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Ehsan AbbasnejadView profile

Associate Professor

• Machine Learning
• Computer Vision
• Natural Language Processing
• +3 more

Dr. Ehsan Abbasnejad is an Associate Professor at Monash University's Department of Data Science and Artificial Intelligence, and holds adjunct positions at the Australian Institute for Machine Learning (AIML, University of Adelaide) and the Centre for Augmented Reasoning (CAR). He specializes in foundational AI, focusing on vision-language tasks, adversarial machine learning, and reinforcement learning. His work bridges theory with real-world applications in agriculture, energy, healthcare, and sports. Education: PhD in Computer Science from Australian National University (ANU). Research Interests: Machine Learning Theory and Adversarial Defenses Neural Network Robustness and Generalization Multimodal Learning (Vision-Language) Continual and Transfer Learning Applications in Energy, Healthcare, and Robotics Awards: Finalist for Australian AI Academic/Researcher of the Year (2024) Multidisciplinary competition wins (e.g., OzMineral Explorer Challenge) Advising & Grants: Australian Research Council (ARC) Discovery Project on Reinforcement Learning CSIRO's Next Generation Graduate Fund Accepting PhD students in foundational AI and applications Labs & Teams: Director of Foundational Machine Learning & Reasoning at Monash, leading global teams in AI competitions and industry collaborations (Microsoft Research, NEC Labs America).

• Healthy Ageing
• Pharmacology
• Pharmacoepidemiology
• +3 more

Dr. Kenji Fujita is a Research Fellow at the Kolling Institute, University of Sydney, specializing in ageing, pharmacology, and pharmaceutical care quality. He holds a PhD, MScMed (ClinEpid), and BPharm from the University of Sydney. Previously a pharmacist in Japan, he leads international initiatives in the Pharmaceutical Care Network Europe (PCNE) and the International Pharmaceutical Federation (FIP). His work focuses on deprescribing strategies, polypharmacy, and developing quality indicators for healthcare services. Dr. Fujita’s research integrates big data analysis, statistical modeling, and machine learning to address challenges in geriatric pharmacotherapy. Current projects include validating frailty indices, evaluating deprescribing interventions in older adults, and developing NLP tools for clinical documentation analysis. He has also contributed to assessing organizational readiness for guideline implementation in community pharmacies. Awards include the 2023 Outstanding Poster Presentation Award at the IAGG Asia/Oceania Congress and 2022 Best Poster at the Japanese Society of Social Pharmacy. He has secured grants such as the 2024 electronic frailty index project and the 2021 systems-approach to medication review initiative. Dr. Fujita collaborates with global networks like J-HOP (Japan Home Visiting Pharmacy Association) and leads multidisciplinary teams across Australia and Europe. His lab focuses on translating data-driven insights into actionable clinical practices to improve medication safety and quality in ageing populations.

Raphaël PhanView profile

Professor

• Security
• Cryptography
• Malicious AI
• +5 more

Prof Raphaël Phan is a Professor and Deputy Head of the School of IT at Monash University Malaysia. His expertise spans security, cryptography, malicious AI, emotion recognition, motion analysis, and generative AI. He has published over 220 papers and led significant projects including privacy-preserving data mining funded by UK MoD and Malaysian government grants exceeding RM4 million. He co-designed the BLAKE hash function (SHA-3 finalist) and has an h-index of 50. Education: PhD in Cryptography (Multimedia University, 2005), MEngSci (2001), BEng (Hons) Computer Engineering (1999). Research focuses on adversarial AI, brain networks, and secure systems. Current projects include Æmbience: emotion-aware virtual assistants using motion magnification. Supervised 15 PhD graduates and 19 current students. Professional affiliations: Chartered Engineer (IET, UK), HEA Fellow, Board of Engineers Malaysia. Recent work emphasizes causal bias detection in micro-expressions, brain tumor detection via advanced YOLOv8, and generative adversarial networks for medical imaging. His work bridges cybersecurity with neuroscience applications.

