Find Academic Supervisors

• Digital Engineering
• Through-life Engineering Services
• Augmented Reality Applications
• +6 more

John Ahmet Erkoyuncu is a Professor of Digital Engineering and Head of the Centre for Digital Engineering and Manufacturing at Cranfield University. His research focuses on digitalization of manufacturing processes, augmented reality (AR) applications in maintenance, and uncertainty management in industrial service contracts. He holds roles such as co-chair of the Through-life Engineering Services Council and is an Associate Member of CIRP. Education: PhD in maintenance cost estimation (collaboration with BAE Systems, Rolls-Royce, and MoD). Current Activities: Leads initiatives in digital twins, AI integration, and AR-driven maintenance solutions. Supervises 8 industry-cofunded PhD projects across aerospace, automotive, and healthcare sectors. Awards: Not explicitly listed in provided texts, though his contributions to digital twin frameworks and AR applications are internationally recognized. Research Interests: Erkoyuncu’s work bridges engineering and data science, emphasizing: - Digital transformation of manufacturing systems, - Human-centric AR tools for maintenance, - Sustainable product-service systems (IPS²), - Uncertainty quantification in industrial contracts. Publications: Over 100 peer-reviewed articles in journals like CIRP Annals, IEEE Access, and Robotics and Computer-Integrated Manufacturing. Recent work includes cognitive digital twin architectures and AI-driven knowledge extraction for degradation analysis. Grants: Actively involved with Innovate UK and EPSRC projects, focusing on autonomous maintenance systems and digital twin applications. Labs/Teams: Oversees the Centre for Digital Engineering, collaborating with industry partners like Airbus, Rolls-Royce, and NHS. Leads the Digital Twin Hub’s Community Council.

• Multi-agent systems
• Reinforcement learning
• Trust management
• +5 more

Athirai Aravazhi Irissappane is an Assistant Professor at Nanyang Technological University's College of Computing and Data Science, School of Computer Science and Engineering. With a PhD in Trust oriented decision making via POMDPs completed in 2016, they have established themselves as a prominent researcher in multi-agent systems and reinforcement learning. Their academic journey shows a clear progression from foundational work in trust management to cutting-edge research in deep reinforcement learning frameworks. Dr. Irissappane's research interests focus on multi-agent systems , reinforcement learning , trust management , and recommender systems . They have made significant contributions to the field of multi-objective reinforcement learning, developing frameworks that address complex policy distributions and cooperative behavior in multi-agent environments. Their work bridges theoretical advances with practical applications, particularly evident in their contributions to the RecSys Challenge 2023. Analysis of their recent publications (2020-2024) reveals a strong emphasis on Advanced reinforcement learning architectures for multi-agent cooperation Privacy-preserving recommendation systems Automated data quality improvement for recommendation tasks Scalable frameworks for reinforcement learning research These works demonstrate increasing sophistication in handling complex decision-making scenarios while addressing practical constraints like computational efficiency and privacy concerns. Dr. Irissappane has been actively involved in major academic initiatives including the RecSys Challenge 2023 and has contributed to comprehensive guides on multi-objective reinforcement learning that have become valuable resources for the research community. Their collaborative work spans multiple institutions and has resulted in publications in top-tier venues including IEEE Transactions, Autonomous Agents and Multi-Agent Systems journal, and conference proceedings of AAAI, IJCAI, and AAMAS.

Rajka PresburyView profile

Adjunct Associate Professor

• Hospitality Management
• Tourism Planning
• Service Quality
• +5 more

Rajka Presbury is an Adjunct Associate Professor at the Centre for Organisational Change and Agility (COCA) within Torrens University Australia. She holds a PhD in Service Quality in the Hotel Sector from Western Sydney University (2010) and a Master of Higher Education from Australian National University (2013). Her research focuses on hospitality service quality, leadership development, and educational-industry partnerships. She has pioneered work on the 'hospitable service mindset' and transformative teaching methods in hospitality workforce training. Presbury is Deputy Chair and Auditor for THE-ICE (International Centre of Excellence in Tourism Hospitality Education), collaborating globally on program accreditation and educational frameworks. Research Interests include guest experience management, inclusive service delivery, and work-integrated learning strategies. She has published extensively on luxury lodge authenticity, service language dynamics, and crisis management in hospitality contexts. Presbury’s work aligns with UN Sustainable Development Goals related to quality education and sustainable tourism. Her 2024 award-winning paper explores adaptive leadership in the hotel sector, while recent publications analyze spoken service language in aged care and luxury hospitality settings. She actively supervises PhD candidates and contributes to industry-academia partnerships through mentorship frameworks and case study methodologies.

