Rika Antonova: Adaptive Kernels and Priors for Data-efficient Bayesian Optimization

Bio

Currently, I am a PhD student at KTH (at the Robotics, Perception & Learning division in the group headed by Danica Kragic). My research is focused on constructing simulation-based kernels to improve data efficiency of Bayesian optimization (BO) for dynamic manipulation tasks, task-oriented grasping and locomotion. More generally, I am interested in reinforcement learning for robotics domains with a challenging sim-to-real gap. Previously, I was a research Masters student at the Robotics Institute at Carnegie Mellon University. At CMU I worked on BO for bipedal locomotion (with Akshara Rai and Chris Atkeson) and on improving data efficiency of BO for personalized education systems (with Emma Brunskill - my MS adviser). Earlier, I was a software engineer at Google. I started in the Personalized Search team in core ranking, then joined the Character Recognition (OCR) team and worked on the open-source OCR engine Tesseract, which was used for Google Books and StreetView.