• Trustworthy Machine Learning
• Adversarial Learning
• Causal Representation Learning
• +6 more

Associate Professor Tongliang Liu is affiliated with the School of Computer Science at the University of Sydney, serving as Director of the Sydney Artificial Intelligence Centre and Trustworthy Machine Learning Lab. He holds a BEng and PhD, and is an ARC Future Fellow. His research focuses on trustworthy machine learning, including adversarial defense, causal representation learning, and robust AI systems. He has authored over 200 papers in top venues like NeurIPS and ICML, and serves as co-Editor-in-Chief of Neural Networks. Research Interests: Developing reliable algorithms for machine learning, emphasizing generalizability and safety. Specific areas include learning with noisy labels, causal inference, and foundational model ethics. He aims to bridge theoretical guarantees and practical applications in computer vision and data mining. Awards: 2024 CORE Award, 2023 IEEE AI's 10 to Watch, 2022 ARC Future Fellowship. Notable recognitions include Eureka Prize shortlist and DECRA. Advising & Grants: Supervises 12 PhD/Master’s students on topics like trustworthy AI, causal discovery, and quantum machine learning. Leads grants on robust learning and AI safety. Labs: Sydney AI Centre and Trustworthy Machine Learning Lab.

• Immersive systems (VR, AR, MR, XR)
• Human-AI interaction and intelligent user interfaces
• HCI theory and methodology
• +3 more

Professor Eduardo Velloso is an academic staff member at the School of Computer Science , University of Sydney . He is a member of the Centre for AI Trust and Governance and holds a PhD in Computer Science from Lancaster University and a Bachelor of Computer Engineering from Pontifical Catholic University of Rio de Janeiro. Teaches COMP4447/5047 - Pervasive Computing and INFO1111 - Computing Professionalism Research Interests focus on distributed collaboration in mixed reality , human-AI interaction , and HCI theory and methodology . His work integrates Engineering, Design, and Psychology to explore gaze interaction, adaptive agents, and multimodal interfaces. Key projects include Blended Whiteboard for remote MR collaboration and GazeGrip for mobile accessibility. Publication Trends show expertise in Virtual Reality , Mixed Reality , and Human-AI Interaction , with recent work on Algorithmic Recourse and Immersive Educational Tools . Awards include ACM Best Paper Awards at CHI, UIST, TOCHI, and DIS venues. Scientific Awards 2024 ACM CHI & DIS Honorable Mentions 2022 UoM-FEIT Teaching & Learning Award 2019 UoM-CIS Excellence in Research Award 2015 ACM UIST Best Paper Award Advising includes supervision of research students Marvin, Tinghui LI, and Wendi YU in projects on asynchronous MR collaboration , situationally-induced impairments , and physical environment integration . His lab explores AI-assisted interaction and context-aware computing through projects like SpinalLog and LiftSmart .

Valentyn PanchenkoView profile

Professor

• Financial Econometrics
• Time Series Analysis
• Dependence Measures and Copulae
• +11 more

Professor Valentyn Panchenko is a leading academic in Economics at the UNSW Business School, specializing in advanced econometric methodologies and financial modeling. Holding a PhD from the University of Amsterdam and an MPhil from the Tinbergen Institute, his research bridges theoretical econometrics with real-world financial applications, emphasizing big data analysis, network structures, and dependence modeling in economic systems. His expertise spans financial econometrics, time series analysis, non-parametric statistics, and agent-based economic simulations. He focuses on Granger causality, model evaluation, structural economic modeling, and bounded rationality with heterogeneous agents. His work has secured significant grants including ARC Discovery Projects and DECRA fellowships, enabling cutting-edge research on market dynamics and economic interactions. Professor Panchenko's publications appear in top-tier journals like the Journal of Econometric Theory, AEJ: Micro, Journal of Economic Dynamics & Control, and Journal of Banking & Finance. His methodological contributions include novel approaches to copula-based forecasting, nonlinear causality testing, and evolutionary learning models in strategic economic environments. While specific student advising details aren't provided, his research leadership demonstrates sustained impact across econometric theory, financial markets, and experimental economics.

