جست‌وجوی استاد و پژوهشگر

Mikael Bodenمشاهده پروفایل

استاد

• Bioinformatics
• Artificial Intelligence
• Machine Learning
• +۶ مورد دیگر

Professor Mikael Boden is a Professor in the School of Chemistry and Molecular Biosciences at the University of Queensland, where he has been since 2003 after holding academic positions at Swedish universities. With a PhD in Computer Science from the University of Exeter, he bridges computational methods with biological research through his work at the Institute for Molecular Bioscience. His research program applies artificial intelligence, machine learning, and data science to solve complex problems in genomics and molecular biology. Key areas include transcriptional and epigenetic regulation during mammalian development, protein evolutionary relationships, and computational tool development for biological data analysis. His group uses probabilistic modeling and data analytics to integrate massive biological datasets across multiple technologies including single-cell RNA-seq and ChIP-seq. Professor Boden's scientific impact is demonstrated through publications in high-impact journals such as Science, Nature Catalysis, and Nature Communications. His research is supported by multiple grants including NHMRC Ideas grants and ARC Discovery Projects focusing on drug design, central nervous system development, autism research, and antibiotic resistance. Associate Editor of Frontiers in Genetics/Computational Biology Docent in Computer Science at Halmstad University, Sweden Ramsay Visiting Scientist Award from University of Birmingham Gastprofessorenprogramm award from Bavarian State Ministry As an educator, he serves as convener of the Bioinformatics dual major and program director of the Master of Bioinformatics. He has supervised 7 postdocs, 11 PhD and 3 MPhil graduates, and currently mentors 6 PhD students including Samuel Davis, Sanjana Tule, and Oliver Hughes. His interdisciplinary collaborations span neuroscience, developmental biology, protein engineering, and bioeconomy research.

Pietro Borghesaniمشاهده پروفایل

دانشیار

• Asset Health Monitoring
• Vibration Sensing
• Signal Processing
• +۶ مورد دیگر

Associate Professor Pietro Borghesani is affiliated with the School of Engineering and the Department of Mechanical and Manufacturing Engineering at the University of New South Wales (UNSW). He holds an Associate Professor rank and focuses on asset health monitoring, vibration sensing, signal processing, and machine learning applications in mechanical systems. His research spans aerospace and rail industries, emphasizing condition-based maintenance optimization and fault diagnostics. Education: PhD in Mechanical Engineering (2013, Politecnico di Milano), postdoctoral work at Politecnico di Milano, and prior experience at McKinsey & Co. as a business analyst consultant. Research interests include: Health monitoring of rotating machinery Signal processing for diagnostics Machine learning in condition assessment Wear mechanisms in gears and bearings Integration of physics-based models with AI Publications highlight advancements in neural network explainability, vibration-based diagnostics, and cepstral analysis. His work bridges signal processing with tribology and mechanical systems. Grants include projects on railway track geometry monitoring (LP200100382), sugar industry maintenance (LP150100545), and aero-engine bearing health (LP180101161). He is an Editorial Board Member for Mechanical Systems and Signal Processing and a member of the International Society for Engineering Asset Management (ISEAM). Labs/Teams: Collaborations with Australian/international companies in aerospace and rail sectors; involvement in experimental setups for vibration and acoustic emission studies.

• Privacy-Preserving Machine Learning
• Reinforcement Learning
• Data Privacy and Security
• +۵ مورد دیگر

Associate Professor Kee Siong Ng is affiliated with the School of Computing at the Australian National University (ANU). His research focuses on privacy-preserving technologies, reinforcement learning, distributed systems, and blockchain applications. He has contributed extensively to areas such as privacy-preserving machine learning, federated learning, entity resolution, and scalable database systems. His work emphasizes balancing computational efficiency with privacy guarantees in data-driven environments. Key research contributions include methodologies for secure data processing in federated learning frameworks, privacy-preserving reinforcement learning for population-level systems, and blockchain-based digital identity solutions. He has led projects like Integrated Graph Analytics and contributed to initiatives involving the Australian Medicare dataset. His research often intersects theoretical foundations with practical implementations, addressing challenges in scalability and real-world applicability. Ng has published over 24 peer-reviewed articles, with notable works appearing in venues like IEEE Transactions on Parallel and Distributed Systems and Transactions on Machine Learning Research . His articles frequently explore cutting-edge topics such as differential privacy, approximation algorithms, and multi-agent systems. Collaborations include industry partnerships and interdisciplinary efforts involving health informatics and financial intelligence. His projects include Integrated Graph Analytics (2018–2021): Focused on scalable graph-based data analysis. Translational Fellowship (2018–2022): Bridging theoretical research with practical applications. Research on Data Sets for Health and Pharmaceutical Schemes (2020): Analyzing Medicare and pharmaceutical data with privacy safeguards. Ng's work prioritizes ethical AI and privacy-by-design principles, with a focus on real-world deployment challenges in distributed and federated systems.

Raj Mehrotraمشاهده پروفایل

پژوهشگر ارشد

• Hydrology
• Climate Change Impact Assessment
• Statistical Modeling
• +۳ مورد دیگر

Dr Raj Mehrotra is a Senior Research Fellow at the Water Research Centre, School of Civil and Environmental Engineering, University of New South Wales. He holds a PhD from UNSW, M.E. from Indian Institute of Technology Roorkee, and B.E. from Jiwaji University. His work focuses on statistical applications in hydrology and hydroclimatology, particularly on multivariate bias correction and stochastic downscaling of climate model simulations. Education: PhD: University of New South Wales M.E.: Indian Institute of Technology, Roorkee B.E.: Jiwaji University, Gwalior Raj develops innovative methodologies for climate data processing, including the Multivariate Bias Correction (MBC) and Multisite Rainfall Downscaling (MRD) software. His research addresses climate change impacts on water resources, reservoir management, and flood/drought risk assessment, with applications in Australia, India, and Thailand. Recent publications reveal a focus on climate projections, bias correction frameworks, and hydrological modeling. Key topics include CMIP5 decadal predictions, stochastic rainfall generation, and uncertainty quantification in climate simulations. His work has been supported by ARC Linkage projects, WaterNSW, Bureau of Meteorology, and international collaborations like the Australia-India Strategic Research Fund. He contributes to climate-hydrology software development and has advised on projects related to water infrastructure, agricultural productivity, and groundwater sustainability. Current projects include multivariate bias correction of regional climate simulations, development of the MINBC package, and ensemble modeling for ungauged catchments.

