Yes… RocketML grew a bit of unicorn tusk in 2019 — although by a hair thickness! Beginning of the new year, we received great news — “equity free” funding from National Science Foundation! #NSF-funded During these formative years, the funding from NSF, will help us significantly in enabling us to hire talent and have the freedom to explore our innovative ideas and dig a little deeper into research!
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As some of you know, right from the start, my team at RocketML, is focused on “building something that people want” but never let go of the “first principles”. We believe this combination is critical in building a company that lasts. “Building something that people want” is a tenet made famous by Ycombinator’s founder Paul Graham. We believe in this tenet. Many startups fail because they don’t follow this simple advice. However challenge of “build something that people want” is that lots of people want lots of different things. If you, as a startup founder, know too much about the domain, you fall into “knowledge trap”. If you are new to the domain, you can spin your wheels forever and could be pushed into wrong directions by wrong customers. One way to overcoming this challenge is by combining “first principles” approach. Although it takes a lot more mental energy, first principles lead you into a path of superior product and innovation. It is the best way to separate “signals” from “noise”. With NSF award, we now have a bit of latitude to go after deep science projects with broader impact on the field, society and apply first principles more rigorously.
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Our project will design, develop, and deploy high-performance computing (HPC) software for unsupervised learning and anomaly detection. In the last decades tremendous successes in machine learning have been achieved in the area of supervised learning that requires compilation of large datasets with labels (for example, grouping of pictures based on the individual depicted on the image). In contrast, unsupervised learning algorithms do not require labels and thus require minimal human participation. However, due to significant technical difficulties they have not been as successful as supervised learning algorithms. This software package circumvents these difficulties and opens the way to scaling unsupervised learning algorithms to large and complex datasets. The main research and development challenges that will be addressed in this project are the ability to integrate this new technology with real world complex datasets through the choice of the correct comparison function between the objects of the dataset and the fully automatic algorithm and algorithm parameter selection. Here is a quote from NSF’s Acting Director of Industrial Innovation and Partnership, Graciela Narcho:
“The National Science Foundation supports startups and small businesses with the most innovative, cutting-edge ideas that have the potential to become great commercial successes and make huge societal impacts,” said Graciela Narcho, Acting Director of Division of Industrial Innovation and Partnerships at NSF. “We hope that seed funding will spark solutions to some of the most important challenges of our time across all areas of science and technology.”
We sincerely hope to make NSF and the community we live in proud of RocketML. If you are passionate about building something that people want, using first principles and solving deeper technical challenges of machine learning ➙ get it touch with me! We have open positions for Internships for PhD and Graduate students, we have open positions for C++ programmers, we have open positions for folks who are passionate about Machine Learning, Data Science, Cloud technology and Container technology!