جست‌وجوی استاد و پژوهشگر

Erick Alfons Lisanganمشاهده پروفایل

مدرس

• Intelligent Transportation Systems
• Artificial Intelligence
• Deep Learning
• +۵ مورد دیگر

Erick Alfons Lisangan is a Computer Science lecturer at Atma Jaya University of Makassar and a Doctor of Philosophy student at the University of Western Australia's School of Physics, Mathematics and Computing. His research integrates artificial intelligence with transportation infrastructure to address urban mobility challenges. Education Master of Computer Science, Gadjah Mada University (2013-2015) Bachelor of Computer Science, Atma Jaya University of Makassar (2006-2010) Research Interests His core focus lies in Intelligent Transportation Systems , analyzing graph traffic networks and spatiotemporal data collection. He develops algorithms for real-time traffic condition inference using embedded systems and AI-driven simulation. Specialized work includes deep learning architectures (LSTM/BiLSTM) for passenger flow prediction, fuzzy logic systems for dynamic route optimization, and wireless sensor networks for smart city applications like flood detection. His research bridges theoretical AI with practical urban infrastructure solutions. Research Output Trends Publications (2018-2025) reveal a trajectory from foundational smart city sensor networks toward advanced AI-driven transportation modeling. Recent work emphasizes hybrid deep learning for Transjakarta bus system prediction, while earlier contributions established fuzzy ant colony systems for traffic-responsive routing and image-based fake news detection frameworks. Advising and Grants Lisangan serves as a casual tutor for Data Structures & Algorithms and Computational Thinking with Python at UWA during his PhD studies. No formal advisees or research grant details are documented in the provided materials. Labs and Teams His doctoral research operates under supervision of Professor Rachel Cardell-Oliver and Professor Amitava Datta at UWA, contributing to intelligent transportation systems research groups with cross-institutional collaborations in Indonesia and Australia.

• Machine Learning
• Materials Science
• Nanoparticle Materials
• +۳ مورد دیگر

Amanda Parker is a Researcher in the School of Computing at the Australian National University (ANU). Her work focuses on applying machine learning to materials science and nanotechnology, with particular emphasis on nanoparticle design, multi-dimensional materials modeling, and computational chemistry. She has supervised research students and contributed to projects such as the 'Computing for Social Good Seed Grants 2023,' exploring efficient high-performance computing methods. Her research interests span machine learning algorithms, data-driven materials design, and the computational analysis of nanomaterials. Recent projects include developing graph-based representations for multi-dimensional materials and optimizing active learning strategies for scientific simulations. She has published extensively on topics like nanoparticle classification, interfacial informatics, and the prediction of material properties using Bayesian inference and neural networks. Her work bridges computational methods with experimental insights, aiming to accelerate the discovery of novel materials for energy, catalysis, and environmental applications. Current efforts emphasize minimizing experimental bias and enhancing computational efficiency through advanced machine learning techniques.

David Ascherمشاهده پروفایل

استاد

• Bioinformatics and Computational Biology
• Protein Structure-Function Relationships
• Genotype-Phenotype Correlations
• +۴ مورد دیگر

Prof David Ascher is a Professor in Biotechnology at the University of Queensland's School of Chemistry and Molecular Biosciences, and Deputy Associate Dean (Research Partnerships) in the Faculty of Science. He leads the Computational Biology and Clinical Informatics group at the Baker Institute. His research focuses on computational modeling of biological data to understand genotype-phenotype links, particularly in genetic diseases and drug resistance. He has developed over 40 widely used computational tools, including platforms for assessing coding variants' molecular consequences. These tools have been translated clinically for diagnosing hereditary diseases and guiding treatments. Education: B.Biotech (University of Adelaide), B.Sci(Hon) (University of Queensland), PhD (University of Melbourne). Previous roles include positions at the University of Cambridge, University of Melbourne, and Baker Institute. Research Interests: Protein structure-function relationships, computational drug design, antimicrobial resistance, and machine learning applications in biomedicine. His tools are integrated into global platforms like PDBe and VEP, and used in clinical trials and industry drug development. Grants: Current funding includes NHMRC Investigator Grants and projects combating antimicrobial resistance. Past grants include collaborations on genetic disease diagnosis and molecular property prediction. Awards: NHMRC Investigator, Associate Editor roles in PBMB and Frontiers in Bioinformatics , honorary positions at Bio21 Institute and Cambridge University. Labs/Teams: Heads the Computational Biology and Clinical Informatics team, collaborates globally on projects like TB-Profiler and SARS-CoV-2 structural analysis.

