Tien Tsin Wong is a Professor in the Department of Data Science & AI at Monash University, Australia. Previously, he served as a Professor at the Chinese University of Hong Kong (1999–2024) and held a Visiting Assistant Professor position at the Hong Kong University of Science and Technology (1998–1999). His research focuses on Generative AI, Computer Graphics, Computer Vision, and Computational Manga, with significant contributions to GPU techniques, image-based rendering, and multimedia compression. Education: He earned a B.Sc. (1992), MPhil (1994), and PhD (1998) in Computer Science from the Chinese University of Hong Kong. Research Interests: His work bridges computational techniques with artistic applications, particularly in manga and animation. Notable areas include generative models, diffusion-based video synthesis, and physically plausible scene generation. His research aligns with UN Sustainable Development Goals through innovations in education and digital accessibility. Awards : He has received the 2004 Young Researcher Award, 2005 IEEE Transactions on Multimedia Prize Paper Award, and two international invention medals (Geneva 2018, Asia Hong Kong 2019). Editorial Roles : He serves as an Associate Editor for Computer Graphics Forum , IEEE Transactions on Visualization and Computer Graphics , and Computational Visual Media . His editorial work underscores his influence in advancing visualization and graphics research. Labs/Teams : While not explicitly named, his collaborations span global institutions, focusing on computational manga, generative AI, and GPU-optimized techniques. His work often involves interdisciplinary teams addressing challenges in digital media and AI.
Dr. Amin Sakzad is an Associate Professor in the Department of Software Systems & Cybersecurity at Monash University's Faculty of Information Technology. His research focuses on lattice-based cryptography, wireless communications, and post-quantum security protocols. He holds a PhD in Applied Mathematics from Amirkabir University of Technology (2011) and has held academic roles at Carleton University and Monash since 2012. Dr. Sakzad’s expertise spans lattice coding theory, MIMO systems, and privacy-preserving technologies for genomic databases and blockchain applications. He leads multiple ARC-funded projects, including work on secure databases (SRDBMS) and post-quantum cryptographic primitives for FinTech and energy sectors. His research has been recognized through awards such as the FIT Dean’s Award for Teaching Excellence (2021). Key collaborations include projects on blockchain security (CollinStar Lab), genomic data privacy, and energy market cybersecurity. His work addresses UN SDGs through contributions to quality education (SDG 4) and industry innovation (SDG 9). Recent publications highlight advancements in lattice-based cryptography (e.g., CRYSTALS-Kyber variants), privacy-preserving energy trading, and secure blockchain protocols like FPPW watchtower systems. His research bridges theoretical cryptography with practical implementations in embedded systems and 5G telecommunications. Grants: 16 active/completed projects including $1.2M in ARC funding Advising: Supervising PhD projects on lattice applications in post-quantum crypto and blockchain Labs: Core member of Monash’s Software Defined Telecommunications (SDT) Lab and CollinStar Lab
Dr. Mao Shan is a Senior Research Fellow at the Australian Centre for Robotics, part of The University of Sydney. He holds a PhD from The University of Sydney (2014) and has held research positions at Nanyang Technological University (2016-2017) and the Australian Centre for Robotics (2014-2016). His research focuses on autonomous systems, V2X communication, cooperative perception, and sensor fusion. Current students include Yaoqi HUANG, Henry LYU, Zhenxing MING, Nguyen TRAN, Tzu-yun TSENG, and Yupeng WANG. His work spans robotics, intelligent transportation systems, and control systems. Recent publications emphasize 3D object detection, cooperative perception frameworks, and autonomous navigation. He has contributed to the development of the University of Sydney Campus Dataset for robust autonomy testing and led cooperative perception projects funded by iMOVE CRC (2018). His research bridges theoretical advancements with practical applications in autonomous vehicles and multi-robot systems. Labs and affiliations include the Australian Centre for Robotics and the Intelligent Transport Systems Group. His interdisciplinary approach integrates probabilistic modeling, sensor fusion, and machine learning to address challenges in autonomous systems.
