Yasemin earned her PhD at KTH Royal Institute of Technology and is a robotics researcher specializing in data-efficient learning from multi-sensory data for robotic applications. Her work spans cognitive robotics, robotic manipulation, and autonomous systems, focusing on enabling robots to learn complex tasks efficiently.

She has worked both in academia and in industrial research (KTH, University of Birmingham, Chalmers, UCL, ABB, Vicarious in Sweden, UK, USA), contributing to several large-scale EU projects, CogX (Cognitive Systems that Self-Understand and Self-Extend), RoboHow (Web-enabled and Experience-based Cognitive Robots that Learn Complex Everyday Manipulation Tasks), RoMaNs (Robotic Manipulation for Nuclear Sort and Segregation), and leading SARAFun (Smart Assembly Robot with Advanced Functionalities). She has led research on robotic grasping and manipulation for industrial tasks at companies, developing prototype systems capable of handling diverse objects and achieving various manipulation goals also learning from demonstrations.

Her contributions to robotics have been recognized with multiple awards, including the Best Paper Award at the IEEE International Conference on Robotics and Automation for Humanitarian Applications (RAHA) in 2016, the Best Manipulation Paper Award at IEEE ICRA in 2013, and recognition as a finalist for the CoTeSys Cognitive Robotics Best Paper Award at IEEE IROS in 2013. She also serves as a reviewer and Associate Editor for robotics conferences and journals.