PROJECTS
Decision Intelligence: Computational Linguistic Modeling and Evaluation
funded by NRF of Korea
2024.06.01 ~ 2027.05.31
PI: Keeheon Lee
Past Meets Future: Deep Learning Innovations in Historical Map Digitization and Infrastructure Insights
This research project proposes a novel approach to accelerating the addition of geospatial and historical data to the Open World Atlanta (OWA - atlanta.urbanspatialhistory.org) interface, a web-based platform that makes accessible information drawn from historical maps, city directories, archival collections, newspapers, web resources, and census data. We will leverage tools from Artificial Intelligence, geospatial analysis, machine learning, and usability studies to develop new approaches and methodologies for data-rich, collaborative research projects. Specifically, our proposed project utilizes machine learning and student-researcher involvement to develop more efficient map processing workflows through four distinct steps: Map Restoration, Intelligent Infrastructure Recognition System, Historical Map Augmentation, and the creation of User-Centric Historical Maps. This partnership brings together an interdisciplinary team of faculty and staff experts from Emory and Yonsei University while also giving opportunities to graduate and undergraduate students to research and publish. Harnessing the Emory-Yonsei team’s combined expertise in machine learning and geospatial mapping technologies will not only build out OWA’s collections to include current bodies of information, but will also better position future teams to expand the body of work. We hope this current phase will result in an exponential increase in data processing efficiency and speed, the streamlining of which has the potential to revolutionize the current workflow with AI capabilities in a way that other digital mapping or archival projects can then replicate.
funded by Emory University-Yonsei University Collaborative Research Grants
2024.03.01 ~ 2026.2.28
PI: Keeheon Lee
Communitas America CASA Project
Information extraction, analysis, visualization of speech data from entrepreneurship program participants in Bronx, NYC.
funded by Communitas America
2021.04.01 ~ 2022.1.31
PI: Keeheon Lee
한국경제발전 논문 데이터세트(Dataset) 구축 및 분석
Information extraction and visualization
funded by KDI
2021.08.01 ~ 2021.12.31
PI: Keeheon Lee
의학 관련 문서로부터 원자료 추출 및 민감도 분석 알고리즘과 소프트웨어 개발 (위탁)
Information extraction and simulation
funded by Ministry of SMEs and Startups, Korea
2021.09.01 ~ 2022.08.31
PI: Keeheon Lee
Big knowledge driven automatic hypothesis inference for new bio-knowledge discovery
funded by NRF of Korea
2021.03.01 ~ 2022.02.29
PI: Min Song, Co: Keeheon Lee
Human-Object Interaction-based Visual Understanding for Smart Work
funded by NRF of Korea
2021.06.01 ~ 2024.02.29
Keywords: Smart Work, Human and Object Interaction Recognition, Visual Understanding, Knowledge Graph, Graph Mining, Optimization
PI: Keeheon Lee
Road Condition Detection and Classification using Deep Learning
July 2019 - August 2019
PI: Keeheon Lee
Towards a Visual Analytics Approach to the Historical Mapping of Cities
funded by Emory-Yonsei Collaborative Research Grants
Nov. 2018 - Current
PI: Younah Kang, Co: Keeheon Lee
Data Science for Measuring and Evaluating the Scholarly Impact of SDGs
funded by Institute for Global Engagement and Empowerment
Sept. 2018 - Current
PI: Semee Yoon, Co: Keeheon Lee
Technology-Society Interaction Data Science: Generative Adversarial Scenario Model for Strategic Planning
funded by National Research Foundation of Korea
Mar 2017 - Current
PI: Keeheon Lee
A Study on Knowledge Data Representation and Analysis for Big Data-driven Future Studies/ funded by Yonsei University
May 2017 - Current
PI: Keeheon Lee
Big Data Analysis for The Historical and Social Origin of Territorial Dispute/ funded by Northeast Asian History Foundation
June 2017 - Current
PI: Whasun Cho Co: Keeheon Lee, Joonsuk Yang, Byung-Jae Lee
RESEARCH EXPERIENCE
Opiniomics: Analysis and Prediction on Dynamic Public Opinion in Smart Society / funded by Yonsei University Future-leading Research Initiative of 2014
Nov 2014 - Feb 2015
Developed a big data based model for public opinion evolution. Raised the main idea that opinion evolves such as biological evolution in gene level. Developing this model as a new perspective of public opinion research.