• Computational Biomechanical Engineering
• Heat and Mass Transfer
• Microfluidics Modelling
• +۶ مورد دیگر

Dr. Suvash C. Saha is a Senior Lecturer at the School of Mechanical and Mechatronic Engineering, University of Technology Sydney (UTS), Australia. His academic journey includes a PhD in Computational Fluid Dynamics from James Cook University and postdoctoral training at Queensland University of Technology. Research Focus: Computational Biomechanical Engineering, Heat & Mass Transfer, Microfluidics Modeling Key Applications: Lung surfactant particle interactions, Red Blood Cell dynamics, Thermal energy systems His recent publications demonstrate expertise in PEM water electrolyzers, natural convection systems, and microplastic inhalation risks. The work spans energy systems optimization, biomedical applications, and environmental health impacts. Dr. Saha's research has been recognized through Stanford University's Top 2% Scientists (2023) and Research.com Mechanical & Aerospace Engineering rankings (2025). His team actively explores: Computational modeling of respiratory and cardiovascular flows Thermal management using phase change materials Multiscale simulations from cellular to organ-level systems Microplastic-nanoparticle interactions in pulmonary systems Scientific Awards: Stanford's Top 2% Scientists (2023) in career-long and single-year categories Research.com Top Mechanical & Aerospace Scientist #86 in Australia (2025) Australia-India Strategic Research Fund Fellowship As lab director of the Computational Biofluid and Heat Transfer (CBHT) Lab, he supervises PhD students in advanced CFD modeling, thermal systems analysis, and biomedical engineering applications using ANSYS Fluent, COMSOL, and OpenFOAM.

Shuang Aoمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Reinforcement Learning
• Curriculum Learning
• +۴ مورد دیگر

Mr. Shuang Ao is a Postdoctoral Research Fellow at the School of Computer Science and Engineering, University of New South Wales (UNSW Sydney). He earned his PhD from the University of Technology Sydney in January 2024. His research focuses on machine learning, reinforcement learning, and curriculum learning, with applications in robotic control and antibody drug discovery. Research Interests: Machine Learning Reinforcement Learning Curriculum Learning Graph Algorithms Optimization Techniques Recent Publication Trends: Shuang's work spans large language models for location-based recommendations, spatio-temporal forecasting, reinforcement learning frameworks, and graph algorithm optimizations. His articles address both theoretical advancements and practical applications in scalable systems and data analysis. Contact: Email: shuang.ao@unsw.edu.au

• Data Mining
• Data Science
• Data Analytics
• +۴ مورد دیگر

Professor Geoff Webb is a world-leading data scientist at Monash University , serving as Director of the Monash University Centre for Data Science within the Faculty of Information Technology . His research focuses on leveraging data science to enable evidence-based decision making and derive actionable insights through artificial intelligence, machine learning, and big data analytics. Core expertise in data mining , bioinformatics , and computational biology Developed Magnum Opus software and contributed to the Weka machine learning workbench Recipient of the Eureka Prize for Excellence in Data Science and leadership roles in major data mining conferences Research Interests Professor Webb's work spans artificial intelligence , machine learning , and data analytics , with a focus on black-box user modelling , interactive data analytics , and statistically-sound pattern discovery . His recent publications highlight advancements in Bayesian networks , time series analysis , and genomic data interpretation , demonstrating his interdisciplinary impact. Scientific Contributions AI in healthcare : EHR-ML framework for clinical records analysis Biological applications : KcatNet for enzyme prediction, PFresGO for protein function Data mining innovations : OPUS search algorithm, proximity forest techniques As a technical adviser to data science company Froomle , he bridges academic research with real-world applications. His work has been recognized through numerous research awards and leadership as Editor-in-Chief of Data Mining and Knowledge Discovery for a decade.

