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 61 of 99
2011
Technical Communications of the 27th International Conference on Logic Programming, ICLP 2011
JP Gallagher, M Gelfond
ICLP (Technical Communications), 2011
ResearchTowards resource usage analysis of miniZinc models
F Bueno Carrillo, M García de la Banda, MV Hermenegildo, ...
Instituto IMDEA Software, 2011
ResearchDiscovery in Text: Visualisation, Topics and Statistics
W Buntine
Proceedings of the Australasian Language Technology Association Workshop …, 2011
Research
2010
Unsupervised object discovery: A comparison
T Tuytelaars, CH Lampert, MB Blaschko, W Buntine
International journal of computer vision 88 (2), 284-302, 2010
Research258 citationsWord features for latent dirichlet allocation
J Petterson, W Buntine, S Narayanamurthy, T Caetano, A Smola
Advances in Neural Information Processing Systems 23, 2010
Research143 citationsA segmented topic model based on the two-parameter Poisson-Dirichlet process
L Du, W Buntine, H Jin
Machine learning 81 (1), 5-19, 2010
Research109 citationsLazy clause generation: Combining the power of SAT and CP (and MIP?) solving
PJ Stuckey
International Conference on Integration of Artificial Intelligence (AI) and …, 2010
Research89 citationsPhilosophy of the MiniZinc challenge
PJ Stuckey, R Becket, J Fischer
Constraints 15 (3), 307-316, 2010
Research83 citationsA Bayesian view of the Poisson-Dirichlet process
W Buntine, M Hutter
arXiv preprint arXiv:1007.0296, 2010
Bayesian Methods83 citationsLock-free parallel dynamic programming
A Stivala, PJ Stuckey, MG de la Banda, M Hermenegildo, A Wirth
Journal of Parallel and Distributed Computing 70 (8), 839-848, 2010
Research77 citationsFast and accurate protein substructure searching with simulated annealing and GPUs
AD Stivala, PJ Stuckey, AI Wirth
BMC bioinformatics 11 (1), 446, 2010
Optimization69 citationsSequential latent dirichlet allocation: Discover underlying topic structures within a document
L Du, WL Buntine, H Jin
2010 IEEE International Conference on Data Mining, 148-157, 2010
Research58 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.