Research Interests

 

 Throughout the journey of my career, my research interest has mainly focused on all aspects of Machine Learning and Data Mining and, recently, with the emphasis on their applications in areas of bio-medical informatics and e-healthcare under IoT environment. I have been currently leading several research projects in such area funded by the Korea Research Foundation, the Korea Science and Engineering Foundation, the ETRI, the KCDC, the Ministry of Food and Drug Safety and so on. Also, another focus of my interest is to develop computational approaches for the mining of popular opinions about commercial products. such as smart-phones, spread over the interest, blogs, and etc. For this purpose, ther issues of applying machine learning techniques for text-mining are being investigated in various aspects.

 

           1. Bio-Medical Informatics

           2. Data Mining and Machine Learning

           3. Text-Mining / Opinion-Mining

 

 

1. Bio-Medical Informatics

 The field of bio-medical informatics concerns about the great use of information technology to handle a large volume of bio-medical data, such as DNP chip data, SNP genotyping data, clinical data, and etc., in an efficient way. So, it deals with the resources, devices, and methods required to optimize the acquisition, storage, retrieval, and use of information in health and biomedicine. This research can contribute to the advance in personalized medicine, new drug development, desease diagnosis and prognosis.

 

           - Cancer diagnosis and prognosis system using DNP chips/SNP chips

           - Gene network modeling using gene expression profiles

           - Computational technology for effective healthcare

           - Clinical decision support systems

           - Bio-medical image processing

 

2. Data Mining and Machine Learning

 The task of data mining is to extract significant patterns or knowledge hidden under a large volume of data. It is currently used in a wide range of fields, such as marketing, fraud detection, and scientific discovery. As a variety of data have grown in size and complexity, data mining tools that transform data into business intelligence or other valuable knowledge became of great demand. The related topics include classification, clustering, neural networks, decision tree, support vector machines, and etc.

 

           - Predictive modeling in a variety of applications

           - Deep learning

           - Interpretable machine learning

 

3. Text-Mining / Opinion-Mining

 Text mining, also frequently referred to as text data mining, refers to the process of extracting high-quality information from text. High-quality information is typically derived through the quessing of patterns and trends by using machine learning or other computational techniques along with natural language processing. Typical text mining tasks include text categorization, text clustering, sentiment analysis, document summarization, relation extraction, and etc.

 

           - Opinion mining (or sentiment analysis) of mobile phones from blogs or internet

           - Gene-gene relation extraction from bio-literature

           - Text-mining based gene raking methods for biomarker discovery

           - Text-mining techniques for healthcare provider quality determination