Andrei AtanovView profile

Researcher

• Computer Vision
• Artificial Intelligence
• Machine Learning
• +3 more

Andrei Atanov is a Researcher at the Laboratoire d'intelligence et d'apprentissage visuels (VILAB) within the School of Computer and Communication Sciences at École Polytechnique Fédérale de Lausanne (EPFL) . His research focuses on advanced topics in artificial intelligence, computer vision, and machine learning, with particular emphasis on multimodal learning, vision-language models, and robust generalization. He is affiliated with the Department of Computer Science and contributes to projects exploring innovative solutions for vision tasks using computationally designed sensors and diffusion models. His work often bridges theoretical advancements with practical applications in robotics and generative systems. Key research areas include developing large vision-language models, optimizing vision algorithms for low-sensor environments, and enhancing model robustness through diversification strategies. His publications span topics from 3D data augmentation to uncertainty estimation in deep learning, reflecting a broad yet technically deep expertise in AI fundamentals and applications.

• Machine Learning
• Predictive Maintenance
• Forklift Activity Recognition
• +5 more

Thorsteinn Rögnvaldsson is a Professor of Computer Science and Deputy Vice Chancellor for Research at Halmstad University. He leads the Center for Applied Intelligent Systems Research (CAISR), focusing on machine learning applications in predictive maintenance, healthcare, and satellite-based poverty analysis. His research spans meta-learning, survival analysis, and synthetic data generation. Education: Not explicitly listed in the provided texts, but inferred to include doctoral training in computer science or related fields given his academic rank. Research Interests: Machine learning for knowledge creation, predictive maintenance for heavy vehicles, forklift activity recognition, synthetic healthcare data, and satellite imagery analysis for poverty mapping. Collaborations include projects with Lund University on neural networks for satellite data interpretation. Articles Trends: Recent work emphasizes meta-learning frameworks, survival analysis model interpretability, and synthetic data evaluation. Key domains include healthcare, industrial IoT, and cross-domain adaptation. Awards: None explicitly listed in the provided texts. Grants/Advising: Coordinates CAISR's research projects and supervises doctoral students in applied machine learning. Active in industry partnerships, particularly with automotive and healthcare sectors. Labs/Teams: Director of CAISR, which hosts interdisciplinary teams in AI, IoT, and big data applications.

Hussein MohammedView profile

Researcher

• Pattern Recognition
• Machine Learning
• Computer Vision
• +3 more

Dr. Hussein Mohammed is a Researcher and Head of the Visual Manuscript Analysis Lab at the University of Hamburg's Centre for the Study of Manuscript Cultures (CSMC). He leads the Cluster of Excellence 'Understanding Written Artefacts' (UWA) and the Visual Manuscript Analysis (VMA) Lab. His roles include Principal Investigator and Project Lead for initiatives like 'Similarity Measurement of Visual Patterns in Written Artefacts' and 'Pattern Recognition in 2D Data from Digitized Images.' He holds a doctoral degree (Dr. rer. nat.) in computer science from Hamburg University, focusing on computational analysis of handwriting styles. His research emphasizes pattern recognition, machine learning, and computer vision applied to historical manuscripts. Key projects include developing tools like the Pattern Analysis Software Tools (PAST), AFAT, and HAT to analyze visual features and aid cultural heritage preservation. His work bridges computer science and humanities, with contributions to palimpsest deciphering via generative AI, artifact feature analysis, and multimodal data integration. Notable awards include the Erasmus Mundus Scholarship (2012). He also contributes to academic outreach through lectures on computational paleography and manuscript digitization.

Andy KeirView profile

Researcher

• Artificial Intelligence
• Electrical Engineering
• Robotics
• +4 more

Andy Keir serves as the Manager of the Research Engineering Facility (REF) at Queensland University of Technology's (QUT) Office of Research Infrastructure. He leads cross-organisational research engineering services in strategic areas like Remotely Piloted Aircraft Systems (RPAS), Robotics and Autonomous Systems, Energy Systems, and Structural/Civil Engineering. He oversees two distributed sites (Da Vinci Precinct and Banyo Pilot Plant Precinct), focusing on efficient service delivery and business development for both internal and external markets. Education: Master of Business Administration (QUT) PGDip(Sc) (University of Queensland) BSc (University of Queensland) Research interests span Artificial Intelligence , Electrical Engineering , and their applications in Robotics, Energy Systems, and Civil Engineering. He emphasizes integrating technology for civilian UAV applications, infrastructure optimization, and sustainable energy solutions. His work combines technical expertise with strategic insights to enhance research impact. Awards and Honors: AAUS Industry Champion Leadership Winner (2020) Fellow, Centre for Emergency and Disaster Management (2015) GMAA Award for Leadership and Strategy (2014) Advising and Grants: While not directly supervising students, Keir contributes to research infrastructure strategy, project management for industry-focused programs, and intellectual property negotiations. Notable projects include semi-automated power pole inspection and advanced terrain detection systems. His collaborations aim to maximize facility utilization and foster innovation in applied research. Labs and Facilities: Core responsibilities include managing the REF and Da Vinci/Banyo sites, which support interdisciplinary research in robotics, energy, and structural engineering. He also participates in the UAV Challenge Steering Committee, promoting safe, civilian-oriented robotics advancements.

