Fujitsu Research of America
AI Lab
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
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
Publications
- Data Diversity Design for Systematic Generalization in Visual Question Answering / journal: TMLR - Amir Rahimi, Vanessa D'Amario, Moyuru Yamada, Kentaro Takemoto, Tomotake Sasaki, Xavier Boix
- Transformer Module Networks for Systematic Generalization in Visual Question Answering / journal TPAMI - Moyuru Yamada, Vanessa D'Amario, Kentaro Takemoto, Xavier Boix*, Tomotake Sasaki* (*equal authorship)
