Find Academic Supervisors

Ly Fie SugiantoView profile

Associate Professor

• Accounting Information Systems
• Data Analytics
• Artificial Intelligence
• +8 more

Dr. Ly Fie Sugianto is an Associate Professor in the Department of Accounting at Monash Business School, Monash University. Her research focuses on the integration of data analytics, artificial intelligence, and machine learning in accounting and business systems, with applications in the energy sector and organizational behavior. Monash Business School, Monash University Department of Accounting Specialization: Accounting Information Systems, Data Analytics, AI Her research interests span Accounting Information Systems , Agent-Based Simulation , Decision Support Systems , and Technology Adoption . She applies computational methods to study competitive dynamics in deregulated electricity markets and the impact of digital tools on employee well-being and organizational resilience. The recent publications reflect a strong trend in using AI and simulation to analyze complex socio-technical systems, particularly in energy markets and leadership dynamics. Keywords across her work include agent-based modeling , data analytics , servant leadership , and enterprise social media , indicating interdisciplinary research at the intersection of information systems, management, and public policy. Her scientific awards include competitive grants from the ARC (SPIRT/Linkage) , the Australia Indonesia Governance Research Partnership (AIGRP) , and the Sumitomo Foundation . ARC Grant: Dispatch Optimisation in the Australian National Electricity Market Sumitomo Foundation: Technology Use and Employee Well-Being AIGRP: Governance and MSME Resilience during Pandemic Dr. Sugianto has advised research projects and collaborated with industry partners such as Western Power , Ecogen Energy , and AEMO . She is currently accepting PhD students and leads externally funded research initiatives. Her work contributes to UN Sustainable Development Goals related to industry innovation and responsible consumption. She is affiliated with research teams focusing on intelligent decision support systems and digital transformation in business , with active collaborations in Australia and Indonesia.

Michael U. GutmannView profile

Senior Lecturer

• Machine Learning
• Bayesian Inference
• Likelihood-Free Inference
• +9 more

Michael U. Gutmann is a Senior Lecturer in Machine Learning at the School of Informatics, University of Edinburgh, and a member of the Institute for Adaptive and Neural Computation. His research lies at the intersection of machine learning, statistics, and scientific applications, with a focus on developing inference methods for complex and implicit models. Education: PhD in Computational Neuroscience, University of Tokyo MSc in Engineering and Applied Mathematics, Swiss Federal Institute of Technology (ETH) Zurich MSc, Ecole Centrale Paris His primary research interests include Bayesian inference, likelihood-free inference, optimal experimental design, unsupervised learning, and applications in computational biology and neuroscience. He is best known for introducing Noise-Contrastive Estimation (NCE), a foundational technique for training unnormalized statistical models. His recent work spans variational inference, density ratio estimation, flow models for missing data, and AI-driven experimental design in behavioral and biological sciences. His publications, including in NeurIPS , ICML , JMLR , and eLife , demonstrate a strong emphasis on methodological innovation for scientific discovery. He has contributed to open-source tools such as ELFI (Engine for Likelihood-Free Inference) and developed practical implementations of robust inference algorithms. Scientific Awards: No specific awards listed in the provided texts. Michael Gutmann actively supervises students and collaborates with leading researchers in machine learning and computational biology. He has secured research funding from EPSRC and BBSRC for projects in generative modeling and infectious disease epidemiology. He teaches advanced courses such as Probabilistic Modelling and Reasoning and Data Mining, reflecting his deep engagement with both theoretical and applied aspects of machine learning. Labs and Research Groups: Institute for Adaptive and Neural Computation (ANC), University of Edinburgh Former affiliations with Department of Mathematics and Statistics and Department of Computer Science at the University of Helsinki and Aalto University

• Parallel Computing
• Optimization Techniques
• High-Performance Computing (HPC)
• +5 more

