Speaker Profile

Ph.D., Customer Success, Tag.bio

Biography
Mark started as a developmental biologist working on transcription factors controlling flower shape and color. Then came to America and worked at Berkeley and Stanford on genes no one but maize-people really care about. He moved from Academia to Industry just as the human genome was published and since then he has worked for a number of genomics related companies ranging from startups to global pharma. Most recently he worked at Thermofisher on FDA approved diagnostic sequencing, DNAnexus on cloud genomics and now at Tag.bio on “question to answer” analytics. If there is a thread to his career, then it has been to work on software products that make the ever-increasing, volume, and variety of data manageable and analyzable by scientists.


AI & Data Sciences Showcase:
Tag.bio

Tag.bio is a data analytics company that specializes in Immuno-Oncology. We enable rapid cloud-based deployment of multi-omic and clinical data, rendering it analyzable via user-friendly "question to answer" apps.

Asking Complex IO-data Questions Simply With Tag.bio
Using immune single-cell data and gene signatures as examples we will show how researchers can ask and answer their own discovery questions. We will review the creation of sets of analyses that follow a line of argument and show how these can be shared and reproduced immediately. How would your organization change if you could ask and answer a thousand questions in the time it took to previously answer one?

 Session Abstract – PMWC 2020 Silicon Valley

Track 7 - January 22 9.00 A.M.-3.45 P.M.,Track 6 - January 23 11.15 A.M.-11.30 A.M.,Track 2 - January 24 11.15 A.M.-4.00 P.M.


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.

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