Viani holds BSc & MSc in Mathematics from University of Yaoundé I, another MSc in Mathematical Sciences from African Institute for Mathematical Sciences & University of Cape Town, and a PhD in Statistics (and Actuarial Mathematics) from Heriot-Watt University.

Prior to joining the Business School, he held a postdoctoral position in Statistics at the University of Oxford, and thereafter, he spent a few years working in the insurance industry  on various projects related to the quantification and management of longevity risk.

His current research interest lies in Statistical Modelling, Machine Learning & Computations, with applications to Credit Risk, Longevity Risk & Demography. Sound methodology for credit risk and longevity risk have gained crucial importance due to the increasing regulation of the financial service industry, in particular the Basel Accords in banking and the Solvency II process in insurance.
Techniques used include: generalised linear models, survival models & multi-states models,  mixed-effect models, non-parametric smoothing methods, time series analysis, bayesian hierarchical modelling, simulations, machine learning and visualisation.