Satalia
Guangyan Song is an experienced data scientist specializing in operational research and optimization, currently working at Satalia since January 2012. Key responsibilities include research and development in constraint satisfaction problems, various optimization techniques, and machine learning applications. Notable projects include optimizing staff scheduling for hospitality, last-mile delivery for a grocery company, and university timetabling. Prior to Satalia, Guangyan Song held positions at Cisco Systems as a data scientist/software engineer focusing on big data analysis and feature extraction, and at OpSource as a software engineer intern. Educational qualifications include a PhD and MSc in Computer Science and Machine Learning from UCL, alongside undergraduate degrees in Computer Science and Software Engineering from Technological University Dublin and Harbin Institute of Technology, respectively.
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Satalia
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The SolveEngine is designed for solving any type of problem enabling you to do things with your business you could previously only dream about.Use the one-stop-source for state of the art optimisation algorithms and you can get immediate access to cutting edge technologies to solve even the hardest problems.Innovative algorithms areproduced in academia yet rarely reach industry. The pace of change means that most planning and optimisation software is ageing and outdated.Their optimisation solutions are available “as-a-service” on demand, or on-premise, as a hybrid cloud or as a standalone solution – whatever suits your individual needs.They take the complexity out of solving optimisation problems, delivering real-world efficiencies and real-time improvements to increase your competitiveness.Companies use Satalia to solve their most challenging optimisation problems in Telecommunications, Financial services, System design and Drug discovery.