PeopleofSuperintelligence

Researcher

Felipe Trevizan

Felipe Trevizan is an AI planning researcher whose work helps agents plan under uncertainty. He completed a machine-learning PhD at Carnegie Mellon in 2013, then worked as a Senior Research Scientist at NICTA and Data61/CSIRO before joining the Australian National University (ANU). His research spans occupation-measure heuristics, neural policies for generalised planning, learned planning heuristics, partial-space search, and constraint generation for stochastic shortest paths. He is an Associate Professor at ANU and convenes its Bachelor of Computing.

Timeline

  1. 2013

    A PhD on short-sighted planning

    He earns a PhD in machine learning at Carnegie Mellon in 2013 under Manuela Veloso. His thesis introduces short-sighted planning as an approach to plan under uncertainty.

  2. 2016–2017

    ICAPS paper awards

    He shares ICAPS Best Paper Awards with coauthors in 2016 and 2017. His 2017 occupation-measure heuristics use action probabilities in stochastic shortest-path planning.

  3. By 2018

    Research at NICTA and Data61

    Before joining ANU in 2018, he works as a Senior Research Scientist at NICTA and Data61/CSIRO.

  4. 2019–2020

    Action Schema Networks

    He coauthors Action Schema Networks (ASNets), a neural architecture that learns generalised policies for probabilistic and classical planning using the relational structure of planning problems.

  5. 2024

    WL-GOOSE heuristics

    With Dillon Z. Chen and Sylvie Thiébaux, he develops WL-GOOSE, which learns planning heuristics from scratch using graph features.

  6. 2025

    Partial-space search

    With Ryan Xiao Wang, he introduces partial-space search, which uses action-schema relations to prune poor actions earlier. Their LazyLifted planner combines this search method with learned action-set heuristics.

  7. 2026

    Constraint generation for SSPs

    The 2026 AI Journal paper with Johannes Schmalz presents a new planner for stochastic shortest-path problems based on efficient constraint generation. It introduces CG-iLAO*, which considers only 40% of iLAO*'s actions on many problems.

  8. Since 2018

    Associate Professor at ANU

    He is an Associate Professor in ANU's School of Computing and convenes the Bachelor of Computing. The School lists him among its Intelligent Systems associate professors in 2026.

Career

  1. Since 2018Australian National UniversityAssociate Professor
  2. National ICT AustraliaSenior Research Scientist
  3. CSIROSenior Research Scientist

Education

  1. 2013Carnegie Mellon UniversityPhD

From the biography.

  1. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other universityIncludes Felipe Trevizan. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other university. We compare doctorate and 5+ year employment links, career timing and current workplaces for 187 major-prize and 1,111 best-paper winners.
  2. The United States employs an estimated 48% of people shaping AIIncludes Australian National University. We estimate where the people shaping AI work today, how universities, companies and research institutes split across countries, and how country shares have changed since the 1960s.
  3. The median h-index is 48 at universities and 14 at frontier AI labsIncludes Australian National University. The median h-index is 48 at universities and 14 at frontier AI labs. We compare publishing careers, recent citations, dual appointments and job moves across 2,420 university researchers and 308 frontier-lab researchers.
  4. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionIncludes Australian National University. We estimate where the people shaping AI studied and where their careers led. Massachusetts Institute of Technology leads with 467 former students, including 291 recorded doctorates.

Colleagues

People named in this biography, and people with dated positions at the same organization in the same years.

Sources and credits

Photo: We have no published photo of Felipe Trevizan yet. Send us a free-licensed photo.

  1. Biography — Felipe Trevizan's Home Page felipe.trevizan.org. Checked 21 October 2026.
  2. Occupation Measure Heuristics for Probabilistic Planning | Proceedings of the International Conference on Automated Planning and Scheduling ojs.aaai.org. Checked 21 October 2026.
  3. Dr Felipe Trevizan | Australian National University anu.edu.au. Checked 21 October 2026.
  4. ASNets: Deep Learning for Generalised Planning arxiv.org. Checked 21 October 2026.
  5. Return to Tradition: Learning Reliable Heuristics with Classical Machine Learning arxiv.org. Checked 21 October 2026.
  6. Leveraging Action Relational Structures for Integrated Learning and Planning arxiv.org. Checked 21 October 2026.
  7. Efficient Constraint Generation for Stochastic Shortest Path Problems arxiv.org. Checked 21 October 2026.
  8. School of Computing 2026 comp.anu.edu.au. Checked 21 October 2026.