Professor Sarath Kodagoda is a leading academic and researcher in robotics and mechatronics at the University of Technology Sydney (UTS), where he serves as Acting Director of the UTS Robotics Institute. He specializes in sensor fusion, data processing, and machine learning, with a focus on robotic solutions for infrastructure inspection and assistive technologies. His work includes developing the Robotic Remote Lab teaching facilities and founding the iPipes lab for wastewater infrastructure research. Affiliations: UTS Robotics Institute, Faculty of Engineering & IT Leadership Roles: President of the Australian Robotics & Automation Association, Ambassador for NSW Smart Sensing Network His research interests span robotics, sensor networks, and infrastructure robotics, with notable contributions to pipeline inspection and tactile sensing. He has published over 170 papers, attracted $6M+ in grants, and supervised 12 PhD students now working at Amazon, Google, and ABB. Awards include the UTS Medal for Teaching & Research Integration and multiple national/international innovation awards. Recent work emphasizes robotic systems for wastewater infrastructure, assistive robotics for vision-impaired individuals, and advanced sensor technologies. His articles highlight innovations in tactile sensing skins, 3D object detection (e.g., CaLiJD, LMIINet), and pipeline defect detection (PIPE-CovNet+). Grants: Contracts with Amplitel, NBN Co Ltd, and ARC Linkage Projects Labs/Teams: UTS Robotics Institute, iPipes Lab
Dr. Ehsan Abbasnejad is an Associate Professor at Monash University's Department of Data Science and Artificial Intelligence, and holds adjunct positions at the Australian Institute for Machine Learning (AIML, University of Adelaide) and the Centre for Augmented Reasoning (CAR). He specializes in foundational AI, focusing on vision-language tasks, adversarial machine learning, and reinforcement learning. His work bridges theory with real-world applications in agriculture, energy, healthcare, and sports. Education: PhD in Computer Science from Australian National University (ANU). Research Interests: Machine Learning Theory and Adversarial Defenses Neural Network Robustness and Generalization Multimodal Learning (Vision-Language) Continual and Transfer Learning Applications in Energy, Healthcare, and Robotics Awards: Finalist for Australian AI Academic/Researcher of the Year (2024) Multidisciplinary competition wins (e.g., OzMineral Explorer Challenge) Advising & Grants: Australian Research Council (ARC) Discovery Project on Reinforcement Learning CSIRO's Next Generation Graduate Fund Accepting PhD students in foundational AI and applications Labs & Teams: Director of Foundational Machine Learning & Reasoning at Monash, leading global teams in AI competitions and industry collaborations (Microsoft Research, NEC Labs America).
Associate Professor Lasantha Meegahapola is a Deputy Head of Department (Teaching & Learning) at RMIT University's School of Engineering in Melbourne, Australia. He holds an IEEE Senior Membership and serves as an Associate Editor for several prestigious journals, including IEEE Transactions on Power Systems and IET Renewable Power Generation. His research focuses on Power System Stability with Renewable Integration, Microgrid Control, and Smart Grid Technologies, addressing challenges like voltage stability, inverter-based grid dynamics, and renewable energy penetration. He has supervised 16 PhD students to completion and published over 200 articles. Key contributions include identifying stability issues in microgrids and advancing grid-forming inverter control strategies. His work aligns with UN Sustainable Development Goals 7 (Clean Energy), 9 (Infrastructure), and 13 (Climate Action). He is actively involved in IEEE committees, including the PSDP Task Force on Microgrid Stability. Teaching roles include Programme Manager for the Bachelor of Electrical Engineering (HK) and Subject Coordinator for Power System Analysis and Control courses. Collaborations span industry and international research institutions, emphasizing real-world applications of his research in power systems and renewable energy integration.
