Speaker Profile

CEO, Omics Data Automation

Biography
Before starting Omics Data Automation, Gans was the chief architect of several generations of multi-core Xeon processors (2001 – 2013) starting with the first multicore server which resulted in more than 92% server market share for Intel. Starting 2014, he focused on Genomics and Precision Medicine driven by “Big Data”. As Director of the Precision Medicine group at Intel, he led the “Collaborative Cancer Cloud” a federated cloud platform for cancer research, collaborating with OHSU (Oregon Health Science University, OR), Broad Institute, [email protected] and various Cancer medical centers including Dana Farber Cancer Institute in Boston and OICR (Ontario Institute for Cancer Research) in Toronto. He also led Heterogeneous computing group and three Intel collaborations with academic centers at UC Berkeley, Technion Israel and UCLA in this area.


AI and Data Science Showcase:
Omics Data Automation

ODA makes tools that aggregate data from EHR, omics and imaging to provide analytics that improve patient outcomes, reduce cost and expand access.

Multi-Modal Learning For AI and IA: Applications To Cancer
The amount and diversity of data generated in the care and treatment of cancer patients is ever increasing, and we will describe how Omics Data Automation Framework store, process, and manage data from large, heterogeneous and siloed data sets such as Omics, Imaging and EHR / Clinical Trials data bases. The talk will cover future directions on how intelligent assistant/causal learning from multimodal data for cancer patients from federated systems will improve patient outcomes in Prostate Cancer and accelerate progress in basic science and clinical trials.

 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.