Dr. Kanchana Thilakarathna is a Senior Lecturer in Distributed Computing at the University of Sydney's School of Computer Science, and a member of the Centre for Distributed and High Performance Computing. They hold a PhD from the University of New South Wales (UNSW) and a B.Sc. Eng (Hons) from the University of Moratuwa, Sri Lanka. Prior to academia, they worked as a Research Scientist at CSIRO/Data61 and had industry experience as a Mobile Radio Network Engineer. Research Interests : Dr. Thilakarathna focuses on cybersecurity, privacy in mobile and IoT systems, mixed reality privacy, and distributed computing platforms. Their work emphasizes user-centric solutions like the Yalut social media app, which enables decentralized data sharing. Key themes include privacy-preserving techniques, edge computing, and secure federated learning frameworks. Recent Work : Recent articles (2023–2025) explore machine unlearning for large language models, federated learning security, and IoT network slicing using P4 programmability. Their work on synthetic video traffic generation (VideoTrain++) and drone detection (DronePrint) demonstrates cross-disciplinary innovation. Awards : Malcolm Chaikin Prize (2015), Meta Research Awards (2020/2022), and Heidelberg Laureate Fellowship (2019). Grants : ARC Research Hub for Future Digital Manufacturing (2024), NSW Defence Innovation Network Projects (2024/2021), and Facebook Research Awards (2022/2020). Students : Advising 4 current PhD students on topics like wireless trust establishment and machine unlearning. Labs/Teams : Part of the Centre for Distributed and High Performance Computing and Sydney Nano Institute.
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.
Olga Kokshagina serves as an Associate Professor in Innovation & Entrepreneurship at The University of Sydney, with adjunct research appointments at Monash University's Emerging Technology Lab and the UNU Hub - Learning Planet Institute. She is also an active member of the French Digital Council. Her research program investigates technology-mediated collaboration in complex innovation systems, focusing on healthcare transformation, deep tech commercialization, and co-design methodologies. Kokshagina has led high-impact projects with global institutions including the World Health Organization, OECD, STMicroelectronics, Vall d’Hebron Hospital, and Roche, demonstrating strong translational research capabilities. Her scholarly work centers on value-based healthcare innovation, digital platform governance, and AI-enhanced collaborative systems. She examines how organizational capabilities evolve during technological transitions, particularly in healthcare ecosystems, and investigates regulatory frameworks for algorithmic control in digital markets. Kokshagina's research bridges theoretical innovation management with practical applications, evidenced by her co-founding of Ninti—an initiative advancing women's health in workplace environments—and her Open Covid-19 crowdsourcing campaign that mobilized global expertise during the pandemic. Analysis of her 2021-2025 publications reveals a cohesive trajectory examining innovation in socio-technical systems. Key themes include value digitalization in healthcare, mission-oriented interdisciplinary collaboration, and the impact of big data on technology management. Her work consistently addresses grand challenges through mixed-methods approaches, spanning conceptual frameworks in journals like Research Policy to applied studies in Technovation and R&D Management, with increasing focus on quantum readiness and AI-augmented learning systems. No scientific awards are documented in the provided materials. Kokshagina currently holds a 2025 research grant for "Co-designing societal readiness and scenario building for quantum" through the University of Sydney Nano Institute/Catalyst program. While no student supervision activities are mentioned, her collaborative projects involve multi-institutional teams across industry, government, and academic sectors. Kokshagina maintains active roles within the University of Sydney Nano Institute and contributes to international policy discourse through the French Digital Council. Her Ninti initiative exemplifies her commitment to human-centered innovation, while ongoing collaborations with healthcare providers like Roche and Vall d’Hebron Hospital demonstrate sustained engagement with real-world implementation challenges in value-based care 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 Yue Rong is a Full Professor at Curtin University's Department of Electrical and Computer Engineering, within the School of Electrical Engineering, Computing and Mathematical Sciences. He holds editorial roles at IEEE Transactions on Signal Processing and IEEE Wireless Communications Letters. His research focuses on signal processing for communications, underwater acoustic systems, wireless networks, and healthcare IoT. Rong has authored over 140 journal and conference papers and received multiple awards, including the 2010 Young Researcher of the Year Award. Education: B.E. (Electrical Engineering), Shanghai Jiao Tong University (1999) M.Sc. (Electrical Engineering), University of Duisburg-Essen (2002) Ph.D. (Electrical Engineering), Darmstadt University of Technology (2005) Research Interests: Rong's work spans cooperative MIMO communications, underwater acoustic systems, OFDM modulation, radar-based healthcare monitoring, and secure wireless protocols. His innovations include adaptive modulation schemes for underwater environments and radar-based vital signs detection. Recent trends in his publications emphasize AI-driven signal processing for healthcare IoT and underwater optical communication systems. Awards: Best Paper Awards (WCSP 2011, APCOMM 2010) Chinese Government Award (2004) DAAD/ABB Fellowship (2001-2002) Grants & Labs: His research is supported by grants focusing on UAV-enabled data collection and underwater network optimization. He leads projects in the Distributed Data Fusion and Emerging Technologies (DDFE) lab, advancing radar-cardiography and wearable health monitoring systems.
