Find Academic Supervisors

• Operations Research
• Management Science
• Optimization Algorithms
• +6 more

Dr. Dmytro Matsypura is an Associate Professor in the Discipline of Business Analytics at the University of Sydney Business School. He holds a BA (Hons) from Kyiv Polytechnic Institute (KPI), an MS (Hons) from KPI, and a PhD from the University of Massachusetts Amherst. His research focuses on optimization methodologies, network science, and their applications in finance, transportation, ecology, and graph theory. He is a recipient of multiple teaching awards, including the Wayne Lonergan Outstanding Teaching Award (Early Career) in 2010. Education: PhD in Management Science, University of Massachusetts Amherst (2006) MS (Hons) in Information Systems, Kyiv Polytechnic Institute (2000) BA (Hons) in Business Administration, Kyiv Polytechnic Institute (1998) Research Interests: Dr. Matsypura’s work spans operations research and management science, with a focus on mathematical optimization and network science. His methodological contributions include developing efficient optimization algorithms, while his applied research addresses real-world challenges in finance, engineering, and ecology. Notable applications include wildfire fuel management, portfolio margining, and credit card fraud detection via graph-based models. Awards and Recognition: Teaching Excellence Award (2008, 2013, 2018) Wayne Lonergan Outstanding Teaching Award (Early Career) (2010) Grants and Projects: Current projects include Bushfire Analytics: Optimization of Fuel Reduction (2023, ARC Discovery Project). His research frequently integrates interdisciplinary collaborations, such as applying graph theory to biomedical problems and cybersecurity. Labs/Teams: Active in the Sydney Environment Institute, contributing to projects at the intersection of analytics and sustainability. Collaborates with industry on fraud detection and supply chain optimization.

• Astrophysics
• Radio Astronomy
• Pulsar Studies
• +3 more

Dr. Ramesh Bhat is a Senior Research Fellow at Curtin University's School of Electrical Engineering, Computing and Mathematical Sciences (EECMS), affiliated with the Curtin Research Institute and Curtin Institute of Radio Astronomy (CIRA). His primary affiliation is with the Faculty of Science and Engineering. He is based at Curtin Perth Campus in Brodie Hall, Room 161. His research focuses on astrophysics and radio astronomy, particularly pulsar timing, gravitational wave detection using pulsar timing arrays, and the study of transient phenomena such as fast radio bursts (FRBs). He contributes to major projects like the High Time Resolution Universe (HTRU) survey, MeerTime, and the Murchison Widefield Array (MWA). Key research interests include pulsar population studies, interstellar medium interactions, signal processing for radio astronomy, and instrumentation development for next-generation telescopes like the Square Kilometre Array (SKA). His work spans theoretical models of pulsar emission mechanisms to observational studies of pulsar nulling, subpulse drifting, and gravitational wave backgrounds. Recent publications highlight advancements in pulsar survey techniques (e.g., GPU-accelerated analysis), discovery of new pulsars and FRBs, and constraints on cosmological models via pulsar timing arrays. His research often involves international collaborations, leveraging facilities like the Parkes radio telescope and the MWA. He actively contributes to radio interferometry, transient detection algorithms, and pulsar timing array data analysis. His work bridges observational astronomy with computational methods, aiming to advance understanding of compact objects and gravitational physics.

Kumudu MUNASINGHEView profile

Professor

• Next Generation Mobile and Wireless Networks
• Internet-of-Things (IoT)
• Cyber-Physical Systems
• +6 more

