October 11: Anant Madabhushi, Case Western Reserve University
October 25: Venugopal Govindaraju, University of Buffalo
November 8: Hugo Aerts, Dana Farber Cancer Center / Harvard Medical School
The growing number of uses for artificial intelligence (AI), machine learning (ML) and deep learning (DL) continues to drive the development of cutting-edge technology solutions. Biomedical research and medical care are fields that are poised to be dramatic change as they start to integrate computer vision, predictive modeling, natural language understanding, and recommendation engines within standard practice. In this talk, we will review why AI and ML are hard problems to tackle, describe some cutting edge examples in biomedical research and other industries that are applying these techniques to create materially better solutions, and then dive into the details of the family of intelligent services at AWS that provide cloud-native machine learning and deep learning technologies to address a wide range of research needs. We will focus specifically on deep learning applications and products, such as the AWS Deep Learning AMI lets you run deep learning in the cloud, at any scale. Whether you’re just getting started with AI or you’re a deep learning expert, this session will provide a meaningful overview of how to improve scale and efficiency with the AWS Cloud.
Modern information systems, storage devices and recording formats have led to unprecedented growths in scientific and social data. These advancements have resulted in the Big Data (BD) paradigm – enormous data collection for processing and analyses that can provide new information not otherwise gleaned from smaller disparate data collections.
This presentation will discuss the Open Archival Information System (OAIS) reference model, to address challenges posed by BD. Examples from Earth Observing Systems and Biomedical research systems will be shown to elucidate the OAIS. An integrated reference architecture for BD life cycle management will be presented.
Intelligent biomedical archives (IBA) concept and characteristics that differentiate IBA from traditional archives will be highlighted. A functional view of the IBA will be presented for increasing transformation of data to knowledge. Scenario-based examples from biomedical research will be provided to stimulate discussion on approaches to operationalize IBA. A vision for developing true knowledge building systems for biomedical research will be shared.
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