Biography

Dr. Felipe Trevizan is currently an Senior Lecturer in the School of Computing (formerly known as Research School of Computer Science) at the Australian National University (ANU). Previously, Felipe was a Senior Research Scientist at NICTA and Data61/CSIRO. Felipe earned his Ph.D. in Machine Learning (2013) from Carnegie Mellon University (CMU) under the supervision of Prof. Manuela Veloso. In his thesis, Felipe introduced short-sighted planning, a novel approach to effectively plan under uncertainty.

Felipe's research interests lie at the intersection of Artificial Intelligence, Operations Research and Machine Learning including automated planning and scheduling, reasoning under uncertainty, heuristic search, and machine learning.

Along with colleagues and students, Felipe is the co-recipient of the 2016 Kikuchi-Karlaftis Best Paper Award of the Transport Research Board and the Best Paper Award at the International Conference on Automated Planning and Scheduling (ICAPS) in 2016 and 2017.

For more details, see Felipe's CV, Google Scholar profile and DBLP entry.

News and Highlights
- Partial-Space Search: New search algorithm designed for learned heuristics (ICAPS'25).
- Efficiently learning heuristics by considering symmetries (ICAPS'25).
- I am organizing the Reliability In Planning and Learning (RIPL) at ICAPS'25.
- I am organizing the Learning for Planning: An Introduction and Primer tutorial at ICAPS'25.
- Optimal Ranking: Guiding GBFS using a ranking between states learned from data (IJCAI'24).
- i2-dual-det: New planner for finding deterministic policies for C-SSPs (ECAI'24).
- GOOSE: First domain-independent method for learning heuristics based on lifted representations (AAAI'24).
- CG-iLAO*: New planner for SSPs based on constraint generation (AAAI'24).
- I was one of the organizers of GenPlan23 at NeurIPS.
- First heuristic search algorithms for Multi-Objective Stochastic Planning (AAAI'23).
- New admissible heuristics for Multi-Objective Deterministic Planning (ICAPS'22).