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 82 of 99
2002
- Research6 citations
- Research5 citations
Fourier elimination for compiling constraint hierarchies
W Harvey, PJ Stuckey, A Borning
Constraints 7 (2), 199-219, 2002
Machine Learning4 citationsFlexible graph layout for the web
T Hansen, K Marriott, B Meyer, PJ Stuckey
Journal of Visual Languages & Computing 13 (1), 35-60, 2002
Research3 citationsImproving GSAT Using 2SAT
PJ Stuckey, L Zheng
International Conference on Principles and Practice of Constraint …, 2002
Research1 citationsUsing the heap to eliminate stack accesses
Z Somogyi, PJ Stuckey
Proceedings of the 4th ACM SIGPLAN international conference on Principles …, 2002
Research
2001
The Cassowary linear arithmetic constraint solving algorithm
GJ Badros, A Borning, PJ Stuckey
ACM Transactions on Computer-Human Interaction (TOCHI) 8 (4), 267-306, 2001
Machine Learning266 citationsA model for inter-module analysis and optimizing compilation
F Bueno Carrillo, M García de la Banda, MV Hermenegildo, K Marriott, ...
International Workshop on Logic-Based Program Synthesis and Transformation …, 2000
Research43 citationsSolving disjunctive constraints for interactive graphical applications
K Marriott, P Moulder, PJ Stuckey, A Borning
International conference on principles and practice of constraint …, 2001
Machine Learning36 citationsOptimizing compilation of constraint handling rules
C Holzbaur, MG de la Banda, D Jeffery, PJ Stuckey
International Conference on Logic Programming, 74-89, 2001
Machine Learning28 citationsLearning as applied to stochastic optimization for standard-cell placement
L Su, W Buntine, AR Newton, BS Peters
IEEE Transactions on Computer-Aided Design of Integrated Circuits and …, 2001
Optimization26 citationsCost-based unbalanced R-trees
KA Ross, I Sitzmann, PJ Stuckey
Proceedings Thirteenth International Conference on Scientific and …, 2001
Research25 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.