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Aaron William ThorntonView profile

Senior Lecturer

• Mathematics
• Computational and Simulation Sciences
• Chemistry
• +11 more

Dr Aaron William Thornton is a Principal Research Scientist at CSIRO and a Senior Lecturer at Southern Cross University, with a strong interdisciplinary focus bridging mathematics, computational sciences, materials engineering, and industrial applications. His work integrates fundamental research with commercial innovation, particularly in energy-efficient separations, gas adsorption, and bio-inspired materials. Education: PhD in Mathematics, University of Wollongong and CSIRO (2010) Bachelor of Mathematics and Finance (1st Class Honors), University of Wollongong (2005) His research interests lie at the intersection of mathematics, computational modeling, separation science, thermodynamics, and machine learning . He develops novel solutions for challenges in aerospace, materials, and biology, with a strong emphasis on real-world impact. His work often involves high-performance simulations and in-silico design of advanced materials. His recent publications and projects reflect a consistent trend toward energy-efficient technologies, carbon capture from air, medical gas devices, and smart membranes . These works combine computational innovation with experimental validation and industrial collaboration, particularly with Air Liquide and through ARC-funded research hubs. Scientific Awards: Performance and Innovation Award, Boeing (2019) Executive Endeavour Fellowship, Department of Education (2018) Julius Career Award, CSIRO (2013–2016) Jacques-Emile Dubois Award, Chemical Structure Association Trust (2013) Young Tall Poppy Science Award - VIC, Australian Institute of Policy & Science (2012) Dr Thornton has secured major research grants from the ARC and the Science and Industry Endowment Fund, supporting projects such as 'Breaking the Limit in Membranes' and 'Engineering Ion Channels'. He has led commercial initiatives including the 'CO2Gen' device and the 'Pocketable Oxygen Device'. He has advised or collaborated on startups and deep tech innovation through programs like the Bright Arena Accelerator. He has also mentored students and early-career researchers, though specific advisees are not listed. He is actively involved in academic service as a member of the editorial boards of Industrial & Engineering Chemistry Research and Membranes (MDPI), and previously served as President of the Membrane Society of Australasia. His international scholarly collaborations include affiliations with UC Berkeley, Chimie ParisTech, Hanyang University, University of Cambridge, and NTUA, reflecting a globally connected research profile.

Vishnu MonnView profile

Associate Professor

• High-performance computing
• Predictive analytics
• Machine learning & Artificial Intelligence
• +7 more

Vishnu Monn is an Associate Professor at Monash University Malaysia's School of IT, serving as Deputy Head of Education and Director of the Advanced Computing Platform. He holds a PhD in Engineering from Multimedia University (2016) and has over 15 years of academic and industry experience, including roles at Panasonic R&D Centre Malaysia (2005–2009) and Multimedia University (2009–2017). His research focuses on high-performance computing, predictive analytics, machine learning, computer vision, and soft robotics, with over MYR 1 million in secured grants as principal investigator. Education: B.Eng. (First-Class Honours) in Electrical and Electronics Engineering (2004) M.Eng. in Electrical and Electronics Engineering (2007) Ph.D. in Engineering (2016) Research interests include: High-performance computing architectures and applications Predictive analytics for industrial and environmental systems Machine learning models for computer vision tasks Soft robotics control systems Recent projects span autonomous driving algorithms, IoT-enabled smart cities, and soft robotics using reinforcement learning. He has published 61+ peer-reviewed articles in top journals and conferences, with recent work emphasizing multimodal learning, generative models, and blockchain optimization. Scientific contributions include leadership in interdisciplinary research, securing major grants, and establishing Monash Malaysia's high-performance computing facility. Teaching commitments include courses on big data, parallel computing, and embedded systems, with roles as chief examiner and program coordinator.

