Research Foundations on Amazon Web Services with Paul Saxman

Paul is a Principal Solutions Architect and leads US Academic Medicine Solutions Architecture at AWS. The team is composed of subject matter experts in electronic health records, medical imaging, healthcare data standards (HL7, FHIR, OMOP), bioinformatics, high-performance computing, genomics, AI/ML, etc. Paul and the Academic Medicine team educate and support customers on optimal cloud architectures to achieve enterprise-level business goals, as well as guidance on solving complex healthcare and biomedical research problems, such as health AI/ML for clinical decision support, high throughput genomics analysis, precision medicine, etc.

About Deep Data Research Computing Center:

We bring in computer scientists’ expertise and need to validate their work to help biologists and biometricians scale research. Over the years, the lowering costs of sequencing leads to an overwhelming amount of biomedical data generated. While this helps biologists and biometricians unlock deeper insights, they often find themselves lack the time or knowledge to acquire, store, and analyze data in a scalable way.
 
Our multidisciplinary team builds cost-efficient systems for research studies with large number of participants and different types of data, with the aim to collect, store and analyze data across multiple platforms, while preserving fast turnaround times. By using robust de-identification and end-to-end encryption methods, we reduce the risk of violating privacy and security regulations.
 
Our vision is the ability to collect and utilize more data at ease accelerates the transformation from conventional medicine to deep medicine. In conventional medicine, we are considered sick when our health status diverts from the population’s average. Deep medicine reveals the wide individual differences by investigating a variety of domains, and unlocks personalized insights to prevent diseases and manage daily health.
 
Connect with us:

Website: https://deepdata.stanford.edu/
Twitter: @DeepDataSU
LinkedIn:   / deep-data-stanford