Fang Chen

Advisor at FYLD

Fang Chen is a Distinguished Professor with extensive experience in academia and industry, currently serving on the Industry Innovation and Science Australia Board at the Department of Industry, Science and Resources. As a Venture Partner at Meraki Venture Capital and a member of the Scientific Advisory Committee at QUT Centre for Data Science, Fang Chen actively contributes to advancements in technology and innovation. The role on the Advisory Board of FYLD and as a Board Director at ITS Australia, along with co-chairing the National Transport Data Community of Practice, highlights a commitment to fostering collaboration in transport data analytics. Additional positions include Chair of the Steering Committee for ACM Intelligent User Interfaces and membership on the National Research Foundation Competitive Research Program Expert Panel in Singapore. Fang Chen holds significant academic leadership as Executive Director of Data Science at the University of Technology Sydney and possesses advanced degrees in Computer Science and Electrical Engineering from prestigious Chinese institutions.

Location

New South Wales, Australia

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FYLD

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FYLD brings real time, remote site visibility to field force operations, leveraging rich data (video, voice, text) collected by field workforces to build a shared view of operations. It enables data driven decisions to be made in real time, allowing a day's operations to be safely delivered. The shift from reactive, sample based ways ofworking to proactive focused interventions drives rapid and high ROI for customers (2 weeks to ROI, by 6 weeks ROI of 8-12 x license fees).As data sets build up, FYLD customers deploy data led process redesign, dynamic scheduling and break down operational silos, achieving even higher ROI.helps utility field teams and managers to make data-driven decisions in real-time. FYLD strips away the need for time-consuming and burdensome administration, while equipping field workers and team leaders with the insights they need to make better, data-driven decisions.


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11-50

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