Find Academic Supervisors

• Transport Engineering
• Traffic modelling
• Microscopic simulation
• +7 more

Dr. Tuo Mao serves as a Research Fellow at the Data Science Institute within the Faculty of Engineering and Information Technology at University of Technology Sydney (UTS). He also holds visiting scientist status at the Intelligent Mobility group at Data61 CSIRO. With a PhD from University of New South Wales (UNSW), Dr. Mao has established himself as a senior engineer specializing in transportation systems research. Dr. Mao's research interests span Transport Engineering with particular expertise in traffic modelling, microscopic simulation, and traffic signal control and management. His work focuses on vehicle infrastructure integration (VII) systems within intelligent transport systems (ITS), with specific applications in motorway modelling, coordinated ramp metering optimization, and V2I communication wireless connection system modelling. He has developed significant expertise in applying genetic algorithms to traffic signal control plan optimization and specializes in machine learning applications, particularly reinforcement learning, for transportation challenges. His recent publications (2023-2025) demonstrate a clear progression toward AI-driven transportation solutions, with emphasis on digital twins for passenger flow prediction, connected vehicle data analysis for railway safety, and micro-mobility pattern analysis. These works reveal consistent application of machine learning techniques to both recurrent and non-recurrent traffic conditions, with practical implementations across Australian transportation networks. EV Charging as a Service (2024-2026) - Australian Renewable Energy Agency UTS Smart Campus for energy saving (2022-2023) - UTS Central Research Budget V2X freight and emergency vehicle priority trial (2021-2022) - iMOVE CRC Safety and risk evaluation of Train Level Crossings (2021) - Transport for NSW Ai-RAP Automation for Australian Road Safety (2020-2022) - iMOVE CRC Dr. Mao actively contributes to the Future Mobility Lab at UTS, where his team develops integrated approaches to transportation challenges through collaboration with government agencies including Transport for NSW and research consortia like iMOVE CRC. His work bridges theoretical optimization methods with practical transportation solutions across Australian contexts, with particular focus on Sydney's transportation infrastructure.

• Statistical Modeling
• Machine Learning
• Regression Analysis
• +3 more

Wang You-Gan is an accomplished academic researcher with over three decades of publication history spanning from 1991 to 2024. His work demonstrates a strong foundation in statistical methodology with applications across multiple domains including environmental science, fisheries management, machine learning, and computational statistics. His research has been published in high-impact journals across statistics, computer science, environmental science, and biology. Wang You-Gan's primary research interests include statistical modeling, regression analysis (particularly support vector regression), longitudinal data analysis, machine learning applications, and environmental statistics. His work shows a clear evolution from early fisheries and environmental applications to broader computational statistical methods with applications in energy systems, genomics, and cloud computing. His research demonstrates strong methodological development coupled with practical applications. His recent publications (2022-2024) show a strong focus on advanced regression techniques, particularly support vector regression with innovations in handling heterogeneous variances, autoregressive processes, and automatic hyperparameter selection. He has also expanded into machine learning applications for energy demand forecasting, air quality prediction, and data center optimization. His work often bridges theoretical statistical development with practical implementation across diverse domains. Wang You-Gan has received significant citation impact for his methodological contributions, with several papers accumulating over 100 citations. His collaborations span multiple institutions and disciplines, indicating his work's broad relevance across fields. His research has important applications in environmental monitoring, energy systems management, computational biology, and cloud computing infrastructure. The consistent publication record over more than 30 years demonstrates his sustained contribution to statistical methodology and its applications.

• Bayesian Statistics
• Approximate Bayesian Computation
• Statistical Modeling
• +6 more

Christopher Drovandi is a Professor at Queensland University of Technology and a Chief Investigator in the Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS). His research spans statistical methodology development and applications across diverse fields including ecology, biomedicine, and environmental science. Dr. Drovandi's primary research interests focus on advancing Bayesian statistical methods, particularly in the areas of: Approximate Bayesian Computation (ABC) and likelihood-free inference Synthetic likelihood methods Sequential Monte Carlo algorithms Bayesian experimental design Statistical computing for complex models His recent work demonstrates a strong emphasis on addressing model misspecification in Bayesian inference, developing robust computational methods for intractable likelihoods, and applying advanced statistical techniques to solve real-world problems in ecology, biomedicine, and environmental science. Drovandi has made significant contributions to both the theoretical foundations and practical applications of modern Bayesian statistics. Dr. Drovandi has received research funding through multiple Australian Research Council grants and collaborates extensively with researchers across disciplines. His work has been published in top-tier statistical and interdisciplinary journals including the Journal of the American Statistical Association, Bayesian Analysis, and PLOS Computational Biology.

Baki KocaballiView profile

Senior Lecturer

• Artificial Intelligence
• Digital Health
• Interaction Design
• +5 more

Baki Kocaballi serves as a Senior Lecturer at the School of Computer Science within the Faculty of Engineering and Information Technology at the University of Technology Sydney (UTS), a position held since May 2020. He concurrently maintains an Honorary Research Fellow appointment at Macquarie University since the same date. His academic foundation includes a PhD in Interaction Design from the University of Sydney, completed in 2013. His research critically examines the convergence of artificial intelligence, digital health, and interaction design, with particular emphasis on multimodal and conversational systems. This interdisciplinary work manifests through projects like the Activating the Everyday Research Program and investigations into spatial collective intelligence developed with UNSW colleagues through the Smart Cities Research Cluster. His methodological approach integrates topological thinking—a conceptual framework he actively develops through dedicated research blogging—to challenge conventional spatial and relational assumptions in design. Analysis of his recent publications reveals dominant trends in healthcare AI applications, particularly conversational agents for clinical settings, digital scribes for documentation reduction, and context-aware systems for chronic disease management. These works consistently bridge technical AI development with human-centered design principles, reflecting his core commitment to usable and trustworthy digital health solutions. Best DC Paper Award at European Conference of Cognitive Ergonomics Doctoral Consortium (2010) Best Poster Award at Research Expo'09, University of Sydney (2009) His teaching portfolio includes advanced subjects in Design Thinking (2015-2017), Installation Studio (2012), and the Interdisciplinary Design Lab (2011), where he emphasizes human-centered methodologies, sensor-based interactive environments, and real-world problem solving. He actively supervises Masters and PhD research while collaborating across institutions through the Visualisation Institute Research Network and Trustworthy Digital Society initiatives. His artistic practice in photography and video complements his academic work, with exhibitions including the MAMA Art Foundation National Photography Prize and multiple international screenings.