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.

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.

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.

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.

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.