جستجوی استاد راهنمای دانشگاهی

• 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.

• Catchment Modelling
• Urban Hydrology
• Climate Change Impact on Flood Estimation
• +۳ مورد دیگر

James Ball is an Adjunct Professor at the School of Civil and Environmental Engineering , University of Technology Sydney. With a career spanning academia, consulting, and government roles, his expertise focuses on catchment modeling , urban drainage systems , and climate change impacts on hydrology. He has held positions at UNSW Australia and contributed to foundational guidelines like Australian Rainfall and Runoff . Education: Doctor of Philosophy, Master of Engineering, and Bachelor of Civil Engineering from the University of Newcastle, Australia. Research Interests: Development of catchment modeling systems for urban environments, integration of deep learning in land use/land cover classification, hydrological software calibration with genetic algorithms, and stormwater-green infrastructure interactions such as green roofs. Recent Trends: His 2024–2021 publications emphasize agent-based groundwater-society interactions , deep learning for impervious surface estimation , and hybrid machine learning methods for dam reservoir evaporation under climate change scenarios. Articles also address flood modeling accuracy , scour mitigation structures for bridges, and socio-hydrology in resource conflicts. Leadership: Editor-in-Chief of the International Journal of River Basin Management , editorial roles in Urban Water Journal , Hydrology , and Water Science and Technology . Past Vice-President of IAHR and technical editor for Australian Rainfall and Runoff . Supervision: Available for Masters and PhD student supervision . Funded Research: Projects include stormwater modeling , hydrological ensemble prediction , phosphorus release studies , and stochastic rainfall generation for flood estimation.

Mark Wallaceمشاهده پروفایل

استاد

• Optimization
• Constraint Programming
• Artificial Intelligence
• +۵ مورد دیگر

Professor Mark Wallace is a renowned academic at Monash University , specializing in modelling and software platforms for planning, scheduling, and optimization problems . He has led groundbreaking projects such as the development of the ECLiPSe constraint programming platform (acquired by Cisco Systems) and the G12 hybrid optimization platform , which was commercialized through his company, Opturion . His research consistently bridges theoretical innovation with real-world applications, supported by industry funding from major companies like British Airways, Qantas, and Woodside Energy. Education: MA in Mathematics and Philosophy from Oxford University; MSc in Artificial Intelligence from Queen Mary College, London; PhD in Communicating with Databases in Natural Language from Southampton University. His work focuses on application-driven research in constraint programming, hybrid optimization systems, and decision support technologies. Recent publications highlight advancements in public transport routing , autonomous vehicle fairness constraints , and human-centred feasibility restoration . He actively collaborates with industry partners on projects involving Melbourne Water , Woodside , and the Alertness CRC , driving innovations in smart infrastructure and transport systems.

Fethi Rabhiمشاهده پروفایل

استاد

• Software Engineering
• Artificial Intelligence Systems Design
• FinTech
• +۶ مورد دیگر

Professor Fethi Rabhi is affiliated with UNSW Sydney as a faculty member of the School of Computer Science and Engineering . His role combines research in software engineering, AI systems design, and FinTech with teaching and fostering university-industry collaborations in financial applications. Research Focus : Software systems, AI design, financial technology, semantic data, and service-oriented computing. Grants : Secured ~$4M in grants across Fintech AI, Smart Services CRC, Ad-hoc Data Grids, and Capital Markets CRC projects. Teaching : 30 years of experience in software engineering, bridging IT and finance disciplines. Leadership : Co-founded the Fintech AI Innovation Consortium (FAIC) in 2022.

• 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.

