Fujitsu Research of America

AI Lab

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Collaborating with people to create new value

Our focus is on realizing a sustainable society by developing cutting-edge AI technologies that not only create new value but also contribute to the transformation of society and business.

Automatic machine learning, commonly known as 'AutoML,' holds great promise in democratizing the use of machine learning (ML) by automating a significant portion of the tasks typically performed by data scientists. However, the vast search space of potential pipelines presents a challenge, often leading to the generation of suboptimal or no pipelines, especially when dealing with large and complex datasets.

Fujitsu Research has actively tackled this issue by developing a unique AutoML technology called 'Fujitsu AutoML.' Leveraging a collection of pre-existing datasets and their human-created pipelines, this technology enables the efficient generation of high-quality pipelines for new datasets with predictive tasks. With Fujitsu AutoML, data scientists can rapidly create and modify AI models, as the code is provided along with detailed explanations. Moreover, citizen data scientists can easily create the desired AI models as well.

Researchers in the AI Lab




  • Avraham Cooper (Avi)

    Multi-model foundation models,
    computer vision,
    large scale AI system design

  • Hiromichi Kobashi

    Machine Learning (ML),
    distributed systems,
    databases

  • Ian Mason

    Machine Learning (ML): domain adaptation, continual learning,
    foundation models

  • Jin Yamanaka

    Machine Learning (ML), Computer Vision

  • Kanji Uchino

    NLP (information retreival,
    semantic web),open education,graph AI,
    AI ethics,AutoML

  • Lei Liu

    Natural Language Processing,
    (NLP),Machine Learning (ML),
    and communication networks

  • Maria Xenochristou

    Machine Learning (ML),
    Computer Vision,
    multimodal learning

  • Michael McThrow

    Machine Learning (ML), AutoML, graph AI,
    databases,programming languages

  • Mehdi Bahrami

    Natural Language Processing (NLP), Machine Learning (ML)

  • Ramya Srinivasan

    Machine Learning (ML),
    computer vision,AI ethics,
    Natural Language Processing (NLP),AI and creativity

  • Shailaja Sampat

    Natural Language Processing (NLP),Computer Vision,
    multimodal learning

  • So Hasegawa

    Machine Learning (ML),
    Computer Vision,
    generative models

  • Trishala Ahalpara

    Machine Learning (ML),
    customer segmentation, AutoML,bias in AI,
    feature selection

  • Wei-Peng Chen

    Machine Learning (ML),
    optimization,
    mobile networks

  • Wing Yee Au

    Machine Learning (ML),
    relational and graph data

  • Xavier Boix

    The Self-Improving Machine Learning group
    Director of Research


Discover more about AI,
one of our 5 key technologies

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Publications


  1. Juhan Bae, Michael R Zhang, Michael Ruan, Eric Wang, So Hasegawa, Jimmy Ba, Roger Grosse, “Multi-Rate VAE: Train Once, Get the Full Rate-Distortion Curve”, International Conference on Learning Representations (ICLR) 2023
  2. So Hasegawa, Masayuki Hiromoto, Akira Nakagawa, Yuhei Umeda, “Improving Predicate Representation in Scene Graph Generation by Self-Supervised Learning”, Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2023
  3. Ramya Malur Srinivasan, Hiroya Inakoshi, Kanji Uchino, “Leveraging Cognitive Science for Testing Large Language Models”, 2023 IEEE International Conference On Artificial Intelligence Testing (AITest), 2023
  4. Youzhi Luo, Michael McThrow, Wing Yee Au, Tao Komikado, Kanji Uchino, Koji Maruhashi, Shuiwang Ji, “Automated Data Augmentations for Graph Classification”, ICLR2023, 2023
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