Falko Schulz is a Distinguished Software Developer in the SAS Platform Products Research and Development division. He works actively on products such as SAS® Visual Analytics further enhancing user experience and analytical capabilities. Falko has a strong background in delivering business analytical applications in a wide range of industries. Prior to joining the R&D division Falko worked in customer facing roles responsible for critical technical implementations and BI architectural designs. During his 19 years at SAS, Falko worked out of various countries including Germany, Australia and US.
As part of this year's IEEE Visual Analytics Science and Technology (VAST) Challenge, a group of SAS data scientists puit SAS Viya and related machine learning tools to the ultimate test - to identify individuals in a complex fishing network. Excitedly, the team received the Honorable Mention Award for Breadth of Investigation!
The IEEE Visual Analytics Science and Technology (VAST) Challenge provides a great opportunity to validate our software against real-world scenarios using complex data sets. Not only do we learn from these projects, but we also send feedback to our development teams to further improve product capabilities for customers.
A team of SAS employees recently participated in a data-for-good project focusing on forest fires in the Amazon. In conjunction with the Amazon Conservation Association (ACA), the team explored options to collect and analyze publicly available imagery and fire data to better understand the drivers for forest fires as well
Technological advancements in connectivity and global positioning systems (GPS) have led to increased data tracking and related business use cases to analyze such movements. Whether analyzing a vehicle, an animal or a population's movements - each use case requires analyzing underlying spatial information. Global challenges such as virus outbreaks, deforestation
Often, when a cybersecurity incident occurs, the clues to how it happened and who caused it are hidden in network data. In the example discussed here, data scientists were asked to identify who caused a global internet outage by examining a large graph of network data with data visualization. This