Raina MacIntyreمشاهده پروفایل

استاد

• Emerging infectious diseases
• Biosecurity
• Epidemiology
• +۸ مورد دیگر

Professor Raina MacIntyre is NHMRC Leadership 3 Fellow and Professor of Global Biosecurity at the University of New South Wales, Faculty of Medicine & Health. She heads the Biosecurity Program at the Kirby Institute, conducting research in epidemiology, bioinformatics, health intelligence, vaccinology, bioterrorism prevention, mathematical modelling, and public health. As of 2024, she is ranked #1 globally on Respiratory Protective Devices and highly ranked in Smallpox research according to Expertscape, with an H-index of 81/64 and over 30,000 citations. Her educational background includes: MBBS (1st Class Honours), University of Sydney, 1988 Master of Applied Epidemiology, Australian National University, 1992 PhD (Epidemiology), Australian National University, 1998 Fellow, Royal Australasian College of Physicians (FRACP), 1994 Fellow, Australian Faculty of Public Health Medicine (FAFPHM), 1995 Professor MacIntyre's research spans four key areas: Personal protective equipment, Vaccinology, Epidemic response and emerging infectious diseases, and bioterrorism prevention. Her innovation, EPIWATCH®, is an advanced AI system for real-time global health intelligence that uses NLP, prioritization algorithms, and LLMs to scan open-source data for early epidemic signals. She has a 31-year track record in pandemics and respiratory virus control, with particular expertise in mask research, having published the largest body of clinical trials on face masks for healthcare workers internationally. Her research publications demonstrate strong focus on emerging infectious disease modeling, vaccine effectiveness, and biosecurity threats. Recent work emphasizes AI-driven health intelligence systems, airborne transmission mitigation, and preparation for re-emergent pathogens like smallpox. Her articles consistently address practical applications of epidemiological research to inform public health policy and emergency response. Professor MacIntyre has received numerous prestigious awards including the Eureka Prize for Leadership and Innovation in Science (2022), the Frank Fenner Award for Research in Infectious Diseases (2003), and the Sir Henry Wellcome Medal and Prize (2007). Her work has been globally recognized, with a quote from her mask research featured on the cover of Lancet in 2020. She currently supervises PhD, ILP, and Masters students in infectious diseases epidemiology and biosecurity. Her research is supported by significant grants including an NHMRC Investigator grant (2023-2027), an MRFF grant for aerosol transmission of SARS-CoV-2, and a $5.39 million gift from Vitalik Buterin's Balvi Philanthropic Fund for EPIWATCH expansion in low and middle-income countries. Professor MacIntyre leads a multidisciplinary team at the Kirby Institute including analysts, epidemiologists, AI specialists, and software engineers developing EPIWATCH® into a global hub for epidemic detection and mitigation. She also co-founded the ARM network for Australian outbreak response and serves on multiple WHO committees including the Technical Advisory Group on COVID-19 Vaccine Composition and the SAGE Ad Hoc Working Group on Smallpox and Monkeypox.

• Machine Learning
• Big Data Analytics
• Computational Intelligence
• +۴ مورد دیگر

Vahid Behbood is a Lecturer at the School of Computer Science within the Faculty of Engineering and Information Technology at the University of Technology Sydney (UTS). He belongs to the Decision Systems and e-Service Intelligence Research Laboratory in the Centre for Quantum Computation and Intelligent Systems. Ph.D. in Software Engineering from UTS (2014) His research spans machine learning , big data analytics , computational intelligence , and transfer learning , with a focus on addressing data shortage challenges through fuzzy systems and domain adaptation. His work includes: Advancing fuzzy regression domain adaptation for cross-domain prediction Developing granular computing techniques for financial failure prediction Exploring SQL query error patterns in computer science education Designing IoT architectural frameworks via systematic reviews Publications demonstrate expertise in neural networks , fuzzy logic , Bayesian methods , and financial data analytics . He applies these techniques to banking ecosystems, real estate valuation, and educational data mining.

• Artificial Intelligence
• Machine Learning
• Federated Learning
• +۸ مورد دیگر

Guodong Long is an Associate Professor at the University of Technology Sydney (UTS) in the Faculty of Engineering and Information Technology. He joined UTS in 2010 and earned his PhD there in 2014. His research focuses on federated learning, trustworthy AI, and pre-trained foundation models with applications in healthcare, IoT, and social media. PhD in Artificial Intelligence, University of Technology Sydney (2014) Leading the Foundation Model and Federated Learning research group (https://www.fmfl.group/) His work addresses challenges in frequency transformation for time series, privacy-preserving healthcare analytics, and spatio-temporal traffic forecasting. He has published extensively at top AI conferences like AAAI, ICLR, and NeurIPS, with significant citation impact (4,682 citations in 2022). Collaborations with industry partners have secured over $4M in external funding. Recent publications emphasize federated foundation models (ICLR'25), privacy-preserving recommendation systems (WWW'25), and adaptive time series analysis. His research integrates domain knowledge and graph learning for multivariate time series imputation and traffic prediction. Dr. Long actively contributes to academic leadership as General Co-Chair for WebConf 2025, Program Co-Chair for AI conferences, and reviewer for top venues. He supervises PhD and master's students and welcomes research visitors for extended collaborations.

