Shunichi Ishihara is a Professor at the School of Culture, History & Language, The Australian National University, where he leads research in forensic linguistics and computational linguistics. His work focuses on forensic text and voice comparison, authorship attribution, and Japanese linguistic studies. He holds qualifications including a PhD (ANU), MSc (Macquarie), MA (ANU), and BEd (Shizuoka). Research Interests: Forensic Voice/Text Comparison Computational Linguistics Intonational Modelling Japanese Language Processing Stylometric Analysis Research Trends: Recent work emphasizes likelihood ratio-based systems for authorship verification, fusion of acoustic and text features for forensic analysis, and applications of deep learning in text evidence evaluation. His studies often explore cross-lingual comparisons (e.g., Japanese, English, Vietnamese) and system validation methodologies. Grants & Projects: "Likelihood project on author recognition" (2024-2026) "Big Australian Speech Corpus" (2010-2015) Multiple forensic voice/text comparison initiatives Labs & Teams: Director of the Speech and Language Lab, collaborating on speech corpus development and forensic linguistic systems.
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.
Professor Spiridon Ivanov Penev is a leading academic in the School of Mathematics and Statistics at the University of New South Wales. He holds a PhD in Mathematical Statistics from Humboldt University (Berlin, Germany) and has been affiliated with UNSW since 1992, progressing from Lecturer to Professor in 2019. His research spans wavelet methods, saddlepoint approximations, structural equation models, and stochastic risk analysis. Education: PhD in Mathematical Statistics, Humboldt University Current Affiliation: Department of Statistics, School of Mathematics and Statistics, UNSW His work focuses on advanced nonparametric techniques, including wavelet-based signal recovery with adaptive sampling rates, and robust inference in structural equation models. He has developed bias-corrected reliability measures for psychometric applications and contributed to stochastic optimization problems in finance and engineering. Recent publications highlight his expertise in semiparametric regression, robust portfolio optimization, and marine engineering applications using machine learning. Key trends include the use of Bregman divergence for shape-preserving estimation and Markov chain methods for climate model weighting. Scientific Awards: DAAD award Elected member of the International Statistical Institute (ISI) He has supervised numerous grants as Chief Investigator, including Australian Research Council projects and industry collaborations. Administrative roles include membership in the School of Mathematics and Statistics Executive Committee. Teaching duties span advanced statistical inference, multivariate analysis, and data science applications.
Dr. Jia Wu is an Associate Professor and Research Director of the Centre for Applied Artificial Intelligence at Macquarie University. He holds a PhD in Computer Science from the University of Technology Sydney (2009) and is an IEEE Senior Member. His research focuses on artificial intelligence, data mining, graph neural networks, and anomaly detection, with over 200 publications in top-tier journals/conferences like IEEE TPAMI, TKDE, and conferences like KDD, IJCAI, and NeurIPS. He has received awards including the Heidelberg Laureate Forum Fellowship (2019) and multiple best paper awards. Education: PhD in Computer Science (UTS, 2009). Current roles include Director of HDR (Higher Degree Research) and Associate Editor for IEEE TNNLS and ACM TKDD. He leads projects in AI-driven cybersecurity, personalized banking solutions, and disaster response systems. Research interests emphasize graph-based learning, fake news detection, and deep learning applications. His recent work explores hypergraph neural networks for fraud detection and brain graph analysis for neurological disorders. He has pioneered scalable semi-supervised clustering techniques and transformer-based hypergraph models for anomaly detection. Awards include CIKM'22 Best Paper Runner-Up, ICDM'21 Best Student Paper, and the 2023 Faculty of Science and Engineering Collaboration Award. His work spans 13 active research projects, including mitigating AI deepfakes in identity systems and enhancing disaster response networks through graph-based simulations. Labs/Teams: Leads teams in the Data Horizons Research Centre, Future Communications Research Centre, and Hearing Research Centre. Collaborates internationally in AI, data mining, and social network analysis.