Opiniomics: 스마트 사회에서 생동하는 여론 분석 및 예측 (Opiniomics: Analysis and prediction on dynamic public opinion in smart society), 연세대학교, 2014.10~2016.09
Acropolis 3.0: Big Data Public Opinion Analysis Framework / funded by National Research Foundation of Korea
Jul 2014 – Aug 2015
Developed a new big data analytics framework for public opinion.
빅데이터 여론 분석 프레임워크 (아크로폴리스 3.0) 개발을 위한 사전 연구, 한국연구재단, 2014.09~2015.08
Agent-based Modeling of Korean Economy / funded by SCIENCE AND TECHNOLOGY POLICY INSTITUTE (STEPI)
Sept 22, 2014
Consulted, as an expert, the agent-based modeling of Korean economy built by IIASA and STEPI.
Satellite Information Application Industry of Korea / funded by KOREA INSTITUTE OF SCIENCE AND TECHNOLOGY EVALUATION AND PLANNING (KISTEP)
Aug 2014
Built scenarios for satellite information application industry of Korea.
Science and Technology Classification for Advisory Support / funded by Korea Institute of Science and Technology Information (KISTI)
Jul 2014- Sept 2014
Developed a new method for science and technology classification.
Big Data Policy Foundation Construction / funded by Ministry of Trade, Industry and Energy of Korea
Jul 2013-Jan 2014
Developed big data mining service for logistics and supply chain management.
빅데이터 여론 분석 프레임워크 (아크로폴리스 3.0) 개발을 위한 사전 연구, 한국연구재단, 2014.09~2015.08
Modeling Virtual Markets with Consumers: Socio-Demographic Properties and Social Network: Application to Product Portfolio Optimization / funded by National Research Foundation of Korea
Sept 2012 – Aug 2014
Raised the core idea and proposed the idea as a research proposal. Developed heterogeneous agent-based simulation for product portfolio optimization.
소비자의 사회 인구 통계학적 특성과 사회연결망을 반영한 가상 시장 모델과 혁신제품 포트폴리오 최적화(Modeling Virtual Markets with Consumers’ Socio-Demographic Properties and Social Network: Application to Product Portfolio Optimization), 한국연구재단, 2013.06~2016.05
Social Scientometrics: Social and Psychological Research / funded by National Research Foundation of Korea
Sept 2012-Feb 2015
Raised the whole idea and proposed the idea as a research proposal. Developed agent-based simulation for forecasting a new research area.
소셜 과학 계량학: 사회심리적 연구자 다이나믹스 시뮬레이션(Social scientometrics: social and psychological research dynamics simulation), 한국연구재단, 2012.9~2015.8
Large-scale Agent-based Distributed Simulation for Product Diffusion and Optimization / funded by National Research Foundation of Korea
May 2010-Apr 2013
Raised the core idea and proposed the idea as a research proposal. Developed a system that has many computers to run agent-based product diffusion models in distributed manner.
신제품 확산예측 및 최적화를 위한 대규모 에이전트 기반 분산 시뮬레이션(Large-scale agent based distributed simulation for product diffusion and optimization), 한국연구재단, 2010.5~2013.4
A study on Analytical Hierarchical Process and Analytical Network Process / funded by Korea Institute of Science and Technology Evaluation and Planning (KISTEP)
Sept 2012–Nov 2012
Developed an analytical network process for an administrative preliminary assessment.
Product Diffusion Simulation and Optimal Product Design Based on Consumer Social Network / funded by Korea Foundation for the Advancement of Science & Creativity
May 2012-Oct 2012
Developed an agent-based product diffusion model to simulate behavioral consumers and optimize product attributes for the consumers. Guide/assist undergraduate students to participate in a research on product diffusion simulation.
소비자 사회 연결망에 근거한 제품 확산 시뮬레이션 및 최적 제품 속성 설계 (Product diffusion simulation and optimal product design based on consumer social network), 한국과학창의재단, 2012
Heterogeneous Consumer-Agent Network Simulation Model for New Product Diffusion: Application to Price and Supply Volume Strategy / funded by Korea Sanhak Foundation
Jun 2008-Jul 2009
Developed an agent-based product diffusion model and determine the price and the supply volume that maximizes the expected profit.