Team.

Dysrupt Labs is privately held. Co-authors of the peer-reviewed publications span the University of Cambridge, Stanford University, the University of Melbourne and the Florey Institute of Neuroscience and Mental Health.

Executive leadership

Karl Mattingly

Chief Executive Officer & Founder

Columbia Business School MBA. Twenty-five-year career at ANZ Banking Group across leveraged finance, private equity and structured products. Seventeen-year track record building and operating collective-intelligence platforms. Author on the peer-reviewed publications underlying the signal suite.

Professor Anne-Louise Ponsonby

Chief Scientific Adviser

Division Head, The Developing Brain, Florey Institute of Neuroscience and Mental Health. Adjunct Professor, Centre of Epidemiology and Biostatistics, University of Melbourne. More than five hundred peer-reviewed publications in The Lancet, BMJ and Annals of Epidemiology. Co-author on the 2014 Annals of Epidemiology, 2023 eBioMedicine and 2024 Journal of Financial Markets papers underlying the signal suite.

Chad Nash

Chief Technology Officer

PhD in Quantum Physics. Architect of the Almanis platform — the machine-learning pipeline, the divergence-detection system and the dataset delivery infrastructure. Ten-year tenure with the company.

Advisory board

Grahame Leonard AM

Chair

Former Victorian Multicultural Commissioner and Chief Executive Officer of Transparency International Australia. Member of the Order of Australia.

William Abbott

Member

Former Partner, HWL Ebsworth Lawyers. Background in institutional investment management and alternative-data evaluation.

Stephen Markscheid

Member

Former GE Capital and Boston Consulting Group. Quantitative strategy and systematic trading operations.

Ian Clark

Member

Former PwC Partner. Institutional due diligence, operational risk and fund compliance.

What the platform does

Dysrupt Labs generates three concurrent signals on a five-indicator US macro basket (CPI, NFP, GDP, PCE, Retail Sales) and on other variables of interest — a general forecaster consensus, a machine-learning-filtered cohort signal and a real-time divergence measure — from the microstructure of the Almanis panel, with public-exchange extraction available as a separate feed.

Co-investigator and academic-partnership enquiries are welcome via the contact address.

Contact — contact@dysruptlabs.com