Professor Chongmin Song is a faculty member at the University of New South Wales (UNSW), affiliated with the School of Civil and Environmental Engineering. His academic rank is Professor, and he specializes in computational mechanics with a focus on innovative numerical methods. He holds a BE and ME from Tsinghua University and a DEng from the University of Tokyo. His research explores computational mechanics, fracture analysis, wave propagation, and soil-structure interactions. Key methodologies include the Scaled Boundary Finite Element Method (SBFEM), image-based modeling, and dynamic simulations of infrastructure systems. He leads significant ARC-funded projects like 'A scaled boundary framework for nonlinear dynamic analysis of structures' (DP250100955) and 'Developing sustainable graded porous cementitious structures' (LP240100123), totaling over $1M in recent grants. Recent publications emphasize adaptive modeling techniques, multiphysics simulations, and high-performance computing applications. Trends include topology optimization for structural dynamics, phase-field fracture modeling for brittle materials, and GPU-accelerated elastodynamics. His work integrates computational efficiency with real-world engineering challenges, particularly in geomechanics and material failure analysis. Professor Song collaborates extensively on projects involving computational fracture mechanics and maintains laboratories focused on numerical simulation advancements. Future work targets scalable algorithms for 3D crack propagation and multiphysics coupling in infrastructure systems.
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 David Scott Taubman is a faculty member and Deputy Head of School (Research) at the School of Electrical Engineering and Telecommunications (EE&T) at UNSW Sydney, Australia. He is also co-director of Kakadu Software Pty. Ltd. and its affiliates Kakadu R&D and Kakadu GPU. He earned his academic credentials from the University of Sydney and University of California at Berkeley: B.Sc. in Mathematics and Computer Science, University of Sydney, 1986 B.E. (Medal) in Electrical Engineering, University of Sydney, 1988 M.Sc. in Electrical Engineering, University of California at Berkeley, 1992 Ph.D. in Electrical Engineering, University of California at Berkeley, 1994 Professor Taubman's research interests span multiple domains within electrical engineering and telecommunications, particularly focusing on: Image Compression (EBCOT algorithm, JPEG2000 technologies) Video Compression (scalable video compression, motion compensated temporal lifting) Image and Video Processing (motion and depth estimation, demosaicing of digital color images, medical image analysis) Multimedia Communication (JPIP standard for interactive imaging, scalable communication systems) He has received numerous scientific awards and honors, including best paper awards from IEEE Signal Processing Society, IEEE Circuits and Systems Society, and IEEE Int. Conf. Image Processing. He has also received teaching awards from UNSW and was recognized with the NSi Inventor of the Year Award. Professor Taubman has contributed significantly to industry standards: Author of the EBCOT coding algorithm adopted in the JPEG2000 standard in November 1998 Author of Verification Model and associated documentation for JPEG2000 Central contributor to IS15444-1, IS15444-4, IS15444-9, IS15444-15 and IS15444-17 Developer of the commercially successful Kakadu Software tools for JPEG2000 He has held various leadership positions at UNSW including Head of the Telecommunications Research Group, Head of the Signal Processing Research Group, and Director of Research at School of EE&T.
Dr. Sirojan Tharmakulasingam serves as a Lecturer and Research and Development Coordinator at the Signals, Information & Machine Intelligence lab within the Faculty of Engineering at the University of New South Wales (UNSW) Sydney. His work bridges theoretical machine learning with practical applications in edge computing and high-performance systems. His research spans multiple cutting-edge domains including machine learning, artificial intelligence, data science, edge computing, and high-performance computing. Dr. Tharmakulasingam specializes in developing next-generation inference models by integrating machine learning, signal processing, mathematical modeling, and computing across diverse data types including images, video, audio, and quantum molecular data. His work has significant implications for scientific computing, telecommunications, and healthcare applications. Analysis of his publication trends reveals a strong focus on practical AI implementations, with increasing emphasis on edge computing solutions, quantum applications, and energy-efficient models. His recent work demonstrates progression from foundational machine learning techniques toward specialized applications in scientific computing and real-time systems. Dr. Tharmakulasingam holds a Doctor of Philosophy from UNSW Sydney and a Bachelor of Science of Engineering from the University of Moratuwa in Sri Lanka. His academic journey reflects a strong foundation in both theoretical and applied engineering principles. As Research and Development Coordinator for the Signals, Information & Machine Intelligence lab, he oversees critical research infrastructure and collaborations. His work location in Room 447 of the EE&T Building (G17) places him at the heart of UNSW's engineering research ecosystem, with access to the Mark Wainwright Analytical Centre's extensive facilities.
