Our laboratory conducts research in Data Engineering, including databases, data mining, information storage, data privacy, machine learning, generative AI, and the analysis of real-world data. Research in our laboratory does not always begin with a predefined topic.
It can start from either: a methodological problem that you want to solve, or an interesting type of data that you want to understand.

Two Ways to Start a Research Project

Method-first: Start from a technical problem

A research project may begin with a methodological question such as:

  • How can we efficiently discover meaningful patterns from large-scale data?
  • How can missing data be reconstructed in an explainable and reliable way?
  • How can we search for similar data while preserving privacy?
  • How can we trace which data, documents, or evidence were used to generate an AI response?
  • How can large-scale data be managed efficiently, reliably, and with lower resource consumption?

From such questions, we develop new methods, algorithms, or systems and evaluate them using real-world datasets.

Methodological Problem
→ New Method / System
→ Evaluation with Real-world or Synthetic Data

Data-first: Start from interesting data

Research can also begin with a type of data that interests you.

Examples include:

  • Medical and healthcare data
  • Music
  • Images
  • Movies and video
  • Dance and human motion
  • Theatre and performing arts
  • Web and social media data
  • Sensor data
  • Other types of real-world data

You might begin by asking:

What can we learn from this data?
What cannot be done well with existing methods?

From there, we identify a research problem and develop a data-engineering approach to address it.

Interesting Data
→ Research Question
→ Data Engineering Method
→ New Knowledge / System

You do not need to work with the same type of data that is currently used in the laboratory.

How We Conduct Research

Research topics are developed through discussions between students and faculty. A typical research process is:

Interest or Problem
→ Literature Review
→ Research Question
→ Method Design
→ Implementation and Experiments
→ Analysis
→ Paper and Presentation

The goal is not simply to build a program or apply an existing method. We place particular emphasis on clearly answering questions such as:

  • What is the research problem?
  • Why are existing approaches insufficient?
  • What is new in the proposed approach?
  • How much does the proposed method improve the situation?
  • To what extent does it solve the original research problem?

For Undergraduate Students

Undergraduate research focuses first on learning how research is conducted. You do not need to have a complete research topic or advanced technical skills when you begin. Students who are interested in areas such as:

  • Databases
  • Data analysis
  • Programming
  • AI and machine learning
  • Multimedia data

are encouraged to visit the laboratory and discuss possible topics. You may join an ongoing research project, or you may develop a new topic based on your own interests. Through undergraduate research, we hope students experience what it means to:
investigate a question whose answer is not yet known, develop a possible solution, and evaluate it through evidence.

For Master’s Students

At the Master’s level, students are expected to investigate a more clearly defined research problem. Rather than simply applying an existing technique to a dataset, we aim to:
identify a limitation of existing research and propose a new idea, method, or system that addresses it.
Possible research areas include:

  • Data mining algorithms
  • Large-scale data management
  • Explainable data analysis
  • Information retrieval and RAG
  • Data privacy
  • Information storage
  • Medical data engineering
  • Cultural and multimedia data
  • General-purpose data engineering methods

Students are strongly encouraged to present their research at domestic and international conferences. Applicants from other universities are also welcome. If you are considering applying to our Master’s program, we recommend reviewing our research topics and contacting us before the entrance examination to discuss your interests.

For PhD Students

At the PhD level, the central question becomes: What new knowledge, method, algorithm, or system can your research contribute to the academic community? Potential directions include:

  • Novel data management architectures
  • New data mining and retrieval algorithms
  • Trustworthy and explainable data management
  • Privacy-preserving data systems
  • Reasoning RAG and evidence-aware AI
  • Data provenance and traceability
  • Data engineering based on large-scale real-world datasets

PhD students are expected to develop their own research direction and disseminate their results through international conferences and journals. If you are interested in pursuing a PhD in our laboratory, we recommend contacting us before applying. It is helpful if you can provide:

  • A brief description of your previous research
  • Your Master’s thesis or a representative publication
  • A rough idea of the research direction you would like to pursue during your PhD

You do not need to have a fully completed research proposal before contacting us. We can discuss how your interests may be developed into a research problem suitable for doctoral study.

Life in the Laboratory

We do not have fixed core working hours, but we place strong emphasis on regular research discussion and interaction among laboratory members. We generally hold laboratory meetings twice a week.

During the first semester, these typically include:

  • Reading and discussing books or papers on current topics in data engineering
  • Research progress presentations

During the second semester, greater emphasis is placed on research progress and discussion. The topics selected for reading are not fixed from year to year. We choose books and papers based on emerging topics in data engineering and the research interests of current laboratory members. Depending on the research topic, students may also participate in collaborative meetings with researchers from other universities or other organizations. Students are also encouraged to present their work actively at academic conferences.

Who Might Enjoy Research in Our Lab?

You do not need to already be an expert in AI, databases, or data engineering. We especially welcome students who:

  • Often ask “why?”
  • Enjoy discovering something from data
  • Like investigating problems independently
  • Want to understand not only how a program works, but why a method is effective
  • Are interested in presenting and discussing their research
  • Are willing to explore new areas

Research in our laboratory is not based on simply completing tasks assigned by the supervisor. We aim to develop research topics through discussion between students and faculty and to gradually turn ideas into academically meaningful research.

Interested in Joining Us?

Please first take a look at our Research and Publications pages to learn more about our current work. However, your interests do not need to perfectly match an existing project in the laboratory. In our lab:

You can start from a method.
You can start from data.

You may already have a question such as:
“I would like to study this type of data.”
“I am interested in this technology.”
“I want to work on data-related research, but I do not yet have a specific topic.”
All of these are reasonable starting points.

If you are interested in joining our laboratory or would like to discuss possible research topics, please feel free to contact us.