Dr. María Yanotti serves as a Senior Lecturer in Economics at the Tasmanian School of Business and Economics (TSBE) within the University of Tasmania. Her academic appointment is based at the Rivers Edge Building, Inveresk campus in Launceston, Tasmania. With a specialization in housing finance, banking, and household economics, Dr. Yanotti has established herself as an expert in mortgage markets and residential property investment, frequently sought for media commentary on these subjects. Dr. Yanotti's research interests span housing finance, mortgage markets, residential property investment, banking economics, and household financial decision-making. Her work frequently examines the intersection of financial systems and housing markets, with particular attention to Australian market dynamics. She has published extensively on mortgage choice determinants, financial inclusion, and the relationship between housing finance systems and economic outcomes. Her research methodology often combines economic theory with sophisticated statistical analysis to address practical questions in housing finance. Analysis of Dr. Yanotti's publication record reveals a strong focus on housing economics and mortgage markets, with significant contributions to understanding residential investment patterns, mortgage choice determinants, and housing finance systems. Her research spans both theoretical and applied economics, with numerous publications in leading domestic and international journals. She has also expanded her research interests to include environmental economics topics such as marine plastic pollution valuation and climate change impacts, demonstrating interdisciplinary reach while maintaining her core expertise in housing finance. Dr. Yanotti has received research funding from the Australian Housing and Urban Research Institute (AHURI) and is affiliated with the Economic Society of Australia and the Women in Economics Network. Her work on residential property investment, particularly the phenomenon of investors favoring properties near their own residence, has received significant attention in both academic and policy circles. At the University of Tasmania, Dr. Yanotti teaches across multiple economics and finance subjects including microeconomics, statistics and quantitative methods, economics for managers, finance for managers, and data analysis for managers. Her teaching reflects her research expertise, connecting theoretical economic concepts with practical applications in housing and financial markets.
Professor Dingxuan Zhou is a distinguished academic serving as Professor and Head of School of Mathematics and Statistics at The University of Sydney, joining the institution on August 29, 2022. He is also a member of The Net Zero Institute and has held significant editorial positions, including editor-in-chief of the journal "Analysis and Application" of "Mathematical Foundations of Computing" and serving on the editorial boards of over ten international journals. Educational Background: BSc in Mathematics from Zhejiang University, China (1988) PhD in Mathematics from Zhejiang University, China (1991) Professor Zhou's research spans learning theory, neural networks, wavelet analysis, and approximation theory, with his current focus on the theory of deep learning. His work aligns with the Faculty of Science Research Strengths in Complex Systems, Precision and Digital Health, Data and Decisions, and National Security. His research demonstrates a consistent progression from foundational mathematical theory to cutting-edge applications in machine learning and artificial intelligence, with particular emphasis on understanding the theoretical underpinnings of neural networks and deep learning systems. His extensive publication record reveals a strong trend toward distributed learning frameworks, approximation theory for neural networks, and the mathematical foundations of deep learning. Recent work focuses on federated learning, transformers, physics-informed neural networks, and the theoretical analysis of over-parameterized networks, reflecting the evolving landscape of machine learning research with increasing emphasis on theoretical guarantees and practical applications. Scientific Awards: Humboldt Research Fellowship (1993) Fund for Distinguished Young Scholars from the National Science Foundation of China (2005) Highly-cited Researcher by Thomson Reuters/Clarivate Analytics (2014-17) World's Top 2% Scientist by Stanford University (2021, 2022, 2023) Professor Zhou has demonstrated exceptional leadership in research and mentorship, having conducted over 40 research grants as Principal Investigator, supervised more than 20 PhD students, and co-organized over 20 international conferences. His collaborative approach is evident in his extensive co-authorship network across multiple institutions globally. He has also served in significant administrative roles including Head of Department of Mathematics (2006-12), Associate Dean of School of Data Science (2018-22), and Director of the Liu Bie Ju Centre for Mathematical Sciences (2019-22) at City University of Hong Kong.
Professor Dino Sejdinovic is a faculty member in the School of Computer and Mathematical Sciences at the University of Adelaide, part of the Faculty of Sciences, Engineering and Technology. Previously, he held positions as Lecturer and Associate Professor at the University of Oxford's Department of Statistics (2014–2022). His academic qualifications include a PhD in Electrical and Electronic Engineering from the University of Bristol (2009) and a Diplom in Mathematics and Theoretical Computer Science from the University of Sarajevo (2006). His research focuses on the intersection of statistical methodology and machine learning, encompassing large-scale nonparametric methods, robust machine learning, multiresolution data fusion, and measures of dependence. He has contributed to kernel methods, Bayesian inference, causal discovery, and applications in climate science, quantum computing, and social science data analysis. Education: PhD in Electrical and Electronic Engineering, University of Bristol (2009) Diplom in Mathematics and Theoretical Computer Science, University of Sarajevo (2006) Sejdinovic's work emphasizes bridging theoretical foundations with practical applications, such as cloud type classification using vision transformers and machine learning-driven quantum device optimization. His recent publications explore topics like kernel-based causal inference, Bayesian neural networks, and uncertainty quantification in statistical models. Advising and grants: Eligible to supervise Masters and PhD students in machine learning and statistics, though specific grants or student advisees are not explicitly listed in the provided texts.