• Cognitive Neuroscience
• Autism Spectrum Disorder
• Substance Use Disorders
• +۵ مورد دیگر

Dr Natalia Albein-Urios is a Visiting Professor at Deakin University's School of Psychology , affiliated with the SEED - Lifespan strategic research center. Her work bridges Cognitive Neuroscience and Clinical Psychology , focusing on Autism Spectrum Disorder (ASD) and Substance Use Disorders (SUD) via Non-invasive Brain Stimulation (NIBS) , fMRI , and EEG . Education : PhD in Neuropsychology (Summa Cum Laude, University of Granada, 2012), Master in Health Psychology (University Miguel Hernandez, 2007), International Master in Clinical Psychology (Spanish Association of Behavioural Psychology, 2009), and BAppSc (Hons) in Psychology (University of Miguel Hernandez, 2007). Research Interests center on: Neurocognitive Mechanisms in ASD and SUD Non-invasive Brain Stimulation (HD-tDCS, TMS, TBS) Neuroimaging (fMRI, EEG) and Behavioral Interventions Emotion Regulation and Executive Dysfunction Her Recent Publications highlight advancements in: Transdiagnostic Approaches to mental health via computational modeling Brain Stimulation Efficacy in ASD and addiction Neuroimaging Biomarkers for dual disorders Scientific Awards include: Best Thesis Prize in Health Sciences, University of Granada (2016) Health Research Postdoctoral Fellowship , Deakin University (2015-2017) First Prizes for Best Poster (ECNP 2018) and Oral Presentation (ICDBMD 2011) Supervision spans 14 Honours Students since 2015 and PhD students like Gillian Clark (2017) and Dinisha Parmar (2021). She has secured grants such as the Research Development Grant (Deakin University, 2014). Her work contributes to the SEED - Lifespan team's mission in translational neuroscience.

• Artificial Intelligence
• Machine Learning
• Probabilistic Inferencing
• +۵ مورد دیگر

Barry Drake serves as an Adjunct Professor in the School of Computer Science at the University of Technology Sydney (UTS), where he is also affiliated with the Faculty of Engineering and Information Technology and the Data Science Institute. Additionally, he holds the position of Co-Chief Scientist at the Digital Health Cooperative Research Centre and participates in government advisory committees. His career bridges academic research and industry application, with significant experience in translating research into practical technology solutions for business and government sectors. Drake earned his PhD in Computer Science from UNSW Australia (1998-2003) and a BSc with First Class Honors in Computer Science from UTS (1991-1996). His educational background is complemented by a Certificate of Proficiency for Radio Fitter/Mechanic Electronic Systems from the Royal Australian Navy. His research focuses on algorithms and software for practical smart systems, with particular expertise in applications of AI and machine learning, probabilistic inferencing and knowledge compilation, learning probabilistic models, and fast-efficient near-neighbor searching in ultra-high dimensional spaces. His work demonstrates a clear trajectory toward health informatics applications , especially in health systems and delivery of health services, where he has made significant contributions through projects like the Lumos statewide linkage programme. The analysis of his publication record reveals a consistent pattern of research bridging theoretical computer science with practical healthcare applications. His recent work shows increasing focus on health data integration, patient journey modeling, and privacy-preserving synthetic data generation for healthcare applications, while maintaining his foundational expertise in probabilistic models and efficient search algorithms. Inventor on 20 filed patents ORCID identifier: 0000-0003-0572-9936 Drake actively supervises Masters and PhD students according to his profile, and his funded research portfolio includes multiple grants from the Digital Health CRC and NSW Health. His industry experience, particularly his 13 years at Canon Information Systems Research Australia where he served as Senior Principal Engineer and lead researcher for machine learning, informs his approach to technology research methods from a commercial perspective. His current projects focus on the impact of integrated care in New South Wales, synthetic data generation, and patient journey modeling. As Co-Chief Scientist at the Digital Health CRC, Drake contributes to a major national initiative focused on digital health innovation. His work with the Lumos programme has created Australia's first statewide linked data asset across primary care and other health settings, providing unique insights about cross-setting healthcare utilization. This initiative represents a significant contribution to health data infrastructure in Australia.

• 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.

• Quantum computation
• Quantum information, computation and communication

Professor Zhengfeng Ji is an Adjunct Professor at the Centre for Quantum Software and Information within the Faculty of Engineering and Information Technology at the University of Technology Sydney. His research focuses on quantum computation, quantum information theory, and quantum communication. Quantum complexity theory Quantum algorithms Quantum network optimization Entanglement characterization Recent publications highlight advancements in quantum network routing frameworks and quantum proof systems. He has secured multiple grants including the Sydney Quantum Academy Postdoctoral Fellowship and ARC Discovery Projects. Quantum Exponential Time Hypothesis Quantum PCP Conjecture Post-quantum Cryptographic Protocols Scientific awards include: SQA Scholarship Sydney Quantum Academy Postdoctoral Fellowship SUSTech Scholarship