data (e.g., applied and clinical) for a better understanding of the factors that influence health, including exercise. His MoTrPAC research includes integration of accelerometry, clinical, and omics data to investigate the relationships between physical activity, physical fitness, and clinically relevant biomarkers.

Dr. Hector Rodrigo Mendez is a Medical Geneticist from Argentina. Rodrigo completed a residency program in Medical Genetics at Centro Nacional de Genetica Medica – ANLIS (Buenos Aires, Argentina) and a Master’s program in Medical Molecular Biology at Buenos Aires University.
Rodrigo continued his scientific career at a German Genomic Start-up, working as a human geneticist and providing his experience in rare disorders, genomic data (WGS/WES/gene panels) analysis, variant interpretation, and its integration with a deep focus on genotype-phenotype correlation.
Rodrigo’s areas of expertise are rare disorders, NGS technology, Whole Genome Sequencing analysis, and ACMG interpretation guidelines, and his research aims are:
– Collection and analysis of clinical data through deep-learning phenotyping approaches.
– Multi-omic data integration to elucidate complex and rare genetic disorders.
– International collaborations to break down barriers to research participation amongst those who have been under-represented.

Samuel Montalvo is a Clinical Exercise Physiologist and Sports Biomechanist with a keen interest in human exercise and sports performance. Currently, he is serving as a Post-Doctoral Research Fellow under the Wu Tsai Human Performance Alliance at Stanford, supervised by Dr. Matthew Wheeler. His research interests revolve around understanding the biomechanical, molecular, and physiological mechanisms of human performance across diverse populations—ranging from clinical and sedentary individuals to athletes.

Bryan was born in Vancouver, British Columbia, grew up in Alaska and returned to Canada to attend the University of British Columbia for undergrad. He completed a PhD in Neuroscience studying the neural control of breathing at the National Institute of Neurological Disorders and Stroke at the NIH and the University of Alaska. He received his MD from the University of Washington in Seattle. He did pediatric residency at UC San Diego and pediatric cardiology fellowship at the University of Michigan before coming to Stanford for Adult Congenital Heart Disease Fellowship. Bryan is interested in the effects of exercise on different ACHD groups and using AI with remote patient monitoring to encourage physical activity. Outside of the hospital Bryan enjoys hockey, golf and spending time with his dog Pickles.

Matthew is a Postdoctoral Researcher at Stanford University in the laboratory of Dr. Euan Ashley and Clinical Scholar in Medical Genetics at Stanford. His earned his MD and PhD from the University of Alabama at Birmingham where he was instrumental in the initial discovery of RALA-Associated Neurodevelopmental Syndrome, also known as Hiatt-Neu-Cooper Neurodevelopmental Syndrome. His current research is focused on advancing genomic technologies for “ultra-rapid” diagnosis and personalized gene therapy for severe metabolic disorders.

Jack is an Australian physician (MD, PhD) currently working as a Post-Doctoral Research Fellow at Stanford University. He is supervised by Professor Euan Ashley and is an active member of the Ashley Lab.

Jack’s fellowship concerns the diagnosis and risk prediction of cardiovascular disease. Jack employs a variety of statistical methods to assess new diagnostic technologies, such as smart phones and smart wearables, and my work also extends to computational cardiac genetics. The data sources he utilizes to conduct my research are numerous, but include large datasets such as the UK Biobank, as well as publicly available dataset (meta-analysis and meta-research). He also has previously used large electronic health records (>250 million EHRs).

Aside from his own research prioritizes (above), Jack also work on studies conducted collaboratively within the Ashley Lab, the Division of Cardiovascular Medicine. These studies broadly include digital health randomized controlled trials (RCTs) and meta-research (including statistical methods such as meta-analysis, meta-regression etc).

Jack previously completed a DPhil (PhD) in clinical epidemiology at the University of Oxford as a Clarendon Scholar. The title of his DPhil thesis was: “Biostatistical and meta-research approaches to assess diagnostic tests”. Jack’s published research is available at the google scholar page (https://scholar.google.co.uk/citations?user=n5l7tL8AAAAJ&hl=en) and some of his code is publicly available at his GitHub (https://github.com/jackosullivanoxford).

Beyond academic institutions, Jack consults to the World Health Organization (WHO); including on WHO guidelines, where he is currently the methodological chair for a WHO guideline concerning the early(ier) detection of disease in adults. Jack also work as an associate editor at one of the BMJ sub-journals: BMJ EBM. During my DPhil Jack worked clinically at Oxford University Hospitals (John Radcliffe Hospital) and intend to return to clinical practice as a Physician-Scientist at Stanford upon the completion of my research Fellowship.

You can follow Jack on twitter (https://twitter.com/DrJackOSullivan): where you will find him tweeting about statistics, surfing, cardiology, medicine, epidemiology, health policy, and, occasionally, politics.

Narayan is a Swiss biomedical engineer with deep expertise in machine learning and signal processing applied to large-scale human health time series data. Currently a postdoctoral Wu Tsai Human Performance Fellow, he earned his PhD in Biomedical Engineering from the ARTORG Center for Biomedical Engineering Research at the University of Bern, Switzerland. He also holds MSc and BSc degrees in Computational and Molecular Life Sciences.

His research focuses on developing, validating, and clinically testing digital health technologies and AI-driven methods for active and passive remote health monitoring. His ultimate goal is to enable the earlier detection of health risks and deliver digital interventions that extend human healthspan and enhance human performance. Specifically, he develops generative deep learning models to simulate large-scale smartphone and wearable data, alongside novel computer vision approaches for artificially supervised functional remote assessment.

Laurens van de Wiel is Dutch scientist from Berghem, The Netherlands. Laurens spent his undergrad in Software Development (BSc, Avans Hogeschool ‘s-Hertogenbosch) and Computing Science (MSc, Radboud University Nijmegen). Laurens continued his career at a start-up, where he created large-scale, real-time analytical software. Laurens continued on his academic trajectory at the Radboudumc in Nijmegen, where he started his PhD in bioinformatics. During his PhD, Laurens integrated genetic data with protein 3D structures and protein domains. He utilized the skills he obtained before setting out on his academic trajectory; building large-scale, robust, reliable software. Exemplified by the MetaDome Web server (https://stuart.radboudumc.nl/metadome/). During his PhD, he developed novel methodologies for the interpretation of genetic variants of unknown clinical significance and, by integrating structural and evolutionary biology with genomics, Laurens identified 36 novel disease-gene associations for developmental disorders. These discoveries enabled diagnosis for over 500 families worldwide. Laurens’ areas of expertise are (bioinformatic) software development, data integration of genetic variation with other omics.