Find Academic Supervisors

• Computational Finance
• Investments
• Asset Pricing
• +3 more

Paul Lajbcygier is an active Associate Professor holding a joint appointment in the Department of Banking and Finance and the Department of Econometrics and Business Statistics at Monash University, Clayton, Australia. His academic career spans appointments at prestigious institutions including The University of Oxford, New York University's Stern School of Business, and London Business School. With a PhD from Monash University (2001), he maintains dual departmental affiliations reflecting his interdisciplinary work at the intersection of finance and quantitative methods. His research focuses on computational finance, investments, asset pricing, and market microstructure, with notable contributions to hedge fund analysis, non-market capitalization weighted indices, and high-frequency trading dynamics. Recent work explores financial education through unconventional channels like television comedy and examines Rosca dynamics in emerging economies using simulation methods. His publications appear in top journals including the Journal of Banking and Finance , IEEE Transactions on Neural Networks , and the Australian Journal of Management . Lajbcygier's research output shows consistent productivity since 1997, with recent emphasis on factor replication, nonlinear hedge fund modeling, and market microstructure analysis. His work demonstrates strong interdisciplinary connections between finance, computational methods, and behavioral economics, particularly evident in his 2023 publications on industry-stratified factor replication and nonlinear hedge fund index clones. Scientific Recognition: SSRN Author Rank 1,221 (2015) among 293,678 authors Extensive citations in leading journals including Journal of Financial Economics and Journal of Financial and Quantitative Analysis Widely cited in international press (Bloomberg, Reuters, CNBC, South China Morning Post) Lajbcygier has secured millions in research funding through ARC Linkage and Discovery grants, including projects like the Superannuation Research Cluster and Systemic Risk modeling. His industry engagement includes providing investment advice to domestic and international fund managers, banks, and hedge funds since 1990. He serves as an Excellence Research Australia Peer Reviewer and Australia Research Council Assessor, bridging academic research with practical financial applications. His collaborative network spans multiple continents, with recent projects involving University of California Irvine, CSIRO, and University of Oxford, demonstrating strong international research partnerships focused on financial systems and behavioral economics.

Min JunView profile

Associate Professor

• Clinical Epidemiology
• Chronic Kidney Disease
• Health Data Linkage
• +5 more

Min Jun is a Scientia Associate Professor and Program Lead at the George Institute for Global Health within the Faculty of Medicine at UNSW Sydney. His research focuses on improving care and outcomes for individuals with kidney disease through large-scale clinical trials and population-based data analysis. He has secured >$5.1M in research funding from top-tier agencies and published over 200 works in high-impact journals like Lancet , JACC , and BMI , with 72% of publications in top 10% journals globally. Educational Background: PhD in Clinical Epidemiology (University of Sydney, 2012) MScMed (Clinical Epidemiology, University of Sydney, 2010-2012) MSc (by research, UNSW Sydney, 2006-2008) BSc (Honours, UNSW Sydney, 2001-2005) Min's research spans clinical epidemiology, nephrology, and health data linkage, integrating observational studies with public health strategies. His work has been cited over 8,000 times and influenced 24 international clinical practice guidelines. He actively mentors students and serves as Associate Editor (Global Health) for Kidney and Blood Pressure Research . Scientific Contributions and Awards: NHMRC Early Career Fellowship (2013-2016) CIHR and AIHS co-funded fellowships Key role in developing clinical practice guidelines for kidney disease in Australia and globally Collaborative Efforts: Min works with institutions in Canada (University of Calgary) and has established international projects using clinical trial data, demonstrating a strong global research network.