Dr. Matthew Brookhouse is a Senior Lecturer at the Fenner School of Environment & Society, part of the Australian National University's Institute for Climate, Energy & Disaster Solutions. With a PhD in Dendroclimatology from ANU, he specializes in using forest structural complexity and tree-ring analysis to understand climate interactions and ecological responses in Australian subalpine environments. Research Focus: Sub-alpine ecology, Dendrochronology, CO2 responsiveness in eucalypt species Teaching: First-year research methods with emphasis on statistical application, advanced modeling and field botany Projects: Leading collaborative snow-gum dieback research and dendrochronological monitoring initiatives His publications span 2006-2025 with recent emphasis on machine learning applications for forest monitoring, tropical tree-ring chronologies for climate change, and climate sensitivity in Australian alpine ecosystems. Key collaborations include institutions like Australian Nuclear Science and Technology Organisation and University of Canberra researchers. Current projects focus on snow-gum woodland dieback mechanisms, high-resolution dendrometric monitoring, and integrating dendrochronology with environmental policy frameworks. He maintains active supervision of research students and contributes to both undergraduate and postgraduate curriculum development.
About Vipul Jain: Associate Professor Vipul Jain leads Supply Chain and Logistics Management at RMIT University’s School of Accounting, Information and Supply Chain Management. With over 20 years of academic experience, he has held senior roles at institutions like Victoria University of Wellington (NZ) and IIT Delhi (India), contributing to curriculum development, research strategy, and academic-industry collaboration. His international collaborations span over 20 global institutions, emphasizing interdisciplinary research in supply chain resilience, sustainability, and Industry 4.0. Education & Academic Leadership: Previously served as Programme Director for Master of Technology (Industrial Engineering) at IIT Delhi, leading the establishment of the Modelling and Analysis of Supply Chain (MASC) Lab. Holds editorial roles for journals like International Journal of Intelligent Enterprise and Computers & Industrial Engineering . Research Focus: Specializes in supply chain design, sustainability, circular economy, and business analytics. His work integrates ICT and big data to address complex supply chain challenges, with notable contributions to vaccine distribution, blockchain adoption, and post-pandemic resilience. Over 145 peer-reviewed publications, including high-impact journals like OMEGA and International Journal of Production Economics , reflect his prolific output. Industry Impact: Conducted management training for organizations like JCB and IBM. Served as an expert assessor for New Zealand’s Ministry of Business and Industry and advised the Indian Railways. His research has been funded by the Department of Science and Technology (India) and EU initiatives like FP6 I*PROMS. Awards & Recognition: Ranked #7 in India’s top logistics academics (2018), recipient of Literati Awards (2024, 2020), and multiple editorial leadership roles. Active in global conferences, including co-chairing ANZAM 2023. Labs & Collaborations: Founded MASC Lab at IIT Delhi. Collaborates with Coventry University, Monash University, Hong Kong Polytechnic, and institutions in Switzerland, Spain, Canada, and the U.S. Focus areas include sustainable supply chains, digital transformation, and crisis resilience.
Roberto Togneri is a Professor and Senior Honorary Research Fellow at the University of Western Australia's School of Electrical, Electronic and Computer Engineering. He has been affiliated with the university since 1988, following his PhD in 1989. His research focuses on signal processing, speech recognition, machine learning, and biometrics, with notable contributions to audio-visual recognition systems and fraud detection. Education: PhD in Electrical Engineering (University of Western Australia, 1989). Research interests include feature extraction for audio signals, neural network models for speech and speaker recognition, and applications of machine learning to fraud prevention. His work has been recognized with awards such as the Education Innovation Award (ICASSP 2019) and grants from the Australian Research Council (e.g., DP110103336 for a 3D Audio-Visual Speech Recognition System). Key projects include developing robust speech recognition systems in adverse environments and advancing graph-based fraudster group detection using spatio-temporal data. He has also contributed to editorial roles in IEEE Signal Processing Magazine and authored over 214 research outputs. Funding highlights include $279,000 for a 3D audio-visual speech recognition system (2011–2013) and $230,000 for robust speech recognition in hostile environments (2010–2012). His research aligns with UN SDGs related to innovation and infrastructure.
Professor Khac Duc Do is a faculty member at Curtin University, holding a position in the School of Civil and Mechanical Engineering within the Faculty of Science and Engineering. He serves in the Office of the Provost and is based at Curtin Perth campus. His research focuses on advanced control systems, nonlinear dynamics, and robotics applications in marine, aerospace, and mechanical systems. He earned a PhD with distinction in 2003 and has held prestigious fellowships including ARC Postdoctoral Fellow (2004) and ARC Australian Research Fellow (2009). His teaching includes courses like Advanced Control and Mechatronics, Navigation and Marine Control Systems, and Advanced Control Engineering. Key research interests encompass control of nonlinear systems, stochastic systems, formation control of mobile agents, fluid-structure interaction, and boundary control of PDE-governed systems. His funded projects include wave-energy converter development (2023-2026), inerter-based damper research (2019-2021), and ocean vehicle control systems. Scientific awards include ARC grants totaling over AUD 2 million. Current opportunities include scholarships in control systems/fluid-structure interaction and a postdoc position in wave-energy conversion.