Professor Scott Anthony Sisson is a leading academic at the University of New South Wales (UNSW) , holding the position of Professor of Statistics and Data Science in the School of Mathematics and Statistics . He serves as Director of the UNSW Data Science Hub (uDASH) and Deputy Director of the UNSW AI Institute (UNSW.ai) . Previously, he was Deputy Director of the Australian Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS) and held leadership roles in the Australasian Society of Bayesian Analysis and Statistical Society of Australia . PhD in Statistics (Bristol University, 2002) MSc in Environmental Statistics and Systems (Lancaster University, 1997) BSc in Mathematics and Statistics (Lancaster University, 1996) His research focuses on computational statistics and Bayesian inference , with expertise in machine learning , extreme value theory , and high-dimensional data analysis . He develops simulation-based algorithms for complex statistical problems and applies these to diverse scientific challenges like seagrass decline, urban flood modeling, and drug delivery systems. His recent work spans quantum computing for statistics, graphon modeling, and synthetic likelihood methods. Scientific awards include: 2024 Fellow of the International Society of Bayesian Analysis 2023 Fellow of the Institute of Mathematical Statistics 2017 ARC Future Fellowship 2010 Queen Elizabeth II Research Fellowship 2006 John Yu Fellowship His advising team has mentored students in statistical modeling, Bayesian computation, and applied data science. Grants from the Australian Research Council and industry collaborations support his research in government and scientific applications. He contributes as Associate Editor for Journal of Computational and Graphical Statistics and Statistics and Computing .
Associate Professor Zhidong Li is a prominent researcher at the Data Science Institute within the Faculty of Engineering and Information Technology at University of Technology Sydney (UTS), Australia. With over a decade of experience in data science and machine learning, he leads impactful research bridging theoretical advancements with practical applications across multiple critical infrastructure domains. Dr. Li earned his PhD from the University of New South Wales, Sydney, Australia, and previously served as a senior engineer at Data61, CSIRO (Commonwealth Scientific and Industrial Research Organisation), Australia's federal government agency for scientific research. His research spans machine learning, data mining, pattern recognition, image processing, and human-computer interaction with applications in water, gas, traffic, urbanization, visitor economy, agriculture, environment, finance, property market, railway, law, electric and health sectors. His work particularly focuses on developing interpretable AI models, temporal point processes, and practical applications for smart infrastructure management. His extensive publication record reveals strong thematic consistency in applying advanced machine learning techniques to infrastructure management problems, with particular emphasis on water systems. His research demonstrates progression from fundamental algorithm development toward increasingly sophisticated applications with real-world impact, especially in temporal modeling, graph neural networks, and fairness in AI systems. Scientific Awards 2022 R&D Excellence Award NSW Water Award 2021 UTS Medal for Research Impact for the Vice-Chancellor's Awards for Research Excellence 2018 Australian Museum Eureka Prize for Excellence in Data Science 2019 Victorian iAwards - Industrial & Primary Industries Merit for 'Predictive Analytics for Water Pipe Maintenance' Multiple AWA research innovation awards (NSW, National, QLD) Dr. Li actively supervises Masters and PhD students and leads numerous funded research projects across diverse sectors. His collaborative approach is evident through partnerships with water utilities, transport agencies, and various CRC projects focusing on Food Agility, Digital Finance, and Smartcrete. His work on the world's first independently-audited ethical talent AI in partnership with Reejig demonstrates his commitment to translating research into real-world solutions that address societal challenges while maintaining ethical standards.
Eric C.K. Cheung is an Associate Professor in the Department of Statistics and Actuarial Science at the University of New South Wales (UNSW), where he has worked since July 2017. Previously, he held positions at the University of Hong Kong (HKU) from 2010 to 2017, including Assistant Professor (2010–2016) and Associate Professor (2016–2017). He earned his BSc (Actuarial Science) from the University of Hong Kong and MMath and PhD in Actuarial Science from the University of Waterloo. His research focuses on insurance risk theory, ruin theory, stochastic processes, and financial mathematics. He has secured multiple grants, including from the Australian Research Council and the Society of Actuaries. Currently, he supervises PhD and Honours students in areas like risk analysis and financial modeling. Cheung teaches actuarial science courses at UNSW, HKU, and the University of Waterloo. His work appears in top journals like Insurance: Mathematics and Economics , and he is an Associate Editor of this journal. He has advised over 10 students at HKU and UNSW.