• Intelligent Systems
• Deep Learning
• Graph Neural Networks
• +۵ مورد دیگر

Professor J. Joshua Thomas is a distinguished academic at University of Wollongong Malaysia, specializing in intelligent systems and advanced computational techniques. He holds a PhD in Intelligent Systems Techniques from Universiti Sains Malaysia (2015) and a Master's degree in Computer Science from Madurai Kamaraj University, India (1999). Professor Thomas has demonstrated strong leadership in academic administration, having served as Head and Deputy Head of the Department of Computing between 2012 and 2017. His research focuses on intelligent systems and interdisciplinary computational algorithms, with recent work emphasizing Deep Learning, Graph Convolutional Neural Networks (GCNN), Graph Recurrent Neural Networks (GRNN), Hyper-Graph Attention Networks, and Quantum Machine Learning. Professor Thomas's projects span diverse applications including end-to-end steering learning systems, algorithm design in drug discovery, and advanced data analytics for electricity consumption and carbon price forecasting. Professor Thomas maintains an impressive publication record with over 50 peer-reviewed publications and 12 books with publishers including Wiley, Elsevier, and IGI Global. His research is supported by multiple active grants at institutional, national, and international levels, including projects on cancer survivor well-being and AI bias in smart cities. Best Paper Award at SCI 2025 Oracle Cloud Infrastructure 2024 Generative AI Certified Professional 2022 Global Staff Awards (UOWGE) - Excellence In Research Winner V-MIIEX2021: COVIDNet - GOLD AWARD Fundamental Research Grant Scheme (FRGS) recipient (2019) As an active researcher and educator, Professor Thomas serves as Principal Investigator on multiple grants, supervises PhD and Master's students, and regularly delivers keynote addresses at international conferences. His collaborative spirit is evident through partnerships with institutions in India, USA, China, and Germany, fostering knowledge exchange and innovation across disciplines.

• Software Engineering
• Human Computer Interaction
• Computer Science Education
• +۴ مورد دیگر

Siti Fazilah Shamsudin is a Senior Lecturer at UOW Malaysia KDU University College with over 20 years of academic experience in Computer Science and Software Engineering. She serves as Program Leader for the Diploma Program in Computing & Creative Media and plays a pivotal role in program development, curriculum design, and accreditation processes for computing degrees, all of which have achieved full accreditation. PhD in Information System (ongoing) at University Technology Mara (UiTM) Master of Information Technology from University Technology Mara (UiTM), 2002-2004 BSc (Hons) in Computer Engineering from De Montfort University, UK, 1996-1999 Dr. Shamsudin's research spans multiple interdisciplinary domains including software engineering, human-computer interaction, and computer science education. Her applied research focuses on medical imaging (particularly kidney stone and ovarian tumor detection using ultrasound), digital forensics for mobile devices, and machine learning applications in aquaculture. Her current PhD research on visual appeal interface design for slow learners bridges educational technology with user-centered design principles. Her publication record demonstrates consistent output in applied computing fields, with recent work focusing on image processing algorithms for medical diagnostics and digital forensics. The research shows a clear trajectory from theoretical foundations to practical implementations with real-world applications in healthcare, cybersecurity, and aquaculture management. Recipient of multiple UOW Malaysia KDU research grants (2014-2021) totaling over RM120,000 Appointed as Assistant Consultant for KiplePay Sdn Bhd through Islamic Science University of Malaysia (2021) Reviewer for 6th International Conference User Science and Engineering 2021 (i-USEr 2021) As the main coordinator for Final Year Projects, Dr. Shamsudin provides comprehensive guidance to students through their research journey. Her grants portfolio demonstrates strong research capability across multiple domains including medical imaging, digital forensics, and aquaculture technology. She maintains active industry connections through consultancy work and applied research projects that address real-world problems.

Sim Kwan Huaمشاهده پروفایل

مدرس

• Time Series Analysis
• Time Series Pattern Recognition
• Data Mining
• +۲ مورد دیگر

Sim Kwan Hua serves as Head of Department for Computer Science and Software Engineering and Lecturer at Swinburne University of Technology Sarawak Campus's Faculty of Engineering, Computing and Science. Joining the institution in 2007 after 6 years in ICT academia, he became Computer Science Program Coordinator in 2011 and has established himself as a key researcher in data-intensive computational fields. His academic credentials include: Bachelor of Information Technology in Computer Science (Honours), Universiti Malaysia Sabah Master of Science in Information Technology, Universiti Malaysia Sarawak Dr. Sim's research program centers on advanced analytical methodologies: Time Series Analysis for dynamic system modeling Time Series Pattern Recognition in complex datasets Data Mining techniques for knowledge discovery Statistical frameworks for data interpretation Predictive Analytic systems for real-world applications His research impact is demonstrated through significant grant funding: Smart Waste Collection System with Intelligent Data Optimization (SDEC TRG 2021, MYR 121,800.00, 2022-2024) Scalable Pattern Recognition in Financial Time Series Data (FRGS 2020, MYR 58,390.00) Prospective graduate students are encouraged to contact Dr. Sim via email to explore research opportunities in his data analytics specialization areas, where he actively mentors the next generation of computational scientists.