Mohammad Asghari-JafarabadiView profile

Associate Professor

• Biostatistics
• Survival Analysis
• Competing Risks
• +7 more

Assoc. Prof. Mohammad Asghari-Jafarabadi is an Associate Professor in the Department of Psychiatry at Monash University, affiliated with Monash Health. With over 15 years of expertise, he specializes in advanced statistical methodologies including Survival Analysis, Competing Risks, and Frailty Models. He has published over 1000 articles with a remarkable H-index and is recognized as a Top 1% scientist globally. His work contributes to UN Sustainable Development Goals related to health and well-being. Education: PhD in Biostatistics (Tarbiat Modares University, 2009) – Frailty Competing Risks Model for Colorectal Cancer Survival MSc in Biostatistics (Tarbiat Modares University, 2006) – Independent Component Analysis in Medical Signal Processing BSc in Statistics (Mazandaran University, 2004) – Psychometric Evaluation of MMPI Research Focus: His interdisciplinary research spans Biostatistics, Machine Learning applications in healthcare, cancer survival modeling, and global health burden analysis. He leads projects like the COX2-driven T cell suppression study in colorectal cancer microenvironments (2024–2028). Publications Trends: Recent work emphasizes Bayesian survival analysis, AI-driven diagnostic tools, and global health burden forecasting. Over 696 peer-reviewed articles highlight collaborations across oncology, cardiology, and epidemiology. Grants & Leadership: Has served as a professor and dean, securing prestigious grants. Current role as Associate Investigator in major research projects demonstrates his collaborative approach. Labs/Teams: Involved in multidisciplinary teams analyzing cancer survival, cardiovascular outcomes, and global health metrics through Bayesian and machine learning frameworks.

Athanasios PantelousView profile

Professor

• Quantitative Research
• Mathematical Modeling under Risk/Uncertainty
• Actuarial Science
• +3 more

Professor Athanasios Pantelous holds a position at Monash University's Department of Econometrics and Business Statistics, where he leads research in quantitative methods and mathematical modeling under risk/uncertainty. He has held roles at the University of Liverpool (Lecturer/Reader) and is affiliated with RiskLab Australia and SWUFE's Belief Analytics Lab. His expertise spans actuarial science, finance, stochastic mechanics, and operational research. Education: Bachelor's in Mathematics (2001), National and Kapodistrian University of Athens M.Sc. in Statistics/OR (2005), Applied Mathematics (2007), National and Kapodistrian University of Athens M.Sc. in Actuarial Statistics (2006), Ph.D. in Statistics (2008), Athens University of Economics and Business Second Ph.D. in Modelling and Systems Science (2013), City University London Research Interests: Focuses on quantitative research methods with applications in actuarial science, finance, and stochastic systems. Develops models for risk management, decision-making under uncertainty, and policy analysis. Active in causal analytics, machine learning, and sustainable supply chain systems. Recent Research Trends: Articles highlight advancements in causal inference algorithms (e.g., patterncausality R package), vaccine distribution frameworks, and stochastic modeling for nonlinear systems. Recent work explores behavioral finance (e.g., Chinese household investment behavior), sustainable supply chains, and environmental policy analysis. Awards & Grants: Co-led the £5.8M EPSRC/ESRC Center for Risk & Uncertainty (2014–2017). Current projects include Economic Scenario Generation for superannuation modeling and blockchain/cryptocurrency research. Recognized as a Fellow of the Hellenic Actuarial Society. Advising & Labs: Supervised 22 PhD students (2009–2023). Currently supervises 6 primary and 3 secondary students. Involved in RiskLab Australia, Belief Analytics Lab (SWUFE), and collaborates internationally in stochastic systems research.

