Bakhtiar Sadeghi is a Doctor of Philosophy researcher at the Faculty of Science and Engineering, Macquarie University. His work bridges Cybersecurity with Ethics and Decision Making , focusing on the application of Serious Games for professional training and ethical awareness development. This interdisciplinary approach aligns with Macquarie University's commitment to innovative research in technology and social responsibility.
Professor Douglas Creighton is a Deakin Distinguished Professor and Director of the Institute for Intelligent Systems Research and Innovation (IISRI) at Deakin University. With a career spanning complex systems modeling, AI, and agent-based simulation, he leads a 100-strong research team working on real-world defense, transport, med tech, and advanced manufacturing solutions. His current research program includes three pillars: systems thinking and quantitative analytics, computational intelligence, and agent-based modeling. PhD in Industrial/Systems Engineering from Deakin University Bachelor of Engineering (Systems Engineering) and Bachelor of Science (Physics) from Australian National University His research strengths include smart transport systems, simulation modeling, robotics, and data analytics. Recent publications focus on stress quantification via EEG analysis, video instance segmentation, and ethical AI implementation in rail systems. He has developed trust estimation algorithms for autonomous agents and contributed to frameworks for rural community health engagement. Scientific recognition includes: SAE Mobility Engineering Excellence Gold Award (2016) IISRI Excellence in Industry Collaboration Award (2020) Three best paper awards at IEEE conferences As a supervisor, he currently guides research on: Mental stress quantification using brain connectivity Multi-cloud decision architecture Immersive technology for Industry 5.0 repair engineers Causal loop diagram representation for public health His industry collaborations span Australian Defence, Alstom Transport, Boeing, and rail/water organizations. Current grants include projects with the Australian Electoral Commission, Flame Security International, and advanced aerial mobility research.
Professor Mark Wallace is a renowned academic at Monash University , specializing in modelling and software platforms for planning, scheduling, and optimization problems . He has led groundbreaking projects such as the development of the ECLiPSe constraint programming platform (acquired by Cisco Systems) and the G12 hybrid optimization platform , which was commercialized through his company, Opturion . His research consistently bridges theoretical innovation with real-world applications, supported by industry funding from major companies like British Airways, Qantas, and Woodside Energy. Education: MA in Mathematics and Philosophy from Oxford University; MSc in Artificial Intelligence from Queen Mary College, London; PhD in Communicating with Databases in Natural Language from Southampton University. His work focuses on application-driven research in constraint programming, hybrid optimization systems, and decision support technologies. Recent publications highlight advancements in public transport routing , autonomous vehicle fairness constraints , and human-centred feasibility restoration . He actively collaborates with industry partners on projects involving Melbourne Water , Woodside , and the Alertness CRC , driving innovations in smart infrastructure and transport systems.
Associate Professor Binghao Li leads the MIoT & IPIN Lab at the School of Minerals and Energy Resources Engineering, University of New South Wales, Sydney. He holds a PhD in Spatial Information Systems from UNSW and advanced degrees in Civil and Electrical/Mechanical Engineering from Tsinghua University and Beijing Jiaotong University. Expertise in indoor/outdoor positioning systems Pioneering mine IoT applications Leader in pedestrian navigation research His research spans indoor positioning technologies, satellite navigation, and mining IoT solutions. Key grant projects include: 2021 CRC-P grant ($2m) for underground mine LoRa networks 2020 ARC Research Hub ($5m) for connected sensors 2018 Digital Grid Seed Funding for indoor navigation 2015-2019 ARC Linkage grants for positioning systems Award highlights: 2019 Best Paper & Presentation Awards 2010 VC's Post-Doctoral Fellowship 2004-2005 student research awards He supervises research in indoor positioning and mine IoT, and teaches courses including ENGG1000 Engineering Design and MINE8710 Mine Slope Stability.