Joel Than Chia Mingمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Deep Learning
• Machine Learning
• +۴ مورد دیگر

Dr. Joel Than Chia Ming serves as Senior Lecturer and Head of Department for Information Technology & Software Engineering at Swinburne University of Technology's Sarawak Campus Faculty of Engineering, Computing and Science. Joining in 2020 after completing his PhD and postdoctoral work at Universiti Teknologi Malaysia (UTM), he leads research in AI-driven medical imaging solutions with national grant funding. His academic foundation includes: Bachelor of Engineering (Biomedical), Universiti Tunku Abdul Rahman (2013) Master of Philosophy, Universiti Teknologi Malaysia (2015) Doctor of Philosophy in Deep Learning & Medical Imaging, Universiti Teknologi Malaysia (2019) Dr. Than's research program bridges artificial intelligence with clinical medicine, focusing on lung disease diagnosis through deep learning and explainable AI systems. His work integrates multi-modal data fusion for applications ranging from pandemic hospital planning to coronary artery disease detection. Current projects emphasize clinical interpretability of AI models to build trust in medical decision support systems. His publication portfolio demonstrates consistent growth in medical AI, with recent work shifting toward explainable frameworks for lung severity classification and cross-domain applications like smart construction. This evolution reflects strategic expansion from pure image analysis to integrated AI solutions with real-world implementation pathways. Key recognitions include: 3-Minute Thesis Champion, IEEE ICSIPA 2019 Best Paper Award, ICBAPS 2018 IEEE Signal Processing Malaysia Best Master Thesis Award (2016) UTM Razak Faculty Best Master Student (2016) IEEE ISSBES Best Student Paper (2015) Dr. Than actively supervises research students through three active FRGS grants, maintaining close mentorship with emphasis on clinical validation and industry translation. His research group operates within Swinburne's engineering infrastructure, collaborating with Sarawak healthcare providers to ensure medical relevance. Prospective students receive direct access to grant resources including GPU computing clusters and clinical imaging datasets. The research environment fosters interdisciplinary work connecting computer science with biomedical engineering, preparing students for careers in healthcare AI through hands-on experience with real clinical challenges and international conference participation.

Marian-Andrei Rizoiuمشاهده پروفایل

دانشیار

• Behavioral Data Science
• Misinformation and Disinformation Analysis
• Information Operations
• +۱۲ مورد دیگر

Marian-Andrei Rizoiu is an Associate Professor leading the Behavioral Data Science lab at the University of Technology Sydney's Data Science Institute, Faculty of Engineering and Information Technology. He holds concurrent appointments as an Honorary Lecturer at Australian National University and Honorary Research Scientist at Data61. Previously, he has held visiting professor positions at Imperial College London, Jean Monnet University, and Max Planck Institute for Software Systems. Associate Professor in Behavioral Data Science, UTS Data Science Institute (Jan 2024 - present) Senior Lecturer in Behavioral Data Science, UTS Data Science Institute (Jul 2021 - Jan 2024) Lecturer in Computer Science, UTS Faculty of Engineering and Information Technology (Feb 2019 - Jul 2021) Dr. Rizoiu's research focuses on interdisciplinary work crossing computer and social sciences, blending psycholinguistics, digital communication, and stochastic modeling to understand human attention dynamics online, the emergence of influence, and opinion polarization. His key contributions include developing theoretical models for online information diffusion that can account for complex social phenomena, and building skill-based real-time occupation transition recommender systems that link social media-predicted personality profiles with occupation skill requirements. His research outputs reveal strong trends in misinformation detection, social influence measurement, and online radicalization pathways. Recent publications demonstrate sophisticated modeling approaches including state space models for early misinformation prediction, multivariate Hawkes processes for analyzing partially interval-censored data, and ideology detection pipelines. His work spans computational social science, machine learning, and practical applications for countering harmful online content. Excellence Award and Academic of the Year at the 2023 Australian Defence Industry Awards ADMA'22 Best Application Paper Dr. Rizoiu has successfully secured over $2.9 million in research funding from selective funders including Meta Research, Defence Science and Technology Group, Department of Home Affairs, and Defence Innovation Network. He has supervised 3 PhD students to completion and more than 10 Honours students, most achieving High Distinction. His research has been applied in real-world contexts including serving as an expert for NSW government's Defamation Law Reform and providing evidence for Australian Federal Senate inquiry into media diversity. He leads the Behavioral Data Science lab which focuses on modeling human behavior in online environments, with particular emphasis on mis- and disinformation detection and labor market analysis. The lab has developed tools like TRACK, UTS OPEN's software for recommending personalized learning pathways, used by over 750 students and professionals.

