Pink After Work with Kognic

Session by: Roshni Navinchandra, Elena Fotina, Sara Thiringer & Sara Roth

Welcome to an evening with the Kognic team  where we will learn about the technology behind increasingly automated driving, and the challenges which keeps us from just relaxing in the back-seat already. We will talk about sensor setups, data needs for machine learning, human-machine collaboration, advanced 2D and 3D data visualisation and ethical dilemmas. Apart from a Kognic-lead presentation, you will have the chance to dive into the world of autonomous driving and try to spot pedestrians, vehicles and the environment through the eyes of a self-driving car. 

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Roshni is a Computer Science Engineer born and raised in India, who ventured to Gothenburg for a taste of new cultures and experiences. Working at Kognic for 3+ years, she works with making sense out of data and building tools and methods to ensure quality of datasets and prevent label errors. Beyond work, she finds joy in sketching and traveling to new destinations!
 
Elena has worked at Kognic for 1,5 years. She builds and maintains pipelines ensuring data accuracy and reliability. With extensive experience handling diverse datasets in various domains, she likes diving into datasets to uncover insights and help drive decisions.
 
Sara is born and bred in Gothenburg and has worked at Kognic for 2 years, she is also a current code mentor at Pink. In her daily work she builds tools and conducts analysis for ensuring data quality and preventing label errors. She has a background in social science as well as informatics and statistics, and likes to discuss and challenge social aspects of AI applications.

Sara is born and raised in Halmstad but moved to Gothenburg to study at Chalmers. She has a background in Mechanical engineering and Systems, control and mechatronics with a focus on Computer vision and Deep neural networks. She has been working at Kognic for just over 1 year. In her daily work, she helps our clients evolve their datasets to make their machine-learning models detect objects in live environments and make autonomous vehicles possible. She spends most of her spare time in the stable.