Prospective Students

Knowledge Acquisition Laboratory (Kato Laboratory) at University of Tsukuba looks for:

  • Research students(研究生)
  • Master course students(博士課程前期)
  • Doctoral course students(博士課程後期)

If you are interested, please contact us via email (see Contact). Please include in the email 1) a summary of a paper from our laboratory, and 2) research topics that you would like to tackle. If you would like to join our lab. as a master or doctoral course student, please also describe how you satisfy the requirements shown at the Q&A section. The subject of the email must contain “KASYS”.

Table of Contents

Primary Research Fields

Our laboratory mainly focuses on the following fields in Information Retrieval:

  • Retrieval models and ranking
  • Learning to rank
  • Search intent estimation
  • Knowledge base development
  • Knowledge base applications
  • Web mining
  • Information extraction
  • Search behavior analysis
  • Search user models
  • Recommendation systems
  • Online evaluation

Primary Research Topics

The primary research topic of our laboratory is knowledge acquisition systems:

  • Developing systems that autonomously acquire knowledge from a large amount of resources
  • Developing search systems for users to retrieve structured knowledge

Primary Research Themes (as of 2022)

The primary research themes of our laboratory are “Universal search models and automation of information retrieval system development” and “dataset search”, as of 2022. Examples of research themes are shown below:

  • Development of universal search models applicable to various search services
  • Search user simulation for information retrieval system evaluation
  • AutoIR: AutoML for information retrieval
  • Search for statistical data from the Web for answering questions such as “Is the music market declining?” and “Which companies abuse employees?”.
  • Automatically generate descriptions about data, for example, the sale of X has been increasing since 2006, and X has increased in Tokyo since 2006.
  • Automatically generate appropriate figures that summarize statistical data.

(Note: While the themes explained above are recommended, Ph. D students can work on their own theme. It is also possible to design research themes based on students’ ability and interest.)

Prerequisite Knowledge

Students at our laboratory are required to have the following knowledge for conducting research themes of our laboratory.

  • English
  • Mathematics
    • Probability theory and statistics
    • Linear algebra
  • Programming
    • Python (any language is OK)

(Prospective students are expected to have mathematics and/or programming skills.)

Knowledge to be Acquired

Students at our laboratory are required to have the following knowledge for conducting research themes of our laboratory, or research students need to learn these basics at the study meetings after they join our laboratory.

  • Information retrieval (see Introduction to Information Retrieval, Cambridge University Press)
    • Retrieval models (Boolean model, vector space model, probability model, query likelihood model, and neural ranking models)
    • Web search (indexing, crawling and link analysis)
    • IR evaluation (test collection development, recall, precision, MAP, nDCG, and ERR)
  • Natural language processing
    • Basics (e.g. tokenization)
  • Machine learning
    • Basics concepts (supervised learning, training data, testing data, and cross validation)
    • Basic models (linear regression, logistic regression, SVM, and decision tree)
    • Deep learning
  • Programming
    • Python
  • Basic operations of Linux
    • Such as ls, cd and cp.

Goals of Our Laboratory

  • Producing experts on information retrieval
    • Producing engineers who can work on research and development of commercial search systems
    • Producing researchers who can publish their work at international conferences
  • Being one of the hubs of information retrieval research
    • Getting ourselves recognized as an IR laboratory in Japan by continuously publishing research outcomes
      • Expecting such reputation (1) helps students find better positions, and (2) attracts more students who like IR


  • Q. Can I join the lab as a master course student (博士課程前期)?
    • A. Yes, but make sure that you satisfy the following requirements:
      • Have studied IR, NLP, and ML and mostly understood a textbook on each field
      • Have studied one or more programming languages and coded an algorithm/system with over 1,000 lines
      • Have a decent English speaking ability (e.g. TOEIC 700pt, TOEFL 76pt, IELTS 5.5pt, or above)
  • Q. Can I join the lab as a doctoral course student (博士課程後期)?
    • A. Yes, but make sure that you satisfy the following requirements:
      • Have satisfied the requirements for master course students explained above
      • Have a refereed paper in a computer science conference