• Data Mining
• Machine Learning
• Computer Vision
• +۵ مورد دیگر

Dr. Bin Liang is a Senior Lecturer at the University of Technology Sydney (UTS), working within the Faculty of Engineering and Information Technology and the Data Science Institute. He joined UTS in December 2018 as a Lecturer and was promoted to Senior Lecturer in July 2022, following his postdoctoral fellowship at Data61 (CSIRO). PhD in Computer Vision, Pattern Recognition, and Machine Learning from Charles Sturt University, Australia (2012-2015) Masters in Computer Science from Taiyuan University of Technology, China (2009-2012) BEng in Computer Science from Taiyuan University of Technology, China (2005-2009) Dr. Liang's research spans data mining, machine learning, computer vision, pattern recognition, and survival analysis, with a strong focus on practical applications in critical infrastructure. His work develops sophisticated machine learning frameworks for anomaly detection, failure prediction, and optimization in water infrastructure, flood mapping, and real estate appraisal. He has pioneered approaches that integrate graph neural networks with temporal analysis for pipe failure prediction and has made significant contributions to water quality optimization through data-driven methods. His research consistently bridges theoretical advancements with practical implementation in industry settings. His recent publications demonstrate a clear trajectory toward increasingly sophisticated models that capture complex temporal and spatial relationships in infrastructure data. There's a notable emphasis on multimodal learning approaches and domain generalization techniques that enhance model robustness across diverse application scenarios. His work on anomaly detection frameworks like MLAD shows innovation in grouping sensors by temporal characteristics for more accurate monitoring. 2022 – AWA R&D Excellence Award (NSW) 2022 – UTS Medal for Research Impact 2022 – Finalist for 2021-22 AWA Young Water Professional of the Year Award (NSW) 2020 – Finalist for the Best Paper Award of the ICARCV 2020 2019 – Industrial & Primary Industries Merit at the Victorian iAwards 2018 – Australian Museum Eureka Prize for Excellence in Data Science Dr. Liang actively supervises Masters Research and PhD students, focusing on data science applications for infrastructure management. His research has been supported through industry partnerships with water utilities and other infrastructure organizations, translating academic research into practical solutions that reduce water loss, improve infrastructure reliability, and enhance environmental sustainability. His work on pipe failure prediction and leak detection has directly contributed to operational improvements in water distribution networks. As a core member of the Data Science Institute at UTS, Dr. Liang collaborates with interdisciplinary teams working on data-driven solutions for urban infrastructure challenges. His research group focuses on developing scalable machine learning models that can be deployed in real-world settings, often working directly with industry partners to validate and implement their approaches. This industry-academia collaboration ensures that his research remains grounded in practical challenges while pushing the boundaries of data science methodology.

David Ascherمشاهده پروفایل

استاد

• Computational Biology
• Bioinformatics
• Structural Biology
• +۳ مورد دیگر

David Ascher is Head of the Computational Biology and Clinical Informatics laboratory at the Baker Institute, Deputy Director of Biotechnology at The University of Queensland, and Head of Systems and Computational Biology at Bio21 Institute. He holds honorary positions at Cambridge University, FIOCRUZ, and the Tuscany University Network. His educational background includes: Bachelor of Biotechnology Bachelor of Science (Honors) Bachelor of Laws Doctor of Philosophy David's research focuses on computational biology and bioinformatics, particularly in modeling biological data to understand fundamental processes. His work centers on developing tools to unravel the genotype-phenotype link, using computational and experimental approaches to assess the effects of mutations on protein structure and function. His group has created a platform of 40 widely used programs for variant effect prediction, which are applied in clinical settings for hereditary diseases, rare cancers, and drug-resistant infections. His recent publications span structural biology, genomics, and drug discovery, with a strong emphasis on protein dynamics, mutation impact prediction, and computational tools for biomedical research. The work often bridges basic science and clinical applications. Scientific awards and fellowships include: Anders Young Investigator Award (2017) Dr Álvaro Romanha Award (2017) Jack Brockhoff Early Career Researcher Award (2016) Dr Antoniana Ursine Krettli Award (2016) Dr Naftale Katz Award (2015) TJ Martin Award (2014) Bionomics Best Thesis Award (2014) NHMRC Investigator Fellowship (2020–2024) MDHS Research Fellowship (2019) NHMRC CJ Martin Fellowship (2014–2018) Victoria Fellowship (2013) Churchill Memorial Trust Fellowship (2013) David Ascher leads the Computational Biology and Clinical Informatics laboratory, which develops computational tools for clinical applications. His work is supported by major grants including the NHMRC Investigator Fellowship. He has not publicly listed his advisees, but his laboratory likely mentors students and researchers in computational biology.