Professor Xue Li is a faculty member in the School of Electrical Engineering and Computer Science at the University of Queensland. His research focuses on machine learning, data mining, and their applications in healthcare, materials science, and computer vision. He has authored over 300 publications, including seminal works on knowledge graph completion, video quality enhancement, and alloy design using machine learning. His work bridges theoretical advancements with real-world applications, such as clinical diagnosis andTinyML systems. Key research interests include graph representation learning, medical informatics, and efficient algorithms for multimedia data. Notable contributions include developing commonsense-enhanced relation extraction models and frameworks for compressed video reconstruction. His research also addresses challenges in federated learning and privacy-preserving genomics. Prof. Li has collaborated extensively with industry and academia, contributing to projects in RFID systems, electronic nose pattern recognition, and cybersecurity. His work is published in top-tier venues like IEEE Transactions and ACM conferences. Despite no listed awards, his prolific output underscores academic impact.
Kanchana Kariyawasam serves as Associate Professor in the Department of Accounting, Finance and Economics within Griffith University's Business School. Holding a PhD in IP Law from Griffith University, an LL.M (Advanced) in IP Law from The University of Queensland, and an LL.B (Hons) from the University of Colombo, she maintains active research affiliations with the Law Futures Centre (2009-2024) and Griffith Asia Institute (2019-present). Her research spans Intellectual Property Law with specialized focus on Copyright and Artificial Intelligence, IP and Right to Repair, Patent Law in Biotechnology, and Gender Equality in IP systems. Current projects examine AI-generated works, digital exhaustion doctrine, and NFT legality under Australian copyright frameworks, reflecting strong alignment with Sustainable Development Goals 4 (Quality Education) and 9 (Industry Innovation). Professor Kariyawasam's funded research portfolio includes significant projects such as the WIPO Study on Illegal Retransmission of Live Broadcasts (2023), Queensland Government Ewaste initiatives (2022), and Griffith University's Right to Repair Workshop (2020). Her publications demonstrate consistent output in high-impact journals including European Intellectual Property Review and International Journal of Law and Information Technology , with recent works analyzing AI copyright challenges, spatial data protection, and medical device repair rights. Deputy Vice-Chancellor's 2024 Student Experience of Teaching Survey Commendation Green Impact Gold Award Recipient (2023, Griffith Repair Cafe) Highly Commended Griffith Award for Excellence in Teaching (Large Classes) Two GBS teaching citations She currently supervises five doctoral candidates researching AI data privacy, patent law intersections, and agricultural IP issues, while having successfully completed supervision of four doctoral theses including works on female entrepreneurship and agribusiness marketing. As Coordinator of Griffith Repair Cafe and Steering Committee Member of Australian Repair Network, she bridges academic research with community engagement on repair rights and e-waste reduction.
Associate Professor Peter Sutton is an academic at the University of Queensland (UQ), holding roles as Deputy Head of School (Teaching and Learning) and Associate Professor in the School of Electrical Engineering and Computer Science. His research focuses on Engineering Education, Embedded Systems, Reconfigurable Computing, and Electronic Design Automation. He has contributed to curriculum design, remote lab management during the pandemic, and hardware-software co-design for embedded systems. Sutton completed his undergraduate studies at UQ and earned advanced degrees at Carnegie Mellon University, with over three decades of experience in computer systems research and education. Education Bachelor of Science, University of Queensland Bachelor (Honours) of Engineering, University of Queensland Masters of Science (Coursework), Carnegie Mellon University Doctor of Philosophy, Carnegie Mellon University Research Interests Sutton’s work spans engineering pedagogy, embedded system design, and reconfigurable computing. Recent projects include adapting hands-on labs for remote learning during the pandemic and optimizing FPGA-based architectures for data compression and encryption. His contributions to cache optimization and multiprocessor systems highlight his expertise in hardware-software integration. Publications His 50+ publications cover topics like FPGA implementations of neural networks, code compression techniques for VLIW processors, and embedded system design tools. Notable contributions include frameworks for reconfigurable system-on-chip development and methods to enhance debugging practices in post-novice students. Labs & Teams He collaborates within UQ’s School of Electrical Engineering and Computer Science, contributing to research groups focused on embedded systems and engineering education innovation.
