Speaker Profile

PhD, VP of Artificial Intelligence, Rosalind

Ben builds computational tools that make life science research faster, more productive, more social and more fun. As a leader of artificial intelligence research and development at Rosalind, Inc., he is currently laying the foundation for trustworthy and accountable generative A.I. services meant to assist scientists at every phase of the research cycle. Prior to joining Rosalind, Ben spent much of his career advancing systems that facilitate the collaborative creation of collective intelligence in biology including: the Gene Wiki, Wikidata, CIViC, and the Gene Ontology Initiative. He has published more than 80 scientific articles in domains ranging from machine learning to games with a scientific purpose.

Empowering Every Scientist with Collective Intelligence
At Rosalind, we believe the path to precision medicine will be found through a collective human intelligence now accelerated by generative AI. Using a BioIntelligence platform, collaborating researchers can harness the network of knowledge to refine their future research while gaining deeper understanding of fundamental biology.

AI and Data Sciences Showcase:

ROSALIND is the leading collaborative research platform for knowledge management and multi-omic data analysis. As a secure unified data hub for sequencing and multiplex technologies, ROSALIND combines deep interpretation and interactive visualization for spatial, single-cell, RNA, NanoString, and clinical data.

 Session Abstract – PMWC 2024 Silicon Valley

Showcase Track S1 - January 24 9.00 A.M.-4.45 P.M.,Showcase Track S1 - January 25 9.45 A.M.-4.00 P.M.,Showcase Track S1 - January 26 11.00 A.M.-3.45 P.M.

The PMWC 2024 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.


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