School of Medicine
Showing 221-240 of 1,566 Results
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Andrea Cipriano
Instructor, Obstetrics & Gynecology - Reproductive Biology
BioDr Andrea Cipriano is an instructor at the Stem Cell Institute and at the Ob/Gyn department at Stanford School of medicine. Since the beginning of his career he was driven by a deep interest in the complexities of life emerging from just a single cell, harboring all the instructions to produce a fully functional organism. His academic journey began with a Bachelor's in Biotechnology and progressed to a Master's in Genomic Biotechnology, where he delved into the intricate world of RNA. During his PhD, Andrea focused on long non-coding RNAs and their pivotal role in cell differentiation, a topic that continues to fascinate him in his current research. He works in the Sebastiano lab, and he is directing several projects, including studying the transcription factor TBX1 during development of the Pharyngeal endoderm, and exploring the impact of time on Chromatin Structure, particularly in the context of aging and its potential reversal. As an instructor, Andrea has been teaching for 4 years at the intensive CIRM stem cell class biology course. Teaching is a big passion that fuels his academic pursuits. His dedication to education stems from a deep-seated belief in the transformative power of knowledge, which is what initially propelled him into the academic world.
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Laetitia Coassolo (Voilquin)
Senior Research Scientist - Basic Life, Pathology Sponsored Projects
Current Role at StanfordI am a Senior Research Scientist in Dr. Katrin Svensson's lab. I am interested in mapping tissue-specific peptide secretion to identify orphan peptide hormones.
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Bernard Mawuli Cobbinah
Postdoctoral Scholar, Anesthesiology, Perioperative and Pain Medicine
BioCobbinah Bernard Mawuli is a Postdoctoral Scholar at Stanford University in the Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine. He is passionate about the intersection of AI and medicine, focusing on developing robust and effective approaches for preventive and predictive healthcare. His research aims to deepen the understanding of high-dimensional multi-omics medical data using advanced machine learning techniques. By exploring innovative ways to analyze this data, his work contributes to improved treatments and enhanced patient care. Through the analysis of large patient datasets, his goal is to create tools that empower clinicians to make more informed decisions, ultimately improving healthcare outcomes for all.
Prior to joining Stanford, he pioneered robust federated learning techniques for evolving data streams and developed methods to reduce multi-center MRI variability in diagnosing brain disorders. -
Caitlin Contag
Clinical Scholar, Medicine - Pulmonary, Allergy & Critical Care Medicine
Postdoctoral Scholar, Pulmonary and Critical Care MedicineCurrent Research and Scholarly InterestsEmerging pathogens, high-consequence pathogens, critical care in resource limited settings, early interventions for sepsis, marginalized patient populations, sexually transmitted diseases, planetary health
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Marimar Contreras Nieves
Clinical Scholar, Medicine - Nephrology
Postdoctoral Scholar, NephrologyBioI am currently in my second year of nephrology fellowship, working on a Masters in clinical research and epidemiology, and also doing postdoctoral research on a U2C-TL1. During my training I have worked on clinical research focused on environmental risk factors and health care barriers that can initiate or accelerate the development and progression of kidney disease. My goal is to reach disadvantaged populations and help reduce the gaps in health that may stem from their vulnerability to determinantal environmental exposures. My projects have included investigating chronic kidney disease of uncertain etiology, with particular interest in California’s Central Valley.
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Tyler Edward Cork
Postdoctoral Scholar, Radiological Sciences Laboratory
Current Research and Scholarly InterestsCurrently, I am involved in two main projects. The first is developing 3D printing techniques to improve the accuracy of ex vivo geometrical and microstructural cardiac modeling from in vivo cardiac MR acquisitions. The second is applying machine learning applications to MRI data as a way to improve overall image quality and reduce acquisition time.
