Angelica Chen

Angelica Chen

New York, New York, United States
732 followers 500+ connections

About

PhD student at NYU Center for Data Science studying deep learning for natural language…

Experience

  • Prescient Design Graphic

    Prescient Design

    New York, New York, United States

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    New York, New York, United States

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    New York, New York, United States

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    Greater New York City Area

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    Greater New York City Area

Education

Volunteer Experience

Publications

  • Reasoning from Radically Incomplete Information: The Case of Containers

    Advances in Cognitive Systems

    In physical reasoning, humans are often able to carry out useful reasoning based on radically
    incomplete information. One physical domain that is ubiquitous both in everyday interactions and in
    many kinds of scientific applications, where reasoning from incomplete information is very common,
    is the interaction of containers and their contents. We have developed a preliminary knowledge base for qualitative reasoning about containers, expressed in a sorted first-order language of…

    In physical reasoning, humans are often able to carry out useful reasoning based on radically
    incomplete information. One physical domain that is ubiquitous both in everyday interactions and in
    many kinds of scientific applications, where reasoning from incomplete information is very common,
    is the interaction of containers and their contents. We have developed a preliminary knowledge base for qualitative reasoning about containers, expressed in a sorted first-order language of time, geometry, objects, histories, and events. We have demonstrated that the knowledge suffices to justify a number of commonsense physical inferences, based on very incomplete knowledge.

    Other authors
    • Ernest Davis
    • Gary Marcus
    See publication
  • Development of an Automatic Algorithm for 3-Dimensional Aortic Annular Measurements for Prediction of Transcatheter Aortic Valve Replacement Outcome

    Institute of Electronical and Electronics Engineers

    2-dimensional aortic annulus measurements such as average diameter, circumference, and area are significant predictors of post-procedural paravalvular regurgitation in transcather aortic valve replacement (TAVR) procedures. In this paper, we propose an efficient automatic algorithm for obtaining these measurements that uses a modified ellipse-detection form of the Hough transform algorithm. The algorithm is tested using data from 31 TAVR patients. The results demonstrate that our algorithm is…

    2-dimensional aortic annulus measurements such as average diameter, circumference, and area are significant predictors of post-procedural paravalvular regurgitation in transcather aortic valve replacement (TAVR) procedures. In this paper, we propose an efficient automatic algorithm for obtaining these measurements that uses a modified ellipse-detection form of the Hough transform algorithm. The algorithm is tested using data from 31 TAVR patients. The results demonstrate that our algorithm is comparable to manual radiologist measurements and offers a fast and consistent method for the quantitative assessment of the aortic annulus.

    Other authors
    • Ming Jack Po
    • Andrew Einstein
    • Omar Khalique
    • Andrew Laine
    See publication

Honors & Awards

  • Nominated to Sigma Xi, scientific research honor society

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  • Outstanding Computer Science Senior Thesis Award

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  • Princeton Innovation 25 Under 25

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    http://princetoninnovation.org/25u25/

  • NSF GRFP Honorable Mention

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