Dr. Kylie-Anne Richards holds dual roles as a Senior Lecturer at the University of Technology Sydney (UTS) Business School and Head of Investment Research at Australia's Future Fund. Her academic work bridges sustainable finance, computational finance, and statistical modelling. She earned a PhD in Mathematics and Statistics from UNSW, a Master of Finance (Financial Engineering) from the University of Hong Kong, and undergraduate degrees in Mathematics, Statistics, and Finance from the University of Melbourne. Her research focuses on energy transition, AI applications in finance, and high-frequency data analysis. Recent studies include developing statistical tests for Hawkes processes and evaluating green bond environmental impacts. She has published in Energy Economics, Annals of Actuarial Science, and the International Journal of Financial Engineering. Dr. Richards leads the Centre for Climate Risk and Resilience at UTS, focusing on climate-related financial risks. Her funded research includes projects on fossil fuel divestment mechanisms and transition risk's impact on sovereign bonds. She regularly engages in industry forums discussing AI-driven financial decision-making and fixed income markets.
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.
Professor Zhifeng Bao is a faculty member at RMIT University's School of Computing Technologies. His research focuses on enhancing data usability across heterogeneous domains, including structured, unstructured, and spatial-temporal data. His work spans database management, keyword search optimization, social network analysis, and spatio-textual data processing. He coordinates the course COSC1169: Intranet and Internet Data Engineering and supervises PhD/Masters students in projects such as trajectory data processing, data asset valuation, and edge computing optimization. Research interests emphasize improving data accessibility and efficiency through methodologies like query relaxation, visual analytics, and provenance tracking. His recent projects include cost-effective edge node placement, traffic accident risk prediction, and differentially private federated learning. Teaching and supervision activities highlight a commitment to bridging theory and practical data engineering challenges.
Katja Ignatieva is an Associate Professor at the School of Risk and Actuarial Studies, UNSW Business School, University of New South Wales. She holds a co-tutelle PhD in Finance from Goethe University Frankfurt (Germany) and Macquarie University Sydney, along with an MSc in Mathematics and Statistics from Humboldt University Berlin and Glasgow University. Her research focuses on quantitative finance, stochastic processes, energy markets, and actuarial risk modeling. Key research areas include energy price dynamics, portfolio risk management, mortality modeling, and systemic risk analysis. She has published extensively in top-tier journals, addressing topics such as volatility modeling, jump-diffusion processes, and commodity market dependencies. Educational background includes: PhD in Finance (2011), Goethe University Frankfurt & Macquarie University MSc in Mathematics & Statistics (2000s), Humboldt University Berlin & Glasgow University Her work bridges theoretical finance with practical applications in risk management and insurance. She has contributed to methodologies for pricing complex financial derivatives and managing longevity risk in variable annuities.
Associate Professor Pierre Lafaye de Micheaux is a statistician based at the School of Mathematics and Statistics, University of New South Wales , where he has worked since 2020. He previously held academic roles at Université Paul Valéry (2020, Associate Professor), ENSAI (2015–2017, Professor), Université de Montréal (2011–2016, Associate Professor), and Grenoble Alps University (2003–present, Assistant Professor). His research spans theoretical and applied statistics , focusing on complex random vectors , neuroimaging genetics , and data science for IoT . Education: PhD in Statistics (2003, Université de Montréal & Montpellier) MSc in Biostatistics (1998, Montpellier) BSc in Mathematics and Physics (1996, Montpellier) MSc in Cognitive Neuroscience (2007, Grenoble Institute of Technology) Research interests include: Dependence Measures : Leveraging complex analysis for big data dependence testing under 3V's (Volume, Variety, Velocity). Neuroimaging Genetics : Developing statistical tools for fMRI/EEG/DTI phenotyping of genetic variation with institutions like CHeBA and INSERM. IoT Data Science : Creating Raspberry Pi-based statistical computing tools for real-time sensor data streams. Complex-Valued Inference : Building a unified framework for complex random vectors in neuroimaging and nuclear engineering. Recent publications demonstrate expertise in circular data analysis , nonparametric testing , and central limit theorem counterexamples , with applications in medical imaging and finance. He has supervised numerous PhD, MSc, and honors students on topics ranging from deep learning to stochastic processes. Scientific achievements include: Université de Montréal Provost Honor List (2003) Editor of the Journal of Statistical Software (2017–present) Co-leader of three research groups: Dependence Measures , Neuroimaging Genetics , and Data Science & IoT He has secured grants from UNSW Research Infrastructure Scheme and NSERC , with industry collaborations including BNP Paribas (credit risk) and Olea Medical (stroke treatment analytics). Current teaching includes Statistical Inference (ZZSC5905) and Data Science (DATA3001) at UNSW.
