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
Dr. Sasha Rubin is a Senior Lecturer and leader of the Computational Logic for AI (LOGIC-AI) group at the School of Computer Science, The University of Sydney. He holds a PhD in Mathematics and Computer Science from the University of Auckland and previously worked at the University of Naples Federico II. His research focuses on logic foundations of AI, including synthesis, planning, formal methods, and multi-agent systems. He teaches courses like Models of Computation and supervises students in topics like probabilistic systems and reinforcement learning. Research Interests: Mathematical Logic, Formal Verification, Temporal Logic Synthesis, Automated Reasoning, and Multi-Agent Systems. He has published extensively in top venues like IJCAI, AAAI, and ACM Transactions. His work includes verification of agent navigation, strategy logic, and planning under uncertain environments. Awards: Recognized as an Australian Research Field Leader in Theoretical Computer Science (2020). He serves on editorial boards for JAIR and conferences like KR, and organizes events such as the Australasian Association for Logic Conference (2024). Supervision and Grants: Current students include Ethan HIRSCHOWITZ and Kunal OSTWAL. Past supervision spans MPhil/PhD projects on probabilistic systems, ML classifier fairness, and symbolic automata. His grants include studies on logic and robots in anonymous graphs. Professional Activities: Member of EATCS, ACM, and mentor for the Sydney Summer Innovation Programme. He leads the LOGIC-AI lab and collaborates internationally, notably with Giuseppe De Giacomo at Sapienza University of Rome.
Associate Professor Nalin Arachchilage is a leading academic in Cyber Security at RMIT University's School of Computing Technologies. He holds an honorary role at the University of Warwick and advises DEFSAFE Cyber Security Inc. His research focuses on usable security, privacy engineering, and AI-driven cybersecurity solutions. Key roles include revamping RMIT's Master of Cyber Security program and leading the Usable Security Engineering group at UNSW ADFA. Education: PhD in Cyber Security (Brunel University), Postdocs at Oxford and UBC. Research spans interdisciplinary areas including HCI, machine learning for threat modeling, and serious games for security education. He has published extensively in top venues like ACM CCS and SOUPS, contributing to global standards like OWASP. Leadership: Previously Assistant Head of School (Research) at the University of Auckland, and Director of the MProfStuds in Digital Security. Active in program committees for major conferences (ACM CCS, SOUPS). Current supervision includes 5 PhD students across RMIT and Auckland. Media Impact: Featured in Sky News Australia, ABC, and TVNZ. Collaborations with HP and Facebook. Awards include impactful contributions to security frameworks and standards. Teaching: Coordinates courses like INTE2625 (Cyber Security) and COSC2738 (Human-Centric Cyber Security). Supervises projects on topics like blockchain security and responsible AI. Non-Academic Roles: Chair of the Academic Board at Canberra Business & Technology College (2020-2021). Advisor to DEFSAFE, focusing on novel cyber-security products.
Professor Will Browne is the Chair in Manufacturing Robotics at QUT, collaborating with the ARM Hub and CSIRO to advance robotics in manufacturing, healthcare, and industry. He holds a Doctorate in Engineering from the University of Wales and has over 30 years of expertise in AI, robotics, and Learning Classifier Systems (LCS). His research focuses on transparent AI systems like LCS for Explainable AI (XAI), human-robot collaboration, and advanced manufacturing solutions. He co-leads the $5M SfTI Robotics Spearhead project and is internationally recognized for LCS contributions, including co-authoring the first LCS textbook with Ryan Urbanowicz. Publications emphasize emotion recognition, embodied AI, and scalable machine learning. His work bridges academia and industry through the ARM Hub, fostering innovation in robotics and design-led manufacturing. Key roles include co-track chair at GECCO (2011–2025) and editorial board memberships. Research spans evolutionary computation, robotics control, and energy systems optimization.
Ilya Verenich is a Researcher at Queensland University of Technology (QUT), known for his work in Business Process Management and Predictive Analytics. He completed his PhD in 2018 with a thesis titled 'Explainable predictive monitoring of temporal measures of business processes.' His research focuses on developing machine learning and AI-driven tools for predictive process monitoring, such as the Apromore platform and Nirdizati web tool. Collaborations with international experts like Marlon Dumas and Marcello La Rosa highlight his contributions to process mining, workflow optimization, and predictive modeling. His work bridges computer science, data mining, and business systems, addressing challenges in real-time process analysis, resource allocation, and anomaly detection. Key areas of expertise include temporal process analysis, LSTM neural networks for sequential data, and minimizing operational waste through predictive activity ordering. He has published extensively in top-tier conferences like CAiSE and BPM, with topics ranging from white-box process performance prediction to symbolic sequence clustering for process monitoring. His interdisciplinary approach combines theoretical frameworks with practical applications in industries requiring efficient workflow management.