Pascal BOUVRY is a full Professor of parallel computing and optimization techniques at the University of Luxembourg's Department of Computer Science , within the Faculty of Science, Technology and Medicine (FSTM) . He currently serves as the Dean of the FSTM and leads the Parallel Computing and Optimisation group . Additionally, he oversees the University's High Performance Computing (HPC) infrastructure. His research focuses on parallel computing, optimization algorithms, distributed systems, and their applications in bioinformatics and distributed environments. Prof. BOUVRY holds a Ph.D. in Computer Science from the University of Grenoble (France) and has extensive industry experience, including roles as CEO/CTO of tech firms and leadership in telecom and financial services sectors. He has pioneered initiatives like the Master in High-Performance Computing and Technopreneurship programs at the University of Luxembourg. His research interests span GPU programming paradigms , federated learning , distributed optimization , and trustworthy AI . Recent work includes advancements in HPC education frameworks, federated learning frameworks (e.g., FedPref), and quantum circuit optimization. He contributes to editorial boards of journals like IEEE Transactions on Sustainable Computing and IEEE Cloud Computing Magazine. Prof. BOUVRY has held international leadership roles, including as Honorary Chair of conferences and active participation in technical committees. His educational efforts emphasize bridging academic research with industrial applications, particularly in HPC and AI.

• Circular Economy
• Materials Science
• Infrastructure Systems
• +7 more

Professor Phil Purnell is a Professor of Materials and Structures at the School of Civil Engineering, University of Leeds. His expertise spans materials science, infrastructure systems, circular economy, and interdisciplinary research. He leads projects addressing decarbonization, waste-to-resource systems, and sustainable construction materials. His roles include Deputy Head of School and advisor to UKRI, DEFRA, and the UK Government’s Circular Economy Taskforce. He holds visiting positions at the Royal College of Art and University of Cambridge. Education: PhD, BEng (Civil), and teaching qualifications (PCPCE, ACTLHE). His research focuses on circular economy applications in textiles, offshore wind, and foundational industries. Notable projects include the Textiles Circularity Centre, Yorkshire Circular Economy Living Lab, and Complex Value Optimisation for Resource Recovery (C-VORR). He advocates for systemic approaches to sustainability, integrating economic, technical, and environmental values. Research outputs emphasize decarbonization pathways, material lifecycle assessments, and robotics in infrastructure management. Collaborations with industry and policy bodies drive practical solutions for resource efficiency. His work bridges engineering, economics, and environmental science to address global challenges like waste management and climate change.

• Medical Imaging
• Breast Cancer
• Diagnostic Imaging
• +8 more

Associate Professor Ernest Ekpo is affiliated with the University of Sydney's Sydney School of Health Sciences, within the Discipline of Medical Imaging Sciences. He holds academic roles as a Scholarly Teaching Fellow and Co-Director of the Medical Image Optimisation and Perception Group (MIOPeG). He is also an Associate Editor of the Journal of Medical Imaging and Radiation Sciences and a member of the Sydney Southeast Asia Centre and Cancer Research Network. Education: Earned BSc (Hons) in Radiography/Sonography from the University of Calabar, Nigeria, and a PhD from the University of Sydney. His research focuses on breast density, cancer biomarkers, medical image perception, radiation dose optimization, and radiology education. He explores applications in low-resource settings and emerging technologies like AI for diagnostic accuracy. Research interests include breast cancer treatment outcomes, image-based phenotypes, and improving imaging pathways for acute abdominal pain. Key themes are Cancer and Medical Imaging, with an emphasis on Women's Health and Chronic Disease. His work combines clinical practice, public health strategies, and technological advancements to enhance diagnostic efficacy and reduce unnecessary procedures. Articles from 2021–2024 highlight his contributions in mammography optimization, radiographer training, and AI-driven diagnostic tools. Recent grants include a 2024 project on CT examination education and a 2023 initiative to streamline breast cancer screening pathways. His awards recognize peer review excellence, early-career teaching, and research leadership. He supervises multiple students investigating breast density biomarkers, imaging modalities for dense breasts, and radiation dose management. His educational efforts aim to improve radiographers' capabilities in identifying urgent findings across CT and X-ray modalities, leveraging blended learning approaches.