Professor Kumudu MUNASINGHE is the Deputy Dean of the Faculty of Science and Technology and Professor of Network Engineering at the University of Canberra. He previously served as Head of the School of IT and Systems (2020–2024) and held the Cisco Research Chair (2022–2023). With a PhD in Telecommunications Engineering from the University of Sydney (2009), his research focuses on next-generation wireless networks, IoT, cyber-physical systems, and blockchain security. He has secured over $8M in research funding from the Australian Research Council, government agencies, and industry, leading to 130+ publications (H-index 21) and prestigious awards like the IEEE Best Paper Award and VC’s Research Award. Education: PhD in Telecommunications Engineering, University of Sydney (2009) Research Interests: 5G/6G Networks & Smart Grid Communications IoT Security and Blockchain Applications Cyber-Physical Systems & Energy Efficiency AI-Driven Network Optimization Grants & Projects: Lead researcher on projects totaling $8M+, including ITTC ARC Plant Biosecurity Training Center ($4.5M) and NetApp Technology Alliance Agreement Co-investigator on 14 projects addressing smart cities, satellite communications, and biosecurity Awards & Recognition: ARC Australian Postdoctoral Fellowship (2023) IEEE Best Paper Awards (2025, 2023, 2022) Fellow of Engineers Australia and Senior IEEE Member Labs & Teams: Leads research groups in Network Engineering and Cyber-Physical Systems Collaborates with industry partners like Cisco and NetApp

Daniel HaraborView profile

Associate Professor

• Artificial Intelligence
• Pathfinding Algorithms
• Optimization
• +3 more

Daniel Harabor is an Associate Professor in the Department of Data Science & AI at Monash University, Australia. He holds a PhD and BSc (Honours) in Computer Science from the Australian National University (ANU). His research focuses on pathfinding algorithms, multi-agent systems, and optimization, with notable contributions to multi-agent pathfinding (MAPF), transportation routing, and constraint-based reasoning. He leads or collaborates in projects like the 2023 MAPF Competition and the Future of Urban Routing initiative. Education: PhD in Computer Science, ANU (2014) BSc (Honours) in Computer Science, ANU (2007) Research Interests: His work emphasizes practical and efficient solutions for combinatorial search problems, including real-time pathfinding, public transport optimization, and large-scale multi-agent coordination. He develops algorithms for dynamic environments with applications in robotics, urban planning, and logistics. Awards: DECRA Research Fellowship (Australian Research Council) Projects & Grants: He has secured funding for projects such as 'Personalised Public Transport' (2019–2025) and 'Improved Constraint Reasoning for Robust Multi-agent Path Planning' (2021–2024). His work often involves collaboration with global experts in AI and robotics. Labs & Teams: Active in the Data Science & AI research group at Monash, contributing to MAPF competitions and open-source pathfinding tools like JPS (Jump Point Search).

• Data Management
• Database Systems
• Graph Data Management
• +4 more

Professor Chengfei Liu is a faculty member at Swinburne University of Technology, leading the Web and Data Engineering research group and serving as focus area leader for Knowledge and Data Intensive Systems in the SUCCESS Centre. His research focuses on web data management, advanced database systems, graph data, and workflow models. He has held academic positions at the University of South Australia, University of Technology Sydney, and the University of Queensland's DSTC. His work spans data management, distributed systems, and information systems, with notable contributions to cohesive subgraph discovery and efficient algorithms. Research interests include keyword queries, uncertain data, graph databases, and artifact-centric workflows. His awards include the Vice-Chancellor's Research Excellence Award (2007) and multiple best paper/demo awards at top conferences. Professor Liu supervises numerous PhD students and holds grants from the Australian Research Council (ARC), including projects on dynamic networks, heterogeneous graphs, and information resilience. He has authored over 297 publications, with recent work addressing topics like Bitcoin transaction prediction, federated subspace clustering, and multimodal eating disorder detection. His professional roles include program committee memberships at SIGMOD, ICDM, and CIKM, reflecting his influence in data science and database research.

Jonghwan RheeView profile

Research Fellow

• Astrophysics
• Radio Astronomy
• Extragalactic Astronomy
• +5 more

Jonghwan Rhee is an Adjunct Research Fellow at the International Centre for Radio Astronomy Research (ICRAR) within The University of Western Australia. He earned his PhD from the Australian National University in 2015 and has since focused on neutral hydrogen gas surveys using the Australian Square Kilometre Array Pathfinder (ASKAP), particularly the WALLABY and DINGO projects. Role : Adjunct Research Fellow, ICRAR (UWA) Education : PhD (2015) from Australian National University Expertise : Radio astronomy, extragalactic studies, HI gas surveys, and machine learning for galaxy classification His research examines galaxy evolution through neutral hydrogen gas distributions, tidal interactions, and star formation processes in low surface brightness galaxies. Recent work includes developing machine learning frameworks for source-finding in radio data and analyzing perturbed galaxies via H i morphometrics. Key collaborations include contributions to the WALLABY pilot survey datasets, with focus areas in stellar mass dynamics, galaxy clusters, and gas scaling relations. His outputs have been referenced in news outlets, academic networks, and software repositories.