Professor Geoff Webb is a world-leading data scientist at Monash University , serving as Director of the Monash University Centre for Data Science within the Faculty of Information Technology . His research focuses on leveraging data science to enable evidence-based decision making and derive actionable insights through artificial intelligence, machine learning, and big data analytics. Core expertise in data mining , bioinformatics , and computational biology Developed Magnum Opus software and contributed to the Weka machine learning workbench Recipient of the Eureka Prize for Excellence in Data Science and leadership roles in major data mining conferences Research Interests Professor Webb's work spans artificial intelligence , machine learning , and data analytics , with a focus on black-box user modelling , interactive data analytics , and statistically-sound pattern discovery . His recent publications highlight advancements in Bayesian networks , time series analysis , and genomic data interpretation , demonstrating his interdisciplinary impact. Scientific Contributions AI in healthcare : EHR-ML framework for clinical records analysis Biological applications : KcatNet for enzyme prediction, PFresGO for protein function Data mining innovations : OPUS search algorithm, proximity forest techniques As a technical adviser to data science company Froomle , he bridges academic research with real-world applications. His work has been recognized through numerous research awards and leadership as Editor-in-Chief of Data Mining and Knowledge Discovery for a decade.
Sam Elman is a Postdoctoral Research Fellow at the School of Computer Science, University of Technology Sydney. His research focuses on Quantum mechanics and quantum information theory Mathematical aspects of free-parafermion solvability Topological entanglement entropy in multiple dimensions Optimal scheduling of quantum graph states Recent work includes publications on path decomposition algorithms for quantum computation and integrability of spin systems via frustration graphs. He is available for Masters Research or PhD student supervision. He is affiliated with the Faculty of Engineering and Information Technology Centre for Quantum Software and Information (QSI) UTS Research Centre
Dr. Madhav Vijayan serves as a Postdoctoral Research Fellow at the Centre for Quantum Software & Information within the School of Computer Science, Faculty of Engineering and Information Technology at the University of Technology Sydney. His research focuses on advancing quantum computing technologies with emphasis on algorithm development and hardware implementation. Dr. Vijayan earned his PhD in Computer Science from the University of Technology Sydney between 2017 and 2021, following an MSc in Physics from the Indian Institute of Technology Madras (2012-2014). His research interests span multiple domains of quantum information science, specializing in quantum circuit compilation , optical quantum computation , and quantum error correction . His work bridges theoretical quantum information with practical implementation challenges, particularly in photonic quantum computing systems where he has developed novel approaches to circuit optimization and 3D architecture design. Analysis of his publication record from 2018-2024 reveals a consistent focus on quantum resource theory, quantum circuit optimization, and photonic implementations. His research demonstrates progression from foundational quantum information theory toward practical quantum computing applications, with increasing emphasis on compilation techniques and hardware-aware algorithm design. Dr. Vijayan has contributed to significant advancements in quantum computing through his service as an assessor for Sydney Quantum Academy HDR scholarships (2022) and his collaborative research projects in quantum software development. His teaching portfolio includes Introduction to Quantum Computing (2022), Methods in Quantum Computing (2020), and mathematical courses such as Introduction to Mathematical Analysis and Modelling (2019-2020). His work at the Centre for Quantum Software & Information positions him at the forefront of quantum software development, contributing to Australia's growing quantum technology sector through both theoretical advances and practical implementations.
Dr. Jing Jiang is an Associate Professor in the School of Computer Science and a core member of the Australian Artificial Intelligence Institute (AAII) at the University of Technology Sydney (UTS). As an ARC DECRA Fellow, she has secured over AU$2 million in research funding through multiple ARC grants, CSIRO/Data61 projects, and industry collaborations. Her work bridges theoretical advances in machine learning with practical applications across various domains. Dr. Jiang's research focuses on machine learning, particularly federated learning, reinforcement learning, and foundation models. She explores how to make these technologies work effectively in heterogeneous environments, addressing challenges like data privacy, non-IID data distributions, and efficient communication. Her work spans both theoretical foundations and practical implementations for real-world applications. Her publications demonstrate a strong trend toward personalized federated learning approaches, with significant contributions to recommender systems, time series analysis, and weather forecasting. She has developed novel techniques like variational autoencoder approaches for federated collaborative filtering and adaptive prompt learning for foundation models on devices. Dr. Jiang has received several notable recognitions: ARC DECRA Fellow Awardee of the Australian International Postgraduate Research Scholarship (IPRS) Dr. Jiang has successfully led multiple major research projects, including two ARC Discovery Projects, one ARC Linkage Project, and a CSIRO/Data61 CRP project where she served as lead Chief Investigator. She has supervised numerous PhD and Master's students and actively collaborates with industry partners on applied research. As a core member of the Australian Artificial Intelligence Institute (AAII) at UTS, Dr. Jiang contributes to a vibrant research ecosystem focused on cutting-edge AI research. She collaborates closely with Professor Guodong Long and other researchers on various machine learning projects, and serves in leadership roles including program co-chair for major conferences like ADMA2023.
