Rate My Professor Timofei Bogomolov

Dr Timofei Bogomolov serves as a Lecturer in the School of Mathematical Sciences within the College of Sciences at Adelaide University. As a lecturer in Data Science and Business Intelligence, his research and teaching interests lie in applications of advanced data analysis to support evidence-based decision making in business, governance, defence, and health. He teaches courses in data science, programming in R, and programming in Python, including COMP 2032 Data Analytics using R (2025, 2024), COMP 5070 Statistical Programming for Data Science (2025, 2024), and INFS 5096 Customer Analytics in Large Organisations (2025, 2024).

Bogomolov's publications cover quantitative finance, health systems, consumer behaviour, and simulation modelling. Key works include 'Pairs trading based on statistical variability of the spread process' (Quantitative Finance, 2013), 'Can time difference deter arbitrage opportunities?' (The Journal of Asset Management, 2013), 'Socio-demographic differences in supermarket shopper efficiency' (Australasian Marketing Journal, 2016), 'Hospital occupancy and discharge strategies: a simulation-based study' (Internal Medicine Journal, 2017), 'Hospital’s instability wedges' (Health Systems, 2020), and 'Analyzing proprietary, private label, and non-brands in fresh produce purchases' (International Journal of Market Research, 2021). He co-authored book chapters 'Identifying patterns in fresh produce purchases: the application of machine learning techniques' (Handbook of Research on Big Data Clustering and Machine Learning, 2020; Research Anthology on Machine Learning Techniques, Methods, and Applications, 2022). Conference contributions feature 'Markov chain modelling of causality in anomalous and non-anomalous combat event series' (MODSIM, 2023) and 'Regression-based approaches for simulation meta-modelling in the presence of heterogeneity and correlation' (MODSIM, 2021). Previously at University of South Australia, he is eligible to co-supervise Masters and PhD students, currently overseeing doctoral projects on sample efficient hierarchical reinforcement learning for simulation, modelling of lead dispersion in air and recontamination rates, and causal relationships in military options.