Felipe Trevizan

Dr. Felipe Trevizan is an Associate Professor BComp Convener in the School of Computing at The Australian National University. His research interests lie at the intersection of Artificial Intelligence, Operations Research, and Machine Learning, focusing on automated planning and scheduling, reasoning under uncertainty, heuristic search, and reinforcement learning. Trevizan earned his Ph.D. in Machine Learning from Carnegie Mellon University under the supervision of Prof. Manuela Veloso. His work includes introducing short-sighted planning and a new model for planning under uncertainty. He has also contributed to research involving Lego Mindstorm robots and has received awards for his work in automated planning and scheduling.

Felipe Trevizan is a researcher at the Australian National University, focusing on Planning under uncertainty, Automated Planning, Heuristic Search, and Artificial Intelligence. His work explores methods for improving planning in uncertain environments, including the use of deep learning for generalized planning and heuristic search techniques for constrained stochastic problems. He has published on topics such as neural network heuristic functions, occupation measure heuristics, and multi-objective planning. His research also addresses probabilistic planning, risk, and Knightian uncertainty, with applications in areas like traffic signal control and flight planning under weather uncertainty.