• Cybersecurity and Privacy
• Artificial Intelligence
• Distributed Computing
• +۶ مورد دیگر

Dr. Keshav Sood is a Senior Lecturer at Deakin University's School of Information Technology, where he leads research in cybersecurity, AI, and next-generation networks. His affiliations include roles as Graduate Research Coordinator and South Asia Country Coordinator. He completed his PhD at Deakin University and a post-doctoral fellowship at the University of Newcastle. Research Interests: Dr. Sood focuses on securing distributed systems, with emphasis on: Federated learning for intrusion detection in IoT/5G networks Biometric privacy in immersive technologies (VR/AR) RF fingerprinting for IoT device authentication Quantum-resistant software-defined networks Adversarial robustness in voice authentication systems His publications consistently explore AI-driven security frameworks, with recent work addressing data sparsity in IoT sensors, cross-domain IIoT authentication, and phishing mitigation using large language models. Awards: Professor of IT Award (2016) IEEE TNSE Excellent Reviewer (2023) Deakin HDR Supervision Award (2023) Course Team Award for Industry Certification Alignment (2021) Supervision & Grants: Dr. Sood currently advises 8 graduate researchers and has secured $655,313 in competitive funding. Key projects include: Smart Farming Cyber Resilience (DFAT Maitri Grant) Secure Access for Critical Infrastructure (Cyber CRC) IoT Data Integrity for Defense Systems (Australian Defence) He leads the Deakin Cyber Research and Innovation Centre, focusing on scalable security solutions for industry partners.

• Robotics
• Artificial Intelligence
• Electronics
• +۶ مورد دیگر

Dr. Matt Felicetti is a dedicated Lecturer in Engineering at La Trobe University's Bendigo campus, specializing in robotics, electronics, and artificial intelligence. He holds a PhD in randomized artificial intelligence for industrial applications and a bachelor's degree in computer systems engineering with top honors. Matt plays a pivotal role in enhancing the engineering capstone program and teaches subjects including Robotic System Design, Advanced Research, and Advanced Engineering Innovation. His educational background includes: PhD in Randomized Artificial Intelligence for Industrial Applications, La Trobe University (2019-2022) Bachelor of Engineering (Computer Systems) with First Class Honors, La Trobe University (2013-2016) Advanced Diploma of Electronics, Swinburne University of Technology (2011-2012) Matt's research primarily focuses on collaborative industry partnerships through the RAMPS R&D group, implementing innovative engineering solutions using electronics, sensors, embedded systems, robotics, AI, and algorithm design. He has a particular interest in field robotics and machine vision in agriculture. Beyond industry applications, Matt specializes in randomized artificial intelligence algorithms, particularly Stochastic Configuration Networks, with a focus on optimizing these algorithms for industrial environments. His research extends to low-level computing aspects including binary operations, data encoding, and hardware implementation on FPGAs or small embedded devices. His recent publications demonstrate a strong progression from theoretical algorithm development to practical implementations in specific industry contexts, with increasing attention to hardware implementation and agricultural robotics applications. The research shows a clear trajectory toward real-world industrial problem solving. Matt has received numerous scientific awards and recognitions: David Myers Medal Nancy Millis Medal SEMS Teaching Award for Industry Relevance and Student Engagement David Myers Research Scholarship D.M. Myers University Medal Fellow of the Higher Education Academy (FHEA) In terms of academic service, Matt serves as an Associate Editor for the journal Industrial Artificial Intelligence and has conducted peer reviews for Neural Computing and Applications, IEEE Transactions on Industrial Informatics, and Information Sciences. He currently leads the funded research project "Robotic Based Sewer Pipe Condition Assessment" through SmartCrete CRC (2024-2027). His teaching portfolio includes coordinating Robotic System Design, Advanced Research, and Ideas for Innovation courses. Matt is an active member of the RAMPS R&D group, which focuses on crafting innovative solutions for industry-specific problems. His work bridges academic research with practical industry applications, particularly in agricultural robotics and industrial AI implementation, demonstrating strong collaboration with researchers like Ross R, Wang D, and Putland S.