A machine-learning scientist whose work in multimodal and graph AI lets RabbitHawk reason across structured, unstructured and contextual data, connecting numbers to meaning.
Pioneer of Adaptive Dependency Learning GNNs
PhD in Machine Learning, Monash University
Former co-founder & CTO, Tymestack (graph-AI pricing engine)
A leading expert in multi-agent systems, reinforcement learning and sequential decision-making, uniting forecasting and optimization into self-learning decision loops.
Designs RabbitHawk’s goal-setting and alignment models, turning advanced analytics into tools that are easy to use, with decades leading multi-disciplinary teams across retail and beyond.
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
RabbitHawk extends your existing systems rather than replacing them, and treats security as infrastructure rather than an afterthought. Each deployment is cloud-agnostic and built to client requirements.
Dedicated & cloud-agnostic
Deployed as a dedicated, per-customer environment in the cloud you prefer. No data-migration project required: RabbitHawk ingests exports and writes optimized results back to your ERP.
Access controls
Role-based access with SSO, MFA for administrators and authenticated, audited administration. Configuration changes are attributable and reversible.
Auditability
Every forecast adjustment records what changed, why, the supporting evidence and a confidence level, giving a full audit trail for every decision.
Data handling
Encryption in transit and at rest, least-privilege access, and clear data-retention practices. We process only what’s needed to deliver forecasts and recommendations.
Reach goals that border on the impossible
Connect with our team to discover how RabbitHawk can transform your planning and drive measurable outcomes.