Max obtained his PhD in computational statistics from the University of Melbourne in 2008, with his doctoral thesis dedicated to the design and implementation of novel exact inference procedures as applied to clinical trials. Capitalising on his knowledge and experience, Max moved to the Bioinformatics Division of the Walter and Eliza Hall Institute of Medical Research (WEHI) where he served as a lead analyst in several international genome-wide association studies (GWAS), also improving the related methods of analysis. One of the GWAS has led to the discovery of association between the IL28B gene and the rate of response to hepatitis C virus (HCV) treatment, the knowledge that since has been implemented in routine clinical practices. In 2010, Max relocated to the Australian Institute of Health Innovation (University of New South Wales), where he continued working on genomic analyses related to pharmacogenomics of multiple sclerosis. In parallel, Max led the quantitative analysis of the study investigating specifics and impact of nation-wide healthcare accreditation programs, the study jointly covering the population of over 2.7 billion people located in 38 countries. In 2014, Max moved to School of Population Health (University of South Australia) where, working in the South Australian Health & Medical Research Institute (SAHMRI), he participated in a number of international studies applying the instrumental variables (Mendelian randomisation) approach to establish causal associations between certain risk factors and human traits. In 2016, Max joined David Lynn and Lisa Butler at SAHMRI, applying bioinformatics and statistical learning methods in order to reveal functional specifics of empirical information contained in datasets generated by high-throughput technologies.
Max authored two registered inventions related to genetics predisposition to opiate addiction and visualisation of interactions among human disorders as observed in an entire population (http://disease-map.net). Max's expertise is in using a combination of statistical learning, categorical data analysis and prediction modelling for extracting informative and functional patterns contained in empirical datasets. Being a loyal, dedicated and enthusiastic team player, Max has a demonstrated ability of generating effective ‘outside-the-box’ solutions to a wide range of applied problems. In his view, technical expertise, natural intuition and the sense of practical relevance are the main ingredients for professional success.