• Data science
• Machine learning
• Business analytics
• +10 more

Masayuki Goto is a Professor in the Department of Industrial Systems Engineering, School of Creative Science and Engineering at Waseda University, Japan, where he has served since 2011. He earned his Doctor of Engineering from Waseda University and leads research integrating statistical science, machine learning, information theory and management engineering to solve business-analytics, marketing, AI ethics and industrial optimisation problems. Education: Doctor of Engineering, Waseda University Research Interests: His work spans data science, machine learning, business analytics, statistical learning theory, generative AI, deep neural networks, natural language processing, network analysis and information theory, with recent emphasis on trustworthy AI and synthetic data generation. Publication Trends: Over 2024-2025 his group has published extensively on deep learning for tabular data, vision-language models, recommender systems, causal inference and ethical AI, demonstrating a shift toward generative-AI-driven business analytics and interpretable models. Scientific Awards: Best Paper Award, CIE51 2024 Outstanding Paper Award, APIEMS 2023 Best Paper Award, APIEMS 2022 Best Paper Award, 20th ANQ Congress 2022 2022 PC Conference Best Paper Award Best Paper Award, JASMIN 2021 Best Paper Award, 19th ANQ Congress 2021 Encouragement Award, AAMSA 2021 IDR User Forum 2020 Enterprise & DBSJ Special Awards Best Paper Award, APIEMS 2019 Best Paper Award, ANQ Congress 2018 World CIST'18 Best Paper Award Best Paper Award, ANQ Congress 2017 JSPS Grant Review Commendation 2016 JIMA Distinguished Research Award 2015 Best Paper Award, Journal of JIMA 2015 IPSJ National Convention Best Paper Awards (2015 & 2012) Advising & Grants: He has mentored a large cohort of graduate students evidenced by co-authorship on over 100 recent papers. He has served as PI on numerous JSPS KAKENHI grants and industry projects focused on data-driven management, AI marketing and ethical AI frameworks. Labs & Teams: He heads the Goto Laboratory within the Waseda Institute for Advanced Study, leading interdisciplinary projects on business AI, data-ethics education and industrial optimisation.

Simone Korsgaard JensenView profile

Research Fellow

• Medical Research
• Artificial Intelligence in Healthcare
• Cancer Treatment Optimization
• +1 more

Simone Korsgaard Jensen is an Honorary Research Fellow at the School of Medicine, University of St Andrews. Her research focuses on applying artificial intelligence to healthcare challenges, particularly in cancer treatment optimization. She contributed to the 'Radical Health AI - Cancer Treatment Optimisation' project, highlighting her engagement with interdisciplinary medical technology. Affiliation: School of Medicine, University of St Andrews Her work bridges computational methods with clinical needs, aiming to enhance treatment efficacy through AI-driven solutions. She holds an honorary role, reflecting her specialized research contributions without formal teaching responsibilities.

• Medical Imaging
• Breast Cancer Diagnosis
• Phase-Contrast Tomography
• +4 more

Dr. Amir Tavakoli Taba is a Senior Lecturer in Medical Imaging Sciences at the University of Sydney, where he co-directs the Medical Image Optimisation and Perception Group (MIOPeG). He specializes in improving medical imaging accuracy, particularly in breast cancer diagnosis, through advancements like phase-contrast tomography and AI integration. His work bridges technological innovations (e.g., low-dose imaging) with clinical practice, emphasizing quality control and radiologist performance analysis. Education: PhD (University of Sydney) MEngSc (University of New South Wales) BSc (University of Tehran) Research Interests: Dr. Taba’s research focuses on phase-contrast computed tomography (PCT), AI-driven diagnostic tools, and clinical workflow optimization. His projects include the world’s first PCT clinical trial (scheduled for 2024 in Melbourne) and collaborations with institutions like ANSTO, Harvard Medical School, and the University of Iowa. He also investigates radiologist expertise development and the role of social networks in medical decision-making. Grants & Awards: NHMRC Synergy Grant (IMPACT: Implementation of X-ray Phase-Contrast Tomography) International recognition, including the SPIE Medical Imaging Award Advising & Labs: Current students: Mohammed ALANAZI (abdominal CT optimization), Jenna ARBID (phase-contrast imaging) Labs: MIOPeG, part of the Sydney Vital and Sydney Catalyst cancer research networks Teaching: Courses in imaging technologies, medical image perception, and clinical capstone projects for diagnostic radiography students.

