Medicine
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Rene Caissie
Adjunct Professor, Medicine - Primary Care and Population Health
BioRene Caissie is an entrepreneur, researcher, and former surgeon who holds the position of CEO and Co-Founder at Medeloop.ai, a company dedicated to revolutionizing clinical research and trials through innovative AI technology. He serves as an Adjunct Professor at the Stanford University School of Medicine, where he teaches entrepreneurship in Digital Health and A.I.. In addition, he lectures within the Stanford Master of Science in Clinical Informatics Management (MCIM) program, mentoring students through their practicum experiences. Furthermore, he provides instruction at the Translational Medicine Program (MTM) at UCSF, focusing on the translational challenges in medicine. He is also a member of the XPRIZE Brain Trust Team, where he lends his expertise to foster healthcare innovations. Additionally, Rene serves as a Venture Partner at the venture capitalist firm OVO Fund
Rene’s entrepreneurial and medical expertise has spurred the creation of several healthcare ventures, such as Medesync EMR, which was acquired by the $37 billion telecommunications giant, Telus. Amid the Covid-19 crisis, he played a crucial role in developing a powered Full Head Protective Hood with an air-purifying respirator and co-founding Dorma Filtration, which introduced Canada's first reusable N95 mask.
Beyond his professional pursuits, Rene is an avid mountain climber, sailboat trans-oceanic racer, SR22 Turbo aircraft pilot, and Ironman World Championship qualifier. His dedication to humanitarian work is evident through his NGO, Volte-Face, which has provided over $1 million in free medical care for life-changing surgeries to underprivileged patients. As a board member for Sprouts, a California-based non-profit, he supports disadvantaged youths through skills coaching and internships. -
TAMER ÇETIN
Affiliate, Medicine - Primary Care and Population Health
BioTamer Çetin is a Research Professor at Stanford whose work focuses on applied econometrics, causal inference, and machine learning. His research develops robust methods for statistically reliable empirical analysis in high-dimensional and complex-data settings, with contributions spanning weak identification, instrumental variables, regression discontinuity, debiased machine learning, and causal estimation.
At Stanford, he connects modern statistical learning methods with classical questions in identification, inference, and policy evaluation. His research seeks to improve the credibility of empirical conclusions when researchers face complex data, competing identification strategies, imperfect instruments, or model uncertainty.
Before joining Stanford, Dr. Çetin held research and teaching positions across academia, consulting, and industry. He has taught courses in economics, econometrics, and data science. His broader research interests include causal inference, health economics, and the use of machine learning in empirical research.