• Optimization
• Data Analytics
• Metaheuristic Methods
• +10 more

Professor Regina Berretta is an Honorary Professor at the University of Newcastle's School of Information and Physical Sciences, specializing in Computing and Information Technology. With expertise spanning computer science, mathematics, and applied optimization, she has established herself as a leading researcher in metaheuristic methods for solving complex combinatorial problems. Currently serving as a Chief Investigator at the ARC Training Centre for Food and Beverage Supply Chain Optimisation, she applies her mathematical modeling skills to address critical challenges in food industry supply chains, with the Centre receiving over $2 million in funding to train next-generation researchers. Professor Berretta earned her PhD, Master of Engineering (Electrical), Bachelor of Mathematics, and Teachers Certificate from Universidade Estadual de Campinas in Brazil. Her educational background in computational and applied mathematics has provided the foundation for her extensive research career focused on integer programming and metaheuristic approaches to optimization problems. Her academic journey includes progression from Lecturer at the University of Newcastle (2003-2007) to various leadership positions including Head of Discipline of Computer Science and Software Engineering (2011-2014) and Assistant Dean of Equity, Diversity and Inclusion (2019-2022). Her research expertise centers on the design of mathematical models and development of efficient computational techniques to tackle large and complex combinatorial optimization problems across diverse application areas. Professor Berretta has made significant contributions to bioinformatics through her work on genetic signature identification from gene expression datasets, and to supply chain optimization through her research on perishable food inventory management, lot sizing, and scheduling problems. Her methodological specialties include memetic algorithms, evolutionary computation, and integer programming approaches that have proven effective for problems that are otherwise computationally intractable. Analysis of her recent publications reveals a strong interdisciplinary focus, with optimization techniques increasingly applied to food supply chain challenges while maintaining connections to bioinformatics applications. Her work demonstrates a consistent pattern of translating theoretical optimization methods into practical solutions for industry problems, particularly in the agricultural sector. Additionally, her more recent publications show growing engagement with gender equity issues in STEM fields, reflecting her leadership in relevant initiatives. Co-founder of HunterWISE, promoting girls and women in STEM Leader of Google CS4HS project for five consecutive years Recipient of over $4 million in research funding through 35 grants Author of more than 80 papers and book chapters Chief Investigator at ARC Training Centre for Food and Beverage Supply Chain Optimisation Professor Berretta has demonstrated exceptional leadership through her administrative roles and community initiatives. As co-founder of HunterWISE, she has developed a comprehensive approach to increasing female participation in STEM through school programs and professional networking events. Her leadership of the Google CS4HS project has directly impacted high school computer science education in the Hunter region. Her research collaborations span multiple disciplines and institutions, reflecting her ability to bridge theoretical computer science with practical industry applications, particularly in the food supply chain sector where her optimization models have demonstrated significant cost and waste reduction potential.

• Intelligent Transportation Systems
• Vehicular Cyber-Physical Systems
• Traffic Flow Modeling
• +3 more

Dongyao Jia is a Senior Research Fellow at the School of Civil Engineering, The University of Queensland. His research focuses on intelligent transportation systems, vehicular cyber-physical systems, and traffic flow modeling, with applications in energy harvesting, car-following model calibration, and vehicle-to-everything communication. Research Themes: Traffic flow prediction, eco-driving optimization, vehicular networks, and cyber-physical system design Key Collaborations: Working with researchers in transportation engineering, computer science, and energy systems Recent publications highlight his work in integrating deep learning with traffic dynamics for acoustic energy harvesting and developing adaptive frameworks for car-following models. His studies on multi-agent reinforcement learning for C-V2X networks and heterogeneous platooning communication demonstrate technical depth across transportation and computer science disciplines. While no specific awards are mentioned in the text, his extensive publication record across high-impact journals like IEEE Transactions on Intelligent Transportation Systems and Transportation Research Part C indicates significant scholarly contributions. Technical Expertise: V2X communication protocols, traffic simulation platforms, machine learning integration for transportation Application Areas: Urban traffic management, sustainable mobility systems, vehicular safety networks

• Transport Engineering
• Public Transit Analysis
• Smart Card Data Applications
• +3 more

Dr. Mahmoud Mesbah is a Senior Research Fellow at the School of Civil Engineering , University of Queensland , specializing in transport engineering and urban mobility . His work leverages smart card data , GIS , and machine learning to analyze passenger behavior , trip purpose inference , and transit network optimization . Research Themes : Public transit modeling, traffic violation analysis, travel time reliability, and data-driven urban mobility solutions. Email : mahmoud.mesbah@uq.edu.au

Zhongwei ChenView profile

Associate Professor

• Mining Geomechanics
• Reservoir Geomechanics
• Machine Learning Applications
• +5 more

Dr. Zhongwei Chen is an Associate Professor at the University of Queensland's School of Mechanical and Mining Engineering. He leads interdisciplinary research focusing on three primary areas: Mining Geomechanics (ground support design, high-stress excavation stability, rock weathering), Reservoir Geomechanics (unconventional gas recovery, carbon sequestration, underground hydrogen storage), and Machine Learning Applications for geomaterial analysis and subsurface characterization. He directs a NATA-accredited Geomechanics Lab with capabilities including rock strength testing, multiphase flow visualization, and acoustic emission analysis. His research integrates experimental and computational approaches to address challenges in geo-energy extraction. Recent publications (2022-2025) demonstrate strong emphasis on: Coal permeability evolution under chemical/mechanical stress Machine learning for geotechnical property prediction Hydraulic fracture propagation in anisotropic formations Underground hydrogen storage mechanics Environmental impacts of mining operations Teaching responsibilities include courses in Applied Mining Geomechanics and Mine Geotechnical Engineering. Research affiliations include the Centre for Multiscale Energy Systems and Future Autonomous Systems and Technologies.