Navid Constantinou is a Senior Lecturer at The University of Melbourne, specializing in Climate & Ocean Geoscience. His research focuses on physical oceanography, geophysical fluid dynamics, and climate modeling, with a particular emphasis on machine learning applications in these fields. He is affiliated with the University of Melbourne and contributes to collaborative projects like Oceananigans.jl and regional-mom6. Research Interests: His work explores ocean circulation dynamics, fluid mechanics, and the interplay between atmospheric and oceanic systems. Key areas include surface wave effects, turbulence decay, and parameterization of ocean mixing processes. He actively develops computational tools to enhance climate modeling accuracy and resolution. Publications: His recent work addresses topics like meridional heat transport in the Atlantic, GPU-based ocean modeling, and the impact of climate change on Antarctic currents. These studies often involve interdisciplinary collaborations with institutions like MIT and the Scripps Institution of Oceanography. Awards: While no explicit awards are listed, his research has been highlighted in prominent journals and media outlets like The Conversation and Nature Climate Change . Advising & Grants: He supervises students such as Dhruv Bhagtani and collaborates on projects funded by initiatives like the Australian Research Council. His software contributions, including OceanBioME.jl and SpeedyWeather.jl, underscore his commitment to advancing computational methods in geosciences. Labs/Teams: Involved in global climate modeling teams and open-source software development communities focused on ocean and atmospheric dynamics.
Dr. Johan Barthelemy is an Honorary Associate Professor at the University of Wollongong's Faculty of Sciences - SAEF, where he leads the SMART IoT Hub and Digital Living Lab. His research focuses on complex systems, agent-based simulation, AI for IoT, and smart cities. He holds a PhD in Applied Mathematics from the University of Namur (Belgium) and has contributed to projects like the mipfp statistical package for R. His work spans environmental monitoring, transportation modeling, and infrastructure resilience. He supervises higher-degree research students and collaborates with NVIDIA as a Deep Learning Institute Ambassador. Education: PhD in Applied Mathematics, University of Namur (2007–2014) MSc in Mathematics, University of Namur (2005–2007) BSc in Applied Mathematics, University of Namur (2003–2005) Research Interests: Agent-based simulation and synthetic population generation AI-driven video analytics for smart cities and environmental monitoring IoT sensor networks and edge computing GPU-based high-performance computing Climate change impact analysis via drones and multispectral imaging Funding & Projects: Lead investigator for ARC grants on future mobility and Antarctic environmental futures CSIRO-funded project on AI-driven sugarcane assessment Multiple University of Wollongong grants for IoT sensors, climate testing facilities, and disaster-resilient transport Labs & Collaborations: SMART Infrastructure Facility (UOW) Namur Center for Complex Systems (Belgium) NVIDIA Deep Learning Institute partnership
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.
Sam McSweeney is a Researcher at Curtin University's School of Electrical Engineering, Computer and Mathematical Sciences (EECMS), part of the Faculty of Science and Engineering. His work focuses on pulsar astrophysics, subpulse drifting phenomena, and radio transient detection using instruments like the Murchison Widefield Array (MWA). Key research areas include magnetar-like radio transients, nulling pulsars, and multiwavelength studies of neutron stars. He has contributed to major surveys like the SMART pulsar survey and developed advanced data processing pipelines for transient detection. Publications since 2023 highlight discoveries of long-period radio transients, emission state-switching phenomena, and multi-instrument follow-up strategies. His work bridges observational astronomy with computational techniques, emphasizing high-time resolution observations and GPU-based imaging for FRB searches.
Professor Anand Veeraragavan is a leading expert in hypersonics and combustion at the University of Queensland's School of Mechanical and Mining Engineering. As Centre Director of the Centre for Hypersonics and Associate Editor of the AIAA Journal of Spacecraft and Rockets, he drives international research initiatives and technical standards. B.Tech (IIT-Madras), MS/PhD (University of Maryland) Co-Director, Centre for Hypersonics Mid-Career Advance Queensland Research Fellow (2017-2020) His research spans supersonic combustion of hydrocarbons, hypersonic aerothermodynamics , advanced optical diagnostics (PLIF, FLDI), and microcombustion power systems . Current projects focus on Boundary Layer Transition (BOLT II) simulations and scramjet cavity optimization for supersonic combustion. Recent publications emphasize fuel injection dynamics in hypersonic flows, cavity flameholding mechanisms, and thermal management for scramjets. His team develops 3D numerical models and experimental diagnostics for shock-turbulence interactions. Best Thesis Award, University of Maryland (2009) Associate Editor, AIAA Journal of Spacecraft and Rockets (2021-present) Advance Queensland Mid-Career Fellowship (2017-2020) Supervising PhD researchers on topics including cavity flame holders, hypersonic boundary layer transition, and turbulent transport mechanisms. Collaborations with UQ's Centre for Hypersonics, MIT, and GE Energy inform his work.
