Dr. Xiaowu Gai is a geneticist and a bioinformatician who has served as the head of bioinformatics at four different biomedical institutions. Currently, he is an Associate Professor of Clinical Pathology at the Keck School of Medicine of USC, and the Director of Bioinformatics at the Center for Personalized Medicine (CPM) at Children's Hospital Los Angeles (CHLA). The CHLA CPM develops and implements a variety of genomics-based tests for both pediatric cancers (e.g. OncoKids®) and for constitutional disorders (e.g. Clinical Exome Sequencing), for which Dr. Gai is responsible for the informatics solutions, following strictly AMP/ACMG guidelines. Dr. Gai currently serves as a Multi-PI for a NICHD-funded U24 grant, curating disease-gene and gene-variant pathogenicity assertions in relation to Leigh and Leigh-like syndromes. In the past, Dr. Gai has had extensive involvement in many other large research projects, having served as PI, Co-PI, or Co-Investigator on a number of NIH-funded grants.
AI and Data Sciences Showcase:
Children’s Hospital Los Angeles
Children's Hospital Los Angeles (CHLA) is a non-profit, pediatric academic medical center located in the East Hollywood district of Los Angeles. Founded in 1901, it is the largest regional referral center for children in critical condition who need life-saving care.
Dynamic Encryption And Watermarking Of Genomic Sequencing Data To Facilitate Privacy-Preserving Ownership-Based Data Governance
To facilitate privacy-preserving and ownership-based governance of genomic data, we developed two novel algorithms to enable flexible fine-grained protection and control consistent with the principles of Data Dignity: a) dynamic privacy-preserving encryption, and b) ownership and utility preserving watermarking.
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.