Timothy CarrollView profile

Professor

• Integrative Human Physiology
• Exercise Science
• Integrative Neuroscience
• +5 more

Professor Timothy Carroll serves as Professor and Deputy Head of School at the School of Human Movement and Nutrition Sciences at the University of Queensland. He also holds the position of Centre Director of the Centre for Sensorimotor Performance. His academic career spans over two decades with significant contributions to integrative human physiology, exercise science, and neuroscience. Dr. Carroll completed his doctorate in Neuroscience at the University of Queensland in 2001. He was awarded an Isaac Walton Killam Memorial Scholarship for postdoctoral studies at the University of Alberta in 2002, followed by a Lecturer position in Human Motor Control at the University of New South Wales in 2003. He joined the University of Queensland as a Senior Lecturer in July 2007, eventually advancing to his current professorial role. Professor Carroll's research focuses on how the central nervous system reorganizes through motor learning and exercise. His work integrates exercise science and neuroscience with emphasis on strength training mechanisms. He employs advanced neurophysiological techniques including Transcranial Magnetic Stimulation (TMS), peripheral nerve stimulation, and electromyography (EMG) in human subject research. The ultimate goal of his work is to develop evidence-based exercise protocols that optimize rehabilitation and injury prevention outcomes. His publication record demonstrates sustained investigation into sensorimotor adaptation, neural plasticity, and exercise physiology. Recent work has expanded into computational modeling of human movement, particularly in cycling biomechanics and musculoskeletal simulations, while maintaining strong connections to fundamental neuroscience questions about motor learning and performance. Isaac Walton Killam Memorial Scholarship Professor Carroll has secured continuous funding from the Australian Research Council since 2004, with current projects including "Subcortical control of human reaching?" (2024-2027) and "A new perspective on how we learn motor skills: two adaptation classes?" (2023-2026). His research program demonstrates sustained excellence and relevance across multiple funding cycles. As Centre Director of the Centre for Sensorimotor Performance, Professor Carroll leads a multidisciplinary team investigating neural control of human movement. His laboratory combines traditional neurophysiological approaches with computational modeling to address complex questions about motor learning, adaptation, and performance optimization in both healthy and clinical populations.

Leigh SperkaView profile

Lecturer

• Outsourcing in Education
• Curriculum and Pedagogy
• Student Perspectives
• +5 more

Dr. Leigh Sperka is a Lecturer at the School of Human Movement and Nutrition Sciences , The University of Queensland . She graduated with First Class Honours in Bachelor of Health, Sport and Physical Education (2013) and earned her PhD (2018) from UQ. Her research interrogates outsourcing in education , particularly its effects on curriculum, pedagogy, and student agency in Health and Physical Education (HPE) . Her work emphasizes student-centered learning and inclusive pedagogy , advocating for marginalized voices in educational practices. She collaborates on projects like Students-as-partners and explores community health models through affiliations with the Centre for Community Health and Wellbeing and Centre for Sport and Society . Recent publications analyze cost-efficiencies in outsourced HPE , feedback activation in physical education , and disability inclusion in sports pedagogy. Her studies span journal quality indicators , curriculum commercialisation , and critical discourse analysis of external providers in education. Fellow of the Higher Education Academy U21 Health Sciences Teaching Excellence Award (2021) UQ Commendation for Student Learning (2020) Faculty of Health Teaching Excellence Award (2020) As an advisor, she supports PhD research on topics such as community dance wellbeing , student voice in community schools , and feedback culture in HPE . Her scholarship bridges education policy , social justice , and pedagogical innovation .

Hrishi PatelView profile

Researcher

• Machine Learning
• Learning Analytics
• Data Science Teaching
• +3 more

Hrishi Patel is a PhD researcher at The University of Queensland (UQ) , affiliated with CIRES (Centre for Innovation in Resource Efficiency and Sustainability). Holding a Master of Data Science from UQ, his work spans machine learning, learning analytics, and data science pedagogy. Education : Master of Data Science, University of Queensland His research focuses on developing outpatient risk prediction models to combat pediatric sepsis, in collaboration with Queensland Health . Supervised by Dr. Sen Wang, Dr. Adam Irwin (Queensland Health), and Professor Shazia Sadiq, his work aims to improve critical care for children through interdisciplinary partnerships. Notable achievements include recognition for outstanding tutoring skills at UQ Business School. He actively collaborates with academic and government teams to bridge data science with healthcare applications.