Dr. Chuong Nguyen is a Senior Research Scientist at CSIRO DATA61 and an Honorary Lecturer at the College of Engineering & Computer Science of Australian National University. His career spans institutions including Monash University, CSIRO CMIS, and Johns Hopkins University. Education : PhD in Mechanical Engineering (Monash, 2010), MEng in Eco-Environmental Civil Engineering (Ritsumeikan, 2003), BEng in Aeronautical Engineering (Ho Chi Minh City University of Technology, 2001), and MBA (Australian National University, 2024). Dr. Nguyen's research focuses on 3D computer vision and machine learning for applications in smart manufacturing, agriculture, and medical imaging. His work bridges advanced imaging techniques like digital holography and hyperspectral scanning with practical industrial solutions. His 15 most recent publications span 3D reconstruction, fluid dynamics, and medical imaging from 2017 to 2010. Key trends involve 3D digitization of biological specimens, noise modeling in sensor systems, and velocity field measurement in complex flows. Scientific Awards : CSIRO Julius Career Award (2020) DICTA Best Paper Award (2019) iAward ACT (2014) CSIRO Innovation Award (2013) Performance Cash Reward (2021) Dr. Nguyen supervises PhD and honors students in machine learning and biomechanical imaging. His $1M CSIRO-Google grant (2024) funds 3D digitization of the Australian National Insect Collection. He leads industry collaborations in mining and manufacturing inspection through advanced 3D vision systems. His lab affiliations include the Immersive Environments Lab at CSIRO and the Australian Centre of Excellence for Robotic Vision . He collaborates with institutions like Microsoft Research, Google Research, and the High Resolution Plant Phenomics Centre.
Dr. Qianqian Yang is a Senior Lecturer in applied and computational mathematics at the School of Mathematical Sciences, Queensland University of Technology (QUT). She holds a PhD in computational mathematics from QUT (2010) and has been recognized with prestigious awards such as the QUT Outstanding Doctoral Thesis Award (2010) and an ARC DECRA fellowship (2014). Her research focuses on fractional differential equations, numerical methods, and their applications in medical imaging, particularly diffusion MRI. She has secured major grants, including an ARC DECRA fellowship (2015–2022, part-time) and an ARC Discovery Project (2019–2022). Dr. Yang’s work bridges computational mathematics and biomedical applications, aiming to model brain tissue microstructure using fractional models. She teaches computational mathematics units and has supervised 2 postdocs, 3 PhDs, and numerous Honours and VRES students. Her research is published in top journals like NeuroImage and SIAM Journal on Scientific Computing. Expertise: Fractional PDEs, Numerical Analysis, MRI Modeling Grants: ARC DECRA, ARC Discovery Project
Dr. Conrad Sanderson is a Researcher and Team Leader at the Data61 division of CSIRO , focusing on artificial intelligence, machine learning, AI ethics, and high-performance numerical computing. He is also an Adjunct Professor at Griffith University . With over 150 publications and 11,000+ citations, he is renowned for developing influential open-source libraries like Armadillo and RcppArmadillo . Research Interests Artificial Intelligence & Deep Learning Responsible AI, Safe AI, and Ethical Trade-offs Numerical Linear Algebra and High-Performance Computing Recent Publications highlight advancements in: Dynamic graph anomaly detection via extreme value theory Fire propagation uncertainty estimation using neural emulators Resolving ethical tensions in AI implementation GPU-accelerated machine learning Scientific Awards Most cited paper award for thesis-based article Armadillo framework: 30+ million downloads Collaborations include researchers from Facebook, NASA, Boeing, and institutions like MIT and Stanford. His work bridges academia and industry through open-source contributions and interdisciplinary applications.