Tomasz Woloszynski is a Lecturer in the School of Civil and Mechanical Engineering at Curtin University. His research focuses on medical image analysis of knee joints and numerical optimization of wear surfaces. He holds dual PhDs in Electronic Engineering from Wroclaw University of Technology and Mechanical Engineering from the University of Western Australia. Research areas include automated classification of wear patterns, surface texture analysis, and computational methods for diagnosing osteoarthritis from medical images. His work develops innovative approaches to characterize tribological surfaces and predict joint degeneration through advanced imaging techniques. Publication analysis shows consistent focus on tribology, surface engineering, and medical diagnostics. Recent work develops automated systems for wear surface classification while maintaining an ongoing research thread in osteoarthritis prediction using bone texture analysis and machine learning methods.
Soo Wooi King serves as a Senior Teaching Fellow at Monash University Malaysia's School of Information Technology, contributing to both academic instruction and research in information technology systems. His professional profile centers on practical applications of computational methods in security and network optimization. His research spans Machine Learning, Network Security, Wireless Networks, and Natural Language Processing, with emphasis on implementing Naive Bayes classifiers for real-world problem solving. Key application areas include malware detection systems, news credibility assessment tools, and telecommunications network management solutions where algorithmic precision directly impacts operational reliability. Publication trends from 2017-2022 reveal consistent specialization in machine learning deployment across cybersecurity and networking domains. His highly cited 2018 survey on wireless load-balancing (18 Scopus citations) demonstrates significant field impact, while recent malware classification research shows evolving technical sophistication in feature engineering approaches. Work consistently bridges theoretical algorithms with industry-specific implementation challenges.
Dr. Yining Hu is a Lecturer at the School of Computer Science , University of Technology Sydney since January 2024. Her research and teaching focus on Artificial Intelligence , Blockchain , Cloud Computing , and Wearable Technology . She holds a PhD from the University of New South Wales and has collaborated with industry partners on blockchain applications for IoT security and sustainability. PhD: University of New South Wales Current role: Lecturer at UTS School of Computer Science Key research areas: Wearables, Blockchain, Cloud Computing Dr. Hu's publications demonstrate her technical expertise in blockchain-IoT integration, with notable works on secure transaction frameworks and carbon offset management. Her recent 2025 paper in Electronics explores blockchain-DNN synergy for IoT security. Earlier works cover wearables' security protocols and adaptive fitness tracking frameworks. She contributes to academic service as a member of the Editorial Board for the International Journal on Data Science and Technology (2024-2027). Her teaching portfolio includes advanced cloud computing and software development courses, with availability for Masters/PhD supervision . Current funded research includes blockchain for food traceability and a 2025-2028 LLM-driven text-to-SQL grant with Hampton Capital.
Emran Ali is a Graduate Researcher (Ph.D. candidate) and Part-Time Lecturer at Deakin University's School of Information Technology within the Faculty of Science, Engineering and Built Environment. He holds concurrent faculty appointments at Hajee Mohammad Danesh Science & Technology University (HSTU) in Bangladesh where he teaches computer science courses while on study leave. His academic journey includes a Master of Science (Research) in Information Technology from Deakin University (2022) and a Bachelor of Science in Computer Science and Engineering from HSTU. Doctor of Philosophy (Ph.D.) in Information Technology, Deakin University (2023–present) Doctor of Philosophy (Ph.D.) in Machine Learning, Coventry University (Cotutelle program, 2023–present) Master of Science (Research) in Information Technology, Deakin University (2020–2022) Bachelor of Science in Computer Science and Engineering, HSTU Bangladesh (2007–2012) Ali's research focuses on algorithm development and applied machine learning in health informatics, specializing in biosignal processing for neurological and sleep disorder detection. His work integrates time-series data analysis with explainable AI techniques to develop clinical decision support systems. Current projects include ML/DL modeling of sleep-stage transitions in aging populations and causal relationship analysis in sleep disorders using EEG data. Analysis of his 10 recent publications reveals strong concentration in biomedical ML applications (60%), particularly EEG-based neurological disorder detection and mental health diagnostics. Secondary focus areas include environmental monitoring systems (20%) and foundational computer science (20%). His work consistently employs ensemble methods and feature optimization techniques across diverse datasets, with increasing emphasis on real-world clinical applicability in recent publications. Deakin University Post-graduate Research Scholarship (DUPRS) through Cotutelle program with Coventry University National Fellowship from Bangladesh Ministry of Science and Technology (2020) Best Presentation Award at Deakin School of IT Conference (2021) AWS AI/ML Scholarships (2023, 2024) Next Generation Tech Booster Scholarship (2024) Ali provides research supervision at HSTU while serving as a Graduate Research Teaching Fellow at Deakin University for Machine Learning and Data Analytics units. His industry collaborations include projects with Monash University, Alfred Health, and AETMOS Australia focused on health informatics applications. Current funding includes AWS-sponsored nanodegrees and Deakin University research scholarships supporting his sleep disorder research. His technical work integrates cloud-based AI/ML platforms (AWS, Azure) with biosignal processing pipelines, utilizing collaborations across Australian healthcare institutions to validate clinical applications. Recent projects emphasize explainability in deep learning models for medical diagnostics, particularly in resource-constrained environments relevant to Bangladesh healthcare contexts.
