Rand Low is an Associate Professor of Quantitative Finance at Bond Business School and Honorary Senior Fellow at the University of Queensland.

Professor Low's research areas are in asset and investments management, specifically correlation/dependence modelling, portfolio optimization, risk management, systematic trading strategies and multi-asset investing strategies. His work has been published in leading academic and industry journals such as Journal of Banking & Finance, Quantitative Finance, Journal of Empirical Finance, Journal of Investing, and Journal of Risk.

Prior to his PhD, Professor Low worked in control systems engineering and management roles for Honeywell and is a Chartered Professional Engineer of the mechanical college. Upon completing his PhD, he won a 3-year postdoctoral research grant on portfolio optimization & risk management techniques for financial crises and was a recipient of the Australia Awards - Endeavour fellowship. He has been a visiting research fellow at the New York University - Stern School of Business.

Professor Low has worked at the global headquarters of Bank of America Merrill Lynch and BlackRock in New York City. He led teams of quantitative analysts in building mathematical models for market/credit/operational risk, securities lending, structured products, asset-backed securities, and portfolio management. Professor Low has also defended quantitative model development practices on behalf of these institutions to US regulators such as the Federal Reserve (FED) and the Office of the Comptroller of Currency (OCC). He is familiar with stress-testing and model risk management practices such as model validation and governance for large financial institutions (i.e., CCAR, DFAST).

Professor Low's research interests are in applying statistical and machine learning techniques in investments management in areas such as corporate credit ratings, robo-advisors, digital assets (i.e., cryptocurrencies), and systematic active strategies.  He is investigating mutual fund and hedge fund performance.