Her research interests include learning for safe and scalable reinforcement learning, learning to learn, motion planning, decision-making, and robot behavior. Aleksandra won IEEE RAS Early Career Award for Industry, the Tom L. Popejoy Award for the best doctoral dissertation at the University of New Mexico in the period of 2011-2014, and was named Distinguished Alumna by the University of New Mexico School of Engineering. Her work has been featured in the New York Times, PC Magazine, ZdNet, VentureBeat, and ​was awarded Best Paper in Service Robotics at ICRA 2018, Best Paper in Reinforcement Learning for Real Life (RL4RL) at ICML 2019, and Best Paper of IEEE Computer Architecture Letters in 2020.