Dr. Thao Duong serves as a Senior Lecturer at the School of Information Technology, Murdoch University, where she contributes to both teaching and research in computer science with a focus on computational problem-solving methodologies. Education Background Ph.D. in Computer Science from Griffith University, Queensland Research Expertise Her work centers on optimisation (particularly combinatorial optimisation and satisfiability problems), artificial intelligence , and machine learning , with significant contributions to data science and data analytics . Dr. Duong develops advanced algorithms for complex decision-making systems, bridging theoretical computer science with practical applications in data engineering. Professional Recognition Best paper award at the Australasian Joint Conference on Artificial Intelligence (2013) Academic Contributions She maintains an active publication record in premier venues including AAAI, IJCAI, and EJOR, though specific student supervision details and grant funding information are not documented in available sources. No current laboratory affiliations or collaborative research teams are explicitly referenced in her professional profile.
Rajeev Gore is a Professor in the Department of Data Science & AI at Monash University. His research focuses on formal verification, automated theorem proving, and logic systems. He has contributed significantly to areas including modal logics, voting system verification, and cryptographic protocols. Gore has been actively involved in developing verified decision procedures for modal logics and exploring applications in electronic voting systems. His work bridges theoretical computer science and practical applications, with a particular emphasis on ensuring correctness through formal methods. Key contributions include the N-PAT nested model-checker and verified verifiability frameworks for voting systems like BeleniosVS and ElectionGuard. His research also addresses trust domains and cryptographic applications, reflecting a deep engagement with both foundational and applied aspects of logic and computation. Gore's publications span over two decades, demonstrating sustained contributions in formal systems, automated reasoning, and security-critical applications. His work often combines rigorous mathematical foundations with practical tool development, emphasizing trustworthiness and verifiability in complex systems.
Alexey Ignatiev is a Senior Lecturer in Monash University's Department of Data Science & AI. His research develops SAT/SMT-based methods for AI applications including explainable AI, automated software debugging, and neuro-symbolic systems. Key projects: Formal Explainability for Neuro-Symbolic AI (ARC-funded) Hierarchical Abstractions for Neuro-Symbolic Systems Recent publications focus on formal explanation techniques for machine learning models, including defect prediction systems and interpretable rule extraction.
Guido Tack is an Associate Professor in the Department of Data Science & Artificial Intelligence at Monash University's Faculty of Information Technology. He leads development of the MiniZinc constraint modeling language and is a core developer of Gecode, a leading constraint programming library. His research focuses on combinatorial optimization, constraint solver architecture, and industrial applications, extending to programming languages and computational logic. Education: PhD (Dr.-Ing.) in Computer Science from Saarland University (2009), Diplom Informatiker (MSc equivalent) from Saarland University (2003). Professional experience includes postdoctoral roles at NICTA, Saarland University, and KU Leuven before joining Monash in 2012 as a Lecturer and Monash Larkins Fellow. Research interests span constraint programming, optimization algorithms, and their applications. Recent projects include improving the MiniZinc ecosystem, optimizing metering systems for smart water networks, and quantum information technology. He has secured over 13 major research grants, including leadership in the ARC Training Centre for Optimization Technologies. Teaching: Chief Examiner for FIT5170, FIT9135, FIT5216; Lecturer for FIT1047, FIT4010, and others. Editorial Roles: Co-Editor-in-Chief of Constraints journal (2015), active in ACM and Association for Constraint Programming. Awards: 2024 Eureka Prize finalist, 2017 FIT Dean's Award, 2010 Doctoral Research Award. Publications span constraint solving techniques, optimization algorithms, and educational tools. Recent work emphasizes real-time path planning, bi-objective search, and energy-efficient systems. He contributes to both academic conferences and industry-focused projects, bridging theoretical research with practical applications.
