Professor Mahdi Jalili is a faculty member at the School of Engineering, RMIT University, Australia. His research focuses on complex networks, dynamical systems, control systems, graph theory, and machine learning applications. He actively supervises research students in areas such as vehicle-to-home (V2H) technology, AI-driven sustainability solutions, and smart grid optimization. His work integrates theoretical frameworks with practical applications in energy systems, transportation, and data analytics. Education: Not explicitly detailed in the text. Research interests include network control, machine learning for energy analytics, and the integration of renewable energy systems. His recent projects address challenges in electric vehicle infrastructure, grid stability, and distributed energy resource management. Collaborations focus on advancing smart grids and sustainable urban transportation systems. Publications span journals like Expert Systems with Applications and IEEE Transactions , covering topics from graph neural networks to grid-forming inverter placement. His work emphasizes interdisciplinary solutions for modern energy and transportation challenges.
Xinghuo Yu is a Distinguished Professor and Vice-Chancellor's Professorial Fellow in the School of Engineering at RMIT University (Australia). He holds leadership roles, including Associate Deputy Vice-Chancellor and Chair of the RMIT Professorial Academy. His academic career spans over three decades, with prior positions at the University of Adelaide and Central Queensland University. Research & Awards: Yu specializes in Control Systems, Complex Networks, Cyber-Physical Systems, Smart Grids, and Artificial Intelligence. He has been recognized with prestigious awards, including the Australasian Artificial Intelligence Distinguished Research Contribution Award (2018) , M A Sargent Medal (2018) , and IEEE Industrial Electronics Society Achievement Award (2013) . He is a Fellow of the Australian Academy of Science, IEEE, and Engineers Australia. Leadership & Engagement: Yu has held executive roles such as President of IEEE Industrial Electronics Society (2018–2019) and Non-Executive Director of Oceania Cyber Security Centre. His research bridges theory and application, with over 1,000 publications and recognition as a Clarivate Highly Cited Researcher in Engineering (2015–2024). Labs & Collaborations: Leads the Intelligent Informatics and Control Research Group , focusing on smart grid optimization, cyber-physical systems, and AI-driven solutions for energy and security challenges. His work addresses real-world issues like renewable energy integration, electric vehicle infrastructure, and resilient power systems.
Alisa Andrasek is a Professor at RMIT University's School of Architecture, Urban and Design (AUD). She leads research at the intersection of design, computational science, and exponential technologies, focusing on high-resolution architecture, robotic fabrication, and AI-driven design processes. Previously, she directed the award-winning Advanced Architectural Design program at UCL and co-founded Biothing, an experimental design lab, as well as AI Build, a robotic fabrication studio. Affiliations: RMIT University, AI Build, Biothing Research Focus: Generative algorithms, computational physics, robotic fabrication, and AI in architecture. Exhibitions: Featured at Centre Pompidou Paris, Venice Biennale, and Sydney Biennial. Her work explores 'data materialization,' creating intricate structures through algorithms and robotic processes. Projects include the Cloud Pergola (Venice Biennale), XenoCells , and MorphoCyte , which blend biological processes with computational design. She supervises research in topics like 'Integral Architectural Design Synthesis in the Metaverse' and 'Generative Practice Research.' Alisa advocates for architecture that resonates with ecological complexity through high-resolution microstructures and adaptive systems. Her teaching integrates cutting-edge technologies like machine learning and large data analytics into design pedagogy.
