Speaker Profile

Ph.D., Health and Life Sciences Solutions Director, KATANA GRAPH

Biography
Dr. Gessner has a background in cheminformatics and network science. He worked in the pharma, biotech, and chemical information industries over the last 20 years with a focus on graph technologies for the past 10 years. His current work focuses on the application of graph analytics and graph AI approaches over large distributed heterogeneous knowledge graphs incorporating multi-omics data, to support advances in precision medicine. As leader of the Health and Life Sciences Solutions team at Katana Graph, he engages with pharma, hospitals, and research institutions to educate and inform on the applications of modern graph data science techniques to problems in the health and life sciences that benefit from representing data as large distributed networks.


Talk


AI and Data Sciences Showcase:
KATANA GRAPH

Katana Graph is an AI-powered graph intelligence company providing faster and more accurate insights on massive and complex data. Our Graph Intelligence platform fuses a combination of novel graph algorithms and graph neural network architectures to rapidly identify business insights at unmatched scale and performance.

Transforming Healthcare with Graph Intelligence
Katana Graph and Intel have created a graph intelligence platform with applications to precision medicine.

 Session Abstract – PMWC 2023 Silicon Valley

Showcase Track S1 - January 25 9.00 A.M.-1.15 P.M.,Showcase Track S1 - January 26 11.30 A.M.-1.15 P.M.,Showcase Track S1 - January 27 11.00 A.M.-1.15 P.M.


The PMWC 2023 AI & Data Sciences company Showcase will provide a 15-minute time slot for selected 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.

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