• Embedded Systems
• System-on-Chip (SoC) Design
• Computer Architecture
• +5 more

Professor Klaus McDonald-Maier is a full Professor in the School of Computer Science and Electronic Engineering (CSEE) at the University of Essex , where he leads the Embedded and Intelligent Systems (EIS) Research Laboratory and heads the Intelligent Embedded Systems and Environments Research Group . He is also Director of Impact , Visiting Professor at the University of Kent, and Visiting Research Affiliate at NASA Jet Propulsion Laboratory, California Institute of Technology. Education PhD in High-Performance Parallel Neural Network Architectures, Friedrich-Schiller-University Jena (Germany, 1999) Electronic Engineering studies, University of Ulm (Germany) Electronic Engineering studies, Cardiff University (Wales) Electronic Engineering studies, École Supérieur de Chimie Physique Électronique de Lyon (CPE-Lyon) (France) Research Interests Professor McDonald-Maier’s research integrates embedded systems , System-on-Chip (SoC) architectures , and AI-driven robotics . He pioneers visual place recognition techniques that remain robust under severe appearance and viewpoint changes, develops cybersecurity frameworks based on ICMetrics for autonomous vehicles and IoT, and designs approximate real-time computing solutions for energy-constrained multicore and FPGA platforms. His work on radiation-tolerant systems supports space and nuclear applications, while his bio-inspired algorithms enable lightweight, neuromorphic perception on resource-limited robots. Publication Trends Between 2022 and 2025 his output converges on FPGA-accelerated AI , secure edge intelligence , visual navigation for autonomous systems , and healthcare analytics . He repeatedly couples rigorous algorithmic innovation with practical hardware deployment, yielding energy-efficient, real-time systems validated in domains ranging from autonomous driving to post-stroke rehabilitation. Scientific Awards & Recognition Best Paper Award – IEEE Transactions on Sustainable Computing (2024) Best Paper Award – IEEE/ACM DATE (2024) Best Paper Award – IEEE Systems Journal (2022) Best Paper Award – IEEE Sensors Journal (2021) Best Paper Award – IEEE Access (2020) Research Grants & Industrial Collaboration He has secured major funding from EPSRC , EU Horizon 2020 , Innovate UK , and industry partners. Current projects span trustworthy autonomy, radiation-hardened edge AI, and AI-enhanced rehabilitation technologies. He is Chief Scientist of UltraSoC Technologies Ltd and CEO of Metrarc Ltd , commercialising University research in semiconductor debug and cybersecurity respectively. Laboratory & Team Leadership As Director of the Embedded and Intelligent Systems Laboratory (EIS Lab) , he oversees a multidisciplinary team of researchers and PhD students, providing state-of-the-art FPGA, robotics, and embedded-systems facilities. The lab collaborates closely with NASA JPL, UK Atomic Energy Authority, and leading semiconductor firms to translate fundamental research into high-impact industrial solutions.

Nagham SaeedView profile

Associate Professor

• Electrical and Electronic Engineering
• Industrial Internet of Things (IIoT)
• Artificial Intelligence in Engineering
• +9 more

Dr. Nagham Saeed is an Associate Professor in Electrical and Electronic Engineering at the School of Computing and Engineering, University of West London, where she has been actively engaged in teaching and research since 2007. She holds a PhD in Intelligent MANET Optimisation from Brunel University and leads the Industrial Internet of Things (IIoT) research group. Her academic service includes editorial and technical committee roles for IEEE and MDPI, and she is a Chartered Engineer (CEng), Senior Member of IEEE, Member of IET, and Senior Fellow of the Higher Education Academy (HEA). PhD in Intelligent MANET Optimisation System, Brunel University (2011) Her research focuses on intelligent systems for smart cities, applying artificial intelligence to telecommunications, energy modeling, and industrial applications. She explores AI-driven optimization in next-generation networks, smart grid integration, battery management systems, and sustainable ICT. Her work also extends to engineering education, particularly feedforward teaching methods and student engagement. The recent publications reveal a strong trend in applying AI and machine learning to solve real-world challenges in energy systems, IoT, transportation, and environmental sustainability, often with a focus on smart cities and renewable integration. Dr. Saeed has been recognized with several awards, including: 2021 University of West London Student Union Best Supervisor/Tutor Award 2022 IEEE Region 8 Outstanding Women in Engineering Section Volunteer Award She mentors early-career engineers and academics and actively promotes electrical and electronic engineering among young girls. She has served as the 2023 IEEE Women in Engineering UK & Ireland Chair and is currently the Vice Chair (Chair-Elect) for the IEEE UK & Ireland Section (2024–2025). Her leadership spans technical innovation, academic service, and diversity advocacy in engineering. She teaches across a range of programs, including MSc Industrial Internet of Things, BEng and MSc Electrical and Electronic Engineering, and supervises PhD research in related fields.