Ben TalbotView profile

Research Fellow

• Cognitive Robotics
• Artificial Intelligence
• Robot Navigation
• +5 more

Dr. Ben Talbot is a Research Fellow at the Queensland University of Technology (QUT) , affiliated with both the Australian Centre for Robotic Vision (ACRV) and the QUT Centre for Robotics (QCR) . He earned a PhD in 2018 and a BEng (Honours I) in Electrical Engineering in 2012 from QUT. Ben specializes in cognitive robotics , artificial intelligence , and real-world robotic applications , with a focus on translating theoretical advances into practical systems. PhD , Robotics, Queensland University of Technology, 2018 BEng (Honours I) , Electrical Engineering, Queensland University of Technology, 2012 His research explores robot navigation using abstract maps , semantic SLAM , and human-inspired spatial reasoning , particularly in unseen environments . He pioneered the Abstract Map framework, which enables goal-directed exploration through symbolic spatial information. His work emphasizes open-source software like BenchBot and OpenSeqSLAM2.0 , enhancing robotics community accessibility. Ben's publications span 2013–2023, showcasing advancements in controller fusion , scene understanding , and sim-to-real transfer . Notable trends include integrating reinforcement learning with classical control and developing dynamic spatial models for navigation. Awarded Best Student Paper at ACRA 2015 and multiple Vice-Chancellor's Awards in 2015, Ben also received the Dean's Scholars Award in 2009. His work has been featured in The LABS and Brisbane Times media outlets. Beyond research, Ben contributed to robotics education as Head Tutor for Advanced Robotics and Microprocessors & Digital Systems during his PhD. He actively supports active scene understanding via high-fidelity simulated environments and robust API designs, aligning with QUT's commitment to bridging classical and modern robotics.

• Artificial Intelligence
• Data Visualization
• Optimization Systems
• +3 more

Assoc. Prof. Michael Wybrow is an Associate Professor in the Department of Human Centred Computing at Monash University's Faculty of Information Technology. His research focuses on visual interfaces for AI explainability, constraint-based optimization, and interactive visualization, with applications in healthcare, energy systems, and decision-making. He holds a PhD from Monash University (2008) and a Bachelor of Computer Science (Honours) from the University of Melbourne (2003). Wybrow has received notable awards, including the Australasian Distinguished Doctoral Dissertation (2009) and the IEEE InfoVis 2015 Best Paper Award. He leads industry projects on chemical plant layout optimization and Bayesian network reasoning, and serves as Director of Work Integrated Learning, overseeing Monash's industry placement programs. Wybrow has developed influential software tools like libavoid and Dunnart, contributing to both academia and industry. His teaching includes creating the ENG1003 course for engineering students, emphasizing app development and software engineering. Over $2.25M in competitive and industry grants fund his research, with collaborations spanning healthcare, energy, and visualization domains. Recent projects include optimizing LNG plant layouts with Woodside Energy, developing near-real-time healthcare dashboards for Eastern Health, and improving causal reasoning through Bayesian networks. Wybrow's work aligns with UN Sustainable Development Goals, particularly in healthcare and sustainable energy.