Matthew StephensonView profile

Lecturer

• Artificial Intelligence
• Machine Learning
• Data Science
• +5 more

Matthew Stephenson is a Lecturer at Flinders University's College of Science and Engineering, specializing in Artificial Intelligence applications for games. He leads the Data for Decisions initiative within the Factory of the Future Transdisciplinary Hub, focusing on AI-powered scenario generation for smart digital twins. Additionally, he is a member of IRL CROSSING, an international lab studying human-autonomous agent teaming dynamics. PhD in Computer Science (Australian National University, 2019) B.Sc.(Hons) in Computer Science (University of Canterbury, 2015) His research applies AI, Machine Learning, and Data Science to game domains, including intelligent agent development for physics-based environments, procedural content generation, and game analytics. He also investigates deceptive behaviors in multi-agent systems and leverages games as testbeds for real-world AI solutions. Recent publications focus on large language models for game benchmarking, physical reasoning challenges, and evolutionary game generation. Scientific awards include an honourable mention at Foundations of Digital Games (FDG'18). He supervises students in procedural generation, game AI, and physics-based task creation, with teaching roles in computational intelligence and neural networks courses.

• Business Analytics
• Artificial Intelligence
• Machine Learning
• +5 more

Adjunct Professor Evgeny Osipov is affiliated with La Trobe University's Business Analytics department. His research spans artificial intelligence, hyperdimensional computing, and neural network architectures. Academic Rank: Adjunct Professor Department: Business Analytics Email: E.Osipov@latrobe.edu.au Research interests include: Hyperdimensional computing and vector symbolic architectures Spiking neural networks and reservoir computing Hardware-efficient AI implementations Causal reasoning in large language models Self-organizing maps and spatiotemporal sequence learning Applications in smart cities and robotic navigation Recent research outputs demonstrate expertise in: Developing unsupervised learning frameworks using hypervectors Optimizing reservoir computing with cellular automata Creating memory-efficient neural network models Advancing hyperdimensional classification techniques Exploring causal graph integration in language models

Siqi MaView profile

Senior Lecturer

• Automated Vulnerability Detection and Repair
• Mobile Security
• IoT Security
• +4 more

Dr. Siqi Ma is a Senior Lecturer at the UNSW Institute for Cyber Security (IFCYBER) within the School of Systems & Computing at the University of New South Wales (UNSW). He previously served as a Lecturer at the University of Queensland's School of Information Technology and Electrical Engineering (ITEE). He holds a Ph.D. in Information Systems from Singapore Management University (2018) and was a Postdoctoral Research Fellow at Data61, CSIRO. He also visited Carnegie Mellon University (CMU) in 2015. Current Role: Senior Lecturer, UNSW Institute for Cyber Security Former Role: Lecturer, University of Queensland Education: Ph.D. (Singapore Management University), Postdoc (Data61, CSIRO) His research spans automated vulnerability detection, mobile security, IoT security, network authentication, and graph-based adversarial robustness. Recent work focuses on drone configuration bugs, Android malware analysis via GNNs, federated learning privacy, and credential leakage in open-source projects. Key trends in his 2024-2025 publications include automated security analysis for embedded systems, deepfake detection in multimedia, and privacy-preserving mechanisms for distributed networks. He collaborates with institutions like Purdue University, Singapore Management University, and CSIRO Data61.

Seojeong LeeView profile

Associate Professor

• Econometrics
• Statistical Inference
• Generalized Method of Moments (GMM)
• +8 more

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.

• Causation
• Artificial Intelligence
• Probabilistic Graphical Models
• +1 more

Professor Javen Qinfeng Shi is a faculty member at the University of Adelaide, holding the position of Professor in the School of Computer and Mathematical Sciences under the Faculty of Sciences, Engineering and Technology. He serves as Founding Director of the Causal AI Group and as one of the directors at the Australian Institute for Machine Learning (AIML), based at the North Terrace campus location. His research centers on causation, artificial intelligence, mind and metaphysics, with Google Scholar rankings placing him 4th globally in causation and 7th in probabilistic graphical models. Shi develops causal AI methods to identify root causes, discover latent variables, eliminate spurious correlations, enhance cross-domain generalization, model intervention consequences, and solve counterfactual queries. His work focuses on optimizing intervention sequences for desired outcomes under resource constraints, applied to material discovery, agriculture, mining, sports, manufacturing, bushfire prediction, healthcare, and education. Professor Shi's industry impact includes the NOBURN bushfire prediction app (released 2023 with 50+ media coverages), energy material discovery via AI catalysts, and smart manufacturing logistics solutions. His work with the Responsible AI Think Tank (2022-2024) and current AI Industry Forum panellist role (2024 onward) demonstrates active contribution to national and state AI ecosystem development. His scientific awards include: 1st place at Open Catalyst Challenge (NeurIPS AI for Science 2023) Winner of AUS/NZ Bushfire Data Quest 2020 Citizen Science Grant 2021 Finalist in SA Department of Energy and Mining Gawler Challenge 2020 (2k+ participants from 100+ countries), recognized for "The most innovative modelling" 2nd place in Explorer Challenge 2019 (1k+ entries from 62 countries) 1st place at SAIC Volkswagen Logistics Innovation Day 2019 Shi is eligible to supervise Masters and PhD students and has secured research funding including the Citizen Science Grant 2021. His industry collaborations span energy, agriculture, mining, and emergency management, translating theoretical causal AI into practical tools like NOBURN. He leads the Causal AI Group at the University of Adelaide and directs research teams at AIML, focusing on causal inference frameworks for distribution shift resilience and intervention optimization. Current projects emphasize bushfire prediction, material science applications, and AI ethics implementation through the AI Industry Forum.

