Deep Data Needs and Challenges in Precision Medicine with Dr. Amir Bahmani

Dr. Amir Bahmani is the Director of Stanford Deep Data Research Computing Center (DDRCC) and the Director of Science and Technology at Stanford Healthcare Innovation Lab (SHIL) and a lecturer at Stanford University. He has been working on distributed and parallel computing applications since 2008.

Currently, Amir is an active researcher in the VA Million Veteran Program (MVP), Human Tumor Atlas Network (HTAN), the Human BioMolecular Atlas Program (HuBMAP), Stanford Metabolic Health Center (MHC) and Integrated Personal Omics Profiling (iPOP).

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