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Project

We have been involved in numerous data mining & machine learning research and industrial projects. We are especially interested in working with companies or organizations with large data that are in need of data mining or machine learning solutions. Please, contact me to determine if such technology would be beneficial to your organization. The followings are examples of recent industrial projects.
  • 데이터마이닝과 딥러닝을 이용한 네트워크 장애 예측 및 분석
  • 지능형 에이전트를 위한 추천 엔진 개발
  • 진동 소음 예측 및 원인 인자 추출 및 분석
  • 딥러닝을 이용한 멀티모달 데이터 통합 학습/모델링 연구
  • 온라인 커뮤니티 댓글 감성 분석
  • Time-series 센서 데이터 마이닝
Below is the list of representative national research projects.

Development of Decision Support System SW based on Next-Generation ML (SW StarLab)

posted Jun 14, 2018, 5:28 AM by Dongha Lee   [ updated Jul 16, 2018, 5:10 AM ]


2018.04.01 - 2025.12.31 (8 years). This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No.2018-0-00584)

In this project, we aim to develop a novel decision support system, METIS, which is short for ML-based Decision Support Information System. Based on the next-generation machine learning, it provides selected information that users really need (i.e., supports users’ decisions) by incorporating a variety of heterogeneous data into a unified modeling framework. Our decision support system has four important and differentiated features compared to existing systems and generic technologies. (1) It supports easy and effective modeling on large-scale heterogeneous data based on the integrated modeling framework. It flexibly constructs models suitable for target domains, target applications, target services, and target data and learns them efficiently. (2) It manages dynamic data and models by using incremental learning. In other words, it efficiently reflects the data accumulated over time in the model at the previous time-stamp. (3) It preserves the privacy of user data by using federated model learning. It trains global models and improves their performances by considering a lot of user data without accessing users’ local data. (4) It provides good scalability and efficiency by fully utilizing limited resources. In terms of model learning, it efficiently processes large-scale data which conventionally can not be handled in a local device, where computation and memory resources are limited.

Click here for more information: METIS

Development of Integration and Inference Technology over Web-scale Complex Data

posted Jun 13, 2018, 10:45 PM by Sangjun Koo   [ updated Jun 13, 2018, 10:48 PM ]

This research was supported by Next-Generation Information Computing Development Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT (NRF-2017M3C4A7063570)

Data on the web are not only large-scale, but also extremely high-dimensional, highly multi-class and heterogeneous types. In addition, most web data are dynamically changing over time and are not structured. We define data with such characteristics as “Big Complex Type Data.” Since Big Complex Type Data on the web consist of various information, it is challenging to analyze them. Thanks to the recent technology improvements, the methods to analyze Big Complex Type Data are now being available. Our goal is to invent strong methods for mining Big Complex Type Data in order to help develop the technologies to extract information from Big Complex Type Data, to integrate the extracted information and to generate knowledge based on inference on the integrated data.

MELOW: Machine learning framework for Embedded LOW-power system

posted Mar 31, 2017, 1:24 AM by 김병주

2016.12.01~2020.11.30 (4 years). This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP) (No.2016R1E1A1A01942642).

This project aims for development of machine learning technology optimized for low-power embedded system. Currently researchers tend to use complex models to obtain highly accurate models via machine learning from big data. However, the training process consumes more power as the size of model increases, which becomes the main obstacle for utilizing machine learning in embedded system with limited power. Therefore, this project suggests developing machine learning technology minimizing power consumption during training while keeping accuracy of the model by exploiting various techniques such as model compression, utilizing various computing resources(flash memory, GPU), and ultimately the framework (MELOW: Machine laerning for Embedded LOW-power system)combining those methodologies. Furthermore, this project will show the practicability of MELOW by developing machine learning applications running on the MELOW framework.

Development of Enabling Software Technology for Big Data Mining

posted Jan 2, 2013, 9:35 AM by Hwanjo Yu   [ updated Jan 2, 2013, 9:38 AM ]

2012.07.01 - 2017.06.30 (5 years). This research was supported by Next-Generation Information Computing Development Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education, Science and Technology (No. 2012M3C4A7033344) 

The goal of this project is the development of enabling software technologies for big data mining. Through this project, we will research data mining techniques for big data in natural sciences and social networks. We will also develop personalized service technologies based on unstructured big data analysis and customer behavior models. Furthermore, we will produce well-trained software engineers who are experts in big data mining.

