Find Academic Supervisors

Jan RiezebosView profile

Professor

• Educational Innovation
• Operations Management
• Process Optimization
• +4 more

Prof. Jan Riezebos is a Full Professor of Educational Innovation at the University of Groningen's Faculty of Economics and Business, holding the academic lead role at the Teaching Academy Groningen (TAG). He has been a professor since 2016 and previously served as Associate and Assistant Professor in Operations Management. His roles include chairing the Lean Management Network and the Educational Leadership Program (Groningen-Twente), alongside fellowships like Comenius Leadership. Education: PhD in Operations Research (2001, University of Groningen), with visiting scholar experience at Ball State University (1993). Key research areas include educational logistics, curriculum internationalization, and lean management applications in education. Projects include EU-funded initiatives like Learn2Inspire (social innovation leadership) and EQUNI (addressing discrimination in higher education). Research focuses on optimizing educational processes, reducing work stress via Value Stream Mapping, and improving teaching methodologies. He leads interdisciplinary collaborations through TAG, fostering innovation across faculties. Active in editorial roles for journals like International Journal of Quality and Reliability Management and Omega .

Georg KremplView profile

Lecturer

• Active Learning
• Data Stream Mining
• Concept Drift
• +2 more

Georg Krempl is Lecturer at Utrecht University's Department of Information and Computing Sciences. His research develops adaptive learning algorithms for data streams with concept drift and verification latency. He specializes in probabilistic active learning methods and statistical approaches to data stream mining. Research Focus : Krempl's work addresses challenges in evolving data environments, particularly developing robust classification under delayed label scenarios and concept drift. Recent projects include generative adversarial networks for domain adaptation and Bayesian methods for anticipatory classification. Professional Activities : Organizer of the Interactive Adaptive Learning workshop series (ECML PKDD) and editorial board member for multiple machine learning journals. His group focuses on both theoretical foundations and practical applications in data stream mining.

Yue XuView profile

Associate Professor

• Data Analytics
• Machine Learning
• Knowledge Discovery
• +6 more

Associate Professor Yue Xu is affiliated with the School of Computer Science at Queensland University of Technology (QUT). She obtained her PhD in 2000 from the University of New England and has held academic positions at Tasmania University (2000–2002) and QUT since 2002, where she was promoted to Associate Professor in 2011. Her research focuses on data analytics, machine learning, and knowledge discovery. Key areas include pattern mining in large datasets, anomaly detection, vulnerability analysis, topic modeling for text summarization, and social media analytics for business and security improvements. She has developed methods to enhance recommendation systems through multi-dimensional user profiling, context-aware recommendations, and semantic-based approaches. Expertise: Text mining, social engineering attack detection, distributed algorithms for big data Research Themes: Cybersecurity policy frameworks, healthcare prediction models, and interpretable machine learning techniques Prof. Xu has received prestigious awards including the Best Paper Award at WEBIST 2014 and iiWAS2012. She leads research projects such as Web Services Reputation Management (funded by Australian Competitive Grant LP0776400, 2007). Her teaching responsibilities include courses like CAB430 and IFN645, and she coordinates the Master of Data Analytics program. Her current PhD supervisions address challenges in industrial control systems vulnerability detection, hospital readmission prediction, senior frailty assessment, and semantic topic modeling. Completed supervisions include impactful works on healthcare e-coaching systems, spammer detection on Twitter, and reputation models for recommender systems.

• Self-Organizing AI
• Machine Learning
• Text Analytics
• +3 more

Damminda Alahakoon is Professor of Business Analytics and AI at La Trobe University, where he founded and directs the Research Centre for Data Analytics and Cognition (CDAC). With dual appointments at Luleå University (Sweden) and University of Moratuwa (Sri Lanka), he bridges international AI research. His 15+ year academic career follows 10 years in industry, including roles in finance and IT across Australia, Sri Lanka, and the Netherlands. Research Impact: Developed the Growing Self-Organizing Map (GSOM) algorithm for adaptive AI systems applied in healthcare, utilities, and smart cities. Current projects include generative AI for healthcare chatbots and quantum optimization for energy systems. Industry Collaboration: Secured $6M+ in research funding for projects like AI-driven stroke rehabilitation monitoring and mining workforce analytics. Recent industry partnerships focus on operationalizing ethical AI frameworks in Australia-India tech corridors. Teaching: Leads the Master of Business Analytics program, specializing in predictive analytics curriculum development.

