Senior Principal Researcher / Director

NEC Corporation


I am a senior principal researcher at NEC Corporation. I am now the director of Knowledge-Based Learning Research Group of Data Science Research Laboratories at NEC Corporation. I received my Ph.D. at University of Tsukuba in March 2018 under supervision of Prof. Hiroyuki Kitagawa.

My research interests are on helping data analysis using data management technologies (DB) and machine learning technologies (ML).



  • Journal of Information Processing Outstanding Paper Award, 2021


  • IPSJ Yamashita SIG Research Award, 2019


  • Best Paper Runner-Up, 2018

    WebDB Forum

  • Annual Conference Award, 2016


  • Best Honorable Poster, 2015


  • Best Paper Runner-Up, 2015


  • Honorable Poster, 2014


  • Dept. Chair Award, 2013

    University of Tsukuba

  • Honorable Student Talk, 2013



  • PhD in Computer Science, 2018

    University of Tsukuba

  • MEng in Computer Science, 2013

    University of Tsukuba

  • BSE in Computer Science, 2011

    University of Tsukuba

We are hiring (Interneship / Full-time)

Our research team (knowledge-based learning) at NEC Corporation is seeking for motivated full-time researchers / internship students who are passionate in working on interdisciplinary research issues that arise from real-world enterprise business. We aim to contribute to both industries and academics. Our research results are commercialized and used in various enterprise companies such as retailers and consumer products companies. We publish our results in top-level venues of computer science (e.g., AAAI, ICDE, ICDM, BigData).

Research topics include but not limited to:

  • Highly scalable ML-aided data integration (DB + ML + HPC)
  • Crowd-sourcing for data cleaning (DB + ML + HCI)
  • Machine learning on data sketches (ML + DB)
  • Knowledge-driven AutoML based on source code analysis (SE + NLP + ML)

Preferred Skills

  • Business-level English (writing & speaking)
  • Basic coding skills for data science tasks
  • Basic knowledge on data structures and algorithms
  • Academic experience (publication) in one of the following research area:
    • Database Systems (DB)
    • Machine Learning (ML)
    • Software Engineering (SE)
    • Natural Language Processing (NLP)
    • Human-Computer Interaction (HCI)
    • Information Retrieval (IR)


Please drop me an E-mail with your CV if you are interested in working with us.


See my GitHub for list of software I have crafted. Here are some selected stuffs.



Provides fully-configurable hardware keyboard functionalities for web browsing on iOS (iPadOS)


Yet another keyboard remapping tool for X environment


An interactive grep tool in your terminal


Allows you to bind commands to key sequences in Mozilla Firefox


A Lisp implementation and REPL written in JavaScript, which supports static-scoping, lexical-closure, macro, and basic special forms


A parser for org-mode notation written in JavaScript


Provides zsh like completion for minibuffer in Emacs

Chaotic Canvas

A chaos fractal generator written in JavaScript


Add jsdoc-related functionalities for Emacs


A major-mode for LEMON Parser Generator.


Complete list of publications is also available.

Table Enrichment System for Machine Learning (Demo)

Low-resource Taxonomy Enrichment with Pretrained Language Models

Entity Matching with String Transformation and Similarity-Based Features

User Identity Linkage for Different Behavioral Patterns across Domains

Efficient Joinable Table Discovery in Data Lake: A High-Dimensional Similarity-Based Approach

Quality Control for Hierarchical Classification with Incomplete Annotations

Continuous Top-k Spatial-Keyword Search on Dynamic Objects

Learning from Unsure Responses

Many annotation systems provide to add an unsure option in the labels, because the annotators have different expertise, and they may not have enough confidence to choose a label for some assigned instances. However, all the existing approaches only learn the labels with a clear class name and ignore the unsure responses. Due to the unsure response also account for a proportion of the dataset (e.g., about 10-30% in real datasets), existing approaches lead to high costs such as paying more money or taking more time to collect enough size of labeled data.

Extracting Feature Engineering Knowledge from Data Science Notebooks

Designing good features for machine learning models, which is called feature-engineering, is one of the most important tasks in data analysis. Well-designed features, which capture the characteristics of data, improve the predictive performance and explainability of the model. Since good features generally reflect the deep knowledge on business domains of the data and the analysis task, feature engineering is considered as one of the most difficult phases in data analysis.

Meimei: An Efficient Probabilistic Approach for Semantically Annotating Tables

Given a large amount of table data, how can we find the tables that contain the contents we want? A naive search fails when the column names are ambiguous, such as if columns containing stock price information are named “Close” in one table and named “P” in another table. One way of dealing with this problem that has been gaining attention is the semantic annotation of table data columns by using canonical knowledge.