• Health Economics
• Pharmacoepidemiology
• Artificial Intelligence
• +6 more

Harvey Koh serves as a Research Fellow at Monash University's Centre for Medicine Use and Safety within the Faculty of Medicine, Nursing and Health Sciences. His research integrates artificial intelligence with pharmaceutical and medical applications, focusing particularly on medication adherence patterns and cardiovascular disease management through analysis of large administrative healthcare datasets. His research interests span Health Economics (including cost-effectiveness analysis and health technology assessment), Pharmacoepidemiology, and Artificial Intelligence applications in Digital Health. Dr. Koh specializes in applying machine learning techniques, statistical signal processing, and computational neuroscience methods to analyze medication usage patterns, particularly examining cardiovascular medications in patients with diabetes and lipid disorders. His recent publications demonstrate a strong focus on medication adherence patterns, particularly examining cardiovascular medications in type 2 diabetes patients and lipid-modifying agents across cardiovascular risk groups. The research consistently employs advanced analytical methods including group-based trajectory analysis and large administrative data mining to identify patient patterns and improve risk stratification. Dr. Koh actively collaborates with researchers including Talic, Zomer, Orman, and Gasevic, with recent work published in high-impact journals such as Diabetes Research and Clinical Practice, Journal of Clinical Lipidology, and PLoS ONE. His research output has shown consistent growth with multiple publications in 2024-2025, indicating an active research trajectory in medical data science and pharmaceutical outcomes research.

Jackie LiView profile

Professor

• Stochastic Mortality Modelling
• Longevity Risk Pricing and Hedging
• General Insurance Claims Reserving
• +3 more

Professor Jackie Li holds a dual PhD in Actuarial Studies (University of Melbourne) and Demography (Macquarie University). As a professor at Monash University's Department of Econometrics & Business Statistics, he specializes in stochastic mortality modeling, longevity risk, and machine learning applications. He leads the Monash Actuarial Program, elevating its global ranking to 7th in UNL's 2020-2024 assessment. Professor Li has secured AUD$800,000 in research funding and serves on editorial boards of journals like Forecasting and Risks . His work addresses UN SDGs through actuarial solutions to longevity challenges. Research Interests: Stochastic mortality modeling Longevity risk pricing and hedging Machine learning in actuarial science General insurance claims reserving Recent Articles: Focus on Bayesian methods, ensemble forecasting, and neural networks applied to mortality and health expectancy analysis. Highlights include systematic vector autoregressive frameworks and manifold learning approaches. Awards: FIAA, FSA, and multiple teaching awards at university, school, and department levels. He is also an Honorary Professor at Macquarie University and serves as an independent examiner for UK actuarial bodies.

Richard BeareView profile

Associate Professor

• Bioinformatics
• Medical Imaging
• Neuroscience
• +3 more

Associate Professor Richard Beare is affiliated with Monash University's Peninsula Clinical School within the School of Translational Medicine. His academic rank is Associate Professor (Research). He specializes in Bioinformatics, Medical Imaging, and Neuroscience, focusing on translational research addressing aging-related chronic conditions. His work bridges clinical and population-level data to improve healthcare outcomes. Key Projects: Imaging and statistical methods for longitudinal brain studies using MRI. Structural covariance analysis for brain network generation. Health services optimization with Google Maps-based travel time modeling. His research aligns with UN SDGs, particularly contributing to good health and well-being (SDG 3). Current funding includes grants from the Brain Research Foundation and NHMRC. He supervises HDR students on topics like thalamic structure-post-concussion links and pediatric concussion biomarkers. Grants and Collaborations: Active projects include national stroke data systems and diabetes-related brain health studies. He collaborates internationally on neuroimaging and health data platforms.

Ole ManeesoonthornView profile

Associate Professor

• Bayesian Inference
• Time Series Forecasting
• Volatility Modelling
• +1 more

Ole Maneesoonthorn is an Associate Professor in the Department of Econometrics & Business Statistics at Monash University. He specializes in Bayesian inference, time series forecasting, volatility modeling, and financial econometrics. Currently accepting PhD students since 2012, his research contributes to UN Sustainable Development Goals through methodological advancements in statistical and financial modeling. He leads two major research projects: Property Price Index Development (University of Melbourne collaboration, 2022-2023) Loss-based Bayesian Prediction (2020-2025, collaborating with G. Martin, D. Frazier, and R. Hyndman) His work emphasizes practical Bayesian methods for economic and financial applications, with recent focus on copula models, hybrid variational inference, and optimal forecasting frameworks. Outputs include peer-reviewed articles in top journals like Journal of Business and Economic Statistics and International Journal of Forecasting .