Dr. Kanika Goel is a Lecturer in the School of Information Systems at Queensland University of Technology (QUT), specializing in Business Process Management (BPM), Data Governance, and Process Analytics. She holds a PhD from QUT and has over 9 years of teaching experience, coordinating programs such as BIT Honours (IN10) and Masters of Philosophy (IN80). Her research focuses on process-oriented data analytics, data quality, and process mining, with industry collaborations spanning health, retail, and asset management sectors. She is a Lean Six Sigma Green Belt certified trainer and a Fellow of the Higher Education Academy (FHEA). Dr. Goel has led several industry-funded projects, emphasizing applied research in data governance, process mining, and process improvement. Notably, she received the Vice-Chancellor's Award for Excellence (2019) for innovative BPM integration in research management systems. Her work bridges academic research and real-world applications, contributing to journals like Business and Information Systems Engineering and IEEE Access . She teaches courses on Business Process Technologies, Modern Data Management, and BPM units in QUT's continuing professional education programs. Her articles explore topics like data imperfections in healthcare systems, process standardization strategies, and privacy risks in NoSQL databases. She advocates for digital literacy and has published on initiatives to build tech-savvy communities. Dr. Goel is also involved in supervising research topics such as prescriptive process analytics and process-data governance patterns.
Professor Kylie Tucker is a distinguished academic at the University of Queensland, serving as Professor and School Director of Teaching and Learning in the School of Biomedical Sciences within the Faculty of Health, Medicine and Behavioural Sciences. She is also an Affiliate of the Centre for Innovation in Pain and Health Research (CIPHeR) and currently serves as President of the International Society of Electrophysiology and Kinesiology (ISEK) for the term 2024-2026. Professor Tucker leads a dynamic research environment focused on advancing knowledge about muscles and movement control, with significant contributions to understanding how pain impacts movement, methods for estimating muscle forces, and assessment of childhood movement control and adolescent skeletal maturity. Professor Tucker earned her Bachelor of Arts, Bachelor of Science, and Doctor of Philosophy from the University of Adelaide. Her academic journey has positioned her as a leader in neuromuscular research, particularly in the areas of motor control and pain adaptation. Within the School of Biomedical Sciences, she has held significant leadership roles including Deputy Director of Teaching and Learning (2018-2020), inaugural chair of the REMEDE committee (2021-2023), and Director of Teaching and Learning (2024-2025). She also co-facilitates UQ's flagship Career Progression for Women program. Her research interests span motor control, pain research, biomechanics, electromyography, neuromuscular control, pediatric movement, scoliosis, and muscle physiology. Professor Tucker's work has transformed understanding of pain's impact on movement and advanced assessment methods for childhood movement control and skeletal maturity. She has recently proposed new insights into scoliosis progression, identifying unique muscle features that can be non-invasively detected early in curve progression. Approximately 3-7% of children worldwide develop adolescent idiopathic scoliosis, often requiring surgical intervention when conservative treatments fail. Analysis of Professor Tucker's recent publications reveals a strong focus on neuromuscular control mechanisms, particularly in relation to pain, scoliosis, and pediatric movement disorders. Her work integrates advanced methodologies including electromyography, shear wave elastography, and biomechanical modeling to investigate muscle function across diverse populations. A notable trend is her leadership in consensus projects (CEDE) establishing standardized methodologies for electromyography research, reflecting her commitment to methodological rigor in the field. Professor Tucker actively mentors the next generation of researchers, supervising numerous PhD students across projects related to scoliosis, knee osteoarthritis, pain research, and pediatric movement disorders. Her research is supported by significant funding including NHMRC MRFF EPCDR grants for chronic musculoskeletal conditions in children and the SRS Research Grant for novel insights into adolescent idiopathic scoliosis. She leads the Motor Control and Pain Research Lab, a collaborative environment bringing together basic science and clinical researchers. The lab focuses on two main research streams: Motor Control and Pain Research and Child and Adolescent Neuromotor Control Research. Professor Tucker teaches across 10 UQ programs with class sizes ranging from 70-1400 students, demonstrating her commitment to education alongside her research leadership.
