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RabbitHawk

The team

A team built for hard decision problems

Most forecasting tools are built around model libraries. RabbitHawk is built around the harder problem: turning uncertain forecasts, messy context and operational constraints into decisions people can trust. That takes forecasting science, optimization, applied AI, product engineering and enterprise delivery in one team.

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Team capability map

How the team maps to the platform

Each discipline shows up directly in the product, so the team is the architecture.

  • Forecasting science

    Probabilistic, hierarchical, reconciled forecasts

  • Optimization & operations research

    Constraint-aware recommendations

  • Applied & agentic AI

    Contextual intelligence and decision agents

  • Product engineering

    The planner workspace and integrations

  • Enterprise delivery

    Governed deployment and implementation

  • Alignment science

    Goal clarity that feeds the engine (90North)

Team members

Scientific leadership

The researchers behind the forecasting, optimization and AI that power the platform.

Dr Christoph Bergmeir

Dr Christoph Bergmeir

Forecasting & AI systems

One of the world’s leading forecasting researchers. His methods are used at global scale, and now power RabbitHawk.

A Senior Research Fellow at Monash University and Senior Fellow in Computer Science & AI at the University of Granada, Christoph is a world-recognized authority on time-series forecasting and uncertainty modeling, spanning adaptive forecasting, explainable AI and coherent prediction across complex hierarchies. He co-authored widely used open-source tools including the Forecast package, NeuralProphet, Kats and Forecastingdata.org, and has collaborated with organizations such as Meta, Walmart, Tokopedia, Honeywell, Worley and Yarra Valley Water. At RabbitHawk he focuses on adaptive forecasting that guides every optimization decision under real-world uncertainty.

  • 13,200+ citations across forecasting & ML research
  • Clarivate Highly Cited Papers (top 1% in forecasting)
  • Maria Zambrano Senior Fellow, University of Granada
  • Australian Research Council DECRA recipient
  • Co-author of NeuralProphet, Kats & the Forecast package
WebsiteScholar
Dr Peter Stuckey

Dr Peter Stuckey

Constraint programming & optimization

A pioneer in constraint programming, modeling and solving complex combinatorial problems.

A pioneer in constraint programming and discrete optimization, with decades of work on solver design and decision systems for difficult industrial problems, Peter is a Professor in the Faculty of Information Technology at Monash University and a project leader in the Data61 CSIRO laboratory. A pioneer of solver-independent modeling languages and high-performance optimization engines, his methods power advanced scheduling, routing and resource-allocation systems for industry and research, working with companies such as Oracle and Rio Tinto. At RabbitHawk he guides integrated optimization: applying the right next action under real-world constraints.

  • 23,000+ citations, h-index 71
  • Fellow of the AAAI (2019)
  • ACM Distinguished Scientist
  • Google Australia Eureka Prize for Innovation in Computer Science
Scholar
Dr Wray Buntine

Dr Wray Buntine

Machine learning & generative AI

Australia’s foremost scholar for the statistical analysis of text and predictive modeling.

A senior machine-learning researcher known for statistical text analysis, predictive modeling and large language models, with a global reputation built over decades of state-of-the-art research spanning 18 book chapters, 48 journal articles and 81 refereed conference papers. Currently Director of the Computer Science Program at VinUniversity, he co-edits ACM Transactions on Probabilistic Machine Learning and has worked with NASA Ames, UC Berkeley and Google.

  • Top 0.75% most-cited AI researchers globally
  • 13,000+ citations and 200+ publications
  • AI Best Paper Award, ECAI
  • General Chair, Asian Conference on Machine Learning 2024
  • Co-editor, ACM Transactions on Probabilistic ML
Scholar

Applied delivery

Translating the science into alignment, product and customer outcomes.

Dr Abishek Sriramulu

Dr Abishek Sriramulu

Graph AI & adaptive modeling

A machine-learning scientist who turns chaos into pattern through multimodal and graph AI.

Focused on multimodal and graph AI, Abishek connects structured and unstructured context to improve prediction, causal understanding and adaptive decision systems. With a PhD in machine learning (forecasting) from Monash University, he is an authority in signal processing, forecasting, neural networks and multimodal learning, and his published work on multimodal AI and hierarchical time-series modeling forms the foundation of RabbitHawk’s unified forecasting backbone. He was co-founder and CTO of Tymestack, a graph-AI pricing engine with beta savings exceeding €300M annually, and has collaborated with large retailers across the USA, Asia and Australia, including Woolworths Group, Tokopedia and Catch of the Day, developing RabbitHawk’s hierarchical forecasting and unstructured-data IP.

  • Pioneer of Adaptive Dependency Learning GNNs
  • PhD in Machine Learning, Monash University
  • Former co-founder & CTO, Tymestack (graph-AI pricing engine)
  • ARC and Meta research-grant recipient
ScholarLinkedIn
Dr Frits de Nijs

Dr Frits de Nijs

Optimization & agentic learning

A world-class optimization scientist working where reinforcement learning meets real-world constraints.

Working at the intersection of optimization, reinforcement learning and real-world constraints, Frits builds autonomous systems that plan, adapt and improve over time. A Research Fellow at Monash University and formerly with CSIRO, he is a leading expert in multi-agent systems, stochastic optimization, reinforcement learning and sequential decision-making. At RabbitHawk he develops the frameworks for autonomous goal alignment and adaptive optimization across complex environments.

  • Research Fellow, Monash University
  • Australian Research Council DECRA grant recipient
  • PhD, TU Delft (multi-agent decision making)
  • 3rd globally, NeurIPS 2021 ML4CO challenge
Scholar
Paul Shale

Paul Shale

Alignment strategy & product design

Turns advanced analytics into tools that are genuinely easy to use.

Paul transforms advanced science into usable systems, helping teams align goals, shape product strategy and turn complex capability into practical delivery. He has led large multi-disciplinary teams across the USA, Australia and New Zealand, with deep experience in retail, construction, infrastructure, consulting, government and tourism. He designs RabbitHawk’s goal-setting and alignment models, facilitates 90North sessions for customers, and develops the architecture that lets customers harness the team’s expertise.

  • B.Com / LLB (Hons), University of Auckland
  • Disruptive Innovation Program, Harvard Business School
  • Former CEO, FCB NZ & Roadtrippers AU; former CCO, Nextspace
  • Led teams of 250+ across the USA, Australia & New Zealand
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Research-backed

Decades of published science, applied to your decisions

Our team’s work spans probabilistic forecasting, hierarchical reconciliation and large-scale optimization, recognized at the field’s leading venues. Explore the peer-reviewed foundations behind the platform.

Browse the research database
peer-reviewed publications
1,181peer-reviewed publications
citations
53,485citations
highly cited papers
Top 1%highly cited papers

Reach possibly impossible goals, with the team behind the science

Connect with our researchers and builders to see how RabbitHawk turns measurement into meaning for your organization.

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