Speaker Profile

Ph.D., CEO, Genialis, Inc.

Biography
Rafael leads Genialis’ effort to integrate and mine vast and diverse sources of biomedical knowledge to realize the promise of precision medicine and therapeutic discovery. He spent nearly 20 years in biomedical research prior to Genialis, publishing on the evolution of innate immune systems, bioengineering of microbes, and genetics of development. He has also nurtured a specialty in developing software for high-throughput molecular design and analyses, co-inventing the j5 DNA assembly design automation tool (which has since been commercialized by TeselaGen Biotechnology). Rafael attended Dartmouth College and then Yale University, where he was an NSF Graduate Research Fellow. He went on to postdoctoral training in Jay Keasling’s synthetic biology group at Lawrence Berkeley National Laboratory, Joint BioEnergy Institute (JBEI), followed by a National Library of Medicine fellowship in Biomedical Informatics at Baylor College of Medicine.


AI and Data Science Showcase:
Genialis, Inc.

Genialis innovates at the nexus of biomedical data, machine learning, and expert engagement to accelerate precision therapies from the lab to patient.

Divining Predictive Signatures In Oncology With Machine Learning
Genialis is developing machine learning workflows to learn from large public compendia and proprietary drug-specific datasets to identify predictive and diagnostic biomarkers. By helping steer drugs to the appropriate cohorts, our biopharma partners run more efficient and successful development pipelines and are better able to reach patients in need.

 Session Abstract – PMWC Silicon Valley


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.