Martin Andersen is an Associate Professor with the Water Research Laboratory and School of Civil and Environmental Engineering at the University of New South Wales (UNSW). His research focuses on hydrogeology, groundwater dynamics, and surface water-groundwater interactions, with particular emphasis on coastal zone processes and reactive flow and transport modeling. Dr. Andersen's research interests span several critical areas in hydrogeology and environmental science. His work on reactive flow and transport modeling examines how chemical reactions affect water movement through geological formations. He investigates geochemical processes and groundwater dynamics in coastal zones, which is crucial for understanding saltwater intrusion and managing coastal aquifers. His research on surface water-groundwater interactions helps inform sustainable water resource management, particularly in Australia's diverse hydrological environments. Andersen has made significant contributions to understanding how climate variability affects groundwater recharge processes and the implications for water security in arid and semi-arid regions. Analysis of Dr. Andersen's recent publications reveals a strong focus on understanding complex hydrological systems through innovative methodologies. His work spans multiple disciplines including hydrogeology, climate science, biogeochemistry, and computational modeling. A key trend in his research is the investigation of groundwater dynamics in various environmental contexts - from arid zone aquifers to coastal wetlands and fractured rock systems. He employs diverse approaches including field monitoring networks, laboratory experiments, statistical modeling, and paleoclimate reconstructions to address pressing water resource challenges. His research has significant implications for sustainable groundwater management, climate adaptation strategies, and ecosystem conservation. Dr. Andersen is actively involved in several research teams and laboratories. As an Associate Director in the School of Civil and Environmental Engineering at UNSW, he contributes to the Water Research Laboratory's mission of advancing water science and engineering. His work often involves interdisciplinary collaborations across hydrology, geology, environmental science, and climate research. Through his leadership in establishing monitoring networks and conducting field experiments, he has helped build valuable infrastructure for ongoing water research in Australia.
Dr Miao Xu is a Research Fellow at the University of Queensland (UQ), affiliated with the School of Electrical Engineering and Computer Science within the Faculty of Engineering, Architecture and Information Technology. She holds an Australian Research Council DECRA Fellowship (ARC DECRA), recognizing her early-career research excellence. Her research focuses on machine learning, data science, and time series analysis, with applications in healthcare, materials science, and algorithmic fairness. Dr Xu's work addresses challenges in noisy label handling, unlearning mechanisms, and adaptive modeling for irregular data. Education: She earned a Doctor of Philosophy (PhD) from Nanjing University. She is actively involved in supervising research and contributes to the Centre for Enterprise AI at UQ. Research Interests: Dr Xu’s expertise spans machine learning , time series analysis , deep learning , and unsupervised learning . Her recent work emphasizes robust learning with noisy or incomplete labels, model unlearning, and applications in alloy design and medical informatics. She explores methods like instance-attention GNNs for irregular time series and confidence-guided techniques for adversarial attack detection. Publications: Her recent work includes advancements in GNN-based time series modeling, bias mitigation in text classification, and active learning for alloy design. Key themes include improving generalization, reducing algorithmic bias, and enhancing model transparency. Awards: Her ARC DECRA fellowship (202X–202X) supports her research on data-driven methodologies. Supervision & Grants: Available for PhD supervision in machine learning and data science. Her grants include funding for projects in unlearning mechanisms and spatiotemporal modeling. Labs/Teams: Affiliated with the Centre for Enterprise AI at UQ, collaborating on enterprise-scale AI applications and interdisciplinary research.
Mahsa Salehi is a Senior Lecturer in the Department of Data Science & AI at Monash University’s Faculty of Information Technology. She holds a PhD in Computer Science from the University of Melbourne and previously served as a postdoctoral researcher at IBM Research Australia. Her research focuses on data mining, machine learning, and time series analysis, with applications in healthcare, cybersecurity, and smart grids. Education: PhD in Computer Science, University of Melbourne (2016) MSc in Software Engineering, Amirkabir University of Technology (2009) BSc in Information Technology & Computer Engineering, Amirkabir University of Technology (2008/2006) Her key research interests include multi-dimensional time series analysis, anomaly detection, brain-inspired machine learning, and non-stationary data learning. She has led or contributed to over 40 research outputs, including high-impact papers on anomaly detection frameworks (e.g., CARLA) and EEG representation learning (EEG2Rep). Her work bridges theoretical advancements with practical applications, such as detecting urinary anomalies in seniors and securing smart grid systems against cyberattacks. Dr. Salehi has secured significant grants, including AU$246K from ARENA (2019–2021) and AU$30K from Emotiv Research (2022–2024). She is an Associate Editor of the ACM Transactions on Knowledge Discovery from Data and has been recognized with awards like the ICDM 2022 Best Paper Runner-Up and IBM’s Manager’s Choice Award (2016). Grants & Projects: Privacy-Preserving Machine Learning (CSIRO Next Gen, 2023–2027) AI for Clean Energy & Sustainability (Monash, 2023–2027) Deep Learning for Brain EEG Analysis (PhD Top-Up, 2022–2025) Her contributions extend to editorial and patent activities, including roles at IBM Research and collaborative projects with industry partners like Emotiv.