Andrea MorichettaView profile

Researcher

• Machine Learning
• Distributed Systems
• Edge Computing
• +7 more

Andrea Morichetta is a University Assistant (Postdoc) at the Institute of Information Systems Engineering, Technische Universität Wien, Austria. He leads research in distributed systems, focusing on the intersection of machine learning and systems design. His work emphasizes edge computing, cloud-native systems, and AI-driven resource management. Education: PhD in Electrical, Electronics, and Communications Engineering (Politecnico di Torino, 2020), supervised by Prof. Marco Mellia. Visiting researcher at Cisco (2017), AIT (2019). Research Interests: ML for cloud/edge systems, federated learning, predictive scaling, cybersecurity in distributed environments, and intent-based systems management. Recent work explores multi-dimensional elasticity in stream processing, asynchronous federated learning, and self-referential digital anatomies. Teaching: Courses include Distributed Systems (VO), Distributed Systems Technologies (VU), and project/seminar courses (SE/PR). Actively supervises bachelor/master theses on topics like federated learning, edge computing, and serverless architectures. Key Contributions: Developed Polaris scheduler for SLO-aware edge scheduling, FL-SERENADE for network anomaly detection, and RainCloud for IoT swarm coordination. Co-authored over 50 peer-reviewed papers in top venues (IEEE Cloud, SOSE, ICSOC). Awards: 2019 Best Poster Runner-Up (TMA Paris), 2018 Best Student Paper (ITC Vienna).

Heitor Murilo GomesView profile

Senior Lecturer

• Machine Learning
• Data Streams
• Online Continual Learning
• +3 more

Heitor Murilo Gomes is a Senior Lecturer (Assistant Professor) in Artificial Intelligence at Victoria University of Wellington (New Zealand) since 2022. Previously, he served as Senior Researcher and Co-Director of the AI Institute at the University of Waikato. His research focuses on adaptive machine learning for data streams, including cybersecurity applications, ensemble methods, and online continual learning. He leads the development of the open-source library CapyMOA and has organized numerous international tutorials and workshops on streaming data techniques. Education : Ph.D. in Computer Science (2013-2017), Pontifícia Universidade Católica do Paraná, Brazil M.Sc. in Computer Science (2011-2012), same institution B.Sc. in Computer Science (2007-2010), UTP, Brazil Research Interests : Adaptive learning systems, concept drift detection, semi-supervised learning for delayed labels, and cybersecurity applications of ML. His work emphasizes practical implementations via libraries like CapyMOA and StreamDM. Key Contributions : Authored foundational papers on Adaptive Random Forests, Streaming Gradient Boosted Trees, and semi-supervised learning for data streams. His grants include a $360,000 Marsden Fund grant (2023-2026) for disagreement-based learning research. Awards : ACM SAC 2014 Best Paper Award (SAE2), PAKDD 2020 Outstanding Reviewer Honor Roll. Grants & Funding : Key researcher in the $13M TAIAO environmental AI program (MBIE SSIF), and sole PI for multiple grants totaling over $400,000 NZD. Active in editorial roles for Machine Learning (Springer) and conference reviewing (KDD, IJCAI, AAAI). Teaching : Leads COMPX523 (Data Stream Mining) and teaches fundamentals of AI. Delivers guest lectures globally on streaming data topics.

