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Jon Herlocker is a distinguished technologist, entrepreneur, and academic, currently serving as the Co-founder and CEO of Tignis, a company pioneering AI-powered process control for complex industrial operations. With a rich background spanning academia and industry, Jon was previously the CTO of Jamf, where he played a pivotal role in scaling the company's technology for Apple device management. Before Jamf, he was a respected Professor of Computer Science at Oregon State University, focusing on human-computer interaction, artificial intelligence, and particularly, recommender systems. He is widely recognized for his foundational work on GroupLens, one of the earliest automated collaborative filtering recommender systems, which significantly influenced the field. Jon's expertise lies in translating cutting-edge research into impactful commercial products and leading high-performing technology teams.
Jon Herlocker's work history includes a series of influential roles in various companies. Here is a detailed list of his professional journey:
Leads Tignis in developing and deploying innovative AI-driven solutions to optimize manufacturing processes, reduce waste, and improve operational efficiency for industrial clients.
Spearheaded the technological vision and strategy for Jamf, contributing to its growth as a leading platform for Apple enterprise management, managing millions of devices worldwide.
Co-developed GroupLens, one of the earliest and most influential collaborative filtering recommender systems, laying foundational work for modern personalization technologies.
Made significant contributions to research and education in computer science, particularly in areas of AI, machine learning, and human-computer interaction, mentoring numerous students.
Authored numerous influential research papers and holds several patents in areas related to recommender systems, AI, and software technology.
University of Minnesota - Year 1994
Lewis & Clark College - Year 1990
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Tignis develops AI-powered software solutions designed to optimize industrial processes in manufacturing and energy sectors. Their platform leverages machine learning and physics-based models to provide predictive analytics, improve operational efficiency, reduce downtime, and enhance sustainability for complex industrial assets and systems. From an end-user perspective, Tignis helps engineers and operators proactively identify potential issues, understand process deviations, and make data-driven decisions to keep their plants running optimally and cost-effectively.
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