Guido TackView profile

Associate Professor

• Combinatorial Optimization
• Constraint Programming
• Programming Languages
• +3 more

Guido Tack is an Associate Professor in the Department of Data Science & Artificial Intelligence at Monash University's Faculty of Information Technology. He leads development of the MiniZinc constraint modeling language and is a core developer of Gecode, a leading constraint programming library. His research focuses on combinatorial optimization, constraint solver architecture, and industrial applications, extending to programming languages and computational logic. Education: PhD (Dr.-Ing.) in Computer Science from Saarland University (2009), Diplom Informatiker (MSc equivalent) from Saarland University (2003). Professional experience includes postdoctoral roles at NICTA, Saarland University, and KU Leuven before joining Monash in 2012 as a Lecturer and Monash Larkins Fellow. Research interests span constraint programming, optimization algorithms, and their applications. Recent projects include improving the MiniZinc ecosystem, optimizing metering systems for smart water networks, and quantum information technology. He has secured over 13 major research grants, including leadership in the ARC Training Centre for Optimization Technologies. Teaching: Chief Examiner for FIT5170, FIT9135, FIT5216; Lecturer for FIT1047, FIT4010, and others. Editorial Roles: Co-Editor-in-Chief of Constraints journal (2015), active in ACM and Association for Constraint Programming. Awards: 2024 Eureka Prize finalist, 2017 FIT Dean's Award, 2010 Doctoral Research Award. Publications span constraint solving techniques, optimization algorithms, and educational tools. Recent work emphasizes real-time path planning, bi-objective search, and energy-efficient systems. He contributes to both academic conferences and industry-focused projects, bridging theoretical research with practical applications.

• Computer Science Education
• E-Learning Technologies
• Artificial Intelligence
• +3 more

Dr. Sue Chng is an Associate Lecturer at the School of Computer Science, The University of Sydney. Her research focuses on computer science education, e-learning technologies, artificial intelligence, and computer vision. She has contributed to improving teaching methodologies through reflective practices, chatbot integration in courses, and hybrid learning models for large undergraduate classes. Her work also spans algorithm design for route optimization, sensor networks, and face recognition systems. Research Interests: Educational Technology: Developing innovative tools like chatbots for FAQ systems and hybrid learning frameworks. Computer Vision: Advancing face recognition algorithms and super-resolution imaging techniques. Algorithm Design: Optimizing pathfinding and route planning under constraints. Publications Overview: Dr. Chng has published in venues such as IEEE conferences and journals like Reflective Practice . Her recent work emphasizes the intersection of education and technology, alongside technical advancements in image processing and algorithmic efficiency. Awards: None listed. Advising & Grants: No advising records or grants mentioned. Her contributions are primarily through publications and teaching innovations. Labs/Teams: No specific lab affiliations noted in the provided text.

• Evolutionary Computation
• Optimization Algorithms
• Wireless Sensor Networks
• +3 more

Dr. Bo Yuan is an Honorary Associate Professor at the School of Electrical Engineering and Computer Science, University of Queensland. His research focuses on Evolutionary Computation, Optimization Algorithms, and their applications in Wireless Sensor Networks and Computer Vision. He has contributed to advancements in parameter tuning of evolutionary algorithms, path planning in robotics, and self-supervised learning techniques for depth estimation. Recent work includes innovations in monocular depth estimation and 3D-aware image generation using frameworks like StyleGAN, while earlier contributions centered on theoretical analysis of evolutionary algorithms (e.g., UMDAc convergence) and optimization in sensor networks. His interdisciplinary research bridges theoretical foundations with practical applications in robotics and AI. Awards: No awards explicitly listed in provided texts. Collaborations include co-authors like Zhao Haimei, Chen Zhuo, and Gallagher Marcus. His publications span journals like Machine Intelligence Research and conferences such as AAAI and IEEE CEC, reflecting a sustained impact in computational intelligence and machine learning.

