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About TieSet


TieSet is at the forefront of the next AI generation

At TieSet Inc., we're leading the charge into the next generation of AI with a vision that transcends traditional boundaries. Our belief is that AI learning shouldn't be confined to a centralized location; instead, it should occur everywhere, across diverse environments, facilitated by our intelligence orchestration platform, STADLE. This paradigm-shifting approach allows AI to integrate more deeply into everyday life, impacting various industry segments. Based in Silicon Valley, our team of top-tier engineers and researchers is dedicated to creating innovative solutions in distributed machine learning, blending artificial intelligence with distributed computing systems. We're not just developing technology; we're crafting a future where collective intelligence redefines the big data AI world.

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TieSet Inc.

Silicon Valley HQ

214 Homer Ave, Palo Alto, CA 94301, USA

The Founders


Kiyoshi Nakayama

Founder & CEO

Kiyoshi is the founder and CEO of TieSet, Inc. Before launching TieSet, Kiyoshi cultivated his expertise as a research scientist at NEC Laboratories America, renowned for its exceptional machine learning research. He also served as a postdoctoral researcher at Fujitsu Laboratories of America, where he developed an innovative distributed system for smart energy. His distinguished career features many international publications and patents, and he has twice received the best paper award. He holds a PhD degree in Computer Science from the University of California, Irvine. As a leading researcher in the field, Kiyoshi is the primary author of Federated Learning with Python, a key text in distributed machine learning.


George Jeno

Co-Founder & CTO

George is the co-founder of TieSet Inc. and the driving force behind the technical development of the STADLE federated learning platform. His profound knowledge in machine learning theory and system architecture design has been pivotal in advancing research on new algorithms and applications for distributed and federated learning. George's academic credentials include a Master’s degree in Computer Science, with a focus on machine learning, from Georgia Tech. Demonstrating his expertise and commitment to the field, George is also the co-author of Federated Learning with Python, a key publication that underscores his significant contributions to machine learning innovation.

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