Dr. Estrid He is a Senior Lecturer at RMIT University's School of Computing Technologies. She obtained her PhD from the University of Melbourne in 2020, where she subsequently served as a postdoctoral research fellow. Her research bridges natural language processing, data mining, and deep learning optimization, with applications spanning healthcare, communications, and algorithmic fairness. Research Focus: Core NLP techniques for knowledge extraction from complex texts (patents, medical records) Enhancing security/efficiency of deep learning models in resource-constrained environments Multimodal learning integrating text, sensor data, and biomedical signals Fairness-aware AI systems for computer vision and graph neural networks Her publication portfolio demonstrates strong cross-disciplinary collaboration, with recent work in: Wireless communications (terahertz signal processing, 6G hardware) Biomedical applications (brain disorder prediction, clinical NLP) Generative models for sensor data and multimodal content Algorithmic fairness in computer vision and graph networks She actively supervises graduate research, with current projects including: Graph learning for brain disorder prediction Multimodal health data mining Privacy-preserving AI for urban sensing Integration of spiking neural networks with LLMs
Professor Xiaodong Li is a faculty member at RMIT University's School of Computing Technologies, serving as Assistant Associate Dean for Data Science & Artificial Intelligence. He holds a Ph.D. in Artificial Intelligence from the University of Otago, New Zealand. His research focuses on machine learning, evolutionary computation, swarm intelligence, and optimization techniques with applications in blockchain security, renewable energy, and logistics. He has received prestigious awards including the 2013 ACM SIGEVO Impact Award and the 2017 IEEE Transactions on Evolutionary Computation Outstanding Paper Award, and is an IEEE Fellow. His academic contributions include editorial roles at IEEE Transactions on Evolutionary Computation and leadership in IEEE Task Forces on Swarm Intelligence and Multi-modal Optimization. Current research interests span automated code generation, quantum AI-driven logistics, and anomaly detection. Supervision projects highlight interdisciplinary applications in AI ethics, solar energy monitoring, and fraud detection. Education: Ph.D. in Artificial Intelligence, University of Otago, New Zealand Key Roles: IEEE Fellow, ARC College of Experts (2023–2025) Publications: Over 280 peer-reviewed articles, including works on niching methods and evolutionary algorithms. Research trends show strong emphasis on hybrid optimization techniques, blockchain security, and AI-driven solutions for sustainability challenges. Recent articles explore dynamic environments, quantum rerouting strategies, and explainable machine learning systems. Awards: ACM SIGEVO Impact Award, IEEE Fellow, ARC College Membership Grants/Projects: Multiple industry-collaborative grants in smart logistics and energy systems. He leads the Data Science & AI team at RMIT, fostering innovation in large-scale optimization and metaheuristics. Active in international conferences like GECCO and IEEE CEC, he promotes open-source benchmark datasets for algorithm testing.
Associate Professor In-Young Yeo is a distinguished academic at the University of Newcastle's School of Engineering, where she serves as an Associate Professor in Civil, Surveying, and Environmental Engineering. With over 15 years of academic experience, she previously held positions at Ohio State University, Cornell University, and University of Maryland. Her work focuses on the critical intersection between landscapes and water resources, using sophisticated remote sensing and modeling tools to improve natural resource management and sustainability. Her research spans the nexus of land and water systems where human and physical systems interact. She pioneers integrative approaches using remote sensing, in-situ data, and process-based models to understand emergent land surface properties and hydrologic processes. Her work addresses water cycle changes across scales, soil moisture-stream flow connectivity, soil and water quality implications of conservation practices, and optimal land use management strategies for environmental sustainability under climate change pressures. Professor Yeo's publication record demonstrates a clear trajectory toward more sophisticated multi-sensor integration approaches and practical applications for agricultural water management. Her recent work increasingly combines machine learning with traditional remote sensing methods, focusing on soil moisture profiling, wetland dynamics monitoring, and developing integrated systems for resource management decision support. Her scientific contributions have been recognized with numerous prestigious awards: 2024 Best Research Paper of 2024, The Soil and Water Conservation Society 2021 Science Note - USDA NRCS Conservation Effects Assessment Project 2018 International Research Visiting Fellowship, University of Newcastle 2017 Women in Research Fellowship, University of Newcastle 2009 Editor's Highlights - AGU Geophysical Research Letters Professor Yeo actively mentors graduate students and offers PhD scholarship opportunities focused on land surface variables and ecosystem health monitoring. She has secured significant research funding from diverse sources including ARC, NASA, NOAA, USDA, Soil CRC, and Sydney Water. Her collaborative approach has led to leadership roles in international research initiatives including NASA-LCLUC and NASA-GRACE programs, the G20 initiative for agricultural monitoring, and the CRC for High Performance Soils. She leads a dynamic research team collaborating with Soil CRC, CSIRO, Australian Universities, and international space agencies. Her work with the US Conservation Effects Assessment Project (CEAP) and US Environmental Protection Agency has been particularly influential in demonstrating the effectiveness of conservation practices for improving soil productivity and water quality. She serves on the educational committee with the Surveying & Spatial Sciences Institute (SSSI) NSW and has contributed significantly to program development at the University of Newcastle.