Konstantinos NikitopoulosView profile

Professor

• Advanced Physical Layer Processing Architectures
• Massively Parallel Algorithms for Communication Systems
• Non-Linear Signal Processing for 6G Systems and Beyond
• +7 more

Konstantinos Nikitopoulos is a Professor at the Institute for Communication Systems, University of Surrey, and Director of the Wireless Systems Lab. He leads research in next-generation wireless communications, including Open RAN, non-linear processing, and neuromorphic computing. His work focuses on advancing MIMO systems, energy-efficient architectures, and practical implementations of 5G/6G standards. He has attracted over £11M in grants, including the HiPer-RAN and NL-COMM projects, and received awards for innovation and teaching. Education : PhD from National and Kapodistrian University of Athens Postdoctoral roles at RWTH Aachen University, UC Irvine, and UCL Research Interests : Non-linear signal processing for 6G systems Massively parallel algorithms and hardware acceleration Open RAN architectures and standard-compliant PHY design Unconventional computing (neuromorphic, quantum annealing) Awards : 2024 Innovator of the Year (University of Surrey) EPSRC First Grant (2021) Incremental Innovation Award (UK Department for Science, 2023) Grants & Projects : HiPer-RAN (£7.8M, DSIT) NL-COMM (£904K, Innovate UK) Tbps Communication System (£520K, Industry) Labs & Teams : Leads the Wireless Systems Lab and contributes to the 5G/6G Innovation Centre’s “Theory and Practice of Advanced Concepts” work area.

Constantinos CostaView profile

Assistant Professor

• Spatial Big Data
• Distributed Systems
• Mobile Computing
• +2 more

Constantinos Costa is a Visiting Research Assistant Professor in the School of Computing and Information at the University of Pittsburgh. He directs research in spatial big data architectures, distributed systems, and mobile computing applications. He earned a PhD from the University of Cyprus and co-founded Rinnoco Ltd., a research innovation consultancy. His work integrates indoor-outdoor navigation, telco data analytics, and context-aware systems. He developed the CAPRIO path recommendation framework and ASTRO pandemic exposure tool, receiving best paper and demo awards at IEEE MDM 2021. Teaching: Created Pitt's first Ableton beat-making course and taught database systems, high-performance computing, and mobile applications.

Massimo TorquatiView profile

Associate Professor

• Parallel Programming
• High-Performance Computing
• Autonomic Computing
• +4 more

Massimo Torquati is an Associate Professor in the Computer Science Department at the University of Pisa. He holds Italian National Scientific Qualifications (ASN) for Full Professor (2023) and Associate Professor (2018) roles in Computer Science and Engineering. His research focuses on high-level tools for parallel programming, autonomic computing, and high-performance data stream processing. He is a member of key organizations like the CINI HPC-KTT National Laboratory, the European HiPEAC Network, and the Parallel Programming Models Group at Pisa. Research Interests: Parallel programming models, distributed systems, concurrency control, and software frameworks like CAPIO and WindFlow. Recent work includes advancements in distributed-memory FastFlow, adaptive streaming, and middleware for scientific workflows. Awards include the ASN qualifications, reflecting his academic standing. He actively contributes to conferences like Euro-Par and workshops on HPC infrastructure. His work emphasizes practical implementations of parallel patterns and scalable computing solutions.

Nicholas LutskoView profile

Assistant Professor

• Climate Science
• Atmospheric Dynamics
• Oceanography
• +3 more

Nicholas Lutsko is an Assistant Professor at the Scripps Institution of Oceanography, UC San Diego, specializing in Climate Sciences within the Department of Climate/Atmospheric Science and Physical Oceanography. His research focuses on modeling and state estimation of oceanic, atmospheric, and climate systems, with expertise in climate sensitivity, cloud feedbacks, and geoengineering strategies. He holds a PhD in Atmospheric and Oceanic Sciences from Princeton University and an MSci in Geophysics from Imperial College London. Education: PhD, Atmospheric and Oceanic Sciences, Princeton University MSci, Geophysics, Imperial College London Research Interests: Climate modeling and feedbacks ENSO dynamics and tropical climate variability Solar geoengineering and climate intervention Atmospheric rivers and extreme precipitation Ocean circulation impacts on Arctic warming Climate sensitivity and radiative forcing diagnostics His recent work emphasizes analytical and data-driven approaches to understand climate system behavior, including hybrid ML-physics frameworks like ClimSim-Online. No scientific awards are explicitly listed in the provided materials. Advising and grants sections remain unspecified in the text. He is affiliated with the Scripps Institution of Oceanography, contributing to interdisciplinary climate research teams.

