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 88 of 99
1998
The Journal of Logic Programming Special Issue: Constraint Logic Programming
K Marriott, PJ Stuckey
Elsevier, 1998
Machine LearningIntroduction to the Special Issue on Constraints and Databases
R Ramakrishnan, PJ Stuckey
Constraints and Databases, 5-5, 1998
Machine LearningThe Study of Clusters of Galaxies and Large Scale Structures
W Buntine, M Shilman
NASA Contractor Report, 10875, 1998
ResearchOn-board Science Understanding: NASA Ames' Efforts
TL Roush, P Cheeseman, V Gulick, D Wolf, P Gazis, G Benedix, ...
Automated Learning and Discovery Conference, 1998
Research- Research
Data Understanding Applied to Optimization
W Buntine, M Shilman
Optimization
1997
Solving linear arithmetic constraints for user interface applications
A Borning, K Marriott, P Stuckey, Y Xiao
Proceedings of the ACM Symposium on User Interface Software and Technology, 87, 1997
Machine Learning169 citationsCompiling constraint solving using projection
W Harvey, PJ Stuckey, A Borning
International Conference on Principles and Practice of Constraint …, 1997
Machine Learning24 citationsMeta-programming in CLP (R)
N Heintze, S Michaylov, PJ Stuckey, RHC Yap
The Journal of Logic Programming 33 (3), 221-259, 1997
Research17 citationsExtending EGENET with lazy constraint consistency
P Stuckey, V Tam
Proceedings Ninth IEEE International Conference on Tools with Artificial …, 1997
Machine Learning16 citationsWell-founded ordered search: Goal-directed bottom-up evaluation of well-founded models
PJ Stuckey, S Sudarshan
The journal of logic programming 32 (3), 171-205, 1997
Optimization14 citationsSolving linear arithmetic constraints for user interface applications: Algorithm details
A Borning, K Marriott, P Stuckey, Y Xiao
Technical Report 97-06-01, Dept. of Computer Science and Engineering …, 1997
Machine Learning11 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.