Accelerating Feature Engineering with Adaptive Partial Aggregation Tree

Range aggregation query is a fundamental operation in the feature engineering phase of the machine learning tasks, which computes statistics, such as the maximum and the standard deviation of a subset of records. Since the feature-engineering process is a trial-and-error process, data analysts repeatedly conduct tons of the range aggregation queries by changing the range conditions, which results in a heavy workload. To accelerate such repetitive range aggregation queries, we propose Adaptive Partial Aggregation Tree (APA-tree), which drastically reduces the amount of I/Os that happen in executing the range aggregation queries.

Compressed Vector Set: A Fast and Space-Efficient Data Mining Framework

In this paper, we present CVS (Compressed Vector Set), a fast and space-efficient data mining framework that efficiently handles both sparse and dense datasets. CVS holds a set of vectors in a compressed format and conducts primitive vector operations, such as lp-norm and dot product, without decompression. By combining these primitive operations, CVS accelerates prominent data mining or machine learning algorithms including k-nearest neighbor algorithm, stochastic gradient descent algorithm on logistic regression, and kernel methods.

Relational Mixture of Experts: Explainable Demographics Prediction with Behavioral Data

Given a collection of basic customer demographics (e.g., age and gender) and their behavioral data (e.g., item purchase histories), how can we predict sensitive demographics (e.g., income and occupation) that not every customer makes available? This demographics prediction problem is modeled as a classification task in which a customer’s sensitive demographic y is predicted from his feature vector x. So far, two lines of work have tried to produce a “good” feature vector x from the customer’s behavioral data: (1) application-specific feature engineering using behavioral data and (2) representation learning (such as singular value decomposition or neuralembedding) on behavioral data.

Link Prediction for Isolated Nodes in Heterogeneous Network by Topic-Based Co-clustering

This paper presents a new probabilistic generative model (PGM) that predicts links for isolated nodes in a heterogeneous network using textual data. In conventional PGMs, a link between two nodes is predicted on the basis of the nodes’ other existing links. This method makes it difficult to predict links for isolated nodes, which happens when new items are recommended. In this study, we first naturally expand the relational topic model (RTM) to a heterogeneous network (Hetero-RTM).

MOARLE: Matrix Operation Accelerator Based on Run-Length Encoding

Data Stream Processing with Concurrency Control

Continuous Query Processing with Concurrency Control: Reading Updatable Resources Consistently

A recent trend in data stream processing shows the use of advanced continuous queries (CQs) that reference non-streaming resources such as relational data in databases and machine learning models. Since non-streaming resources could be shared among multiple systems, resources may be updated by the systems during the CQ execution. As a consequence, CQs may reference resources inconsistently, and lead to a wide range of problems from inappropriate results to fatal system failures.

Efficient Invocation of Transaction Sequences Triggered by Data Streams



Senior Principal Researcher / Director

NEC Corporation

Apr 2022 – Present Tokyo, Japan
Senior Principal Researcher and the Director of a research group that focus on data management, natural language processing, and data mining issues. Also the product manager of NEC Data Enrichment service, an ML-based data preparation platform.

Principal Researcher

NEC Corporation

Apr 2020 – Mar 2022 Tokyo, Japan
Principal investigator of a research team (knowledge-based learning) and product manager of an ML-based data management software.

Senior Researcher

NEC Corporation

Apr 2017 – Mar 2020 Tokyo, Japan

Principal investigator of a research team (knowledge-based learning). Research topics include

  • Data Management (Data Integration, Data Indexing, …)
  • Machine Learning (Multi-label Classification)
  • Human-Computer Interaction (Crowd Computing)
  • Information Extraction (Knowledge Extraction)


NEC Corporation

Apr 2013 – Mar 2017 Tokyo, Japan

Research on customer behavior data analytics. Research topics include

  • Bayesian Modeling of Customer Behavior
  • Context-aware Recommendation
  • Statistical Relational Learning

Research Staff (Internship)

NTT Research Laboratories

Aug 2011 – Aug 2011 Tokyo, Japan
Research and development on a data stream processing system.

Software Engineer (Part-time)

Clear-Code Inc.

May 2011 – Mar 2013 Tokyo, Japan
Software development. Developed web browser extensions, E-mail reader extensions, and search engine backends. (JavaScript, Ruby, C++).

Software Engineer (Internship)

Hatena Inc.

Aug 2010 – Aug 2010 Tokyo, Japan
Developed a browser extension of social bookmark web service (Hatena Bookmark) for Safari.