Tongguang Li is a Research Fellow at the Department of Human Centred Computing, Monash University. His research focuses on learning analytics, self-regulated learning, and AI applications in education. He has contributed to the development of the FLoRA engine, an AI tool designed to enhance hybrid human-AI regulated learning. Li’s work explores adaptive scaffolding, large language model (LLM) feedback systems, and the integration of multimodal data for educational insights. His recent studies investigate how LLMs like ChatGPT can provide effective feedback to students, analyze rhetorical patterns in writing, and measure the impact of scaffolding on learning processes. Li has been recognized for his research with the Conference Best Full Student Paper Award from the Australiasian Society for Computers in Learning in Tertiary Education (2022). Key themes in his work include understanding self-regulated learning strategies through trace data, optimizing adaptive systems for learner engagement, and leveraging AI for educational innovation. His research bridges cognitive science, data analytics, and educational technology to improve learning outcomes and pedagogical practices.
Associate Professor Jenni Ilomaki holds a position at Monash University's Centre for Medicine Use and Safety. With expertise in clinical pharmacy, epidemiology, and public health, she leads a research group analyzing administrative claims data. Her work spans collaborations with governmental and non-governmental organizations globally, yielding over 150 peer-reviewed publications and $4 million in grants. She previously served as Chair of ASCEPT's Pharmacoepidemiology Special Interest Group and currently serves as Science Lead for the Monash Addiction Research Centre and Executive Editor of the British Journal of Clinical Pharmacology. Education includes a Bachelor of Science (Pharmacy) from the University of Kuopio (1999) and Master of Science (Pharmacy) from the University of Kuopio (2004). She completed a PhD in alcohol epidemiology at the University of Eastern Finland (2011) and a postdoc at the University of South Australia (2011-2014). Notable recognitions include Young Epidemiologist of the Year (2011) and the Ronald D. Mann Best Paper Award (2021). Key research focuses on quality use of medicines, medicine safety, large population studies, and innovative epidemiological methods. Major projects include developing clinical decision support tools for cardiovascular prevention, analyzing preventable hospitalizations in aged care, and investigating psychotropic medication trends in youth. Her work contributes to UN Sustainable Development Goals related to health equity and non-communicable disease reduction. Recent publications emphasize drug repurposing (e.g., SGLT2 inhibitors), opioid prescribing patterns, hip fracture outcomes, and dementia detection algorithms. She chairs multinational studies on stroke and myocardial infarction cost burdens, demonstrating interdisciplinary impact across pharmacology, epidemiology, and health economics.
Tao Zou is an Associate Professor at the Research School of Finance, Actuarial Studies and Statistics, Australian National University. His research spans covariance regression modeling, network data analysis, and applications in financial and environmental statistics. He earned a Ph.D. in Statistics in 2016. Ph.D. in Statistics, 2016 Dr. Zou’s work pioneers covariance regression, where covariances are modeled as functions of covariiates. Key contributions include robust estimation techniques, spatio-temporal modeling, missing data imputation via semi-supervised learning, and distributed data aggregation. His methods address challenges in high-dimensional and non-Euclidean data analysis. Recent publications (2025–2023) explore quasi-score matching for spatial autoregressive models, regularization in network regression, functional principal component analysis for complex data, and environmental applications like PM2.5 pollution studies. These works emphasize robustness, scalability, and interdisciplinary relevance in economics, finance, and environmental science. Dr. Zou collaborates on projects like the 2023 Data Analysis App to Empower Assessment of Immunogenicity of Biologics (Co-Investigator). While his student supervision list isn’t explicitly provided, his methodological advancements influence big data and spatial statistics. He contributes to open-access software and continues expanding covariance regression for non-normal and functional data.
Andrew Zammit Mangion is an Associate Professor at the University of Wollongong , affiliated with the School of Mathematics and Applied Statistics . His research focuses on spatio-temporal statistics, computational methods, and environmental informatics, with applications in climate science and geospatial data analysis. Education : PhD in Statistics (University of Sheffield, 2012), B.Eng. (University of Malta, 2007) Research Themes : Spatio-temporal modeling, Bayesian inversion frameworks (e.g., WOMBAT v2.S), deep learning integration, and statistical software development (e.g., FRK package) Grants & Projects : ARC DECRA Fellow (2018), Chief Investigator on ARC Discovery Project (greenhouse gases), ARC Special Research Initiative (Securing Antarctica's Environmental Future), and ARC Industrial Transformation Hub (TIDE). Collaborations : University of Bristol, University of Edinburgh, ESA CCI, NASA OCO-2 data projects Scientific Awards include the prestigious Australian Research Council Discovery Early Career Researcher Award (DECRA). His work spans Antarctic ice sheet analysis, CO2 flux inversion, and scalable spatial statistical models for environmental monitoring.