Professor Rita Kay Henderson is a distinguished academic at the School of Chemical Engineering, University of New South Wales (UNSW) , serving as Deputy Dean (Societal Impact & Translation) at UNSW Engineering. Her expertise lies in water quality engineering, algal biotechnology, and environmental technologies. She leads the Algal and Organic Matter (AOM) Lab , collaborating closely with the Australian water industry to develop innovative solutions for water treatment and resource recovery. Education: PhD in Water Sciences, Cranfield University (2008) MSc in Water Pollution Control Technology, Cranfield University (2004) MChem in Environmental Chemistry, University of Edinburgh (2002) Research Interests: Design and optimization of separation processes for microalgae and organic matter. Development of monitoring protocols for water quality events using advanced characterization techniques. Resource recovery from wastewater and algae-based biotechnology. With over $7M AUD in research funding, including six ARC grants, her work emphasizes collaboration with industry partners like Melbourne Water and Sydney Water. She has authored >90 peer-reviewed articles and chairs key committees, including the UNSW Sustainable Development Goal (SDG) Steering Committee. Awards: AWA NSW Kamal Fernando Mentoring Award (2016) Finalist, Australia Museum 3M Eureka Prize (2015) AIPS NSW Young Tall Poppy Science Award (2012) Advising & Grants: Current and past supervision of over 20 PhD/Master’s students. Key grants include ARC Discovery DP200102195 and industry-funded projects. Her lab, the AOM Lab, focuses on four core themes: natural organic matter, nuisance algae, algae harvesting, and resource recovery. She also contributes to global conferences and editorial roles at Water Research and AWWA Water Science .
Professor Alberto Motta is a Professor of Economics at the University of New South Wales (UNSW), affiliated with the School of Economics within the UNSW Business School. His research focuses on Contract and Organization Theory, Development Economics, and Labor Economics, with an emphasis on applying experimental methods and randomized controlled trials to improve educational outcomes through technology. He is a Fellow of UNSW's Scientia Education Academy and co-chair of the STEP UP initiative in Education. His work explores organizational design effectiveness in firms, enforcement agencies, and media, and he combines theoretical, empirical, and experimental approaches to address socio-economic challenges. Motta has secured significant funding through grants, including a $770,000 UNSW Business School Strategy Knowledge Hub grant (2020) and a $105,000 initiative on Good Decisions in Education (2019). He has also received prestigious awards such as the UNSW President’s Award for Building Collaborations (2019) and the Wharton School Gold Medal (2017). His research spans experimental economics, microfinance, and corruption policy, with notable publications in journals like the Economic Journal , Journal of Development Economics , and Games and Economic Behavior . Motta is actively involved in educational innovation, including the design of serious games and gamified content to enhance learning outcomes. He collaborates widely, engaging in initiatives like the International Growth Centre and the Scientia Education Academy.
Lachlan O'Neill serves as a Research Fellow at Impact Labs, Monash University, within the Department of Data Science & AI. His interdisciplinary work bridges computational methods with social science applications, focusing on data-driven analysis of policy and media narratives. His research centers on Natural Language Processing and Data Visualization, with specific expertise in narrative extraction, policy analysis, and time series classification. He develops innovative frameworks like CANarEx for contextually aware text analysis and narrative exploration maps for visualizing complex policy storylines, primarily applied to Australian social inequality research. Recent publications (2022-2023) demonstrate his focus on visualizing historical and current policy narratives, contextually rich text-as-data applications, and model training optimization through techniques like slingshot learning. His highly cited 2019 work on the Proximity Forest algorithm remains influential in time series classification. O'Neill served as Chief Investigator on the 2023 Strengthening Democracy Project funded by the Scanlon Foundation Trustee, which analyzed opportunity and disadvantage narratives through collaborative research with Angus, Dwyer, and Goodwin. He maintains active affiliation with Impact Labs at Monash University, contributing to interdisciplinary initiatives that merge data science with societal impact research.
