M.D., CEO & Co-Founder, Univfy Inc.
Biography
Dr. Yao has led Univfy from founding through stages of technology invention and commercialization. She is now leading the team to scale Univfy’s business. Dr. Yao has over 20 years of experience in clinical and scientific research in reproductive medicine. Prior to founding Univfy, she was on the faculty at Stanford University, where she led NIH-funded fertility and embryo genetics research and developed the Univfy technology with the academic founding team. Dr. Yao graduated from medical school at the University of Toronto and completed her obstetrics and gynecology residency training at McGill University. She received her clinical subspecialty training in reproductive endocrinology and infertility at Brigham and Women’s Hospital at Harvard University. Dr. Yao received multiple research awards for her fertility research work, including pre-implantation embryo development, the role of stem cell genes in the embryo, and uterine receptivity at implantation.
AI & Data Sciences Showcases:
Univfy
Univfy is dedicated to helping individuals and couples realize their dream of becoming parents. We are IVF and fertility analytics experts passionate about providing you with the tools to choose IVF with greater confidence, success, and cost-success transparency.
A Global AI Platform to Enhance IVF Access and Fertility Therapeutics Innovation
Univfy is dedicated to helping individuals and couples realize their dream of becoming parents. We are IVF and AI experts passionate about providing you with the tools to choose IVF with greater confidence, success, and cost-success transparency.
Session Abstract – PMWC 2020 Silicon Valley
Track 7 - January 22 9.00 A.M.-3.45 P.M.,Track 6 - January 23 11.15 A.M.-11.30 A.M.,Track 2 - January 24 11.15 A.M.-4.00 P.M.
The PMWC 2020 AI Company Showcase will provide a 15-minute time slot for selected AI companies to present their latest technologies to an audience of leading investors, potential clients, and partners. We will hear from companies building technologies that expedite the pre-clinical and clinical drug discovery and development process, accelerate patient diagnosis and treatment, or develop scalable systems framework to make AI and deep/machine learning a reality.
