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 63 of 99
2010
An introduction to minizinc
K Marriot, PJ Stuckey, L De Koninck, H Samulowitz
University of Melbourne G 12, 2012, 2010
Research6 citationsImproved consensus clustering via linear programming
N Downing, PJ Stuckey, A Wirth
Proceedings of the Thirty-Third Australasian Conferenc on Computer Science …, 2010
Research6 citationsTowards a lightweight standard search language
H Samulowitz, G Tack, J Fischer, M Wallace, P Stuckey
The 9th international workshop on constraint modelling and reformulation …, 2010
Optimization6 citations- Bayesian Methods6 citations
Introduction to the special issue on the Fourteenth International Conference on Principles and Practice of Constraint Programming (CP 2008)
PJ Stuckey
Constraints 15 (2), 149-150, 2010
Machine LearningMAU Abedin, V. Ng, L. Khan Non-Transferable Utility Coalitional Games via Mixed-Integer Linear Constraints...................... 633 G. Greco, E. Malizia, L. Palopoli, F …
S Katrenko, PW Adriaans, M van Someren, C Cayrol, FD de Saint-Cyr, ...
Machine LearningBeyond 2D-grids
N Quadrianto, K Kersting, T Tuytelaars, WL Buntine
International Conference on Multimedia Information Retrieval (MIR) 2010, 2010
Research
2009
Propagation via lazy clause generation
O Ohrimenko, PJ Stuckey, M Codish
Constraints 14 (3), 357-391, 2009
Research352 citationsLazy clause generation reengineered
T Feydy, PJ Stuckey
International Conference on Principles and Practice of Constraint …, 2009
Research173 citationsConfidence-based work stealing in parallel constraint programming
G Chu, C Schulte, PJ Stuckey
International conference on principles and practice of constraint …, 2009
Machine Learning120 citations- Research115 citations
Why Cumulative Decomposition Is Not as Bad as It Sounds
A Schutt, T Feydy, PJ Stuckey, MG Wallace
International conference on principles and practice of constraint …, 2009
Research98 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.