Find Academic Supervisors

• Electron device and electronic equipment
• Chemical reaction and process system engineering
• Wireless sensor networks
• +4 more

Shigeyuki Tateno is a Professor at the Graduate School of Information, Production and Systems within the Faculty of Science and Engineering at Waseda University. Holding a Doctor of Engineering degree from Kyushu University, he has established himself as a leading researcher in wireless sensor networks, fault diagnosis systems, and corrosion engineering. His academic journey spans over three decades, with significant contributions to both theoretical frameworks and practical industrial applications. Dr. Tateno received his graduate education from Kyushu University's Division of Engineering, specializing in chemical engineering, completing his studies in 1992. His academic career progressed from Research Assistant in the Department of Chemical Engineering at Kyushu University (1992-2003) to Associate Professor at Waseda University's Graduate School of Information, Production and Systems (2003-2009), culminating in his current position as Professor. He maintains active membership in four professional societies: Japan Facility Management Society, Institute of Electronics, Information and Communication Engineers, Society of Instrument and Control Engineers, and Society of Chemical Engineers. His research portfolio demonstrates remarkable breadth across multiple engineering disciplines. Dr. Tateno's primary research thrusts include wireless sensor networks for human motion detection and indoor positioning, fault detection and diagnosis using signed digraphs and graph theory, and corrosion engineering with particular focus on corrosion under insulation in petrochemical plants. His work bridges chemical engineering with computer science and electronics, creating innovative solutions for challenges in industrial maintenance, autonomous systems, and healthcare monitoring. Recent projects have expanded into autonomous driving systems, elderly monitoring technologies, and rescue operation support systems. Analyzed across his 67 publications with 276 citations and an h-index of 8, Dr. Tateno's research shows clear evolution from theoretical fault diagnosis methods toward practical applications in safety-critical systems. His most recent work demonstrates strong interdisciplinary connections between electronics, chemical engineering, and computer science, with increasing emphasis on real-world implementations in automotive technology, elderly care facilities, and industrial maintenance systems. The consistent thread throughout his career is the application of measurement technology to solve practical engineering problems. Japan Facility Management Society Institute of Electronics, Information and Communication Engineers Society of Instrument and Control Engineers Society of Chemical Engineers Dr. Tateno has supervised numerous research projects addressing critical engineering challenges in industrial maintenance, safety systems, and sensor technology development. His research has been applied in diverse settings including petrochemical plants, elderly care facilities, and automotive systems. The practical impact of his work is evident in implemented systems for pipe corrosion management, autonomous vehicle control, and rescue operation monitoring. His approach consistently bridges theoretical frameworks with practical implementations, ensuring research outcomes deliver tangible benefits in real-world environments. His laboratory focuses on developing wireless sensor network applications for positioning systems, human motion detection, and industrial monitoring. The research team combines expertise in electronics, chemical engineering, and computer science to create innovative solutions for challenges in elderly care, rescue operations, and petrochemical plant maintenance. Current projects include autonomous driving systems for elderly mobility, real-time logistics monitoring, and drowsiness detection systems based on physiological changes, reflecting the team's commitment to addressing societal challenges through engineering innovation.

• Blockchain
• Decentralized Systems
• Federated Learning
• +3 more

J.A. Pouwelse is a Professor at the Data-Intensive Systems department within the Electrical Engineering, Mathematics and Computer Science school at Delft University of Technology . With 125 research outputs and 4 supervised works, his work focuses on Blockchain , Decentralized Systems , Federated Learning , and Peer-to-Peer Networks , particularly for Web3 applications. His recent publications analyze topics such as: Decentralized adaptive ranking Serverless federated learning Green smart contracts Search index optimization Zero-trust frameworks Notable recognition includes the LCN Best Paper Award 2021 . Research spans decentralized infrastructure design, privacy preservation, and scalable system implementation.

Erik SchaffernichtView profile

Senior Lecturer

• Robotics
• Sensor Systems
• Machine Learning
• +6 more

Erik Schaffernicht serves as a Senior Lecturer in the Department of Natural Sciences and Technology at Örebro University's School of Science and Technology. His research is primarily conducted through the Center for Applied Autonomous Sensor Systems (AASS) where he leads work in the Adaptive and Interpretable Learning Systems and Robot Navigation and Perception research groups. Dr. Schaffernicht's research spans multiple areas in robotics and artificial intelligence, with particular expertise in sensor systems, behavior trees, and gas distribution mapping. His work bridges theoretical computer science with practical applications in autonomous systems, environmental monitoring, and human-robot interaction. His research often involves developing novel algorithms for robot perception, control, and decision-making in complex environments. His recent publications demonstrate a strong focus on behavior trees for robot control, gas distribution mapping techniques, and applications of machine learning in robotics. The research shows increasing sophistication in using deep learning approaches for environmental sensing and robot navigation, with applications ranging from industrial safety to healthcare monitoring. Dr. Schaffernicht maintains an active research agenda with numerous publications in top robotics and AI venues, including IEEE Robotics and Automation Letters, Robotics and Autonomous Systems, and various IEEE conference proceedings. His work shows consistent collaboration with researchers across Europe, particularly with the AASS research center at Örebro University. His research projects include both ongoing work on automatic cognitive screening tests using eye-tracking technology and completed projects such as AIR (Action and Intention Recognition), RAISE (Robotic System for Air Quality Assessment), and SmokeBot (Mobile Robots for Disaster Site Inspection).