Dennis FehrenbacherView profile

Associate Professor

• Information Systems
• Management Accounting
• Digital Technologies
• +12 more

Dennis Fehrenbacher is an Associate Professor in the Department of Accounting at Monash Business School, Monash University. He is actively engaged in research and teaching at the intersection of information systems and management accounting, with a strong focus on digital technologies and their organizational impact. Monash Business School, Monash University Department of Accounting Chair, Monash Business School Behavioral Laboratory Organizer, Experimental Research Cluster Associate Editor, Decision Support Systems Editorial Board Member, Behavioral Research in Accounting His research interests center on information system success, performance measurement, analytics, and the behavioral aspects of accounting. He employs empirical methods such as experiments, surveys, and archival analysis, often integrating emerging technologies like eye tracking and big data analytics to study cognitive biases in performance evaluation and decision-making. Dennis has developed innovative teaching methods, including workshops on Social Media Analytics using Python and MongoDB, and machine learning for business decisions. His recent research outputs span topics such as professional scepticism, multitasking, accountability in capital budgeting, and causal inference in judgment using balanced scorecards. These works reflect a consistent trend toward understanding human behavior in accounting systems through technological and experimental lenses. He has received recognition for his work, including: Best Paper Award - Management Accounting Stream (2020) Dennis has served as a reviewer for top journals like Journal of Management Information Systems and Accounting, Organizations and Society , and has been invited to speak internationally. He has led two major research projects funded by CIMA and AFAANZ, focusing on performance evaluation and information processing. His work contributes to the UN Sustainable Development Goals, particularly those related to quality education and decent work. He leads the Monash Business School Behavioral Laboratory, supporting experimental research in accounting and management. The lab facilitates advanced research using tools like eye tracking and data analytics, fostering a collaborative environment for empirical inquiry.

• Causal Machine Learning
• Topological Data Analysis
• Riemannian Optimization
• +5 more

Qian Li is a Lecturer in Computing at the School of Electrical Engineering, Computing and Mathematical Sciences (EECMS) at Curtin University, Australia. She holds a Ph.D. from the Chinese Academy of Sciences and M.Sc. degrees from Shandong University and the University of Luxembourg. Her research focuses on causal machine learning, topological data analysis, and optimal transport, with applications in computer vision, data science, and recommendation systems. She has published over 50 articles in top-tier venues like IEEE Transactions and ACM conferences. Education Ph.D., Chinese Academy of Science (CAS) MSc (Research), Shandong University MSc (Research), University of Luxembourg Research Interests Dr. Li explores causal reasoning for machine learning, leveraging mathematical tools like Riemannian geometry and optimal transport to address challenges in robustness and interpretability. Her work spans causal inference, counterfactual fairness, and explainable AI, with applications in healthcare, energy, and commerce. Recent projects include causal-based recommendation systems and topological data analysis techniques. Key Achievements Secured a $120k grant from China's National Natural Science Foundation (2020-2024). Lead researcher on AI-driven solar energy storage projects with UNSW and Providence Asset Group. Recipient of prestigious scholarships including Chinese National Graduate Scholarship (2016, top 1%). Grants & Students Current Ph.D. students include Xiangmeng Wang and Tri Dung Duong. She has supervised graduates like Yangyang Shu (Adelaide University Research Associate) and Jun Yin (UTS). Labs & Teams Leads research in causal AI and topological data analysis, collaborating with institutions like UTS and the University of Melbourne.

