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Current Projects

We are conducting multiple projects.

CURRENT PROJECTS

  • Development of Experiential Knowledge Platform

The goal of this project is to develop a knowledge management platform that digitalizes and manages experiential knowledge of field experts in order to support mission-critical decision making. More specifically, this project aims at building a self-evolving experiential knowledge platform, which minimizes the involvement of knowledge expert by interacting with field experts and supports their decision making by exploiting expertienal knowledge automatically obtained through the self-learning process. The project is funded for three years from 2015 to 2018.

  • Big-data Analytics

The goal of this project is to develop a predictive model of the current value of real estate properties. The project involves big-data processing, machine learning algorithm design, and predictive model evaluation. Working with a government-sponsored company, the project is expected to radically transform the current dominant practice of real estate valuation, mostly dependent on laborious human efforts. The project is funded for 2016 to 2018.

  • Next Generation Virtual Assistant’s Intelligence Modeling

The goal of this study is to support the effective interaction between people and Internet of things based on object and connected intelligence. This project aims at exploring case studies for UX frameworks based on object and connected intelligence, and modeling UX-based user profile elements for effective human-object interaction. We also provide a UX research methodology that is suitable for object and connected intelligence to other researchers. The project is funded for 2017.

  • Movie Recommender Systems

The goal of this project is to develop a movie recommender system for SK broadband, Oksusu. The project incorporates various techniques such as NLP, text-mining and deep learning. By utilizing the textual contents and metadata related to the movie, the project is expected to provide a reliable and reasonable recommendation to the user. In addition, the project suggests a customized recommender system by using the hybrid recommender system based on deep learning algorithms. The project is funded for 2017.

  • Evaluator Recommender Systems

Based on NRF's research related to Big Data such as research pagers and patent documents, we are developing a new system that automatically recommends appropriate research project reviewers by applying machine learning and natural language processing algorithms. We analyze structured and unstructured research-related big data, make prototypes, and check the validity of the system that we developed. The project is funded for 2017.

  • Phone: +82 42 350 1602
    Fax: +82 42 350 1610

  • Location: Room 1201, Bldg. E2-1, KAIST 291 Daehak-ro, Yuseong-gu, Daejeon 305-701

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