School of 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 CETIN
Affiliate, Medicine - Primary Care and Population Health
BioTamer Çetin is a research scientist at Stanford working in applied econometrics, causal inference, and machine learning, with a focus on developing robust methods for statistically reliable empirical analysis. His research spans econometric theory and applications involving weak identification, instrumental variables, debiased machine learning, and high-dimensional causal estimation.
At Stanford, his work connects modern statistical learning tools with classical questions in identification, inference, and policy evaluation. His research aims to improve the reliability of empirical conclusions when researchers and scientists face complex data, multiple identification strategies, or imperfect instruments.
Before joining Stanford, Dr. Çetin held research and teaching roles across academia, consulting, and industry. He has taught courses in economics, econometrics, and data science. His broader interests include causal inference, health and labor economics, machine learning for empirical research, market regulation, and antitrust.