• Medical Research
• Biomedical Informatics
• Data Stream Processing
• +2 more

Dr. Ke Wang is an Associate Research Scientist at the Yale School of Medicine, Yale University. His work focuses on advancing biomedical informatics through innovative data processing techniques, including algorithm design for dynamic graphs and streaming data frameworks. His research intersects computer science and healthcare, addressing challenges in real-time data analysis and cybersecurity for web applications. Academic Role: Researcher in biomedical data science and algorithm development Key Areas: Streaming algorithms, graph theory, cybersecurity, and persistent memory optimization

• Carbon Capture and Storage
• Corrosion Engineering
• Material Science
• +3 more

Kenneth René Simonsen serves as a Postdoctoral Researcher at Aalborg University's Department of Energy within the Faculty of Engineering and Science, Esbjerg campus. His research focuses on critical infrastructure challenges in carbon capture, utilization, and storage (CCUS) systems, particularly material integrity and corrosion dynamics in CO 2 transport networks. His primary research domains include carbon capture engineering (100% fingerprint match), corrosion material science (64%), and energy requirement analysis (30%). Current investigations address impurity-induced corrosion mechanisms, energy optimization for capture plants, and material performance validation under real-world CCUS conditions. This work directly supports Denmark's climate goals through technological development for safe CO 2 storage infrastructure. Recent publications (2025) demonstrate concentrated expertise in pipeline corrosion modeling (3/5 papers), CO 2 impurity monitoring frameworks (1 paper), and energy requirement analysis (1 paper). These contributions span environmental engineering, materials science, and analytical chemistry, reflecting interdisciplinary approaches to CCUS challenges with strong industry applicability. No scientific awards were documented in the source materials. His research is sustained through active participation in major national projects including C-TECH (Carbon Transformation & Energy Control Hub, 2025-2028) and CarbonAdapt (infrastructure adaptation for CO 2 storage, 2022-2025), alongside his principal investigator role in the Material Integrity of Carbon Capture Storage Infrastructure project. Simonsen collaborates extensively within Aalborg University's energy research ecosystem and participates in technical workshops such as the 2021 Mineral Deposition Technology forum. His work emphasizes practical solutions for existing infrastructure adaptation, aligning with Denmark's commitment to the Paris Agreement through actionable carbon management technologies.

Wei JieView profile

Professor

• Distributed Computing
• Data Analytics
• Computing Security
• +7 more

Professor Wei Jie is a faculty member in the School of Computing and Engineering at the University of West London. His research focuses on distributed computing, data analytics, and computing security, with significant contributions to cloud and grid computing, big data, and e-Science. He has secured substantial research funding from organizations such as the Royal Society, Innovate UK, JISC, and AWS. PhD in Computer Science (exact institution and year not specified) Extensive research experience in grid and cloud computing infrastructure Professor Jie's research interests center on distributed systems, cybersecurity, and data-intensive computing. He has published extensively in top-tier journals and conferences, with a strong emphasis on practical applications in cloud environments, remote sensing, and intelligent monitoring systems. His work integrates parallel computing, swarm intelligence, and real-time analytics to solve complex computing challenges. The recent publications of Professor Jie reflect a consistent focus on distributed computing, data analytics, and security. His work spans cloud infrastructure optimization, trajectory data compression, remote sensing data management, and secure computing environments. Key themes include scalability, real-time processing, and intelligent system design, with applications in environmental monitoring, healthcare, and network security. Successfully attracted research funding from the Royal Society, Innovate UK, JISC, and AWS Professor Jie has supervised numerous research projects and contributed to major collaborative efforts in grid and cloud computing. He has led the development of secure information services, resource management frameworks, and large-scale data processing platforms. His research has been supported by significant grants and has resulted in impactful publications and systems. Professor Jie has been instrumental in developing academic programs at UWL, including the BSc Cyber Security and leading the MSc Cyber Security course. He teaches across various computing disciplines, including data science, distributed systems, and cybersecurity, contributing to undergraduate and postgraduate education.

Asra AslamView profile

Lecturer

• AI and Machine Learning
• Health Data Science
• Computer Vision
• +6 more

Dr. Asra Aslam is a Lecturer in Data Science at the University of Sheffield's School of Information, Journalism and Communication. She holds a PhD in Computer Vision and Deep Neural Networks from the University of Galway, Ireland, and has extensive industry experience as a Machine Learning Research Scientist at mindtrace.ai. Currently leading the NIHR DynAIRx project's AI efforts and serving as General Chair of Women in Computer Vision (WiCV), she specializes in health data science, medical imaging, and smart city applications. **Research Interests**: Machine Learning for Healthcare (clinical codelists, electronic health records) Computer Vision (fire detection models, object detection systems) Transportation Analytics (London Underground rail asset identification) Data-driven medical equity projects **Grants & Collaborations**: £100k NIHR Team Science Grant on Medical Equity Alan Turing Institute collaboration with Transport for London Co-PI on University of Liverpool/Alan Turing Institute project **Notable Achievements**: 2024 Computing.co.uk Rising Star of the Year 2024 WeAreNovi Education Leader Award Organized 13th WiCV Workshop at CVPR 2024 Secured NVIDIA GPU grants for PhD research **Teaching**: Develops data science and health informatics modules focusing on AI applications in healthcare and smart cities.