Scarlett Abramson is a Researcher in the Department of Electrical and Electronic Engineering, specializing in signal processing for gravitational astronomy. Her work focuses on advancing methodologies for analyzing gravitational wave data and optimizing communication systems. She supervises thesis research under Prof Robin Adams Close, contributing to interdisciplinary projects at the intersection of astrophysics and engineering. Key research interests include real-time signal processing, smart wearable devices, and next-generation wireless networks. She is involved in projects such as 'Real-time Internet of Things with Performance Guarantees' and 'Multi-Modal Deep Dictionary Learning Framework for Managing Smart City Assets,' reflecting her expertise in applied signal processing and networked systems. Scarlett collaborates on initiatives like 'Hypersonic vehicle design' and 'Neuro-Autonomy,' demonstrating her engagement with cutting-edge control systems and autonomy challenges. Her contributions to low-carbon energy systems, such as 'Green Water is Good: Control and Design of Low-Carbon Water Pumping Systems,' highlight her commitment to sustainable engineering solutions.
Sergio Rojas is a Senior Lecturer in the Applied and Computational Mathematics section at Monash University's School of Mathematics. His expertise spans numerical analysis, scientific computing, and mathematical modeling, focusing on residual minimization-based methods for solving complex partial differential equations. He holds a PhD and MSc in Engineering Sciences from Pontificia Universidad Católica de Chile, a Master's in Mathematics from the University of Pavia, and a Bachelor's in Mathematics from Pontificia Universidad Católica de Valparaíso (PUCV). Education: PhD and MSc in Engineering Sciences, Pontificia Universidad Católica de Chile Master's in Mathematics, University of Pavia, Italy Bachelor's in Mathematics, PUCV, Chile Research Interests: Dr. Rojas develops advanced numerical methods including Finite Element, Discontinuous Galerkin, and Physics-Informed Neural Networks. His work emphasizes robust algorithms for complex PDEs, with applications in computational fluid dynamics, geophysics, and solid mechanics. Recent studies explore adaptive stabilization techniques and solver optimization for parallel architectures. Article Trends (2023-2025): Recent publications highlight robust neural network approaches for PDEs, adaptive finite element methods, and computational efficiency in parallel solvers. Key themes include residual minimization, stability analysis, and integration of machine learning in numerical simulations. Supervision: Accepts undergraduate, master’s, and PhD students in numerical analysis and scientific computing. Current PhD projects include variational physics-informed schemes and residual minimization methods. Labs/Teams: Collaborates on interdisciplinary projects involving computational mathematics and engineering applications, though specific lab affiliations are not detailed in the provided text.
Dr. Alireza Moayedikia is a Lecturer in Information Systems at the School of Business, Law and Entrepreneurship, Swinburne University of Technology. His research focuses on Artificial Intelligence, Machine Learning, and Data Science, with expertise in social network analysis, natural language processing, and back-end software development. He has over ten years of professional experience in AI and ML, emphasizing data preparation for machine learning tasks. Research Interests: Dr. Moayedikia’s work spans optimization algorithms for crowdsourcing, microtask management, and feature selection. His recent contributions include advancements in matrix factorization, dynamic payment systems for microtask platforms, and community detection in attributed networks. His methodologies often integrate swarm intelligence (e.g., bee colony optimization) and probabilistic models. Publications: His 19 publications reflect a strong focus on AI-driven solutions, including contributions to data-driven healthcare systems and green logistics. His articles address challenges in sparse data handling, worker reliability estimation, and multi-criteria decision-making frameworks. Grants: He contributed to the SCOPE 3 research contract (2020–2021), developing prototypes for tracking Green House Gas emissions in last-mile delivery systems. Labs/Teams: Collaborates within interdisciplinary teams at Swinburne, focusing on applied AI and sustainable logistics solutions.