Chun Ouyang is a Professor at Queensland University of Technology (QUT) in the School of Computer Science within the Faculty of Science. With an extensive publication record spanning over two decades from 2002 to 2025, Professor Ouyang has established themselves as a leading researcher in Business Process Management, Process Mining, and Explainable AI. Their work bridges theoretical foundations with practical applications across healthcare, finance, and industrial sectors. Professor Ouyang's research interests primarily focus on Business Process Management systems, Process Mining techniques, Explainable Artificial Intelligence, and Healthcare Process Analysis. Their work has evolved from foundational BPMN/BPEL translation research in the early 2000s to sophisticated process mining approaches in the 2010s, and most recently to cutting-edge Explainable AI applications in clinical and business contexts. They have developed novel methodologies for process querying, predictive process analytics, and XAI evaluation frameworks that have significantly advanced the field. Their research consistently emphasizes practical applicability while maintaining strong theoretical foundations, with publications in top-tier journals and conferences including IEEE Transactions, Springer journals, and major BPM conferences. Analysis of Professor Ouyang's recent publications (2023-2025) reveals a strategic research trajectory that integrates traditional process mining with modern AI techniques, particularly focusing on explainability and trustworthiness. Their work demonstrates a consistent pattern of addressing real-world challenges through rigorous methodological development, with increasing emphasis on healthcare applications, clinical decision support systems, and the ethical implications of AI deployment. The publications show strong interdisciplinary collaboration patterns, particularly with medical researchers and industry partners. Professor Ouyang has mentored numerous PhD students and early-career researchers who have gone on to establish themselves in the BPM and AI communities. Their research group at QUT has secured multiple competitive grants supporting innovative work in process analytics and AI. They maintain active collaborations with leading researchers globally, including Catarina Pinto Moreira, Arthur ter Hofstede, and Moe Wynn. Professor Ouyang leads the Process Analytics Research Group at QUT, which focuses on developing advanced techniques for business process analysis, prediction, and optimization. The group maintains strong industry connections with healthcare providers, financial institutions, and government agencies, ensuring their research has practical impact. Current projects include developing trustworthy AI systems for clinical decision support, cross-organizational process analysis frameworks, and next-generation process mining techniques for complex, distributed systems.
Professor Clinton Fookes is a faculty member at the Queensland University of Technology (QUT) within the School of Electrical Engineering & Robotics . His research focuses on leveraging computer vision and artificial intelligence to develop automated systems that understand, anticipate, and interact with human behaviors, with applications in medical diagnostics, autonomous vehicles, defense, and industrial efficiency . Research areas include AI adaptability, multimodal biosignal analysis, and human-machine interaction Collaborates with CSIRO Data61, Defence Science and Technology Group, Orica, Airbus, and Sentient Vision Systems Develops systems for human action detection, infrastructure monitoring, and stress response prediction His work addresses critical challenges in AI deployment, such as environmental adaptability and reducing diagnostic errors in medical and autonomous systems. Recent publications highlight trends in self-supervised learning, zero-shot knowledge transfer, multimodal integration , and 3D reconstruction for healthcare , while exploring ethical AI use in sectors like mining and defense . Professor Fookes emphasizes interdisciplinary collaboration, bridging engineering, medicine, and social sciences to advance AI systems capable of real-world impact. His research agenda includes improving AI memory capabilities and explainability for safer, more reliable automation.
Associate Professor Seojeong Lee is a faculty member at the University of New South Wales (UNSW) Business School, School of Economics, specializing in advanced econometric theory. She joined UNSW in 2012 after completing her PhD at the University of Wisconsin-Madison and has established herself as a leading researcher in robust inference methods under complex data conditions. Her educational background includes: Ph.D. in Economics, University of Wisconsin-Madison (2008-2012) M.A. in Economics, Seoul National University (2006-2008) B.A. in Economics and Political Science (dual major), Seoul National University, summa cum laude (2000-2006, with military service 2002-2004) Professor Lee's research centers on developing theoretically rigorous methods for econometric inference, with primary focus on generalized method of moments (GMM), instrumental variables (IV), and two-stage least squares (2SLS) under model misspecification. Her work addresses critical challenges including invalid/many/weak instruments, heterogeneous treatment effects, and clustered sampling, contributing foundational advances to statistical inference in economics. Analysis of her recent publications reveals a strong trajectory in refining methods for many-instrument settings and misspecified models, with increasing emphasis on computational implementations (e.g., Stata packages) and applications to causal inference. Her work bridges theoretical econometrics with practical policy-relevant analysis. Her scientific achievements include: Australian Research Council DECRA Fellowship (2017-2019) UNSW Dean's Research Fellowship (2020-2022) Zellner Thesis Award Honorable Mention from American Statistical Association (2014) Multiple competitive UNSW research awards Professor Lee actively supervises PhD candidates Wei Tian and Fangzhou Yu, and has secured over AUD 700,000 in research funding including ARC Discovery Projects. She teaches undergraduate and postgraduate econometrics courses, integrating her research into pedagogy. Her ongoing work continues to push boundaries in robust econometric methodology for modern data challenges.