Romaric DuvignauView profile

Associate Professor

• Distributed Systems
• Network Security
• IoT/CPS Applications
• +7 more

Romaric Duvignau is an Associate Professor at Chalmers University of Technology since 2024, affiliated with the Department of Computer Science and Engineering within the IT Faculty. His primary research focuses on distributed systems, network security, and cyber-physical systems (CPS), with an emphasis on optimizing communication and computational efficiency in distributed applications. Key projects include the READY project for continuous monitoring systems and contributions to P2P energy sharing optimization via the DEEP and HyperP2P initiatives. He has been at Chalmers since 2017 and also holds teaching responsibilities in computer networks and data-driven systems. Research Interests: Distributed Algorithms & Optimization Cybersecurity & Privacy Peer-to-Peer Energy Sharing Vehicular Networks Edge Computing & Streaming Analytics Notable Articles: His recent works include advancements in P2P energy matching algorithms (2025), Byzantine fault-tolerant consensus (2025), and vehicular data localization (2021). Earlier contributions include impactful studies on vehicular networks (DRIVEN framework, 2020) and network traffic analysis (2019). Teaching & Grants: Responsible for courses like Computer Communication (EDA343) and Data-driven Support for CPS. Active in educational innovation, exploring AI impacts on teaching and active learning tools like Kahoot/Mentimeter. Labs/Teams: Member of the Distributed Computing and Systems group at Chalmers Networks and Systems division, collaborating with researchers like Marina Papatriantafilou and Vincenzo Gulisano on energy systems and distributed algorithms.

Ingrid Kristine GladView profile

Professor

• Statistics and Data Science
• Machine Learning
• Anomaly Detection
• +5 more

Ingrid Kristine Glad is a Professor at the Department of Mathematics, University of Oslo, affiliated with the Faculty of Mathematics and Natural Sciences. She serves as co-director of the Integreat Centre of Excellence and BigInsight Centre for Research-Based Innovation, and chairs the Abel Board (2022–2026). Her research focuses on statistical and machine learning methodologies for high-dimensional data, particularly in genomics, sensor systems, and maritime applications. She has pioneered methods like monotone regression, tailored graphical lasso, and Shapley-value-based explainability frameworks. Research Interests: Glad’s work integrates theoretical statistics with practical applications in anomaly detection, change-point analysis, and predictive modeling. She develops novel algorithms for analyzing large-scale datasets from genomics (e.g., gene networks) and industrial sensor streams (e.g., battery degradation in maritime batteries). Her methods emphasize interpretability and scalability, addressing challenges in both supervised and semi-supervised learning contexts. Publications: Her recent work includes advancements in Shapley-value explanations, maritime battery health monitoring, and biofouling impact analysis. Key themes across her articles are statistical methodology development, machine learning applications in engineering, and computational tools for genomic data integration. Over 50 peer-reviewed papers span journals like Expert Systems with Applications , Journal of Machine Learning Research , and BMC Bioinformatics . Grants & Leadership: Leads interdisciplinary projects funded by the Norwegian Research Council and EU initiatives. Her roles in major centers highlight her influence in shaping statistical research agendas. Supervises active PhD students focused on topics like lifetime analysis models and maritime system analytics. Labs & Collaborations: Central to the Genomic HyperBrowser platform and collaborations with maritime industry partners. Active in both theoretical statistics (e.g., penalized regression) and applied domains (e.g., autonomous ship safety modeling).

• Hydrology
• Environmental Science
• Civil Engineering
• +3 more

Dr. Sevvandi Kandanaarachchi is an Associate Professor in the School of Science at RMIT University's City Campus, Australia. Her research focuses on environmental data analytics, hydrology, and civil engineering applications. Key areas include water quality monitoring, sediment transport modeling, and algorithm development for environmental systems. Her work integrates machine learning with environmental science, addressing challenges in early event detection, anomaly identification, and predictive modeling for water resources. Notable contributions include frameworks for high-frequency sensor data analysis and meta-modeling approaches for stream turbidity prediction. Dr. Kandanaarachchi collaborates on interdisciplinary projects involving civil engineering, atmospheric sciences, and computational methods. She holds a PhD and has published extensively in peer-reviewed journals such as Hydrological Processes and PLOS One . No specific grants, awards, or student advisees are listed in the provided information.