• Cybersecurity
• Machine Learning
• Data Analytics
• +5 more

Dr Zhiyuan (Thomas) Tan is an Associate Professor at Edinburgh Napier University’s School of Computing, Engineering and the Built Environment . He is internationally recognised for his cybersecurity research and has been listed among Stanford University’s Top 2% Scientists for 2021–2023. Education BEng (2005) with high distinction – North-eastern University, China MEng (2008) – Beijing University of Technology, China PhD in Computer Systems (2014) – University of Technology Sydney, Australia Research Interests Dr Tan’s research integrates cybersecurity with machine learning and data analytics. His core areas include: Intrusion detection and defence of critical service systems Adversarial machine learning for malware and anomaly detection Virtualisation security through non-parametric behaviour modelling IoT and vehicular network security—cloud/edge/cloudlet frameworks Privacy-preserving AI and federated machine unlearning Smart-city digital forensics and cyber-physical system resilience Research Output Trends His recent articles (2020–2025) reveal a strong focus on federated learning, edge & mobile computing, UAV coordination, and AI-driven security. Topics span advanced steganography, metamorphic malware, graph injection attacks, and trustworthiness in vehicular platoons, all anchored in real-world IoT and transportation applications. Awards & Distinctions Stanford University Top 2% Scientists List (2021, 2022, 2023) National Research Award 2017 – Research Council of the Sultanate of Oman Best Paper Awards (three instances) Kaspersky Lab Student Cyber Security Conference – Finalist Award SICSA Supervisor of the Year 2019 – Honourable Mention Grants & Leadership Dr Tan has attracted over £200k in external funding including: Carnegie Trust (£73,564) – Federated Machine Unlearning ENU Development Trust (£29,998) – Machine Unlearning Royal Society (£12,000) – VANET Security & Privacy SICSA & other awards for MemoryCrypt, AI Secrets, behaviour biometrics, and visiting-fellow schemes. Supervision & Mentoring Since 2013 he has supervised or co-supervised 16 doctoral candidates and several master’s students; six PhDs have successfully graduated. Roles range from Director of Studies to additional supervisor across diverse topics from malware evolution to VR olfactory interfaces. Research Groups & Collaboration He is affiliated with the Centre for Artificial Intelligence and Robotics , the Centre for Distributed Computing, Networking and Security , and the Centre for Cybersecurity, IoT and Cyber-physical Systems at ENU, fostering interdisciplinary collaboration with national and international partners.

Francesca PianosiView profile

Associate Professor

• Water Resource Management
• Hydrology
• Environmental Modelling
• +5 more

Dr Francesca Pianosi is an Associate Professor in Water & Environmental Engineering at the University of Bristol 's School of Civil, Aerospace and Design Engineering. She contributes to the Cabot Institute for the Environment and leads research on data analysis, mathematical modelling, and uncertainty quantification for hydrology and water engineering. Specialises in simulation and optimisation methods for water resource management Focuses on uncertainty propagation in natural hazard models Developed the open-source SAFE Toolbox for sensitivity analysis Research Trends Her recent publications (2023-2025) demonstrate expertise in: Groundwater flow and recharge in data-scarce regions Digital Twin applications for watershed management Climate change impact on landslides and droughts Multi-objective optimisation for reservoir operations Integration of machine learning with hydrological models Scientific Awards Arne Richter Award for Outstanding Young Scientists (2015) Best Research Oriented Paper - Journal of Water Resources Planning and Management (2024) Early Career Research Excellence (ECRE) award (2014) Francesca leads the Water Management and Adaptation based on Watershed Digital Twins project (2024-2027) and contributes to the USARIS project on uncertainty quantification for infrastructure systems (2023-2025).

