Christos Tryfonas
Sr. Director, VMware


Christos Tryfonas is CTO and Co-Founder of Cetas. A computer scientist, technologist and successful entrepreneur, he co-founded Cetas in 2010 with focus on driving the technology around the emerging area of analytics and Big Data. He has several years of experience as a computer scientist and architect in both academia and industry research labs, leading innovative projects ranging from information retrieval and large scale distributed systems, to storage networking, multimedia networking and network security.

In 2003, he co-founded Kazeon Systems, Inc. with focus on information classification, management, and retrieval where he was responsible for the technical leadership, the architecture and design of a complex distributed software product. Kazeon was acquired by EMC Corporation in September 2009. Prior to co-founding Kazeon Systems, Tryfonas was a Principal Member of Technical Staff at Sprint Advanced Technology Labs, where he was responsible for research and development in emerging networking and multimedia technologies. He contributed to several innovative Sprint services, and was part of the world-class IP research group at Sprint that provided accurate large-scale Internet measurements and analytics for worldwide IP capacity planning and traffic analysis to the Internet community. He has also worked at AT&T Bell Laboratories doing research and development in the area of traffic analysis. Christos holds several patents (issued and pending) in the areas of large-scale information classification, management, and retrieval, cloud services, distributed systems, congestion control in packet networks, and network transport of multimedia/video traffic.


Christos Tryfonas (VMware), Karthik Kannan (Cetas by VMware)
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Hospitals, healthcare providers & insurance companies have traditionally been using standard statistical & Bayesian approaches for data modeling and predictive analytics. But these approaches fall short in the light of the very large volume, variety and velocity of Big Data. In this session, we talk about machine learning & combinatorial algorithms that can enable personalized recommendations. Read more.


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