Pioneering fundamental research into AI efficiency.

We believe that by increasing the information density of data we can help make AI more impactful and useful for everyone.

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“Fundamental research is at the core of what we do at Granica and we've combined that with our team's systems engineering expertise to build a platform that drives information density and efficiency, at cloud scale.”

Andrea Montanari, Chief Scientist at Granica

Featured Research

Towards a statistical theory of data selection under weak supervision

Sep 25, 2023 | INFORMATION DISTILLATION

Given a sample of size N, it is often useful to select a subsample of smaller size n<N to be used for statistical estimation or learning. Such a data selection step is useful to reduce the requirements of data labeling and the computational complexity of learning.

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Sampling, Diffusion, and Stochastic Localization

May 18, 2023 | ALGORITHMS

Diffusions are a successful technique to sample from high-dimensional distributions that are not given explicitly but rather learnt from a collection of samples. We generalize the construction of stochastic localization processes.

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Compressing Tabular Data via Latent Variable Estimation (ICML 2023)

Feb 20, 2023

Feb 20, 2023 | TABULAR COMPRESSION

Data used for analytics and machine learning often take the form of tables with categorical entries. We introduce a family of lossless compression algorithms for such data.

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Inline Data Detection in Large Data Streams

Sept 14, 2021

Sep 14, 2021 | LOSSLESS DATA REDUCTION

We present a a novel approach to data processing and reduction method that involves receiving an input data stream and computing a set of features that are representative of or unique to the stream.

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Efficient Data Deduplication through Sketch Computation and Similarity Metrics

Mar 2, 2021

Mar 2, 2023 | LOSSLESS DATA REDUCTION

The methods provide a more efficient and effective way of handling large data streams, which can be particularly beneficial in applications that require real-time data processing and reduction.

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Let's build the future of AI efficiency, together.

A diversity of products requires a diversity of perspectives. We're on a mission to build tools that are easy to use and impossible to ignore — and we want your unique voice.

Whether you're a researcher, a systems engineer, an ML engineer, or simply someone interested in a more efficient future for AI, reach out.

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