© 2020 Strange Loop
Can we empower data engineers to use AI to explore, monitor, clean, and predict data streams, without having to learn math?
InferenceQL was made by some of the same people behind BayesDB and Empirical Systems, first presented at Strange Loop in 2015. Unlike BayesDB, InferenceQL is not a database, but instead can be embedded alongside data tables, data streams, and client-side web applications. We are just starting to seek external beta testers, contributors, and volunteers.
Ulrich Schaechtle is a research scientist at MIT. He leads the research engineering efforts around InferenceQL. Ulrich holds a PhD in computer science from Royal Holloway, University of London, as well as an MSc in computing from Imperial College London and a BSc in applied congitive sciences from the University of Duisburg-Essen. Ulrich leads an MIT team for the DARPA Synergistic Discovery and Design (SD2) programs. He has publications in major conferences and journals for programming languages and AI. He currently works on applications of InferenceQL to data journalism, psychiatry, and synthetic biology. In 2018, Ulrich was selected by DARPA as a DARPA Riser, one of 50 early career scientists developing breakthrough technologies.