Matt DuckhamView profile

Professor

• Geospatial AI
• Spatial Algorithms
• Geovisualisation
• +5 more

Matt Duckham is a Professor in Geospatial Sciences and Director of the Information in Society Enabling Impact Platform at RMIT University. He has held previous academic roles at the University of Melbourne as an Australian Research Council Future Fellow and at the University of Maine's National Center for Geographic Information and Analysis. His work bridges theoretical and applied GIScience, with a focus on AI integration and spatial knowledge infrastructure. Academic Leadership: Director, Information in Society EIP (2021–present); Acting Dean, STEMM Diversity and Inclusion (2020–2021) Research Themes: Geospatial AI, Qualitative Spatial Reasoning, Decentralized Geosensor Networks, Spatial Data Trusts Recent Publications demonstrate his commitment to advancing spatial computing through machine learning integration and explainable AI frameworks. His teaching resources include open-access textbook materials and practical exercises spanning relational spatial databases and data visualization. Professional Contributions include co-founding the Journal of Spatial Information Science and developing open educational resources under a Creative Commons Attribution license. His work addresses critical issues in data ethics , urban resilience , and emergency response systems .

• Public Communication Evaluation
• Organizational Listening
• Health Communication
• +3 more

Jim Macnamara is a Distinguished Professor at the University of Technology Sydney (UTS), where he serves as Interim Dean of the Faculty of Arts & Social Sciences and leads research in the School of Communication. He holds additional appointments as a Visiting Professor at the London School of Economics and the London College of Communication. His PhD (University of Western Sydney, 2005) and extensive 30-year industry background in journalism, PR, and media research underpin his academic authority. Macnamara's research centers on evaluation methodologies for public communication , organizational listening , and health/digital communication . His frameworks for measuring communication impact have been adopted by the WHO, UK Government, and European Commission. Current projects include community-driven health crisis communication (NHMRC grant) and refugee digital literacy (NSW Health). Recent publications (2022-2025) reveal a focus on ethical digital communication, post-pandemic societal transformation, and advanced evaluation frameworks (MEL). His work consistently bridges theory with policy and corporate practice, particularly in crisis response and stakeholder engagement. Awards & Fellowships: Pathfinder Award (IPR, USA), Don Bartholomew Award (AMEC), Triple Gold/Platinum industry awards, and fellowships from AMEC, AMI, and PRIA. He convenes the Health Communication Research Group at UTS and has led evaluation projects for WHO during COVID-19. His $1.2M+ grants include NHMRC, NSW Government, and EU Commission collaborations.

• Autonomous Systems
• Robotics
• Transportation Engineering
• +3 more

Dr Philip Gun is a Researcher at the University of Sydney's Faculty of Engineering, affiliated with the Rio Tinto Centre for Mine Automation and the Australian Centre for Field Robotics (ACFR). His work focuses on autonomous systems, robotics, and optimization in transportation networks. He holds a research position and contributes to projects involving multi-vehicle coordination, mining automation, and simulation methodologies. Research Interests: His research emphasizes trajectory optimization for autonomous vehicles, rail network logistics, and hybrid simulation models. He explores topics like cooperative control in road networks, heuristic-based algorithms for freight scheduling, and iterative model development approaches. Labs/Teams: He is part of the ACFR Research Facilities and collaborates with the Rio Tinto Centre for Mine Automation. His work bridges robotics engineering with industrial automation challenges in mining and transportation sectors.

Lyle NoakesView profile

Professor

• Computer Vision
• Optimal Control
• Variational Problems
• +3 more

Lyle Noakes is a Professor in the Department of Mathematics and Statistics at The University of Western Australia (UWA), within the School of Physics, Maths and Computing. His research focuses on geometric optimization, differential geometry, optimal control, and variational problems. He has contributed to advancements in computer vision, spline theory, and Riemannian manifolds. Noakes has held grants related to nonlinear approximation theory, geometry in computer vision, and inverse scattering. His work spans theoretical developments and applications in fields such as robotics path planning and quantum algorithms. Research Interests: Geometric optimization of curves and surfaces Optimal control in non-Euclidean spaces Applications of differential geometry to computer vision Scattering theory and inverse problems Recent articles highlight innovations in minimizing curvature/spirality in curves, convergence analysis of numerical methods, and geometric rigidity. His work on Napoleonic triangles in hyperbolic and spherical geometries demonstrates cross-disciplinary geometric exploration. Noakes has advised 10 students and collaborated on projects funded by ARC Small Grants and UWA initiatives.