Infinidata Lab at TU Delft builds the next generation of data systems — uniting data lakes with machine learning, privacy-preserving federated learning, and quantum data management.
We rethink how data is integrated, shared, and processed — from the model lake to the quantum processor.
Bringing data integration and machine learning together, so heterogeneous data and rich model zoos meet in one lake.
Training and synthetic-data generation across organisational silos — without ever sharing the raw data.
Reimagining query optimisation, entity matching, and anomaly detection for the NISQ-era quantum processor.
Model Lake
Amalur explores the convergence of data integration and machine learning — automating how scattered training data across silos is integrated for downstream models. It is the foundation of the group’s Model Lake vision, where heterogeneous data and rich model zoos meet in one place.
Synthetic data
SiloFuse generates cross-silo synthetic tabular data using latent diffusion models, so organisations can share realistic data without ever exposing raw, feature-partitioned records.
Time series
WaveStitch performs flexible and fast conditional time-series generation with diffusion models, stitching together realistic signals under user-specified constraints.
LLM serving
TranSQL serves large language models with relational queries — compiling model inference to SQL so that LLMs can run inside a database engine, even on low-resource hardware.
We're always looking for sharp PhD and MSc students. Thesis topics and open positions are posted year-round.
See open positions