Dr. Hamid Alinejad-Rokny is a Scientia Senior Lecturer at UNSW Sydney and Adjunct Associate Professor at Concordia University. He leads the UNSW BioMedical Machine Learning (BML) Lab within the Graduate School of Biomedical Engineering. His research focuses on applying machine learning, bioinformatics, and statistical methods to understand genomic mechanisms underlying diseases like cancer and neurodevelopmental disorders. Dr. Rokny holds a PhD in Biostatistical Machine Learning from UNSW and has secured over $13M in grants as a principal or co-investigator. He has published 80+ papers, including 10 as first author and 45 as senior author. Education: Bachelor’s in Software Engineering (2004-2009), Master’s in Artificial Intelligence (2009-2012), PhD in BioMedical Machine Learning (UNSW, 2014-2018), Postdoc at Harry Perkins Institute (2017-2019). Research interests include medical AI, deep learning, genomic data analysis, and systems biology. He actively supervises 20+ PhD/Master’s students and collaborates with industry partners like CSIRO and 23Strands. Awards include the DECRA 2023, NHMRC MERIT, and International Autism Fellowships. He also serves as a keynote speaker at conferences like HUGO and an Honorary Lecturer at Macquarie University. Grants total $2.75M as lead investigator and $10.6M as co-investigator. Industry partnerships include PORSPA Advance ($4.7M) and Australian Digital Domains ($3.6M). His lab develops tools like MaxHiC and DeepGenePrior for genomic analysis. Labs/Teams: Director of UNSW BML Lab, Health Data Theme Leader at UNSW Data Science Hub. Active in mentoring 13 researchers globally and co-supervising international teams.
Mark Dras is a Professor in the School of Computing at Macquarie University, specializing in Natural Language Processing (NLP), computational linguistics, and AI ethics. He holds academic roles including Director of Research Training and Deputy Director of the Frontier AI Research Centre. His research focuses on NLP applications in privacy, native language identification, and healthcare, with over 140 publications and 21 active research projects. Education: PhD in Natural Language Processing (Macquarie University, 2000) BSc(Hons) in Computer Science (Macquarie University, 1993) BEc in Actuarial Studies (Macquarie University, 1991) Research Interests: Dras' work spans privacy-preserving NLP, adversarial attacks, clinical dialogue systems, and multilingual NLP. He leads projects on combating AI-generated misinformation and securing health intelligence systems, funded by organizations such as the Australian Research Council and Department of Defence. Grants & Awards: 2023 Faculty of Science & Engineering Inter-School Collaboration Award $3M Defence ASCA grant for authenticity verification via adversarial detection $2.9M Medical Research Future Fund project for health data silo integration Students & Mentorship: Supervised over 30 PhD/MRes students, including current advisees in AI ethics and healthcare NLP. Notable alumni include Omid Mohamad Nezami (Oracle), Yasaman Motazedi (Flamingo AI), and Shervin Malmasi (NLI expert). Labs & Initiatives: Active in the Data Horizons Research Centre, Performance and Expertise Research Centre, and Macquarie's AI ethics working groups. Teaches Algorithms and Data Structures at both undergraduate and postgraduate levels.
Professor Alan Wee-Chung Liew serves as Head of School at Griffith University's School of Information and Communication Technology, Australia. He joined Griffith University in 2007 after holding positions as Assistant Professor at Chinese University of Hong Kong and Senior Research Fellow at City University of Hong Kong. Professor Liew's research spans Artificial Intelligence, Machine Learning, Medical Imaging, Computer Vision, and Bioinformatics . His work focuses on developing AI solutions for healthcare applications, image analysis, and pattern recognition problems. He leads methodological innovations in machine learning algorithms while maintaining strong connections to real-world applications. His recent publications demonstrate a clear trend toward interdisciplinary AI applications, particularly in medical imaging, healthcare analytics, and trustworthy AI systems. The research shows increasing focus on explainability, privacy preservation, and practical deployment of AI solutions in critical domains. Professor Liew has received significant recognition including: Fellow of the Queensland Academy of Arts and Sciences Fellow of the Australia Computer Society Senior member of IEEE (USA) Stanford University's World's Top 2% Scientists (Computer Science: AI & Image Processing) He actively supervises numerous PhD students across diverse AI topics including medical imaging, graph neural networks, and trustworthy AI. His research is supported by substantial funding from government agencies including ARC, NHMRC, and international collaborations. Professor Liew co-leads the AI4Health lab and the TrustAGI lab , which focus on developing ethical, reliable, and safe AI technologies with strong industry and hospital partnerships.
