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Upcoming Speakers: Oct 25, 2017
Dr. Venu Govindaraju
Science of Authentication: Beyond the Physical and Behavioral Biometric Modalities
An invitation: If you are interested in presenting your work to our diverse audience of informaticists; basic, translational, and clinical researchers; software developers; and others interested in exploring the uses of informatics in cancer research, contact Eve Shalley at eve.shalley@nih.gov or 240-276-5194.
Welcome to the CBIIT Speaker Series Wiki
The NCI Center for Biomedical Informatics and Information Technology (CBIIT) Speaker Series presents talks from innovators in the research and informatics community. The biweekly presentations allow thought leaders to share their work and discuss trends across a diverse set of domains and interests. The goals of the Speaker Series are: to share leading edge research; to inform the community of new tools, trends, and ideas; to inspire innovation; and to provide a forum from which new collaborations can begin.
Speakers represent many different institutions, and the topics they address are wide-ranging. View a list of all past speakers, and view their presentations on our NCI CBIIT Speaker Series YouTube playlist!
For help accessing NCI CBIIT Speaker Series files, go to Help Downloading Files.
Location: 9609 Medical Center Drive, Rockville, Maryland 20850
Speaker Series Guidelines for Speakers: Download Word document
Questions or suggestions? If you have questions or would like to recommend a speaker, please email Eve Shalley at eve.shalley@nih.gov
Please refer to the Speaker Calendar below for upcoming speakers.
Upcoming Speakers:
October 25: Venu Govindaraju, University of Buffalo
November 8: Hugo Aerts, Dana Farber Cancer Center / Harvard Medical School
CBIIT Speakers
Given the pervasive use of e-commerce transactions and personal data storage in the cloud, society has an urgent need for a robust process that authenticates and protects the privacy and online assets of individuals and organizations. We recommend a totally new approach that rethinks the entire "science of authentication." The biometrics and cyber security communities have approached the challenge from different vantage points. The former focuses on "individuality" and "liveness" of human characteristics whereas the latter has primarily considered encryption and elaborate software protocols. This talk explores methods that go beyond the traditional biometrics of physical and behavioral modalities by integrating tests for humanness and identity in a cognitive framework. We are creating a comprehensive solution that will address a host of challenging AI problems ranging from OCR, object recognition to natural language understanding. We also will show how our holistic process allows for a more practical approach to security within the framework of a continuous authentication scenario.
Traditional biology generally looks at only a few aspects of an organism at a time and attempts to molecularly dissect diseases and study them part by part with the hope that the sum of knowledge of parts would help explain the operation of the whole. Rarely has this been a successful strategy to understand the causes and cures for complex diseases. The motivation for a systems based approach to disease understanding aims to understand how large numbers of interrelated health variables, gene expression profiling, its cellular architecture and microenvironment, as seen in its histological image features, its 3 dimensional tissue architecture and vascularization, as seen in dynamic contrast enhanced (DCE) MRI, and its metabolic features, as seen by Magnetic Resonance Spectroscopy (MRS) or Positron Emission Tomography (PET), result in emergence of definable phenotypes. At the Center for Computational Imaging and Personalized Diagnostics (CCIPD) at Case Western Reserve University, we have been developing computerized knowledge alignment, representation, and fusion tools for integrating and correlating heterogeneous biological data spanning different spatial and temporal scales, modalities, and functionalities. These tools include computerized feature analysis methods for extracting subvisual attributes for characterizing disease appearance and behavior on radiographic (radiomics) and digitized pathology images (pathomics). Unlike radiomics and pathomics which are supervised feature analysis approaches, there has also been a great deal of recent interest in deep learning which enables unsupervised feature generation. In this talk I will discuss the development work in CCIPD on new radiomic and pathomic and deep learning approaches for capturing intra-tumoral heterogeneity and modeling tumor appearance. I will also focus my talk on how these radiomic and pathomic and deep learning approaches can be applied to predicting disease outcome, recurrence, progression and response to therapy in the context of prostate, brain, rectal, oropharyngeal, and lung cancers. Additionally I will also discuss some recent work on looking at use of pathomics in the context of racial health disparity and creation of more precise and tailored prognostic and response prediction models.
When left to their own devices, scientists do a terrible job creating the metadata that describe the experimental datasets that make their way in online repositories. The lack of standardization makes it extremely difficult for other investigators to find relevant datasets, to perform secondary analyses, and to integrate those datasets with other data. At Stanford, we are leading the Center for Expanded Data Annotation and Retrieval (CEDAR), a center of excellence in the NIH Big Data to Knowledge Program, which has the goal of enhancing the authoring of experimental metadata to make online datasets more useful to the scientific community. CEDAR technology includes methods for managing a library of templates for representing metadata, and interoperability with a repository of biomedical ontologies that normalize the way in which the templates may be filled out. CEDAR uses a repository of previously authored metadata from which it learns patterns that drive predictive data entry, making it easier for metadata authors to perform their work. Ongoing collaborations with several major research projects are allowing us to explore how CEDAR may ease access to scientific data sets stored in public repositories and enhance the reuse of the data to drive new discoveries.
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.
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