We are InfiniData Team

Philosophy: Simplicity, Best Practices and High Performance

What we focus on

Empowering the Future of Data, Today

Multimodal data & GPU acceleration

Data privacy and security

Data Management for Quantum Computing and Quantum Internet

Projects

Amalur - AI in data lakes

Objective

We tackle the effectiveness and efficiency aspects on ML workflow.

Target

Model Lake, leveraging heterogeneous data from data lakes and rich models from model zoos.

Approach

We introduce advanced algorithms and optimizers, and accelerate with the sheer power of GPU.

Federated Learning

Objective

Our commitment to data privacy and security, ensuring data privacy without compromising the quality of insights.

Approach

We train ML models on decentralized devices and silos, ensuring data privacy without compromising the quality of insights.

Outcome

We envision novel algorithms and software tools bolstering privacy and fostering a trust-based environment for data handling and AI.

Publications

VLDB 2024

Towards Quantum Data Structures for Enhanced Database Performance

Tim Littau, Ziyu Li, Rihan Hai
International Workshop on Quantum Data Science and Management, QDSM 2024

VLDB 2024

On Efficient ML Model Training in Data Lakes

Wenbo Sun
VLDB PhD Symposium

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Ziyu Li , Hilco van der Wilk, Danning Zhan, Megha Khosla, Alessandro Bozzon, Rihan Hai
In Proceedings of the 2024 IEEE 40th International Conference on Data Engineering, ICDE 2024

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Ziyu Li, Wenbo Sun, Danning Zhan, Yan Kang, Lydia Chen, Alessandro Bozzon, and Rihan Hai.
IEEE Transactions on Knowledge and Data Engineering (2024).

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Aditya Shankar, Hans Brouwer, Rihan Hai, and Lydia Chen.
In Proceedings of the 2024 IEEE 40th International Conference on Data Engineering, ICDE 2024

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The digestion of our on-going projects