Prof. Ping Yu is a Professor at the University of Wollongong's School of Computing and Information Technology, where she has held leadership roles such as Director of the Centre for Digital Transformation (2013–2020). Her work focuses on digital health, health informatics, and data-driven solutions for aged care, with collaborations involving the World Health Organization (WHO), NSW Health, and over 11 aged care providers. She has pioneered projects like the WHO’s eSTEPS platform and AI-driven analytics to optimize healthcare resources. Her research integrates socio-technical perspectives to address challenges in technology adoption and healthcare system improvements. Her research interests span data engineering, data quality, ontology development, and generative AI applications in healthcare. Key areas include extracting insights from unstructured health records, improving clinical decision-making, and enhancing patient outcomes through mHealth and telemedicine innovations. She also explores the impact of green spaces on healthy aging and disparities in healthcare access across regions. Prof. Yu has secured over $4.5 million in research funding, including significant grants from the Australian Research Council. She has mentored 29 postgraduate students to completion, emphasizing interdisciplinary research and practical implementation. Her awards include the prestigious 2022 Telstra Brilliant Women in Digital Health Award and the 2008 Don Walker Award for contributions to health informatics. Her advising and grants narrative highlights her role as a mentor and her success in securing competitive funding. Her work bridges academia and industry, with projects like improving pressure injury risk management in aged care using EHRs and analyzing agitation in dementia through AI. She contributes to education policy, including co-designing academic leadership programs and enhancing clinical informatics curricula in the AI era. Prof. Yu leads multi-disciplinary teams at UOW and collaborates with global institutions, focusing on labs and initiatives like the Appendicectomy Surgical Pathway Ontology (ASPO) and the 6A framework for hypertension management via mHealth. Her research emphasizes real-world impact, from reducing hospital readmissions to optimizing surgical workflows and improving public health communication strategies during crises.
Yuan-Fang Li is an Associate Professor in the Department of Data Science & AI at Monash University's Faculty of Information Technology. He also serves as Associate Dean International. His research focuses on knowledge graphs, natural language processing, multimodality, and graph representation learning. He holds a PhD from National University of Singapore (2006) and a Bachelor of Computing (Honours) from the same institution (2002). Affiliations: Monash University (since 201?), National University of Singapore (PhD 2002-2006) Key Projects: Leading research on neuro-symbolic systems (HARNESS project), large-scale multimodal knowledge management, and maritime knowledge graphs Teaching: Taught courses including FIT4002, FIT4004, and supervised over 20 PhD students Research interests include complex question answering over knowledge graphs, knowledge extraction from text/images, and structural/temporal graph learning. He has published 152+ works with notable contributions to scene graph generation, event extraction, and LLM-based reasoning. Key awards include the 2020 Best Student Paper Award and 2017 Kurzweil Prize. Grants: ARC Discovery Projects, industry collaborations (e.g., Outotec Oy) Labs/Teams: Active in Monash's Data Science & AI research groups, leading neuro-symbolic AI initiatives
David Lo is the OUB Chair Professor of Computer Science at Singapore Management University's School of Computing and Information Systems, where he directs the Information Systems and Technology Cluster and the Center for Research on Intelligent Software Engineering. An ACM Fellow, IEEE Fellow, and ASE Fellow, his research focuses on AI for Software Engineering (AI4SE), leveraging machine learning, data mining, and NLP to enhance software analytics and automation. Research Highlights: AI4SE, code LLMs, human-AI synergy in software engineering, software reliability, and empirical studies of practitioner pain points Awards: IEEE TCSE Distinguished Service Award, university-wide Teaching Excellence Award, Outstanding Graduate Supervisor Award, 2 Test-of-Time Awards, and 11 ACM SIGSOFT/IEEE TCSE Distinguished Paper Awards Leadership: General Chair of ASE'16 and MSR'22, PC Co-Chair for ASE'20, FSE'24, and ICSE'25, ACM SIGSOFT Executive Committee member His work has received over 20 awards, 37,000 citations, and an H-index of 100. As an educator, he has mentored trainees who became faculty and R&D experts globally.