Piotr Koniusz is a Principal Research Scientist at Data61/CSIRO and an Honorary Associate Professor at the Australian National University (ANU), with an Adjunct role at UNSW. He holds a PhD in Computer Vision from the University of Surrey (2013) and a BSc from Warsaw University of Technology (2004). His research focuses on Foundation Models, Representation Learning, and Few-shot Learning, with contributions to Graph Neural Networks and Adversarial Robustness. Key roles include Program Chair for NeurIPS’25, Senior Area Chair for ICML’25 and ICLR’25, and Workshop Co-Chair for WWW’25. Awards include the Sang Uk Lee Best Student Paper (ACCV’22) and recognition as an Outstanding Area Chair (ICLR 2021–2023). Research interests span Vision-Language Models (VLMs), Generative Adversarial Networks (GANs), and Domain Adaptation. He supervises PhD students at ANU and collaborates with industry on projects like traffic forecasting and ecotoxicology prediction.
Professor David Taubman is a distinguished academic serving as Professor and Deputy Head of School (Research) at the School of Electrical Engineering and Telecommunications (EE&T) at the University of New South Wales (UNSW) in Sydney, Australia. He is also co-director of Kakadu Software Pty. Ltd. and its affiliates Kakadu R&D and Kakadu GPU. With a career spanning over three decades, Professor Taubman has made significant contributions to the field of image and video compression, most notably as the author of the EBCOT coding algorithm adopted in the JPEG2000 international standard. Professor Taubman earned his B.Sc. in Mathematics and Computer Science (1986) and B.E. (Medal) in Electrical Engineering (1988) from the University of Sydney, followed by an M.Sc. (1992) and Ph.D. (1994) in Electrical Engineering from the University of California at Berkeley. His professional journey includes engineering work at the Electricity Commission of N.S.W. (1988-1990), research positions at Hewlett-Packard Laboratories in Palo Alto (1994-1998), and an academic career at UNSW where he progressed from Senior Lecturer (1998-2003) to Associate Professor (2004-2009) and finally to Professor (2009-present). He has held various leadership roles including Head of the EE&T Telecommunications Research Group (2003-2014), Head of the EE&T Signal Processing Research Group (2014-present), Director of Research for the School of EE&T (2011-2016), and Deputy Head of School (Research) since 2017. Professor Taubman's research interests center on image and video compression, with particular expertise in JPEG2000 standards and implementations. His work spans signal processing, wavelet transforms, scalable video coding, motion modeling, and multimedia systems. He has pioneered numerous compression algorithms and frameworks, including the EBCOT coding algorithm that became central to the JPEG2000 standard. His recent research focuses on efficient motion modeling with cuboidal partitioning, learned lifting-based transform structures, and high-throughput implementations of JPEG2000 for video applications. His work bridges theoretical foundations with practical implementations, as evidenced by the commercially successful Kakadu Software tools that have garnered around 500 commercial licensees. Analysis of Professor Taubman's recent publications reveals a consistent focus on advancing compression technologies with particular emphasis on scalability, efficiency, and adaptability. His work spans traditional image compression (JPEG2000 extensions), video coding (cuboid-based partitioning for UHD/360-degree video), and emerging applications (nanopore sequencing data compression). A notable trend is the integration of machine learning techniques with traditional compression frameworks, as seen in his work on learned lifting-based transform structures. His research maintains strong connections to real-world applications across diverse domains including medical imaging, astronomical data processing, and genomic sequencing. IEEE Fellow Engineers Australia Fellow (by invitation) Professor Taubman has served as Associate Editor for the IEEE Transactions on Image Processing for two four-year appointments (2003-2005 and 2010-2013). He has been actively involved in numerous research grants focused on image and video compression technologies, particularly those related to the JPEG2000 standard and its extensions. His work has received significant industry support, reflected in his consultancy with various U.S., Japanese, and Australian corporations. He has also contributed to international standards development as a member of Standards Australia Technical Committee MS-065 (mirroring ISO TC42 on Digital Photography) and as a constitutional member of Standards Australia Technical Committee IT-029 (Coded Representation of Picture, Audio and Multimedia/Hypermedia Information). Professor Taubman co-directs Kakadu Software Pty. Ltd. and its research affiliates Kakadu R&D and Kakadu GPU, which have developed the commercially successful Kakadu Software tools for JPEG2000. His research group at UNSW focuses on advanced image and video compression techniques, with particular expertise in wavelet-based methods, scalable coding, and motion modeling. The group maintains strong industry connections and has contributed significantly to the development and standardization of image compression technologies worldwide.
