The systems we build to test our ideas — each links out to its code, paper, and data.
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.