Our team leverages sophisticated concepts from contemporary mathematics to enhance state-of the art analytics methods. We specialize in capturing and leveraging the shape of your data, extracting information from complex signals, fusing multiple data modalities for maximum knowledge potential, and developing explainable, verifiable machine learning and artificial intelligence solutions.

Data fusion

Heterogeneous data sets promise more insights into your business, but that may not be true with traditional data fusion. We use topological approaches to put time series data from heterogeneous modalities on a common geometric footing, enabling principled and unsupervised data fusion for improved machine learning and maximum potential for extracting information from your data.

Machine learning

Machine learning and artificial intelligence approaches determine rules or behaviors from existing data that can be applied to new data. We bring practical know-how and clever methods to apply machine learning and AI in an effective, reproducible, and interpretable manner. It can be challenging to trust and integrate AI into your business workflows. Our explainable, verifiable AI approaches that remove the blackbox and reduce the uncertainty of results in novel scenarios.

Shape analytics

The term shape analytics describes our ever-growing suite of techniques for the data-driven analysis of real-world problems. Beginning with the cutting-edge area of topological data analysis (TDA), our toolkit has expanded to incorporate ideas from geometry, measure theory, information theory, graph theory, and analysis.

Signal processing

Temporal signals are extremely information-rich, often underutilized resources. Our signal processing and featurization approaches capture frequency structure and dynamics in a computationally efficient way.

Whether you are looking for signal compression solutions without losing valuable information or want to maximally leverage your signal data to enhance your team’s decisions, we would love to hear from you.