Dr. Jun Li is a Senior Lecturer at the School of Computer Science, Faculty of Engineering and Information Technology, University of Technology Sydney (UTS), Australia. He received his Ph.D. in Computer Science from Queen Mary University of London in 2009 and is affiliated with the Australian Artificial Intelligence Institute (AAII) at UTS. His research spans multiple domains within artificial intelligence, with primary focus on Machine Learning applications in computer vision and 3D geometry. Dr. Li has published extensively in high-impact journals including IEEE Transactions (TPAMI, TIP, TNNSLS) and Pattern Recognition, with recent work expanding into interdisciplinary research in earth science and marine applications. His research output demonstrates consistent productivity with numerous publications each year across diverse AI application areas. Dr. Li's work shows strong thematic progression from foundational computer vision techniques to applied interdisciplinary research. Early work focused on face hallucination and video super-resolution, while more recent publications address environmental applications using Graph Neural Networks for wave prediction and damage classification for disaster response. His research consistently bridges theoretical AI advances with practical real-world applications across healthcare, autonomous systems, and environmental science. AI to assist disaster emergency response (2023-2026) Applying Generative Adversarial Network in Medical Image Analysis (2020-2021) Big Massive Open Online Course (MOOC) Data Retrieval (2017-2020) As an educator, Dr. Li teaches core courses including '31005 Machine Learning' and '32513 Advanced Data Analytics Algorithms' at UTS, and is available for Masters Research and PhD student supervision, contributing to the development of next-generation AI researchers.
Distinguished Professor Chin-Teng Lin is a leading academic at the University of Technology Sydney (UTS) , where he serves as Co-Director of the Australian AI Institute (AAII) and Director of the Computational Intelligence and Brain Computer Interface Lab . With a career spanning decades, he has pioneered advancements in artificial intelligence (AI) and brain-computer interfaces (BCI) , focusing on human-machine collaboration, wearable EEG systems, and neuroergonomics. School of Computer Science, UTS Co-Director, Australian AI Institute Director, Computational Intelligence and BCI Lab Lin’s research interests are deeply rooted in machine-intelligent systems , cognitive neuroscience , and human-centric AI . He has developed groundbreaking technologies like fuzzy neural networks (FNNs) in 1992, which revolutionized AI by integrating human-like reasoning. His work extends to multi-agent reinforcement learning for cybersecurity, wearable EEG devices for real-world applications, and neurofeedback interventions for chronic pain management. His research outputs include over 950 peer-reviewed publications , with a focus on deep learning , transformer models , and clustering algorithms . Articles like the MGRW-Transformer (2025) and Autonomous Clustering (2025) highlight his leadership in interpretable AI and parameter-free methods. Scientific awards and recognitions include: IEEE Fellow (2005) IFSA Fellow (2012) IEEE Fuzzy Systems Pioneer Award (2017) Outstanding Achievement Award, Asia Pacific Neural Network Assembly Lin has supervised 72 PhD candidates , 30 postdoctoral fellows , and 237 research Masters students since 1992, mentoring notable alumni such as Dr. Zehong Cao (ARC DECRA Fellow) and Prof. Chia-Feng Juang (IEEE Fellow). His funding portfolio includes $10M+ from the US Army Research Lab , $3.8M from the Australian Defence Innovation Hub , and $30.2M in industry collaborations .