Professor Daniel Oron is affiliated with the University of Sydney, where he joined in 2004 after completing his PhD in Operations Research at the Hebrew University of Jerusalem. His research focuses on Combinatorial Optimization, particularly Scheduling Theory, addressing challenges like batch scheduling with setups, customer delivery models, and scheduling under deteriorating conditions. He teaches courses such as Quantitative Business Analysis, Management Science, and Business Analytics Honours. His editorial role includes serving on the board of the Journal of Industrial & Management Optimization . Recent research contributions span multi-agent scheduling, energy recharging in scheduling, and coupled task optimization. He advises two current PhD students: Johnson (Two-agent scheduling problems) and Renjie Yu (Multi-agent scheduling with parallel batching). Publications highlight advancements in scheduling algorithms, resource allocation, and optimization under constraints. Notable works include minimizing late jobs with step-learning models and analyzing parameterized complexity in single-machine scheduling.
Dr. Edoardo Bertone is a Senior Lecturer at Griffith University's School of Engineering and Built Environment - Architecture and Design. He holds a PhD in Water Resources Engineering from Griffith University and Bachelor/Master degrees in Civil Engineering from the Polytechnic University of Turin. His research focuses on data-driven modeling, Bayesian Networks, and System Dynamics applied to water resources management, climate change adaptation, and the water-energy nexus. He is affiliated with Griffith's Cities Research Institute and Australian Rivers Institute, collaborating on projects with water utilities, governments, and private entities. Dr. Bertone has received awards such as the 2024 PVC Science Excellence in Teaching and the JSPS Fellowship (2022). He supervises doctoral and master's students in areas like water quality management and climate change impacts. Education: PhD in Engineering (Griffith University, 2015); MEng and BEng in Civil Engineering (Polytechnic University of Turin, 2009-2011). Research Interests: Water quality modeling, drinking water optimization, data-driven prediction, climate change adaptation, and sustainable development goals. He leads over 25 funded research projects, including initiatives on reservoir water quality management in Thailand, real-time nutrient monitoring, and cyanobacteria bloom modeling. Dr. Bertone’s work integrates advanced sensors, machine learning, and Bayesian networks to address environmental challenges. Awards: Listing includes PVC Excellence Awards (2024, 2017), JSPS Fellowship, and recognition as a Rising Star in Queensland Science (2015). Grants & Supervision: Principal supervisor for 10+ doctoral candidates and collaborator on projects funded by Seqwater, CSIRO, and the Ian Potter Foundation. Key grants include $745k for biofertilizer combatting eutrophication and $269k for coagulation optimization models. Dr. Bertone’s contributions extend to urban sustainability, co-editing the book *SeaCities: Urban Tactics for Sea-Level Rise* and developing frameworks for integrating SDGs into architectural education.
Dr. Sam Ferguson is a Senior Lecturer at the School of Computer Science, University of Technology Sydney (UTS), with a multidisciplinary background in music performance, cognitive science, and psycho-acoustics. His research explores the intersection of sound, music, and human experience through creative coding, machine learning, and interactive systems. Key Research Areas: Sound and Music Computing, Human-Computer Interaction, Creative Coding, Cognitive Science, Installation Art, and Acoustics. Current Projects: ARC Linkage project on creative coding and multiplicitous media; industry collaborations on IoT-based audiovisual systems. Recent Publications: Focus on spatial audio complexity, gestural interaction with networked sound, music emotion recognition frameworks, and robotic performance through genre-based cultural platforms. Leadership Roles: Director of Teaching & Learning Engagement; former Deputy Head of School (Teaching and Learning); active in ACM Creativity and Cognition Steering Committee. Teaching: Courses like Digital Media Studio , Prototyping Physical Interaction , and Data Processing using R within UTS's interdisciplinary Software Development Studio.