Michael MahoneyView profile

Professor

• Randomized Numerical Linear Algebra
• Machine Learning
• High Dimensional Statistics
• +3 more

Michael Mahoney is Professor of Statistics at the University of California, Berkeley, with additional affiliations at the International Computer Science Institute (ICSI) where he is Vice President and Director of the Big Data Group, the Lawrence Berkeley National Laboratory (LBNL) where he leads the Machine Learning and Analytics Group, and the EECS department's RISELab. He is also an Amazon Scholar. Education: While specific degrees are not listed in the provided text, his extensive record of teaching, research leadership, and publications indicates doctoral-level training in Statistics and Computer Science. Research Interests: Mahoney's work centers on the applied mathematics of data , spanning algorithmic and statistical foundations of big data, randomized numerical linear algebra (RandNLA), high-dimensional statistics, machine learning, and scientific machine learning. He develops theory, scalable implementations, and real-world applications in internet analytics, social networks, genetics, astronomy, and climate science. Recent software contributions include the RandBLAS and RandLAPACK libraries (standardizing RandNLA routines), Landscaper for visualizing deep-learning loss landscapes, and packages such as FreeAlg , DetKit , Imate , and LeaderBot . Awards & Honors: NeurIPS 2020 Best Paper Award (co-authored work on column subset selection) Director, NSF TRIPODS UC Berkeley FODA Institute Key contributor to the BALLISTIC project for next-generation BLAS/LAPACK Grants & Leadership: Principal Investigator, NSF TRIPODS FODA Institute (Foundations of Data Analysis) Group Lead, Machine Learning and Analytics, LBNL Vice President & Director, Big Data Group, ICSI Advising & Mentoring: Mahoney has an extensive network of current and former PhD students, postdocs, and visiting researchers, including placements at MIT, Stanford, Waterloo, Stevens, Tsinghua, and Georgia Tech. Current advisees include Shengaho Yang, Zhichao Wang, Hyunsuk Kim, and Pu Ren, among many others. Labs & Teams: He directs research efforts across UC Berkeley Statistics, ICSI’s Big Data Group, LBNL’s Machine Learning and Analytics Group, and the RISELab (formerly AMPLab), fostering cross-disciplinary collaboration between statistics, computer science, and domain sciences.

Francis HuiView profile

Associate Professor

• Ecological Statistics
• Longitudinal and Spatio-Temporal Data Analysis
• Mixed Effects Models
• +5 more

Francis Hui is an Associate Professor in Statistics at the Research School of Finance, Actuarial Studies and Statistics (RSFAS), The Australian National University (ANU), specializing in ecological statistics, longitudinal data analysis, and statistical computing. His work bridges methodological advancements with practical applications in environmental science, actuarial studies, and multivariate data analysis. Education: PhD in Statistics from the University of New South Wales (2015) BSc/BA (First Class Honours, University Medal) from the University of New South Wales (2012) Research Interests: Approximate likelihood methods for estimation and inference Ecological statistics and joint species distribution modeling Dimension reduction and model selection for complex data Variational approximations and semiparametric regression Spatio-temporal and multivariate longitudinal data analysis Publication Trends: Francis's recent work focuses on robust statistical methods for ecological modeling, including generalized linear mixed models, composite likelihood techniques, and dimension reduction. His research also explores fair actuarial models for retirement outcomes and computational efficiency in handling large-scale environmental datasets. Projects: Current projects include developing modern statistical methods for clustering community ecology data, enhancing predictive accuracy in spatio-temporal models, and creating scalable tools for multivariate abundance data analysis. Software: He has contributed to R packages such as assam , COQUE , HPGEE , and gllvm , which implement advanced statistical techniques for ecological and longitudinal data.