Professor Robert Fitch is a distinguished academic leader and one of Australia's foremost authorities in robotics at the University of Technology Sydney (UTS), where he serves as Professor in the School of Mechanical and Mechatronic Engineering. He holds significant leadership roles as Director of the NSW Defence Innovation Network and was previously Director of UTS Tech Lab and Head of School of Mechanical and Mechatronic Engineering from 2019-2023. His career bridges academia, research, and industry collaboration to develop engineering solutions for tomorrow's societal challenges. PhD from Dartmouth College (1998-2004) Former Senior Research Fellow at Australian Centre for Field Robotics, University of Sydney Current Director of NSW Defence Innovation Network (since 2023) Founding Co-Director of NSW Space Research Network (2021-2023) Professor Fitch specializes in autonomous field robotics, developing outdoor robotic systems for diverse industry applications from agriculture to aeronautics, environmental science to space exploration. His research spans multiple domains including multi-robot coordination, hydrogen storage systems, maritime robotics, and AI-driven automation. He operates at the critical nexus of academia, research, and industry, collaborating with businesses and government organizations to deliver innovative solutions to complex real-world challenges. His work demonstrates exceptional translational impact, turning academic research into commercial ventures and practical applications. His recent publications reveal a strong focus on advancing robotics through novel planning algorithms for multi-robot systems operating in complex environments. There's a clear trend toward practical applications in maritime operations, hydrogen energy storage, and warehouse automation, reflecting his commitment to solving real-world engineering challenges. His research integrates theoretical advances with industry needs, particularly in marine robotics, autonomous systems for agriculture, and energy storage technologies. Professor Fitch's work has received significant recognition with numerous accolades and extensive media coverage including appearances on ABC's flagship 7.30 program, Weekend Australian, BBC, and The Hindu. His research has been featured in over 15 news outlets and blogged about extensively, demonstrating its broad societal impact. He has graduated 20 PhD students who are now leading work in field robotics, mechanical and mechatronic engineering, and autonomous systems. Since 2011, he has secured nearly $30 million in grants funding over 50 projects, including major collaborations with industry partners like Space Machines Company, Advanced Navigation, and Australian Wool Innovation. His leadership has been instrumental in growing startups from small beginnings to significant operations, such as Space Machines Company which launched the largest Australian-made satellite into orbit aboard a SpaceX transporter in March 2024. Professor Fitch leads the UTS Robotics Institute and has been instrumental in establishing collaborative spaces like UTS Tech Lab, which has fostered successful partnerships with multiple companies. His work with Wildlife Drones demonstrates how academic research can translate into successful commercial ventures, having grown from research projects into an established business. His leadership extends to numerous industry and academic organizations, boards, and committees, where he serves as director, chair, and co-chair.
Professor J. Joshua Thomas is a distinguished academic at University of Wollongong Malaysia, specializing in intelligent systems and advanced computational techniques. He holds a PhD in Intelligent Systems Techniques from Universiti Sains Malaysia (2015) and a Master's degree in Computer Science from Madurai Kamaraj University, India (1999). Professor Thomas has demonstrated strong leadership in academic administration, having served as Head and Deputy Head of the Department of Computing between 2012 and 2017. His research focuses on intelligent systems and interdisciplinary computational algorithms, with recent work emphasizing Deep Learning, Graph Convolutional Neural Networks (GCNN), Graph Recurrent Neural Networks (GRNN), Hyper-Graph Attention Networks, and Quantum Machine Learning. Professor Thomas's projects span diverse applications including end-to-end steering learning systems, algorithm design in drug discovery, and advanced data analytics for electricity consumption and carbon price forecasting. Professor Thomas maintains an impressive publication record with over 50 peer-reviewed publications and 12 books with publishers including Wiley, Elsevier, and IGI Global. His research is supported by multiple active grants at institutional, national, and international levels, including projects on cancer survivor well-being and AI bias in smart cities. Best Paper Award at SCI 2025 Oracle Cloud Infrastructure 2024 Generative AI Certified Professional 2022 Global Staff Awards (UOWGE) - Excellence In Research Winner V-MIIEX2021: COVIDNet - GOLD AWARD Fundamental Research Grant Scheme (FRGS) recipient (2019) As an active researcher and educator, Professor Thomas serves as Principal Investigator on multiple grants, supervises PhD and Master's students, and regularly delivers keynote addresses at international conferences. His collaborative spirit is evident through partnerships with institutions in India, USA, China, and Germany, fostering knowledge exchange and innovation across disciplines.
