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.

Due to space limitations, our laboratory does not accept research students (non-degree students). We accept only degree-seeking students who have passed the entrance examination for the doctoral, master’s, or undergraduate programs, as well as exchange students from universities that have academic exchange agreements with Ochanomizu University.

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.

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.

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 students from departments other than the Department of Humanities Data Engineering are also welcome to contact us if you are interested in our research.

For undergraduate research, we place particular emphasis on learning how to conduct research. You do not need to have a clearly defined research topic or specific technologies in mind from the beginning.

If you have developed an interest through your coursework or practical classes in areas such as:

• databases
• data analysis
• programming
• AI and machine learning
• multimedia data

we encourage you to visit our laboratory and talk with us.

You may join one of the research projects already underway in the laboratory, or develop a new research topic starting from data or a problem that interests you. Through your undergraduate research, we aim to help you learn how to approach research questions for which there is no predetermined answer.

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.

In particular, due to space limitations, we do not accept research students for the purpose of preparing for the graduate school entrance examination. Whether or not an applicant has previously been affiliated with our laboratory as a research student has no influence on the graduate admissions selection process. If you wish to join our laboratory as a master’s student, please apply through the regular graduate school entrance examination.

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

Our laboratory does not have fixed core hours, but we place a strong emphasis on discussion and interaction among lab members.

In principle, we hold two lab seminars each week. During the spring semester, these mainly consist of reading and discussing books and research papers related to data engineering, together with research progress presentations. In the autumn semester, we increase the opportunities for discussion centered on research progress.

The topics covered in the reading sessions are not fixed every year. They are selected each year based on emerging topics in data engineering and the research interests of current lab members.

In addition to the regular seminars, individual meetings and discussions are arranged as needed to discuss research topics and how to proceed with research. Depending on the research topic, students may also participate in collaborative research meetings with researchers from other universities or institutions.

We actively encourage students to present their research results at both domestic and international conferences.

Who Might Enjoy Research in Our Lab?

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.

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

Interested in Joining Us?

Please first take a look at our Research page, as well as the publication pages of the laboratory and Le 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.