Loc DoView profile

Professor

• Oral epidemiology
• Dental public health
• Risk and benefit of fluoride
• +3 more

Professor Loc Do is a dentist and oral epidemiologist specializing in social and clinical oral epidemiology. He holds a PhD from the University of Adelaide and currently serves as Director of Research at the School of Dentistry, University of Queensland. His research focuses on child and adult oral health disparities, fluoride efficacy, and water fluoridation policies. He has led major studies like the National Child Oral Health Study (2012–14) and the National Study of Adult Oral Health (2017–19). Education: BDS (Dentistry), MScDent, PhD (Oral Epidemiology). Research Interests: Oral epidemiology, dental fluorosis, socioeconomic inequalities in oral health, fluoride risk-benefit analysis, and complex systems science in dental research. His work frequently addresses public health interventions and policy implications. Awards: 2022 IADR Distinguished Scientist H Trendley Dean Award, 2023 IADR Abrey Sheiham Award for Distinguished Research in Dental Public Health. Grants and Projects: Principal Investigator for NHMRC/MRFF-funded studies on water fluoridation, child oral health inequities, and aged care oral health. Current projects include the Queensland Child Oral Health Study (2020–2024) and the SMILE Birth Cohort Study (2013–22). Advisement: Supervises PhD and Master’s students in areas like oral health policy, fluoride safety, and aged care interventions. Notable supervision includes studies on water fluoridation’s social impact and oral health in residential care facilities. Labs/Teams: Leads research teams in the School of Dentistry and collaborates with the Centre of Research Excellence on Achieving the Tobacco Endgame. Active in multidisciplinary projects addressing global oral health challenges.

Gloria RobertsView profile

Research Fellow

• Neuroscience
• Psychiatry
• Bipolar Disorder
• +3 more

Dr. Gloria Roberts is a Research Fellow at the Black Dog Institute, affiliated with the University of New South Wales' Faculty of Medicine, School of Psychiatry. Her research focuses on identifying predictors of bipolar disorder development in high-risk populations, with particular emphasis on neural mechanisms of executive functioning and emotional processing. Location: Black Dog Institute, Hospital Road, Prince of Wales Hospital, Randwick NSW 2031 Contact: +61 2 9382 8324 | ORCID: https://orcid.org/0000-0002-1966-5120 Education Background: B.Sc in Applied Psychology (University College Cork, Ireland, 2002) M.Sc in Neuropharmacology (National University of Ireland Galway, Ireland, 2003) Diploma in Statistics (Trinity College Dublin, Ireland, 2006) PhD in Neuroscience (Trinity College Dublin, Ireland, 2008) Dr. Roberts' research program centers on the neural basis of emotional dysregulation characteristic of mood disorders, employing structural and functional Magnetic Resonance Imaging as her primary research tool. Her work integrates advanced neuroimaging analysis techniques including diffusion tensor imaging tractography, dynamic causal modeling, graph theory, and machine learning approaches. She maintains active collaborations with Queensland Institute of Medical Research (Brisbane), Neuroscience Research Australia (Sydney), and the Centre for Healthy Brain Ageing (Sydney). Analysis of Dr. Roberts' publication record (94 journal articles, 2 book chapters, 25 conference papers) reveals a consistent research trajectory focused on neurocognitive patterns in bipolar disorder. Her recent work increasingly incorporates machine learning techniques to identify predictive biomarkers, with a growing emphasis on longitudinal studies tracking high-risk populations. The interdisciplinary nature of her research bridges neuroscience, psychiatry, and computational methods to address fundamental questions about mood disorder development. Scientific Contributions: Extensive publication record across multiple formats (journal articles, book chapters, conference presentations) Development of innovative neuroimaging analysis techniques for bipolar disorder research Establishment of multi-institutional collaborations across Australia Integration of machine learning approaches with traditional neuroimaging methods Dr. Roberts actively mentors junior researchers and contributes to the broader scientific community through peer review activities and participation in research networks focused on mood disorders. Her work has significant implications for early intervention strategies and the development of novel therapeutic approaches for bipolar disorder.