Dr. Bin Liang is a Senior Lecturer at the University of Technology Sydney (UTS), working within the Faculty of Engineering and Information Technology and the Data Science Institute. He joined UTS in December 2018 as a Lecturer and was promoted to Senior Lecturer in July 2022, following his postdoctoral fellowship at Data61 (CSIRO). PhD in Computer Vision, Pattern Recognition, and Machine Learning from Charles Sturt University, Australia (2012-2015) Masters in Computer Science from Taiyuan University of Technology, China (2009-2012) BEng in Computer Science from Taiyuan University of Technology, China (2005-2009) Dr. Liang's research spans data mining, machine learning, computer vision, pattern recognition, and survival analysis, with a strong focus on practical applications in critical infrastructure. His work develops sophisticated machine learning frameworks for anomaly detection, failure prediction, and optimization in water infrastructure, flood mapping, and real estate appraisal. He has pioneered approaches that integrate graph neural networks with temporal analysis for pipe failure prediction and has made significant contributions to water quality optimization through data-driven methods. His research consistently bridges theoretical advancements with practical implementation in industry settings. His recent publications demonstrate a clear trajectory toward increasingly sophisticated models that capture complex temporal and spatial relationships in infrastructure data. There's a notable emphasis on multimodal learning approaches and domain generalization techniques that enhance model robustness across diverse application scenarios. His work on anomaly detection frameworks like MLAD shows innovation in grouping sensors by temporal characteristics for more accurate monitoring. 2022 – AWA R&D Excellence Award (NSW) 2022 – UTS Medal for Research Impact 2022 – Finalist for 2021-22 AWA Young Water Professional of the Year Award (NSW) 2020 – Finalist for the Best Paper Award of the ICARCV 2020 2019 – Industrial & Primary Industries Merit at the Victorian iAwards 2018 – Australian Museum Eureka Prize for Excellence in Data Science Dr. Liang actively supervises Masters Research and PhD students, focusing on data science applications for infrastructure management. His research has been supported through industry partnerships with water utilities and other infrastructure organizations, translating academic research into practical solutions that reduce water loss, improve infrastructure reliability, and enhance environmental sustainability. His work on pipe failure prediction and leak detection has directly contributed to operational improvements in water distribution networks. As a core member of the Data Science Institute at UTS, Dr. Liang collaborates with interdisciplinary teams working on data-driven solutions for urban infrastructure challenges. His research group focuses on developing scalable machine learning models that can be deployed in real-world settings, often working directly with industry partners to validate and implement their approaches. This industry-academia collaboration ensures that his research remains grounded in practical challenges while pushing the boundaries of data science methodology.
Zhang Fangyi is a research fellow at Queensland University of Technology's School of Electrical Engineering and Robotics, specializing in robotics, computer vision, and machine learning. With a PhD completed in 2018 titled 'Learning real-world visuo-motor policies from simulation,' Zhang has established a strong research trajectory focusing on bridging the gap between simulation and real-world robotics applications. Zhang's research interests center around robotic perception and manipulation, with particular expertise in sim-to-real transfer techniques, tactile sensing systems, and graph neural networks. Their work spans multiple domains including robotic grasping, fabric manipulation, face clustering algorithms, and graphene-based sensor development. A consistent theme throughout Zhang's research is the development of robust systems that can effectively transition from simulated environments to real-world applications. The publication record shows a clear evolution from foundational work in sim-to-real transfer (2015-2019) toward more specialized applications in tactile sensing and material science (2021-2024). Recent work demonstrates expanding interests into graphene-based sensor technology while maintaining core expertise in robotic perception. Zhang frequently collaborates with leading researchers at QUT including Peter Corke, with whom they've published multiple papers on robotic grasping and tactile sensing. Zhang's research has practical applications across multiple domains including assistive robotics, sensor development, and computer vision systems. Their work on laser-induced graphene sensors shows particular promise for next-generation tactile interfaces and wearable technology.