Developing Search and Mining Technologies for Mobile Devices (*우수 국과 과제 선정)

posted Jan 2, 2013, 9:28 AM by Hwanjo Yu   [ updated Mar 10, 2017, 1:05 PM ]

2011.05.01 - 2014.04.30 (3 years). This work was supported by the Brain Korea 21 Project in 2010 and Mid-career Researcher Program through NRF grant funded by the MEST (No. KRF-2011-0016029).
 

Combining the highly profitable information search industry and the mobile computing paradigm, mobile information search industry has been growing rapidly despite the global economy recession. Thus, development of mobile search technology will impact on the economy positively. This project aims at advancing the technologies in the areas of mobile search and mining, low-power consumption utility mining, and mining for mobile online advertizing.

User-Friendly Search Engine for PubMed

posted Jan 2, 2013, 9:27 AM by Hwanjo Yu   [ updated Jan 2, 2013, 9:27 AM ]

2009.05.01 - 2012.02.28 (3 years). This work was supported by the Brain Korea 21 Project and Mid-career Researcher Program through NRF grant funded by the MEST (No. KRF-2009-0080667).


PubMed MEDLINE, a database of biomedical and life science journal articles, is one of the most important information source for medical doctors and bio-researchers. Finding the right information from the MEDLINE is nontrivial because it is not easy to express the intended relevance using the current PubMed query interface, and its query processor focuses on fast matching rather than accurate relevance ranking. This project develop techniques for building a user-friendly MEDLINE search engine.

Novel Recommendation for Digital TV

posted Jan 2, 2013, 9:26 AM by Hwanjo Yu   [ updated Jan 2, 2013, 9:26 AM ]

2011.02.01 - 2012.01.31 (1 years). This work was supported by Samsung Electronics.

Existing recommendation systems (e.g., the Netflix competition) focus on an accurate prediction of purchase, as the systems are evaluated based on the prediction accuracy. However, such systems tend to recommend popular items. Recommending popular items, however, might not be effective or affective on users' purchase decisions, as users likely already know the items and likely have pre-made decisions on the purchase of items, e.g., recommend to watch Star Wars or Titanic. Effective recommendation must recommend unexpected or novel items that could surprise users and affect users' purchase decision. This project is to develop an effective recommendation for digital TV customers.

Feature Weighting for Ranking

posted Oct 11, 2011, 9:58 PM by Hwanjo Yu   [ updated Jan 2, 2013, 9:31 AM ]

This work is supported by MSRA (Microsoft Research Asia).
 
Feature weighting for ranking has not been researched as extensively as for classification.  This project develops various feature weighting methods for ranking by leveraging existing methods for classification. The developed methods are used on the Live Search query log data to identify key features that determine the users’ click-through behaviors. The developed methods will also be used to build the feature selection component of RefMed -- relevance feedback PubMed search engine.

Enabling Relevance Ranking in Databases for User-Friendly Data Retrieval

posted Aug 8, 2011, 4:26 AM by Hwanjo Yu   [ updated Aug 8, 2011, 4:33 AM ]

2008.07.01 - 2011.06.30 (3 years). This work is supported by the Brain Korea 21 Project and the Korea Research Foundation Grant funded by the Korean Government (KRF-2008-331-D00528).

    Most online data retrieval systems, built based on relational database management systems(RDBMS), support fast processing of Boolean queries but offer little support for relevance or preference ranking. A unified support of Boolean and ranking constraints in a query is essential for user-friendly data retrieval. This project develops foundational techniques that enable such data retrieval systems in which users intuitively express ranking constraints and the system efficiently process the queries.

    Development of Kernel Based Real-time Recommender System through Structured Web Data Analysis

    posted Sep 4, 2010, 5:51 PM by Hwanjo Yu   [ updated Aug 8, 2011, 4:34 AM ]

    With Prof. Jaewook Lee (PI), 2008.07.01 - 2010.06.31 (2 years). This work was supported by the Brain Korea 21 Project and the Korea Research Foundation Grant funded by the Korean Government (KRF-2008-314-D00483).

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