Ruth SimView profile

Lecturer

• Epidemiology
• Health Economics
• Data Analysis
• +3 more

Ruth Sim is a Lecturer at the Malaysia School of Pharmacy, Monash University. Her expertise lies in epidemiology and health economics outcomes research, with a focus on real-world studies, economic modeling, and cost-effectiveness analysis. She has over five years of experience in quantitative research, including systematic reviews, network meta-analyses, and advanced statistical methods like machine learning. Dr. Sim’s research spans therapeutic areas such as oncology, dermatology, vaccines, and medical technologies. She emphasizes translating health data into actionable insights, particularly for low-resource settings. She currently serves as Faculty Advisor for the ISPOR Student Chapter at Monash University Malaysia and is open to supervising Master’s and PhD candidates in epidemiology and health economics. Her work contributes to UN Sustainable Development Goals, focusing on improving healthcare solutions through evidence-based approaches. Notable research areas include chronic disease predictive modeling, healthcare policy analysis, and pandemic impact studies.

• Multiple Sclerosis (MS)
• Neuro-ophthalmology
• Cerebellar Dysfunction
• +3 more

Associate Professor Anneke van der Walt is an academic neurologist specializing in multiple sclerosis (MS) and neuroimmunology at Monash University's School of Translational Medicine. She leads the MS and Neuro-ophthalmology Research Group, focusing on cerebellar dysfunction, cognitive impairment in MS, and digital biomarkers. With over $17.6 million in competitive grants, her work has been cited over 1,800 times and includes 162 peer-reviewed publications. Her clinical roles include heading the MSNI Unit and Neuro-ophthalmology Service at Alfred Health, Melbourne. She actively supervises postdocs and PhD students and chairs committees for MS-related initiatives. Notable awards include the NHMRC Investigator Grant (2021) and the MS Australia President’s Medal (2022). Education: PhD in Neuroscience (University of Melbourne, 2012), MBChB (University of Pretoria, 1998) Research Interests: MS progression mechanisms, digital health tools, women’s health in neurology, and autoimmune neurology Awards: MRFF Project Grant (2022), Cleveland Award (2012) Grants: Over $17.6M from NHMRC, MRFF, and industry partnerships Teams/Labs: Van Der Walt Lab, MSBase Registry, and the MSBae Foundation Her work bridges clinical practice and research, emphasizing translational outcomes and patient-centered care through innovative biomarker development and global collaboration.

Abubakar BalaView profile

Senior Lecturer

• Artificial Intelligence
• Machine Learning
• Cybersecurity
• +4 more

Dr. Abubakar Bala is a Senior Lecturer at the Malaysia School of Business, Monash University. His research focuses on artificial intelligence (AI), machine learning, and their applications in cybersecurity, edge computing, environmental engineering, and optimization algorithms. He actively contributes to the UN Sustainable Development Goals (SDGs), particularly in sustainable resource management and innovation. His work spans diverse domains such as drone detection via machine learning, AI-driven machine maintenance, and hybrid desalination plant optimization using deep learning. He has collaborated internationally on projects addressing cyber-physical systems security, multi-object tracking, and cloud computing efficiency. Publications highlight a trend toward applying metaheuristics in reservoir computing and neural networks, alongside optimizing industrial processes like virtual machine placement and fault prediction in aviation engines. His research also emphasizes energy efficiency, security protocols for IoT/drone systems, and Six Sigma methodologies in plant analytics. No scientific awards or formal advisees are explicitly listed. His work bridges theoretical advancements with practical applications in engineering and computer science.