Samira Abbasgholizadeh-RahimiView profile

Assistant Professor

• Artificial Intelligence in Primary Healthcare
• Digital Health Technologies
• Medical Machine Learning
• +5 more

Dr. Samira Abbasgholizadeh-Rahimi serves as Assistant Professor at McGill University in the Faculty of Medicine and Director of Artificial Intelligence in Family Medicine (AIFM) . She holds the Canada Research Chair in Advanced Digital Primary Health Care and maintains affiliations with Mila – Quebec AI Institute and Lady Davis Institute for Medical Research . B.Eng., Ph.D. from McGill University President-Elect, Canadian Operational Research Society Her interdisciplinary research bridges Artificial Intelligence and Primary Healthcare , with specific focus on: AI-enabled decision support tools Explainable machine learning for clinical contexts Medical knowledge graphs and NLP applications Addressing algorithmic bias in vulnerable populations Ageism mitigation in health AI systems Digital support for neurocognitive disorders Recent publications demonstrate her expertise in: AI ethics and EDI integration (4 papers since 2024) Geriatric depression prediction models (2025 JMIR study) Shared decision-making frameworks (2022-2023 series) Conscious AI debates in clinical settings (2024 commentary) Scientific recognition includes: 2022 NAPCRG New Investigator Award Cross-institutional collaborations across Canada, Europe, and Asia Research funding sources: Fonds de recherche du Québec – Santé Natural Sciences and Engineering Research Council Brocher Foundation (Switzerland) CIHR SPOR grants Industry partnership with Roche Canada Lab activities including: Developing HOPE (Home-Based Older Adults' Depression Prediction) model Creating incorpoRATE SDM assessment framework Building evidence-based decision support tools for dementia caregivers Training next-gen AI healthcare researchers through Mila collaborations

• Indoor Positioning
• Mobile Computing
• Context-Aware Computing
• +4 more

Adriano Jorge Cardoso Moreira is an Associate Professor with Habilitation at the Department of Information Systems, School of Engineering, University of Minho, Portugal. He holds multiple leadership roles including Scientific Coordinator of Urban Computing at Centro de Computação Gráfica and Director of the MAP-tele PhD Program. His research is conducted primarily through the Urban Computing Lab , focusing on smart place technologies. Education: PhD in Electrical Engineering (1997) and Bachelor's in Electronic and Telecommunications Engineering (1989), both from University of Aveiro, Portugal. Research Focus: His work spans indoor positioning, mobile/context-aware systems, urban computing, and wireless network simulation. Key innovations include fingerprinting algorithms for localization, multi-sensor fusion techniques, and human mobility analysis. Research outputs consistently address real-world industrial challenges such as warehouse management, factory automation, and urban infrastructure. Research Output Trends: Recent publications (2021-2023) emphasize practical applications of Wi-Fi/LoRaWAN fingerprinting, machine learning for sensor calibration, and industrial vehicle tracking. Over 70% of recent works involve experimental validation in real environments, reflecting a strong applied research focus. Key thematic clusters include radio map optimization, multi-sensor datasets, and scalability of positioning systems. Awards & Recognition: First Prize, EvAAL-ETRI Indoor Localization Competition (Off-site track, 2015 & 2017) Second Prize, EvAAL-ETRI Indoor Localization Competition (2016) IEEE Senior Member status Patent in computational geometry Projects & Funding: He leads/participates in numerous EU/national projects including: ORIENTATE (2021-2023): Low-cost indoor positioning for factories Lab4U&Spaces (2021-2023): Urban space solutions AR WARE (2018-2022): AR for warehouse management SAMU (2015-2018): Smart autonomous mobile units Lab & Team: He established/leads the Urban Computing Lab developing technologies for smart environments. Previously headed the Computer Communications and Pervasive Media Group (until 2016). Current team includes PhD/Master students working on wireless positioning and mobility analysis.

