Department of Biomedical Informatics
Guest Speaker: Jonas Almeida, Ph.D. Chief Technology Officer and Professor, Department of Biomedical Informatics , Stony Brook University Interoperable
Title: Biomedical BigData at Data Commons scale
When: Wednesday, December 12, 2018
Where: BMI Conference Room HSC-L3 Room 045
With the funding of several NIH initiatives, Data Commons frameworks are now emerging as the interoperable data layer for Biomedical BigData. The motivation is that of allowing management and analysis of data in a manner that a) does not require the data assets to be downloaded; b) is aligned with the governance of acquisition os personal data; c) can take full advantage of the scalability of Cloud-hosted data-intensive systems; and d) maximizes the opportunities for signal extraction by machine learning. This presentation will have two parts. This first part reviews the early stage development of Data Commons, starting with Genomic Data Commons (https://gdc.cancer.gov) and including work at Stony Brook. The second part is an open discussion of opportunities and obstacles to the realization of Data Commons as a resource shared between Biomedical Research and Clinical Operations.
Jonas S Almeida is Chief Technology Officer and Professor at the Dept of Biomedical Informatics (jonasalmeida.info). He has published over 200 manuscripts, with a h-index of 42, covering topics of machine learning and web computing in data intensive life sciences research applications. He has served in numerous NSF and NIH panel’s including charing the Data Sciences working group at NSF’s division of Biological Sciences Advisory Committee (2010-2014). His accomplishments include the development of a formal computational governance model for web-scale applications (https://en.wikipedia.org/wiki/s3db) and more recently the validation of a novel architecture for web computing that is the most highly accessed manuscript ever published in BMC Bioinformatics (www.biomedcentral.com/bmcbioinformatics/mostviewed/alltime). The machine leaning applications to autoimmune diseases includes the use of neural computing to identify proteomic biomarkers for lupus nephritis (PMID16316334). His current activity is largely driven by the opportunities for end-user facing mobile computing in Health Care
**CME Credit Available**
The School Of Medicine, State University of New York at Stony Brook, is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians.
The School of Medicine, State University of New York at Stony Brook designates this live activity for a maximum of 1 AMA PRA Category 1 Credit(s) ™. Physicians should only claim the credit commensurate with the extent of their participation in the activity. Disclosure Policy: All those in control of CME content are expected to disclose any relevant financial relationship with the provider of commercial products or services discussed in the educational presentation or that have directly supported the CME activity through an educational grant to the sponsoring organization(s). All commercial relationships that create a conflict with the planners, speakers, author’s control of content must be resolved before the educational activity occurs.
- Everyone MUST register with the CME office by using this link: https://cme.stonybrookmedicine.edu/my-cme-account/sign-up. ALL attendees are tracked by the CME office
- All CME credits are now being entered onto the online Stony Brook system.
- You will be able to view and print your credits yourself whenever you need them.