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 32 of 99
2019
Compiling CP subproblems to MDDs and d-DNNFs
D De Uña, G Gange, P Schachte, PJ Stuckey
Constraints 24 (1), 56-93, 2019
Research22 citationsPath planning with CPD heuristics
M Bono, AE Gerevini, DD Harabor, PJ Stuckey
Proceedings of the Twenty-Eighth International Joint Conference on …, 2019
Optimization22 citationsClosing the gap in surveillance and audit of invasive mold diseases for antifungal stewardship using machine learning
D Baggio, T Peel, AY Peleg, S Avery, M Prayaga, M Foo, G Haffari, M Liu, ...
Journal of clinical medicine 8 (9), 1390, 2019
Machine Learning20 citationsConstraint programming for dynamic symbolic execution of JavaScript
R Amadini, M Andrlon, G Gange, P Schachte, H Søndergaard, PJ Stuckey
International Conference on Integration of Constraint Programming …, 2019
Machine Learning20 citationsRAIRE: Risk-limiting audits for IRV elections
M Blom, PJ Stuckey, V Teague
arXiv preprint arXiv:1903.08804, 2019
Machine Learning18 citationsRegarding jump point search and subgoal graphs
DD Harabor, T Uras, PJ Stuckey, S Koenig
International Joint Conference on Artificial Intelligence 2019, 1241-1248, 2019
Optimization17 citationsOptimal context-sensitive dynamic partial order reduction with observers
E Albert, MG De La Banda, M Gómez-Zamalloa, M Isabel, PJ Stuckey
Proceedings of the 28th ACM SIGSOFT International Symposium on Software …, 2019
Research16 citationsDynamic Adaptive Gesturing Predicts Domain Expertise in Mathematics
A Sriramulu, J Lin, S Oviatt
2019 International Conference on Multimodal Interaction, 105-113, 2019
Machine Learning13 citationsToward computing the margin of victory in single transferable vote elections
M Blom, PJ Stuckey, VJ Teague
INFORMS Journal on Computing 31 (4), 636-653, 2019
Research13 citationsDisjoint splitting for conflict-based search for multi-agent path finding
J Li, D Harabor, PJ Stuckey, A Felner, H Ma, S Koenig
International Conference on Automated Planning and Scheduling 2019, 279-283, 2019
Optimization12 citationsCutting the size of compressed path databases with wildcards and redundant symbols
M Chiari, S Zhao, A Botea, AE Gerevini, D Harabor, A Saetti, M Salvetti, ...
Proceedings of the International Conference on Automated Planning and …, 2019
Research11 citationsLocal rapid learning for integer programs
T Berthold, PJ Stuckey, J Witzig
International Conference on Integration of Constraint Programming …, 2019
Research8 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.