Sergiy Shelyag is an Associate Professor in Applied Mathematics and Data Science at Flinders University. His research lies at the intersection of applied mathematics, computational physics, data science and machine learning, with a particular focus on developing mathematical and computational methods for complex systems in nature and technology.

His fundamental research has centred on plasma physics, magnetohydrodynamics, computational fluid dynamics, radiative transport, nonlinear and delay dynamical systems, inverse problems, and data-driven modelling. Over time, this methodological expertise has developed into a broad interdisciplinary research program applying mathematical modelling, numerical simulation and machine learning to problems in defence, environmental and marine science, hydrology, biofouling, combustion, health, complex networks as well as other data-intensive systems.

He has a sustained record of nationally competitive and externally funded research, including an ARC Future Fellowship, ARC Discovery funding, Defence-supported projects and current interdisciplinary programs involving Defence, government and industry partners. At Flinders, he has led and contributed to projects in mathematical modelling of complex conflict environments, predictive modelling of marine biofouling, environmental variability and other strategically important applications. His work increasingly focuses on building mathematical modelling and data science as enabling capabilities that connect fundamental methodology with problems of national and industrial significance.

Sergiy has published extensively across applied mathematics, astrophysics, computational physics, data science and interdisciplinary applications. According to Google Scholar, his research has attracted more than 4,000 citations, with h-index of 32. He has supervised and mentored HDR students across several institutions and disciplines and regularly contributes to international peer review and research assessment for journals and funding agencies.

He currently leads several externally funded projects at Flinders and works with collaborators across Australia and internationally to develop interdisciplinary research programs linking mathematical innovation, computation and data-driven methods with real-world scientific and technological challenges.

Sergiy's research expertise is in applied mathematics, computational physics, data science and machine learning, with particular strengths in mathematical modelling and numerical simulation of complex dynamical systems. He has extensive experience in magnetohydrodynamics and plasma physics, computational fluid dynamics, radiative transport, partial and delay differential equations, inverse problems, complex networks, statistical analysis and data-driven modelling. He develops and applies high-performance numerical methods, machine-learning techniques and physics-informed computational approaches to both fundamental scientific problems and interdisciplinary applications. This expertise has supported research across defence, environmental and marine science, hydrology, combustion, health and other complex systems.

Sergiy's current research interests focus on the development and application of mathematical, computational and data-driven methods for complex physical and technological systems. These include plasma dynamics; magnetohydrodynamics and higher-order fluid models; nonlinear and delay dynamical systems; machine learning for physical systems and inverse problems; data-driven reduced-order and predictive modelling; complex networks; environmental and hydrological modelling; marine biofouling; and mathematical modelling of defence and decision-making systems. He is particularly interested in research that connects fundamental mathematical and computational methodology with interdisciplinary problems where modelling, simulation and machine learning can provide new predictive capability, physical insight and practical impact.

• ARC Future Fellow (2013–2016), awarded for research in computational plasma physics.
• Sustained competitive research funding, including ARC Discovery funding and a portfolio of Defence, government and industry-supported interdisciplinary projects; more than AU$1 million in competitive and external research funding across the career.
• Project leadership in strategically important research, including Defence-related mathematical modelling and predictive modelling of marine biofouling, with current projects exceeding $300,000 individually.
• Broad interdisciplinary research record spanning applied mathematics, solar and space physics, computational fluid dynamics, machine learning, complex systems, environmental modelling, hydrology, combustion and health applications.
• Extensive teaching and researcher-development experience, with approximately 20 years of university teaching across mathematics, statistics, physics, computing and data science, together with HDR supervision across several institutions.
• International professional esteem, including regular peer review for major journals and research-funding agencies and for Mathematical Reviews (MathSciNet).