Carl ZhangView profile

Assistant Professor

• Social Network Privacy
• Data Science
• Machine Learning
• +4 more

Carl Zhang serves as Assistant Professor of Computer Information Systems and Paul Engler Professor of Business Innovation at West Texas A&M University's Department of Computer Information and Decision Management within the Paul and Virginia Engler College of Business. Joining in 2020, he teaches programming, social network analysis, and data visualization courses while leading research in privacy and machine learning applications. His academic credentials include: Ph.D. in Computer Science, George Washington University (2020) M.S. in Computer Science, George Washington University (2017) B.S. in Information Management, Shandong Normal University (2015) Dr. Zhang's research centers on social network privacy vulnerabilities, data science methodologies, and machine learning implementations. His work bridges theoretical security frameworks with business applications, particularly in deanonymization resistance, malware detection systems, and consumer trust modeling in digital marketplaces. Current investigations focus on federated learning optimization and psychological factors in AI-driven e-commerce. Publication trends reveal increasing emphasis on practical security solutions, with recent work (2024-2025) addressing real-world challenges in data heterogeneity and malware classification. His output spans high-impact journals including IEEE Transactions and Journal of Computer Information Systems, demonstrating consistent contributions to both theoretical foundations and business applications. Key recognitions include: Paul Engler Professor of Business Innovation (2024) College of Business Teaching Excellence Award (2023) Dr. Zhang actively mentors 9-18 undergraduate students per term through structured projects like the ABET accreditation system and community initiatives. His $2,950 WTAMU Foundation grant (2022) supports IT knowledge dissemination via guest lectures and learning platforms. He leads student engagement through BuffTeks and Buff Analytics communities, fostering hands-on technical development. Though without a formal lab, he cultivates research opportunities through course projects, competition mentorship (e.g., USITCC winners), and software development initiatives like the Program Assessment Reporting System deployed on WT's network infrastructure.

Ajitha RajanView profile

Professor

• Software Testing
• Software Verification
• Test Automation
• +6 more

Ajitha Rajan is a Professor (Personal Chair of Software Testing & Verification) at the School of Informatics, University of Edinburgh. She joined the university in December 2012 as a Reader (equivalent to Associate Professor in American terms) and was promoted to Professor in 2024. Prior to her position at Edinburgh, she held postdoctoral positions at Oxford University and Laboratoire d'Informatique de Grenoble in France. She earned her PhD in Computer Science from the University of Minnesota in August 2009 under Professor Mats Heimdahl. Her research focuses on two primary directions: Automated Software Testing (including test input generation, test oracles, and coverage measurement) and Biomedical AI (particularly cancer survival models and interpretability for biological sequences and medical images). Her work has applications in safety-critical systems, blockchains, embedded systems, and medical diagnostics. She has made significant contributions to explainable AI for healthcare applications, especially in lung cancer detection and cancer survival analysis. Her recent publications demonstrate a strong trend toward interdisciplinary research at the intersection of software engineering and biomedical applications. She has numerous publications in top venues including ICSE, ASE, and healthcare-focused conferences. Her work increasingly focuses on making AI systems more interpretable and trustworthy, particularly in medical contexts where model decisions can have life-or-death consequences. ACM SIGSOFT Distinguished Reviewer Award, ISSTA 2025 Best Paper Award at ICHI 2025 Promoted to Professor (Chair in Software Testing & Verification) 2024 SICSA Best Supervisor Award 2024 ACM Distinguished Paper Award 2008 Professor Rajan actively supervises PhD students in both software testing and biomedical AI domains. She leads several funded projects including MANIFEST (a cancer immunotherapy response research platform), a Huawei Joint Lab project on RobustCheck, a Royal Society Industry Fellowship on AutoTest, and the KATY project on clinical knowledge for personalized medicine. Her research group includes current PhD students working on explainable AI for medical image analysis, scenario-based testing for autonomous driving, and protein design applications.

José Luis Alonso BerrocalView profile

Professor

• Information Retrieval
• Web Cybermetrics
• Digital Libraries
• +5 more

Professor José Luis Alonso Berrocal is affiliated with the University of Salamanca, where he works in the Department of Computer Science and Automation within the Faculty of Engineering. His research spans information systems, web technologies, and educational applications of computer science. Dr. Berrocal earned his PhD from the University of Salamanca with a thesis titled "Cibermétricos. Análisis de los dominios web españoles" (2001), supervised by Dr. Luis Carlos García de Figuerola Paniagua. His academic journey has positioned him at the intersection of computer science and information systems. His research interests focus on Information Retrieval , Web Cybermetrics , Digital Libraries , and Educational Technology . Dr. Berrocal has made significant contributions to understanding web structure, information retrieval systems, and the application of technology in education. His work often bridges theoretical computer science with practical applications in academic and research settings. Analysis of his publication history shows a consistent focus on information retrieval systems and web analysis techniques. His early work centered on foundational tools like SACARINO (a web crawling and processing system), while more recent research examines the structure of institutional websites, particularly in biomedical research centers. He has explored both theoretical aspects of information retrieval and practical implementations using libraries like Lucene. Active researcher in the SOCIOLOGICAL ANALYSIS GROUP Member of the UNIVERSITY INSTITUTE FOR SCIENCE AND TECHNOLOGY STUDIES PhD School affiliation: Logic and Philosophy of Science