We’re building predictive analytics to ensure big machines keep running. Our platform will underpin the Industrial Internet.
Artificial Intelligence, and machine learning more specifically, is driving new efficiencies in industry. Dramatic reductions in compute and data storage costs, coupled with an increasing number of sensors, open new applications for machine learning. Unfortunately, equipment data is often managed in data silos, requires complex system integration to capture and store, and is rarely consistent outside of an operating environment.
Tagup addresses this problem: we’ve built a common data platform to manage equipment data across the world. With the data in a canonical format, sophisticated machine learning methods can be applied to predict equipment failure and reduce operating costs.
Tagup was founded in 2015 at MIT and is headquartered up the street in Somerville, Massachusetts. Tagup has a diverse global team with expertise in data science, software development, and field engineering.
Jon's background is in industrial information systems. Prior to Tagup, he worked at GE Energy in Atlanta. His first role at GE was as a software product manager on the Grid IQ product line. In this role he was responsible for working with a development team of over twenty engineers in Atlanta, San Ramon, and Shanghai to build analytics applications for electric utilities.
In his most recent role at GE, he worked as fulfillment lead on a joint venture with a Chinese high voltage equipment manufacturer. Before GE, Jon studied physics and economics at MIT. After GE, he earned his MBA from Harvard Business School. He is the principal inventor on one granted and several pending patents.
Will is a final year PhD candidate at MIT, in the Computer Science and Artificial Intelligence Laboratory (CSAIL). Working in machine learning and artificial intelligence his research focuses on ways to ground symbolic planning in the real world, and involves software engineering from micro-controllers through high-level web programming.
As an MIT undergraduate Will double majored in physics and mechanical engineering. He subsequently received his masters in mechanical engineering from MIT. Will has published articles on machine learning in journals including NIPS, IEEE, and RSJ.
Outside of work, Will is a jiu-jitsu black belt and sensei.