Dr. Ali Ahrari is a Lecturer at the School of Systems and Computing, University of New South Wales, Canberra. He holds a Ph.D. in Mechanical Engineering from Michigan State University (2016) and has extensive experience in research and academia, including roles as a Research Fellow and Associate at UNSW-Canberra and the University of Sydney. His research focuses on evolutionary algorithms, multimodal and multi-objective optimization, and surrogate-assisted optimization. Ahrari is a recipient of prestigious awards, including the ARC-DECRA 2023 and multiple international competition wins in optimization (e.g., CEC/GECCO competitions). He leads research groups like the Canberra Evolutionary Optimization (EvOpt) and serves on editorial boards, including Applied Soft Computing. Education: Ph.D. (2016, Michigan State University), M.Sc. and B.Sc. (University of Tehran). Awards: ARC-DECRA, ISCSO, and GECCO/CEC competition wins. Grants: ARC DECRA (2023), NCI Adapter Schemes, UNSW HPC allocations. Supervision: Currently advising 1 PhD student at SEIT, UNSW-Canberra. Engagements: Chair of IEEE Task Force on Multi-modal Optimization, organizer of optimization competitions (GECCO'2024, CEC'2022). His research emphasizes computational optimization, evolutionary computation, and swarm intelligence, with applications in engineering design and dynamic environments. He actively contributes to academic communities through editorial roles and conference organization.
Dr. Michael Stevens is a Senior Lecturer at University of New South Wales (UNSW) Canberra , where he focuses on advanced manufacturing and biomedical device control systems . His work bridges digital manufacturing for SMEs with smart artificial heart technologies , emphasizing industry collaboration and translational research. Specializes in physiological control systems for rotary blood pumps Develops unobtrusive fall detection systems for dementia patients Leads international projects on total artificial heart development Education : B.Eng (Medical - First Class Honours), Queensland University of Technology (2010) PhD in Physiological Control for Biventricular Assist Devices, University of Queensland (2014) Research Trends show consistent focus on: Machine learning for biomedical diagnostics (2018–2025) mmWave radar and thermal sensors in patient monitoring (2021–2024) Computational fluid dynamics in artificial heart modeling (2016–2024) Physiological control algorithms for rotary blood pumps (2011–2025) Scientific Awards : UNSW Scientia Education Award (2021) for contextual teaching Heart Foundation Runner-up for "Smart Artificial Hearts" pitch (2021) ARC PGC Supervisor Award (2017) for mentoring Grants & Supervision : Holds over $6 million in competitive funding including MRFF and ARC grants. Currently supervises 4 PhD students while maintaining industry partnerships with VitalCare and BiVACOR. Labs & Facilities : Works across UNSW Engineering labs and Graduate School of Biomedical Engineering platforms, including mock circulation loops and high-performance computing clusters for CFD simulations.
Dr. Zhi Chen is a Lecturer in Computing at the School of Mathematics, Physics and Computing, University of Southern Queensland, specializing in Artificial Intelligence and Machine Learning with applications spanning digital agriculture and healthcare systems. Education: Master of Information Technology (MIT), University of Queensland, 2018 PhD, University of Queensland, 2023 Research Focus: His work centers on zero-shot learning, domain adaptation, and multimodal systems, addressing core challenges in computer vision and deep learning. Current projects integrate AI with agricultural risk modeling and medical diagnostics, emphasizing real-world deployment of robust algorithms under data-scarce conditions. Publication Trends: Recent output (2022-2025) shows concentrated expertise in source-free domain adaptation and generalized zero-shot learning, with significant contributions to plant disease recognition (via mobile multimodal systems) and diabetes subgroup analysis. His work consistently appears in premier venues including AAAI, CVPR, and ACM MM, demonstrating methodological innovation applied to critical domains like climate-resilient agriculture and precision medicine. Supervision: Currently serves as Associate Supervisor for a doctoral candidate developing parametric insurance models for oyster farms to mitigate climate-related risks from king tides and extreme weather events. Awards: No scientific awards were documented in the provided materials.
