Research & Decision Science
Where forecasting research becomes operational decision infrastructure
RabbitHawk is built on peer-reviewed work in probabilistic forecasting, hierarchical reconciliation, optimization and applied AI, translated into systems that improve real decisions under real constraints.
- publications
- 1,181publications
- citations
- 53,485citations
- highly cited papers
- Top 1%highly cited papers
Scientific foundations
Methods proven in the literature, applied in production
Our team’s research is recognized at the field’s leading venues and used at scale by major organizations. We bring that rigor to enterprise planning, but judge every method by whether it improves decisions in practical settings.
Research → product
| Research area | Enterprise problem | RabbitHawk capability |
|---|---|---|
| Hierarchical forecasting | Local plans do not add up to executive targets | Reconciled forecasts across every level |
| Probabilistic forecasting | Point forecasts hide risk | Full predictive distributions, intervals and scenario ranges |
| Optimization | Forecasts do not tell teams what to do | Constraint-aware recommendations |
| Agentic AI | Context is trapped in emails and notes | Context ingestion and decision agents |
| Human-in-the-loop AI | Automation without trust creates resistance | Approval, override and audit trails |
| Alignment science | Teams optimize against conflicting goals | 90North alignment and goal clarity |
Hierarchical forecasting
Local plans do not add up to executive targets
Reconciled forecasts across every level
Probabilistic forecasting
Point forecasts hide risk
Full predictive distributions, intervals and scenario ranges
Optimization
Forecasts do not tell teams what to do
Constraint-aware recommendations
Agentic AI
Context is trapped in emails and notes
Context ingestion and decision agents
Human-in-the-loop AI
Automation without trust creates resistance
Approval, override and audit trails
Alignment science
Teams optimize against conflicting goals
90North alignment and goal clarity
Selected publications
The full peer-reviewed database: search by title, author, venue, category or year.
1,181 results · page 30 of 99
2020
Nested ECBS for bounded-suboptimal multi-agent path finding
SH Chan, J Li, D Harabor, PJ Stuckey, G Gange, L Cohen, S Koenig
Proceedings of the IJCAI Workshop on Multi-Agent Path Finding. Yokohama, 2020
Multi-Agent Systems2 citationsAlgorithm selection for dynamic symbolic execution: A preliminary study
R Amadini, G Gange, P Schachte, H Søndergaard, PJ Stuckey
International Symposium on Logic-Based Program Synthesis and Transformation …, 2020
Research1 citationsAggregation and Garbage Collection for Online Optimization
A Ek, M Garcia de la Banda, A Schutt, PJ Stuckey, G Tack
International Conference on Principles and Practice of Constraint …, 2020
Optimization1 citationsDiscriminative, generative and self-supervised approaches for target-agnostic learning
Y Jin, W Buntine, F Petitjean, GI Webb
arXiv preprint arXiv:2011.06428, 2020
Research1 citationsLeveraging Cross Feedback of User and Item Embeddings with Attention for Variational Autoencoder based Collaborative Filtering
Y Jin, H Zhao, M Liu, Y Zhu, L Du, L Gao, H Zhang, Y Li
arXiv preprint arXiv:2002.09145, 2020
Research1 citationsSelf-organising Neural Network Hierarchy
S Borgohain, G Kowadlo, D Rawlinson, C Bergmeir, K Loo, H Rangarajan, ...
Australasian Joint Conference on Artificial Intelligence, 359-370, 2020
Machine LearningRecent Developments in the Design and Implementation of Programming Languages
FS de Boer, J Mauro
Schloss Dagstuhl-Leibniz-Zentrum für Informatik GmbH, 2020
ResearchRandom errors are not politically neutral.
ML Blom, A Conway, PJ Stuckey, V Teague, D Vukcevic
CoRR, 2020
Research- Machine Learning
2019
Searching with consistent prioritization for multi-agent path finding
H Ma, D Harabor, PJ Stuckey, J Li, S Koenig
Proceedings of the AAAI conference on artificial intelligence 33 (01), 7643-7650, 2019
Optimization350 citationsSales demand forecast in e-commerce using a long short-term memory neural network methodology
K Bandara, P Shi, C Bergmeir, H Hewamalage, Q Tran, B Seaman
International conference on neural information processing, 462-474, 2019
Machine Learning286 citationsShort-term planning for open pit mines: a review
M Blom, AR Pearce, PJ Stuckey
International Journal of Mining, Reclamation and Environment 33 (5), 318-339, 2019
Optimization162 citations
Learn the science behind the engine
“Forecasting for Data Scientists”, a free 30-chapter video course by co-founder Dr Christoph Bergmeir, from fundamentals to advanced deep learning.