Nima Talebian is an Assistant Professor at Bond University's Faculty of Society & Design, affiliated with the Centre for Comparative Construction Research. He is currently accepting PhD students and focuses on sustainable structural engineering, including performance evaluation, composite materials, and cold-formed steel design. His research aligns with UN Sustainable Development Goals, particularly sustainable construction practices. Education: PhD in Structural Engineering from Griffith University (2014–2018). Research Interests Dr. Talebian explores sustainable development principles in structural engineering, with emphasis on: Structural health monitoring and control Earthquake-resistant design and resilience Modular and prefabricated construction systems Energy-efficient materials and smart technologies in construction Recent Research Trends His 2023–2025 publications highlight advancements in machine learning for seismic performance assessment , IoT-based concrete monitoring , and gender equity in construction workforces . Collaborative projects include FSD-funded initiatives on robotics and composite materials. Grants & Projects 2023 FSD Research Project Grant: Investigating smart technologies in construction safety 2023 FSD Deans Award: Robotics applications in modular construction Research Infrastructure He leads research through the Centre for Comparative Construction Research, focusing on industry-academia partnerships to advance sustainable and resilient building practices.
Dr Thiru Balasubramaniam is a Research Fellow at Queensland University of Technology (QUT), working within the Faculty of Science, School of Computer Science. His expertise lies at the intersection of data science, machine learning, and real-world applications, with a specific focus on tensor factorization methods for managing multifaceted data from IoT and Web 3.0 applications. His educational background includes a PhD from Queensland University of Technology and a Bachelor of Engineering from Anna University. Prior to his doctoral studies, he worked as a Research Assistant at the Singapore University of Technology and Design - Massachusetts Institute of Technology (SUTD-MIT) International Design Centre, where he analyzed mobility data to personalize city environments for elderly citizens in Singapore. Dr Balasubramaniam's research interests span multiple areas of data science: Tensor and Matrix Factorization methods Pattern Mining and Text Mining applications Recommender Systems development IoT data processing Web 3.0 applications Real-time analytics for multifaceted data His publication record demonstrates consistent contributions to high-impact venues including IEEE TKDE, ACM TKDD, WWW, WISE, AusDM, and PRICAI. The trend in his recent work shows increasing application of tensor factorization techniques to diverse real-world problems including environmental monitoring, pandemic modeling, social media analysis, and smart grid technology. His research often involves interdisciplinary collaborations, particularly with Professor Richi Nayak at QUT. Scientific recognition includes: QUT-CDS first byte research funding worth 30,000 AUD Dr Balasubramaniam has been actively involved in teaching data analytics subjects at QUT since 2017, including Data Exploration and Mining, Data and Web Analytics, Data Mining Technology and Applications, and Web Computing. His teaching spans both undergraduate and postgraduate levels. His research has been supported through various collaborative grants and institutional funding mechanisms at QUT.
Dr. Hussain Ahmad is an Assistant Professor (Lecturer) at the School of Computer and Mathematical Sciences, University of Adelaide, Australia. His research focuses on Cyber Security, Software Engineering, and Generative AI, with a particular emphasis on industry-driven innovation and real-world applications. He collaborates with prestigious institutions such as the Department of State Development South Australia, Defence Science and Technology Group Australia, and Cisco. Dr. Ahmad completed his PhD at the University of Adelaide, where he explored enhancing C2 network security, software vulnerability management, and scalable microservice architectures. He has led over 15 R&D projects, resulting in 12 peer-reviewed publications in top-tier venues like CORE A* and A-ranked conferences. His teaching philosophy integrates industry relevance, continuous feedback, and cutting-edge content, ensuring students gain practical skills aligned with modern IT demands. He advises students on research projects and maintains a strong focus on cloud-native systems, self-adaptive frameworks, and AI applications. Key contributions include frameworks like Smart HPA for resource-efficient auto-scaling and Co-Evolutionary Defence for Active Directory systems. His work bridges academia and industry, addressing challenges in cyber situational awareness, DevOps practices, and large-scale system resilience.