Ricardo Baeza-YatesView profile

Professor

• Algorithms
• Responsible AI
• Web Search and Mining
• +7 more

Ricardo Baeza-Yates serves as Director of Research at the Institute for Experiential AI at Northeastern University's Silicon Valley campus. He also maintains part-time professor positions at Universitat Pompeu Fabra in Barcelona and Universidad de Chile in Santiago. Previously, he was VP of Research at Yahoo Labs (2006-2016) and CTO of NTENT, a semantic search technology company. His academic career spans over three decades with significant contributions to both industry research and academia. Dr. Baeza-Yates earned his Ph.D. in Computer Science from the University of Waterloo, Canada, in 1989. His academic journey includes professorships at Universidad de Chile (1989-2004), ICREA Professor at Universitat Pompeu Fabra (2004-2005), and ongoing part-time appointments at both institutions since 2004 and 2006 respectively. His research spans algorithms, information retrieval, web search and mining, data science, with increasing emphasis on responsible AI and ethical considerations in technology. He is particularly renowned for his work on bias in search systems, algorithmic fairness, and the societal impacts of AI. His expertise uniquely bridges technical aspects of search and data mining with ethical and societal implications, making him a leading voice in the responsible AI movement globally. His recent scholarly output demonstrates a strong focus on bias detection, fairness metrics, responsible technology development, and the societal impacts of algorithms. The work spans technical machine learning and information retrieval while increasingly addressing ethical and social dimensions across diverse application areas from finance to healthcare to social media. Dr. Baeza-Yates has received numerous prestigious awards throughout his distinguished career: 2019 Corresponding Member of the Brazilian Academy of Sciences 2019 Award Salvà i Campillo to the Person of the Year 2018 National Research Award for applied research and technology transfer 2012 ASIS&T Best Information Science Book Award for Modern Information Retrieval 2011 IEEE Fellow for contributions to computer science development 2009 ACM Fellow for contributions to information retrieval algorithms 2002 Corresponding member Chilean Academy of Sciences As an educator and mentor, Dr. Baeza-Yates has advised numerous students throughout his academic career at multiple institutions. His textbook 'Modern Information Retrieval' has become a standard reference in the field, influencing generations of computer scientists. He has secured significant research funding through his leadership roles at Yahoo Labs and academic grants, demonstrating his ability to bridge theoretical research with practical applications. Dr. Baeza-Yates is actively involved with the DATA Lab at Northeastern University's Khoury College of Computer Sciences and previously founded the Web Science and Social Computing Research Group at Universitat Pompeu Fabra. He serves on multiple high-impact advisory boards including the Global AI Ethics Consortium, Global Partnership on AI, and IADB's fAIr LAC Initiative for Latin America and the Caribbean, reflecting his global influence in shaping responsible AI practices.

Cem ErsoyView profile

Professor

• Computer networks
• Pervasive computing
• Edge computing
• +1 more

Cem Ersoy is a Professor at Bogazici University 's Department of Computer Engineering, with a career spanning over 30 years. He has held visiting positions at the University of Trento, Italy, and previously served as Department Head at Bogazici University multiple times. Education : Ph.D. in Electrical Engineering (Polytechnic University, 1992), M.S. in Electrical Engineering (Bogazici University, 1986), B.S. in Electrical Engineering (Bogazici University, 1984) His research focuses on computer networks , pervasive computing , edge computing , and wireless networks . Recent work highlights AI integration in air computing, stress monitoring with wearables, and edge network optimization. Articles span applications of multi-agent DRL, probabilistic tracking, and network slicing. Scientific awards : Elected to the Academy of Sciences of Turkey (2022) Bogazici University Research Excellence Awards (Junior 2007, Senior 2014) He has supervised 24 PhD and 58 MS theses. As principal investigator, he led 35 funded research projects by agencies such as the EU, TUBITAK, and Bogazici University Research Fund. His Google Scholar profile shows 11,856 citations, h-index 43, and i10-index 130.

• Virtual Reality
• Augmented Reality
• Human-Computer Interaction
• +7 more

Professor Yi-Ping Hung is a distinguished faculty member in the Department of Computer Science and Information Engineering at National Taiwan University's College of Electrical Engineering and Computer Science. With a prolific publication record spanning over three decades from 1988 to 2025, Professor Hung leads research at the intersection of virtual reality, human-computer interaction, and computer vision. Their work bridges theoretical innovation with practical applications in health monitoring, biomechanics, and immersive technologies. Professor Hung's research interests focus on creating novel interaction paradigms for virtual and augmented reality environments, with particular emphasis on multisensory feedback systems. Their lab has pioneered approaches for haptic feedback, gaze-based interaction, and physiological response integration in immersive environments. Recent work explores breathing-integrated relaxation techniques, wind simulation for VR, and personalized biofeedback systems that demonstrate the translational impact of their research. Analysis of publication trends reveals a consistent focus on making virtual experiences more natural and embodied, with increasing integration of machine learning techniques in recent years. Professor Hung's team has developed innovative systems for Tai Chi training, archaeological simulation, and mental wellness applications that showcase practical implementations of their research. Professor Hung has mentored numerous graduate students who have become first authors on significant publications, including Ping-Hsuan Han, Yang-Sheng Chen, and Kuan-Wei Tseng. The research group maintains strong collaborations across disciplines and has secured funding for projects spanning VR hardware development, health monitoring systems, and cultural heritage applications. Current research directions include blockchain applications for environmental stewardship, advanced avatar creation techniques using generative models, and sophisticated sensor fusion approaches for precise indoor localization. The lab continues to push boundaries in creating more immersive, natural, and beneficial human-computer interaction experiences.

