Hojung (Ashley) Kwon

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About Me

I am a computer science PhD student at Brown University. Previously, I received my undergraduate degrees at Duke University in computer science (BS) and art history (AB). My research interests include augmented reality (AR) and virtual reality (VR) technologies, digital interface designs using intuitive metaphors, computer science education for marginalized students, and novel uses of game engines. To learn more about my projects in interdisciplinary areas, bridging computer science to art history, social science, and mathematics, please see my portfolio below.



Digital Restoration of Early Renaissance Paintings

Virtual restoration project for generating 3D models of restored Early Renaissance Italian panel paintings displayed at the North Carolina Museum of Art (NCMA)

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Pigment Separation Using X-Ray Elemental Maps of a Painting

Design project for an algorithm that can separate restored areas in a painting from its original areas and generate images showing areas where specific pigments were used

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User Movement Characterization in VR Systems

Data collection project for extracting characteristics of users' translational and rotational movements in VR systems

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AR-based Labeling for DNN Fine-tuning

A smartphone AR application where a user and a fine-tuned Faster RCNN model collaboratively generate image labels for a training dataset for a deep neural network.

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Visualization of Author Collaboration and Publication Relationships – Insight Engine 2.0

An interface built with the Unity game engine that represents author collaboration relationships and publications data extracted from more than 2 million papers published in the ACM journal until 2016

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Poly-association Generator Using a Knowledge Graph Structure

A website that helps users visualize connections between two or more concepts related to diverse disciplines, including art history and philosophy

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Contemporary Art Recommendation System

A website that recommends paintings of contemporary artists that were born in the 1980s or later based on users' preferences in extrinsic and intrinsic characteristics of artworks to raise awareness about emerging artists' work

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