Adjunct Associate Professor Nick Reid is an emeritus academic at the University of New England (UNE), specializing in Australian Aboriginal linguistics and language revitalization. He retired in 2019 but continues research on Daly River languages (NT), including Ngan'gikurunggurr and Ngan'giwumirri. His work bridges descriptive linguistics, ethnomusicology, and environmental science, notably linking Indigenous oral traditions to sea-level chronologies. A pioneer of online education, he co-developed UNE's Master of Applied Linguistics and created multimedia resources like the Phonetics: An Interactive Introduction CD-ROM. Reid holds a BA (Hons) and PhD from ANU, with research interests spanning morphosyntax, nominal classification systems, and Indigenous language maintenance. He collaborates with institutions like the Northern Institute (CDU) and the University of Melbourne on projects blending linguistics with environmental science. Awards include the Carrick Citation (2006) for student learning innovations and UNE's Vice-Chancellor's Award (2000) for teaching excellence. His publications emphasize language documentation, including grammars and curriculum resources for Aboriginal communities. Recent work focuses on collaborative projects with the Ngan’gi Schools Network and coastal inundation studies with Patrick Nunn. He has supervised numerous higher-degree students and remains active in eResearch initiatives, such as digitizing the Ngan’gi language collection.
Dr Estelle Wallingford is a Lecturer in the Department of Business Law and Taxation at the Monash Business School, Monash University, where she also serves as Deputy Director of Education. Her work bridges law and emerging technologies, with a strong focus on artificial intelligence, digital twins, and data privacy. PhD in Law, Monash University Juris Doctor, University of Sydney Diploma of Legal Practice, Australian National University Bachelor of Arts (Honours), University of Melbourne Diploma in Languages, University of Melbourne Her research investigates the legal challenges posed by AI and related technologies, proposing innovative frameworks such as the Tri-Phase AI Liability Model to address private law liability. She explores whether AI should be granted legal personhood, function as a legal agent, or remain classified as property, advocating for a flexible, context-sensitive approach. Her scholarship contributes to global discussions on AI governance, regulation, and ethical deployment. Her forthcoming monograph with Routledge, Liabilities and Modern Artificial Intelligence: A Tri-Phase Model , synthesizes years of research into a comprehensive theoretical framework. The work analyzes causation, liability thresholds, and stakeholder responsibilities in AI systems, offering practical guidance for legal professionals and policymakers. She has been recognized with the Dean's Award for Innovation in Learning and Teaching (2023) for integrating AI into pedagogy and encouraging responsible use among students. Her teaching includes undergraduate business law and technology law units. Dean's Award for Innovation in Learning and Teaching (2023) Dr Wallingford has contributed to multiple academic events, including the Monash Law 1st Annual Digital Law Symposium and workshops on digital platform regulation and digital twins. She has also engaged with public discourse through media appearances and blog posts on generative AI. Her prior legal practice involved complex financial regulatory cases, insolvency, and litigation, including work during the 2018 Australian Royal Commission into Misconduct in the Banking, Superannuation and Financial Services Industry. She is affiliated with research initiatives addressing UN Sustainable Development Goals, particularly through education and ethical technology governance.
Musa Mammadov is a Senior Lecturer in Data Science at Deakin University's School of Information Technology, part of the Faculty of Science Engineering and Built Environment. His research focuses on data science, machine learning, and computational mathematics with applications in environmental modeling, healthcare analytics, and financial systems. Education: Doctor of Philosophy from University of Ballarat Research Interests: Specializing in numerical and computational mathematics, Mammadov develops advanced machine learning techniques for complex classification problems while exploring optimization methods in mathematical economics. His work spans environmental modeling applications in Sri Lanka's Kalu River Basin, healthcare fraud detection algorithms, and financial market analysis. Scientific Contributions: The recent publications highlight his work in hydrological forecasting using deep learning architectures, anomaly detection in medical billing systems, and probabilistic modeling of financial indices. His methodological contributions include improving Bayesian network classifiers and developing novel dependency estimation techniques. Academic Roles: Mammadov serves as editorial board member for Optimization Letters and Annals of Data Science . He supervises doctoral students in data science projects including health provider billing analysis and satellite downlink scheduling optimization.