• Optimization
• Stochastic Modelling
• Artificial Intelligence in Health
• +5 more

Dr. Indu Bala is a Lecturer in the School of Computer and Mathematical Sciences at the University of Adelaide, affiliated with the Faculty of Sciences, Engineering and Technology. Her research focuses on computational science, optimization algorithms, stochastic modeling, and applications of AI/ML in healthcare and infrastructure systems. She is eligible to supervise Masters and PhD students as a co-supervisor. Her work bridges theoretical research with practical solutions, particularly in power systems, water management, and biostatistics. Notable contributions include advancements in particle swarm optimization, gravitational search algorithms, and machine learning applications for medical data analysis. Recent research explores optimization techniques for cost-effective water distribution networks and adverse event classification in healthcare. Publications span 2018–2025, emphasizing heuristic algorithms, healthcare analytics, and multi-modal optimization challenges. She actively collaborates on interdisciplinary projects involving renewable energy, climate adaptation, and clinical decision-making systems.

• Stochastic Modelling
• Mathematical Epidemiology
• Evolutionary Biology
• +3 more

Dr Andrew Black is a Senior Lecturer in the School of Computer and Mathematical Sciences at the University of Adelaide, within the Faculty of Sciences, Engineering and Technology. His research focuses on stochastic modelling, mathematical epidemiology, evolutionary biology, probabilistic learning, Bayesian inference, and data science. He is eligible to supervise Masters and PhD students in these areas. Key research interests include applying generative machine learning techniques (e.g., diffusions, normalising flows) to stochastic models, analyzing household outbreak data for influenza and COVID-19, and modeling evolutionary transitions from cells to multicellular life. He also develops new simulation methods and importance sampling algorithms for stochastic systems. Recent work spans topics such as viral dynamics inference, smart grid optimization with low-resolution data, and barriers to genetic disease detection. His interdisciplinary approach bridges mathematical theory with real-world applications in public health and energy systems.

Sandun Malpriya SilvaView profile

Research Fellow

• Biostatistics
• Data Science
• Health Informatics
• +3 more

Dr. Sandun Malpriya Silva is a Senior Research Fellow at the Australian Institute of Health Innovation (AIHI), Macquarie University, specializing in biostatistics and data science. He holds a PhD in Statistics from Swinburne University of Technology and a BSc in Statistics from the University of Colombo. His research focuses on statistical and machine learning models for health-related problems, including physical activity programs, falls prevention in aged care, and medication management. Previously, he worked as a biostatistician at the NHMRC Clinical Trials Centre, University of Sydney, and held teaching roles at Monash University, Melbourne Business School, and Swinburne University of Technology. Research interests include predictive analytics in healthcare, systematic reviews of intervention efficacy, and the application of statistical methods to real-world health challenges. Notable projects include developing predictive tools for fall risk in aged care, analyzing medication use patterns, and optimizing pharmacist-led medication reviews. His work has been published in journals such as Journal of Safety Research , Cancer Medicine , and Biometrical Journal . Dr. Silva has received awards including the Rising Star Award (2022) and the Swinburne University Postgraduate Research Award (2017). He collaborates with institutions like the Victorian Department of Transport and Peter MacCallum Cancer Research Centre, addressing complex health issues through data-driven approaches.

Ibrahim JoudahView profile

Lecturer

• Artificial Intelligence
• Machine Learning
• Statistics
• +4 more

Ibrahim Joudah is an Academic Casual in the School of Mathematical and Physical Sciences at Macquarie University, where he is pursuing his Doctor of Philosophy. His work bridges advanced statistical methods with practical educational applications. His research interests focus on artificial intelligence applications in education, with particular emphasis on predictive modeling of student outcomes using advanced machine learning techniques. His work combines traditional statistical methods with cutting-edge AI approaches to address challenges in educational data analysis. Key areas include generative adversarial networks for synthetic educational data generation, feature selection in high-dimensional datasets, and robust statistical estimation methods. His publication record shows a clear trajectory toward applying sophisticated AI methods to educational contexts, with recent work focusing on enhancing prediction accuracy for student learning outcomes. His research demonstrates strong technical expertise in both theoretical statistics and practical AI implementation, particularly using Python and related computational tools. Joudah has been active in academic dissemination, including an invited talk at the 26th International Conference on Computational Statistics in August 2024. His publications have garnered attention across academic platforms, with significant readership on Mendeley and mentions on professional social networks.