Ufora

Ufora, founded in 2011 by Alexander Leeds and Braxton McKee, operates as a data science and high-performance computing platform from its headquarters in New York City. The company secured $3 million in a seed funding round on April 25, 2011, with participation from investors such as Neu Venture Capital, Two Sigma Ventures, NYC Seed, and Contour Venture Partners.

The core of Ufora's business is a scale-agnostic data science platform designed to empower data scientists and analysts to address complex challenges in statistics, machine learning, and predictive analytics. It provides services for quantitative modeling and numerical computing. The platform is engineered to automate the distribution and parallelization of algorithms while computations are actively running, a feature it calls "Smart Compute." This implicitly parallel system adaptively and automatically manages computations and data across a cluster of machines, allowing users to work within their familiar coding environments without needing to adapt their problems to specialized parallel programming frameworks. The primary aim is to grant users seamless access to substantial computing power for handling large datasets.

Ufora's business model is centered on providing its data analysis platform to clients who require significant computational resources for their data-intensive tasks. The company targets professionals in the field of data science and analytics who work with large-scale data and sophisticated modeling. The platform is designed to handle demanding computational problems in areas such as genomics and bioinformatics, which are often characterized by massive datasets and the need for high-speed analysis.

Keywords: data science platform, high-performance computing, machine learning, predictive analytics, quantitative modeling, numerical computing, big data analysis, parallel computing, big compute, data analysis platform, computational research, cluster computing, scalable computing, Alexander Leeds, Braxton McKee, data-intensive computing, scientific computing, enterprise infrastructure, algorithm parallelization, big data solutions