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 83 of 99
2001
A framework for analysis of typed logic programs
V Lagoon, PJ Stuckey
International Symposium on Functional and Logic Programming, 296-310, 2001
Research20 citationsBoolean constraints for binding-time analysis
K Glynn, PJ Stuckey, M Sulzmann, H Søndergaard
Symposium on Program as Data Objects, 39-62, 2001
Machine Learning18 citationsBuilding constraint solvers with HAL
MG de la Banda, D Jeffery, K Marriott, N Nethercote, PJ Stuckey, ...
International Conference on Logic Programming, 90-104, 2001
Machine Learning14 citationsEffective strictness analysis with HORN constraints
K Glynn, PJ Stuckey, M Sulzmann
International Static Analysis Symposium, 73-92, 2001
Machine Learning8 citationsA systematic approach in type system design based on constraint handling rules
PJ Stuckey, M Sulzmann
Third Workshop on Rule-Based Constraint Reasoning and Programming, Cyprus, 2001
Machine Learning7 citationsHigher-precision groundness analysis
M Codish, S Genaim, H Søndergaard, PJ Stuckey
International Conference on Logic Programming, 135-149, 2001
Research7 citationsA general type class framework
K Glynn, PJ Stuckey, M Sulzmann
Technical report, Department of Computer Science, The University of Melbourne, 2001
Research4 citationsUntitled-Preface
N Boland, M Krishnamoorthy, P Stuckey
Annals of Operations Research 108 (1-4), 13-17, 2001
Research1 citations- Research1 citations
Solving Disjunctive Constraints for
K Marriott¹, P Moulder¹, PJ Stuckey, A Borning
Principles and Practice of Constraint Programming: Proceedings 1520, 361, 2001
Machine LearningTrying out alternatives for type class support in a new back end for HAL
B Demoen, M García de la Banda, P Stuckey, E Pontelli
Proceedings of the Colloquium on Implementation of Constraint and Logic …, 2001
ResearchComparative Usability of One-way and Multi-way Constraints for Diagram Editing
M WYBROW, KIM MARRIOTT, L MCIVER, PJ STUCKEY
ACM Journal Name 2 (3), 09, 2001
Machine Learning
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“Forecasting for Data Scientists”, a free 30-chapter video course by co-founder Dr Christoph Bergmeir, from fundamentals to advanced deep learning.