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.
Dr. Wibowo Hardjawana is a Senior Lecturer in Telecommunications Engineering at the School of Electrical & Computer Engineering , University of Sydney. He holds a PhD from the University of Sydney and serves as an ARC DECRA Research Fellow. His research focuses on wireless network softwarisation, enabling programmable radio interfaces to address traffic elasticity in 5G/6G systems. Education : PhD (University of Sydney) Grants : ARC DP210100744 (2021), ARC DECRA DE140101114 (2014) His work spans 5G/6G network architectures , machine learning for wireless systems , and open radio interfaces . Key contributions include graph representation learning for interference management, Bayesian neural network detectors for OTFS modulation, and NOMA decoding techniques . Recent publications analyze ultra-reliable low-latency communications , UAV-enabled networks , and stochastic geometry in wireless systems . He has collaborated with institutions in China, Indonesia, and UAE, and engaged with industry partners like Telstra and Ausgrid.
Abhinav Dhall is an Associate Professor in the Department of Data Science & AI at Monash University. His research focuses on computer vision, affective computing, and human-centered AI, with a particular emphasis on deepfake detection, multimodal analysis, and ethical AI applications. He is actively involved in organizing workshops like the Multimodal and Responsible Affective Computing (MRAC) and chairs conferences such as ACCV. Dhall accepts PhD students and has contributed significantly to datasets like AV-Deepfake1M and EmotiW challenges. His work spans topics including HDR imaging, facial expression recognition, and AI ethics in multimedia systems.
Michael Henderson serves as a Lecturer at Monash University within the School of Curriculum, Teaching and Inclusive Education. His academic profile reflects deep engagement with contemporary educational challenges through research spanning adult learning, digital technologies, and pedagogical innovation. His research interests encompass: Adult and Vocational Education Higher Education Systems Educational Technology Integration Feedback Literacy and Assessment Practices Digital Literacy for Marginalized Populations Artificial Intelligence in Learning Environments Creativity in Educational Contexts Henderson investigates how generative AI transforms feedback mechanisms, with emphasis on student perceptions of AI-generated versus teacher feedback. His work critically examines digital empowerment frameworks for refugee and migrant learners, addressing systemic barriers in technology access. Recent publications reveal growing focus on decolonizing creativity research, ethical AI implementation in Australian policy contexts, and play-based digital safety education for young children. This trajectory demonstrates consistent attention to equity, cultural responsiveness, and practical applications of emerging technologies in diverse educational settings. His scientific recognition includes: Dean's Award for Programs that Enhance Learning (2019) Henderson currently leads the international research project "Active Learning about Academic Publishing through Collaborative Online International Learning" (2024-2025), examining cross-cultural academic skill development. His upcoming presentation at the 2025 Australian Association for Research in Education Conference will address collaborative learning frameworks. Though specific student mentoring details are unavailable, his project leadership suggests active involvement in guiding emerging researchers through international collaborations focused on educational technology and publishing practices.
Dr. Madhushi Bandara is a Lecturer at the School of Computer Science, University of Technology Sydney (UTS), specializing in knowledge representation, complex system modeling, and data analytics. She leads the data management research stream at the UTS DigiSAS lab and is a core member of the Biomedical Data Science Laboratory within the UTS Australian Artificial Intelligence Institute. Her industry collaborations include Telstra, Cancer Australia, and Capsifi, focusing on AI integration in healthcare and finance. She coordinates the Business Information Systems major in UTS's Master of Information Technology program and convenes the Future Generation Enterprise Architecture Community of Practice. Education PhD in AI Systems Engineering, University of New South Wales (2020) BSc (Hons) in Engineering, University of Moratuwa, Sri Lanka (2015) Research Interests Madhushi's work bridges machine learning, knowledge graphs, and enterprise architecture to address challenges in data governance for SMEs, ESG metric management, and healthcare pathway analysis. Her research emphasizes translating cutting-edge AI into industry solutions through contextual domain knowledge integration. Scientific Awards UNSW-UTS Trustworthy Digital Society Scholarship Teaching & Leadership She teaches enterprise information systems, digital strategy, and AI for enterprises in UTS's online postgraduate programs. Her service roles include co-chairing tracks at the Australasian Conference on Information Systems and reviewing for Expert Systems with Applications.