Mohammad Polash is a Lecturer at the School of Computer Science, The University of Sydney, since 2019. Previously, he served as an Associate Professor in the Department of Computer Science and Engineering at Jagannath University, Bangladesh. He holds a PhD from Griffith University, Australia, and degrees in Computer Science and Engineering from the University of Dhaka, Bangladesh. His academic roles include Executive Member of SIGCSE Australia Chapter, Fellow of the UK Higher Education Academy (FHEA), and Member of the Internet Society Bangladesh Chapter. He advises Jagannath University IT Society. Education: PhD in Computer Science, Griffith University (Australia), 2017 MSc in Computer Science and Engineering, University of Dhaka (Bangladesh) BSc in Computer Science and Engineering, University of Dhaka (Bangladesh) Research interests focus on Combinatorial Optimization, Operations Research, Evolutionary Computation, Data Mining, and Machine Learning. He develops algorithms for constraint-based problem-solving and explores applications in logistics, scheduling, and educational technology. His recent work emphasizes AI's impact on IT professions and computing education. Earlier research includes optimization in SAT solvers, aircraft sequencing, and Golomb rulers, reflecting a blend of theoretical and applied computer science. Scientific Awards: University of Sydney Faculty of Engineering Dean’s Commendation for Teaching (2024) University of Sydney Vice Chancellor's Award for Excellence in Teaching & Learning (2023) Dean’s Award for Excellence in Innovative Education (2022) Research Impact Award-2016 (Griffith University) Best Paper Runner-up at PRICAI 2018 IEEE Best Paper Award (2009) Advising and Grants: Supervises Zifan ZHENG on Sign Language Production via Deep Learning Recipient of IPRS and Publication Assistance Scholarships during PhD Mentored competition teams at Dhaka IT Fest-2018 and Sydney Coding Fest 2023
Vlad Demsar is a Senior Lecturer in Marketing at Swinburne University of Technology's School of Business, Law and Entrepreneurship. He serves as Director of the Consumer Experience & Innovation (CXI) Research Group and Major Discipline Coordinator for Marketing. With extensive experience bridging academic research and practical business applications, Demsar specializes in helping organizations solve complex marketing challenges related to strategy, communications, emerging technologies, consumer experience, and sustainability. Doctor of Philosophy (Marketing), Monash University Bachelor of Business (Honours), Monash University Bachelor of Business (Marketing), Monash University Bachelor of Business (Management), Monash University Demsar's research spans consumer behaviour, advertising, digital marketing, and digital cultures, with a strong focus on the intersection of artificial intelligence and marketing. His work explores how generative AI is reshaping advertising creativity, consumer responses to AI-generated content, and the ethical implications of emerging technologies in marketing. He investigates diversity representation in virtual environments, the impact of sex appeals in influencer versus brand marketing, and how the concept of the 'common good' manifests in consumer decision-making and brand perception. His extensive publication record in leading journals reveals a clear research trajectory focused on the transformative impact of AI on marketing practices, with particular attention to consumer responses, ethical considerations, and practical applications. Recent work demonstrates increasing specialization in generative AI's role in advertising creativity, customer service paradoxes, and circular consumption patterns, while maintaining longstanding interests in consumer behavior fundamentals and marketing strategy. 2024 Journal of Advertising Research Award - Best Reviewer 2024 Journal of Advertising Research Awards - Runner Up Best Paper Monash Postgraduate Publication Award Australian Postgraduate Award Monash Dean's Postgraduate Research Excellence Award (awarded twice) Demsar actively supervises doctoral research, including projects on customer experience evaluation frameworks. His grant portfolio demonstrates significant research leadership, with current projects including 'Understanding the role and function of Artificial Intelligence in Enhancing Social Housing Services' and 'The role of generative artificial intelligence in facilitating circular consumption behaviour.' He has secured both internal university funding and external contracts with organizations like The Lumery Pty Ltd and The Trustee for Graham Family Foundation. As Director of the Consumer Experience & Innovation (CXI) Research Group, Demsar leads a team focused on cutting-edge research at the intersection of consumer behavior, emerging technologies, and sustainable business practices. The group collaborates with major Australian organizations across telecommunications, banking, retail, and technology sectors to translate academic insights into practical business solutions.
