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 78 of 99
2004
Type annotations in Haskell
P Stuckey, M Sulzmann, J Wazny
Technical report, National University of Singapore, 204, 2004
Research3 citations- Research3 citations
An abstract interpretation framework for constraint handling rules
G Duck, T Schrijvers, P Stuckey
CW Reports, 37-37, 2004
Machine Learning2 citationsFunctional and Logic Programming: 7th International Symposium, FLOPS 2004, Nara, Japan, April 7-9, 2004, Proceedings
Y Kameyama, PJ Stuckey
Springer Science & Business Media, 2004
Research2 citationsBuilding Design Optimization using Constraint Logic Programming
JS Mashford, RM Drogemuller, PJ Stuckey
INAP/WLP Proc. of the International Conference on the Applications of Prolog …, 2004
Machine Learning2 citationsA unifying inference framework for Hindley/Milner with extensions
PJ Stuckey, M Sulzmann
Technical Report TR12/04, The National University of Singapore, 2004
Bayesian Methods2 citationsA note on the definition of constraint monotonicity
CW Choi, W Harvey, JHM Lee, PJ Stuckey
Machine Learning1 citationsHerbrand constraints in HAL
B Demoen, MG de la Banda, W Harvey, K Marriott, D Overton, PJ Stuckey
Program Development in Computational Logic: A Decade of Research Advances in …, 2004
Machine LearningAutomated Synthesis of Data Analysis Programs: Learning in Logic
W Buntine
International Conference on Inductive Logic Programming, 1-1, 2004
ResearchEDITORIAL BOARDS, PUBLISHING COUNCIL
AP Železnikar, M Gams, M Luštrek, D Torkar, S Alagic, A Ardo, ...
Informatica 28 (1), 2004
Research
2003
Flexible access control policy specification with constraint logic programming
S Barker, PJ Stuckey
ACM Transactions on Information and System Security (TISSEC) 6 (4), 501-546, 2003
Machine Learning173 citationsFinding all minimal unsatisfiable subsets
MG de la Banda, PJ Stuckey, J Wazny
Principles and Practice of Declarative Programming: International Conference …, 2003
Research121 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.