David ToalView profile

Associate Professor

• Design optimisation & automation
• Machine learning & deep learning
• Computer aided design
• +3 more

Dr. David Toal is an Associate Professor at the University of Southampton, specializing in the application of machine learning techniques to aerospace system design optimization. His research focuses on automated geometry creation, prediction of simulation outputs, and fundamental machine learning advancements. He is affiliated with the Computational Engineering and Design Group and the Institute for Life Sciences. His teaching interests include engineering design methods, optimization, reliability, and CAD integration. He supervises multiple PhD students in areas such as aerodynamic geometry generation and structural design automation. Dr. Toal has led projects funded by the European Union and EPSRC, including E-Break (FP7) and equipment grants for advanced computational tools. His work emphasizes multidisciplinary collaboration, leveraging CAD systems and deep learning for applications in advanced aerial mobility and turbine optimization. Recent publications highlight advancements in Kriging models, adversarial auto-encoders, and semantic segmentation for engineering design. Dr. Toal's research bridges computational methods with practical aerospace challenges, aiming to accelerate design processes through data-driven and AI-enhanced approaches.

• Architected materials
• Metamaterials
• Additive manufacturing
• +5 more

Farooq Azam is a Research Fellow at the Department of Mechanical Engineering , University College London , focusing on Modelling and Optimisation of Sustainable and Smart Technical Textiles . He works in the Roberts Engineering Building, London, United Kingdom (WC1E 7JE). Research Interests: Architected materials, Metamaterials, Additive manufacturing, Structural Imaging, Structural optimisation, AI/Machine Learning, Applied Mechanics, Mechanics of Materials Email: farooq.azam.20@ucl.ac.uk Recent Research Trends : His work spans additive manufacturing applications in biomedical components, structural optimization of architected materials, turbulence modeling in engines, and machine learning-driven metamaterial design. Key areas include Ti6Al4V scaffolds, hexagonal honeycomb configurations, and carbon microlattices. Scientific Awards : FHEA (Fellowship of the Higher Education Academy), 2024 Teaching : He has taught modules such as Group Manufacturing Challenges (MECH0099) , Micro/Nano Architected Composite Materials (MECH0096) , and Elasticity and Plasticity (MECH0026) at UCL.

Martijn R.K. MesView profile

Professor

• Transportation and Logistics Management
• Artificial Intelligence for Logistics
• Synchromodal Transport
• +11 more

Martijn Mes is a full professor of Transportation and Logistics Management and chair of the Industrial Engineering & Business Information Systems section at the University of Twente, Netherlands. He leads research and education initiatives that integrate AI, simulation and optimisation into logistics and supply-chain innovation. Education: Ph.D. in Operations Research, University of Twente – 2008 M.Sc. in Applied Mathematics, University of Twente – 2002 Post-doctoral researcher, Princeton University, Dept. of Operations Research & Financial Engineering Research focus: Mes develops quantitative models and AI techniques for strategic, tactical and operational logistics challenges. His work spans three application pillars: Emergency & humanitarian logistics – rapid relief distribution with trucks and UAVs Urban logistics – city distribution, self-organising systems and last-mile innovations Sustainable logistics – synchromodal transport, green ports and electric/autonomous fleets Methodologically he combines approximate dynamic programming, reinforcement learning, multi-agent simulation, discrete-event simulation and stochastic optimisation to create decision-support tools for industry and government. Publications trend: Recent articles (2025-2022) exhibit a strong emphasis on integrating reinforcement learning and stochastic optimisation into dynamic vehicle routing, drone-assisted delivery and post-disaster inventory allocation, signalling a shift towards data-driven, real-time logistic systems. Grants & projects: Mes has coordinated and participated in numerous national and European projects on sustainable logistics, urban distribution, port optimisation and healthcare logistics, frequently collaborating with industry partners and public bodies. Teaching & supervision: He coordinates and lectures in the BSc and MSc programmes Industrial Engineering & Management, offering courses on simulation, queueing theory, dynamic programming, Markov chains, transportation management and technology management. He has authored a widely used Plant Simulation tutorial and supervises PhD candidates working on AI-driven logistics, autonomous vehicles and digital twins.