Software Development

Visualizing Local Information Inequality in South Korea: An AI-Based Approach Using Public Library Data

The goal of the project is to understand information inequality across geographical regions in South Korea and visualize them using an AI-backed visualization tool. Our plan, spanning three years, revolves around the development of an intuitive platform for the purpose of visualizing these disparities. During the first year, we aim to construct comprehensive metrics for assessing the level of informational inequality, based on the theory of local information landscapes (LIL theory).

Exploring How Convergence Methods Foster Shared Accountability to Reveal, Map, and Mitigate the Sources and Dynamics of Bias across Social Service Provisioning Systems

Social services, traditionally, have been organized around their missions, such as education or safety or health. A newer approach, called "wrap-around services" or "systems of care," organizes services around individuals and their specific context and needs. These systems face many challenges when applied in real-world settings. Application processes often focus more on the potential of technologies and less on the realities, histories, and needs of communities. The proposed research addresses this gap by evaluating the implementation of a system of care in a real-world setting.

Fundamental Technologies for the Multi-Scale Mass-Deployable Cooperative Robots

"Multi-scale mass-deployable cooperative robots are a next-generation robotics paradigm where a large number of robots that vary in size cooperate in a hierarchical fashion to collect information in various environments. While this paradigm can exhibit the effective solution for exploration of the wide area consisting of various types of terrain, its technical maturity is still in its infant stage and many technical hurdles should be resolved to realize this paradigm."  (Choo et al., 2013)

Heuristics for Assessing Computational Archival Science (CAS) Research: The Case of the Human Face of Big Data Project

Computational Archival Science (CAS) has been proposed as a trans-disciplinary field that combines computational and archival thinking. To provide grounded evidence, a foundational paper explored eight initial themes that constitute potential building blocks. In order for a CAS community to emerge, further studies are needed to test this framework. While the foundational paper for CAS provides a conceptual and theoretical basis of this new field, there is still a need to articulate useful guidelines and checkpoints that validate a CAS research agenda.

Cycle Atlanta: Seeing Like a Bike

The Cycle Atlanta project aims at creating sensor systems that allow a bike to "see" its environment and collect data as a participatory effort so that we can help the City of Atlanta to make informed decisions about biking infrastructures. Specifically, a sensor box equipped with sonars, lidars, PM sensors, gas sensors, gyroscope, accelerometer, and others was developed to detect environmental factors that can give rise to cyclists' stress level. I participated in this project as a Data Science for Social Good (Atlanta's DSSG) Summer fellow in 2017.

Remapping Southside Community: Storytelling Urban Renewal Impact

This project aims to build a map-based platform that presents historical documents of the nation-wide, urban renewal project in 1960's and 70's and to provide easy-to-use interfaces that can be used by former residents, archivists, researchers, and citizens. Ultimately, this platform aims to reconstruct a virtual neighborhood where people can share their memories by creating social networks of former residents.

Mapping Inequality

Over the past four years, teams of archivists have digitized portions of National Archives RG195: General Records of the Home Owners’ Loan Corporation [HOLC]. The records include surveys, memorandums, and maps of American neighborhoods in the 1930’s. Now that the records are available digitally, students and faculty members are working to curate the collection and “data-fy” the information contained within.

MapReduce Framework for Swarm Robot Systems

This was a part of research projects conducted under the Basic Research Laboratory Grant from Ministry of Education, Science, and Technology in South Korea. This project is two folds: (1) simulating an application of swarm robot systems; (2) designing a software framework for the swarm robot systems to reduce the complexity of developing applications while minimizing the amount of transmitted data by adopting MapReduce paradigm. The video above is a simulation of a swarm robot system application that searches for red pillars (foraging).

Image Retrieval Systems based on Color Similarity and Edge Detection

This project was conducted in 2008 for my bachelor's thesis in the Department of Electrical Engineering at Seoul National University (it was more like a capstone project rather than a thesis, since the focus of the project was mainly at implementing algorithms rather than analyzing the performance of algorithms using concrete measures, e.g., recall and precision). I implemented an image retrieval system prototype that takes an image as input, and outputs most similar images from the image database.

Open Data Impact Map

The Center for Open Data Enterprise is a non-profit organization that aims to maximize the value of open data as a public resource that anyone can use. As a means to promote the impact and value of using open data, the center designed and developed the Open Data Impact map. As a Data Science & Technology Fellow at the Center for Open Data Enterprise, I have worked on the Open Data Impact Map, which is a searchable, centralized database of open data use cases from around the world. The map shows the distribution of organizations in the world that make use of open data.

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