Hilary Davis is a Senior Research Fellow at Swinburne University of Technology, affiliated with the School of Business, Law and Entrepreneurship and the Centre for Social Impact. She leads the Place-based Economies and Approaches research stream, focusing on digital technologies for social impact, particularly in health, rural resilience, and inclusive community development. Her research interests span Human-Computer Interaction (HCI) , digital health , rural mental health services , participatory design , and community resilience . She emphasizes co-design with marginalized groups and the use of digital storytelling to empower communities. Her work aligns with UN Sustainable Development Goals, especially SDG 3 (Good Health) and SDG 11 (Sustainable Cities and Communities). Hilary’s recent publications (2023–2025) explore digital interventions for internet addiction in children, aged care technologies, bushfire early warning systems, and participatory planning for sustainable development. These works reflect a strong trend toward community-led innovation , behavior change design , and technology for social equity . Special Award (for More Than Human Food Futures Cookbook), UMEA Food Symposium 2022 Hilary actively supervises PhD students and has led major research grants from the Australian Research Council (ARC), Department of Health, and community organizations. Her projects include evaluations of mental health outreach programs, digital health interventions (e.g., VerilyConnect), and national educational initiatives like the ELLA language app program. She is a member of the Australian Evaluation Society (AES) and the Association for Computing Machinery (ACM), and serves on program committees for international HCI conferences. She leads the SMART (Small Rural Health Services Team) network, which supports rural health innovation through digital storytelling and co-design. Her work bridges academia and community practice, fostering sustainable, equitable, and resilient solutions.
Associate Professor Wayne Wobcke is a faculty member in the School of Computer Science and Engineering at the University of New South Wales (UNSW), where he has been employed since 2002. His academic career includes previous positions at the University of Sydney until 1998, British Telecom Labs in the UK for three years, and the University of Melbourne for one year. He holds a PhD in Computer Science from the University of Essex (1989), an MSc from the University of Queensland (1985), and a BSc (Hons) in Mathematics/Computer Science from the University of Queensland (1984). Dr. Wobcke's research spans both theoretical and practical aspects of artificial intelligence and data science. His work encompasses intelligent agents, data mining, agent-based modeling, dialogue management, personal assistants, recommender systems, and computational social science. He has collaborated extensively with industry through three Cooperative Research Centres (Smart Internet Technology CRC, Smart Services CRC, and Data to Decisions CRC), where he served as a Programme Manager and Project Leader for over 10 years. Notable achievements include developing a voice-controlled mobile application for email and calendar interaction (a precursor to Apple's Siri) and deploying a people-to-people recommender system for online dating on one of Australia's largest dating sites. His recent research focuses on data science in humanitarian contexts and machine learning applications in official statistics, conducted in collaboration with BPS (Statistics Indonesia) and STIS (Politeknik Statistika, Indonesia). His publication record shows a consistent trajectory of impactful research, with recent work concentrating on poverty targeting, domain adaptation, natural language processing for recommender systems, and political opinion mining. Scientific Awards: Best Paper Nomination, 11th Workshop on Argument Mining (2024) UNSW Arc Postgraduate Research Supervisor Award (2017, 2018) AAAI Deployed AI Application Award, Twenty-Sixth Annual Conference on Innovative Applications of Artificial Intelligence (2014) Best application paper runner up, 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining (2013) Dr. Wobcke has successfully supervised numerous research students, with Irwan Rahadi currently working on 'Causal Modelling and Machine Learning for Official Statistics'. His grant portfolio includes significant funding from the Australian Research Council and various Cooperative Research Centres, totaling over $3.7 million since 2003. He teaches COMP9414 Artificial Intelligence and COMP9727 Recommender Systems at UNSW.
