Rate My Professor Hanna Kurniawati

Hanna Kurniawati is a Professor in the School of Computing at the Australian National University and holds the SmartSat CRC Professorial Chair for System Autonomy, Intelligence and Decision-Making. She earned a BSc in Computer Science from the University of Indonesia and a PhD in Computer Science from the National University of Singapore in 2008 for work on robot motion planning. Following her PhD, she served as a Research Fellow at the National University of Singapore, then as Postdoctoral Associate and Research Scientist at the Singapore-MIT Alliance for Research and Technology, MIT. She advanced from Lecturer to tenured Senior Lecturer at the University of Queensland School of Information Technology and Electrical Engineering. In 2019, she joined ANU as tenured Senior Lecturer with an ANU Futures Fellowship, was promoted to Associate Professor in 2021, and to Full Professor in 2023.

Her research focuses on robotics, decision-making under uncertainty, motion planning, computational geometry, integrated planning and learning, and reinforcement learning, particularly scalable techniques under the Partially Observable Markov Decision Processes framework for robust autonomous systems. She founded the Robot Decision Making group, leads the Robust Decision-making & Learning Lab, and is ANU Node Lead and Planning & Control theme lead for the Australian Robotics Inspection and Asset Management Hub. Awards include the Robotics: Science and Systems 2021 Test of Time Award, ICAPS 2015 Best Paper Award, ICRA 2015 Best Paper Finalist, and Australian Computer Society ICT Researcher of the Year 2015 Gold Award. She delivered keynotes at IROS 2018, ICRA 2025, and ICAPS 2025, served as ARAA President 2019-2020, Senior Editor of IEEE RA-L, ICRA 2022 Program Co-Chair, and IEEE TRO Editor. Key publications encompass 'Sampling-based Motion Planning for Optimal Probability of Collision under Environment Uncertainty' (2024), 'Partially Observable Reference Policy Programming' (2025), and 'POSGGym: a library for decision-theoretic planning and learning in partially observable multi-agent environments' (2025).