Naveen Kumar ElumalaiView profile

Senior Lecturer

• Renewable Energy
• Materials Science
• Photovoltaics
• +3 more

Naveen Kumar Elumalai serves as a Senior Lecturer in Mechanical Engineering within the Faculty of Science and Technology at Charles Darwin University. His academic profile demonstrates strong institutional integration through his affiliation with the Energy and Resources Institute and membership in the Higher Degree by Research (HDR) Cohort, where he is registered to supervise postgraduate research. His extensive collaboration network spans Charles Darwin University, University of New South Wales, Curtin University of Technology, and international institutions including Sunway University and University Malaysia Pahang. Dr. Elumalai's research focuses on cutting-edge renewable energy technologies, with particular expertise in photovoltaic systems, advanced materials for energy applications, and sustainable engineering solutions. His work bridges theoretical materials science with practical engineering implementations, especially in solar energy conversion and storage systems. The research portfolio demonstrates consistent growth from 2011 through 2025, with significant output in perovskite solar cells, energy harvesting technologies, and nanomaterial applications for renewable energy systems. Analysis of his 85 research outputs reveals a strategic evolution from fundamental materials research toward applied sustainable engineering solutions. Recent publications (2023-2025) show increased focus on commercialization pathways, system integration, and environmental impact assessment of renewable energy technologies. His collaborative work with over 50 researchers demonstrates strong interdisciplinary connections between mechanical engineering, materials science, and environmental sustainability. With an h-index of 35 and 4,483 citations, his scholarly impact is substantial within the renewable energy research community. His research trajectory shows consistent publication activity with 5 projects in 2022 alone, indicating sustained research momentum and competitive grant acquisition. Dr. Elumalai actively supervises postgraduate research through Charles Darwin University's HDR program and maintains extensive collaborative networks with major Australian research institutions. His laboratory work appears centered within the Energy and Resources Institute, focusing on experimental validation of novel materials and energy conversion systems. Current research directions suggest growing emphasis on practical implementation challenges of renewable energy technologies and their integration into existing infrastructure.

Jan ZikaView profile

Associate Professor

• Oceanography
• Climate Science
• Fluid Dynamics
• +5 more

Jan Zika is an Associate Professor in the School of Mathematics & Statistics at the University of New South Wales, Sydney. His research focuses on the role of ocean dynamics in climate change, particularly water mass transformation, heat transport, and the interaction between ocean circulation and the global water cycle. PhD from CSIRO (Hobart) Prior affiliations: Université Joseph Fourier, University of Southampton, Imperial College London Key research areas include: Ocean mixing and its impact on climate sensitivity Water mass transformation as a diagnostic for ocean circulation Heat engine efficiency in warming climates Biogeochemical tracer transport Recent publications emphasize: Advancements in water mass transformation frameworks Machine learning applications for salinity mapping Constraints on ocean carbon uptake mechanisms Historical trends in freshwater transport Scientific awards: 2018 EGU Outstanding Early Career Researcher Award 2020 Australian Academy of Science Anton Hales Medal Grants include NERC Postdoctoral and Senior Fellowships. He leads interdisciplinary teams studying Southern Ocean dynamics and has contributed to ocean modeling benchmarks in CMIP5/CMIP6 frameworks.

• Hydrogen Fuel Cells
• Electrochemistry
• Renewable Energy
• +4 more

Dr. Quentin Meyer is a Lecturer at the School of Chemistry, University of New South Wales (UNSW), specializing in advanced electrochemical systems and fuel cell technologies. His research focuses on optimizing catalysts, membrane electrode assemblies, and electrochemical diagnostics for hydrogen fuel cells. Recent publications highlight his work on platinum-free catalysts , oxygen reduction reaction mechanisms , and electrochemical impedance spectroscopy . Collaborations span institutions in Australia and international partners in Europe and Asia. Key research areas: Hydrogen fuel cell engineering Electrochemical stability and degradation Catalyst layer optimization Operando and in situ diagnostics

Alice TowlerView profile

Part-Time Lecturer

• Forensic Psychology
• Face Identification
• Cognitive Science
• +3 more

Dr. Alice Towler is a Casual Academic at the School of Psychology within the University of New South Wales (UNSW). Her research focuses on improving face identification accuracy and efficiency in forensic settings, including the study of super-recognition abilities, development of evidence-based facial comparison methods, and optimization of human-software collaboration in identity verification. Her primary research interests include: Forensic face identification techniques and error reduction Predictors of superior face recognition ability (super-recognition) Development of evidence-based training protocols for facial image comparison Integration of human expertise with facial recognition software systems Identity fraud detection in critical applications like passport verification Dr. Towler collaborates extensively with the UNSW Forensic Psychology Lab under Dr. David White and Prof. Richard Kemp. She maintains an active partnership with the Australian Passport Office to enhance identity fraud detection capabilities. As a member of the Evidence-Based Forensics Initiative, she advocates for scientifically validated practices in forensic sciences through interdisciplinary collaboration between legal professionals, forensic scientists, and cognitive researchers. Her recent publications demonstrate consistent focus on face recognition mechanisms, perceptual expertise development, and forensic applications. Research themes include optimization of training protocols, individual differences in recognition abilities, validation of forensic methodologies, and real-world implementation challenges.