빅데이터 처리 및 데이터베이스 연구실 Big Data & Database Lab

How to store well and find well

The Big Data Processing and Database Laboratory revisits the founding question of databases - how to store data well and find it well - at the scale and in the forms data takes today. It studies clustering algorithms, an unsupervised learning technique, for analysing big data across many fields efficiently, and develops mining techniques for the large graph data generated on social media such as Twitter, Facebook and Instagram. Indexing techniques and query processing optimisation for searching large multi-dimensional spatial data are also central subjects. More recently the laboratory has extended to indexing and query processing suited to searching geospatial data stored on a blockchain.

정보보안 연구실 Information Security Lab

Detecting software faults and vulnerabilities automatically

Software is used across every field today, and the errors and security vulnerabilities inside it can cause serious incidents and damage. The Information Security Laboratory studies how to detect such errors automatically and remove them in advance. It uses static analysis, which approximates a program's behaviour and predicts errors without running it, and fuzz testing, which finds errors by running a program repeatedly on varied inputs, to detect faults in software from many domains. Recently it has been combining the strong code understanding of large language models with program analysis and testing to open new possibilities in software error detection.

인간 중심 AI 및 시각화 연구실 Human-Centered AI & Visualization Lab

Making the answers of AI understandable to people

However good an answer artificial intelligence produces, it does not lead to a real decision unless a person can understand and trust it. This laboratory works where human-centered AI meets information visualisation, studying interactive systems that let people and AI reach better judgements together. Combining visual analytics with HCI methodology, it turns complex data into a form people can read, and explores situated analytics in which analysis happens naturally within the physical and occupational context a user is in. It is one of the most interdisciplinary areas in the College of Computing, demanding technical rigour in computer science and an understanding of people at the same time.