We are InfiniData Team

Affiliated with TU Delft, the Netherlands, Infinidata Team stands at the forefront of innovative data management technologies, blending the power of AI, Federated Learning, and Quantum Data Management to reshape the landscape of data analysis and security. AstroWind: Production-ready.
By focusing on these key areas, Infinidata Lab aims to redefine what's possible, making significant strides toward a future where data's full potential can be realized effectively, efficiently, and securely.

Philosophy: Simplicity, Best Practices and High Performance

What we focus on

Empowering the Future of Data, Today

AI in data lakes

Multimodal data & GPU acceleration

Federated Learning

Data privacy and security

Quantum Data Management

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.

Talks and Tutorials

QDSM: Quantum Data Management in the NISQ Era

Rihan Hai

Quantum Data Management: From Theory to Opportunities

Rihan Hai , Shih-Han Hung, Sebastian Feld

Publications

QDSM: Towards Quantum Data Structures for Enhanced Database Performance

Tim Littau, Ziyu Li, Rihan Hai

On Efficient ML Model Training in Data Lakes

Wenbo Sun

Model Selection with Model Zoo via Graph Learning

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

Amalur: the Convergence of Data Integration and Machine Learning

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

SiloFuse: Cross-silo Synthetic Data Generation with Latent Tabular Diffusion Models

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

Find out more content in our Blog

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The digestion of our on-going projects

We are InfiniData Team

What we focus on

Amalur - AI in data lakes

Objective

Target

Approach

Federated Learning

Objective

Approach

Outcome

Talks and Tutorials

Publications

QDSM: Towards Quantum Data Structures for Enhanced Database Performance

On Efficient ML Model Training in Data Lakes

Find out more content in our Blog

Quantum Data Management in the NISQ Era

Post-Doc Position AI and Data Lakes

Data management for Quantum Computing 25/09/2024

PhD Position Quantum Data Management