Xiao GuView profile

Researcher

• AI for Healthcare
• Biomedical Signal Processing
• Pervasive Healthcare
• +3 more

Xiao Gu is a Researcher at the Computational Health Informatics Lab, Department of Engineering Science, University of Oxford. He holds a PhD in Computing from Imperial College London (2023), an MRes in Medical Robotics and Image Guided Intervention (2019), and a BEng in Electronic Engineering from Fudan University (2018). His research focuses on integrating pervasive sensing and deep learning for healthcare applications, addressing challenges in generalization and data scarcity. He has been recognized with awards including the BMVC Outstanding Reviewer (2024) and IEEE ICRA Student Travel Grant (2023). Key research interests include AI-driven healthcare solutions, biomedical signal processing, and the development of robust models for real-world medical applications. His work spans datasets like CFP for biomedical sensing and contributions to workshops on wearable intelligence. Xiao Gu has authored over 30 peer-reviewed publications, with recent work focusing on medical imaging, domain adaptation, and vision-language models. He actively contributes to academic activities, including editorial roles and conference reviewing.

Jorma LaaksonenView profile

Senior Lecturer

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +4 more

Jorma Laaksonen is a Senior Lecturer at the Department of Computer Science, Aalto University (School of Science). His research focuses on artificial intelligence, machine learning, computer vision, and multimedia systems, with notable contributions to cross-lingual evaluation, hyperspectral imaging, and audio-visual attention mechanisms. He holds a Doctoral degree in Engineering and Technology from Helsinki University of Technology (1997). Education: Doctoral degree, Engineering and Technology, Helsinki University of Technology (1997) Licentiate degree, Engineering and Technology, Helsinki University of Technology (1991) Master's degree, Engineering and Technology, Helsinki University of Technology (1990) His research interests span AI-driven visual computing, human-computer interaction, and multimedia systems. Key projects include USSEE (audio-visual scene understanding) and ARTISDIG (AI for forest spectral analysis). He has published over 280 papers and supervised multiple theses. Awards include the IEEE Best Paper Award (2017) and Microsoft's Best Practice Award (2016). Recent work includes cross-cultural language modeling, efficient text-to-video retrieval, and open-world video instance segmentation. He leads the Lecturer Laaksonen Jorma group and actively participates in editorial roles and doctoral thesis evaluations.

Mehdi KhakiView profile

Senior Lecturer

• Data Assimilation
• Geodesy
• Satellite Remote Sensing
• +4 more

Dr. Mehdi Khaki is a Senior Lecturer in the School of Engineering at the University of Newcastle, Australia. He specializes in geodetic and remote sensing techniques applied to hydrology and environmental science. His research focuses on integrating satellite data products with hydrological models to improve water resource management and climate change studies. Educations: Bachelor of Civil Engineering, University of Tehran, Iran Master of Civil Engineering, University of Tehran, Iran PhD in Civil Engineering, Curtin University, Australia (2018) Research Interests: Dr. Khaki's work emphasizes the application of advanced statistical and machine learning methods to analyze satellite remote sensing data, particularly in areas such as data assimilation, GRACE satellite products, and environmental monitoring. His research spans global scales (e.g., water storage changes in the Middle East, Africa, Australia) and focuses on topics like dam impacts, soil moisture, and climate-driven hydrological changes. Publications: His contributions include over 48 journal articles, 6 conference papers, and a book on Satellite Remote Sensing in Hydrological Data Assimilation . Recent work highlights include studies on dam impacts in Saudi Arabia, tidal analysis in China Seas, and vegetation dynamics downstream of dams. Awards & Grants: 2023 Alexander-von-Humboldt Fellowship 2020 ASIA-PACIFIC Spatial Excellence Awards Total grants: $1,084,320 (e.g., ARC Discovery Projects, Alexander-von-Humboldt Foundation) Teaching & Supervision: Dr. Khaki supervises PhD and postdoctoral researchers in areas like remote sensing applications, hydrological modeling, and geodetic techniques. He actively contributes to teaching and curriculum development in environmental and geospatial engineering.