Research

Data Discovery Center

The Data Discovery Center, led by Prof. Gunwoon Park, sits within IDIS and manages the project “Data Discovery Methodology Development”, which aims to revolutionize the way scientific discoveries are made by leveraging advanced data analysis techniques. This initiative focuses on the following key areas:
Key Objectives and Activities:
1. Data-Driven Scientific Discovery:
  • Development of methodologies to efficiently analyze and understand vast amounts of data, enabling the extraction of new knowledge and insights.
  • Application of these methodologies to solve complex scientific problems across various fields, promoting collaborative research efforts.
2. Research Goals:
  • Exploration of the fundamental principles of data science, including mathematical, computational, and inferential theories, to develop and share new analytical methodologies.
  • Providing theoretical and methodological foundations for data-driven research across all natural sciences, offering new perspectives for scientific inquiry.
  • Development of a system for sharing newly developed analytical methodologies.
Research Groups and Sub-Projects:

1. Advanced Analysis Method Development Team (Gunwoong Park):

A-1-1:

Applied Statistics (AI/Big Data Statistical Analysis): Development of methodologies to analyze data generation mechanisms. (Gunwoong Park, LAMP faculty)

A-1-2:

Probability/Theoretical Statistics (Statistical Learning Theory): Development of methodologies for analyzing non-Euclidean data. (Sungkyu Jung, LAMP faculty)

A-1-3:

Applied Statistics (Medical/Biostatistics/Survival Analysis): Development of causal inference methodologies. (Kwonsang Lee, LAMP faculty)

A-1-4:

Applied Mathematics (AI/Machine Learning): Research on epistemology and ethics in data science. (Hyundeuk Cheon, LAMP faculty)

2. Natural Science Data Analysis Team (Seung-Sup B. Lee):

A-2-1:

Statistical Physics/Biophysics/Complex Physics (Statistical Mechanics): Modeling of rare events based on diffusion models. (Yongjoo Baek, LAMP faculty)

A-2-2:

Algebra (Number Theory): Modeling of arithmetic data. (Dohyeong Kim, LAMP faculty)

A-2-3:

Physics (Strongly Correlated Systems): Development and application of quantum many-body calculation methodologies. (Seung-Sup B. Lee, LAMP faculty)

A-2-4:

Organic Chemistry (Organic Synthesis Methodology): Modeling of catalytic organic reactions. (Seung Youn Hong, LAMP faculty)

A-2-5:

Applied Statistics (AI/Big Data Statistical Analysis): Development and application of Pareto front exploration methodologies for molecular generation. (Min-hwan Oh, LAMP faculty)

This comprehensive project is supported by the SNU Center for LAMP project and aims to establish a robust framework for data-driven research, facilitating new discoveries and advancements in natural sciences through innovative data analysis techniques and interdisciplinary collaboration.