جستجوی استاد راهنمای دانشگاهی

• Artificial Intelligence in Healthcare
• Oncology
• Machine Learning
• +۷ مورد دیگر

Le Minh Thao Doan is a PhD candidate and part-time Lecturer in Computer Science at the School of Computing, Engineering & Digital Technologies, Teesside University. Her research focuses on AI applications in healthcare and oncology, particularly leveraging machine learning and deep learning for cancer diagnosis and prognosis. She emphasizes Explainable Artificial Intelligence (XAI) to enhance early cancer detection and improve patient outcomes. Previously, she worked as a financial auditor and data analyst at multinational firms like EY and AB InBev. Later, she contributed to SME digitalization as a data analytics developer at Teesside University. Her expertise spans big data, business intelligence, and data analytics for solving complex business challenges. Key research areas include AI-driven healthcare solutions, cybersecurity (intrusion detection systems, payment fraud prevention), and digitalization in healthcare supply chains using smart wearables. She has published extensively on topics like machine learning in oncology, cybersecurity, and healthcare technology innovation. Her work bridges technical and clinical domains, aiming to translate AI advancements into practical tools for healthcare and enterprise sectors. Collaborations include projects with clinical partners to evaluate real-time respiratory monitoring in neonatal care.

• Climate Science
• Ecohydrology
• Machine Learning
• +۳ مورد دیگر

Dr. Shijie Jiang is a Project Leader at the Max Planck Institute for Biogeochemistry , leading the Machine Learning for Hydrological and Earth Systems group within the Department of Biogeochemical Integration. His research focuses on integrating hybrid AI methods with domain knowledge to study climate-water-ecosystem interactions under anthropogenic and climatic pressures. Key areas include ecohydrological processes, explainable machine learning applications in earth systems, and climate extremes predictability. Education: Ph.D. in Environmental Science (2017–2021), National University of Singapore M.Sc. in Environmental Science (2013–2016), Beijing Normal University B.Sc. in Environmental Engineering (2009–2013), Wuhan University Research Interests: Dr. Jiang explores interactions between climate, water, and ecosystems using AI-driven frameworks. Specific foci include: Physics-AI hybrid models for ecohydrology and climate processes Machine learning interpretability in earth system science Predictability of climate extremes and their impacts on water cycles Global hydrological drought mechanisms and compounding climatic drivers Labs/Teams: His work is anchored in the Biogeochemical Integration Department, collaborating with the ELLIS Unit Jena. Research outputs emphasize bridging data-driven discovery with traditional geophysical modeling. Grants/Advising: No specific grants or advisees listed in current records. Activities focus on leading interdisciplinary projects and postdoctoral mentorship within his group.

• Wireless Sensor Networks
• Machine Learning
• Deep Learning Methods
• +۵ مورد دیگر

Shagufta Henna is a Lecturer in Computer Science at the Department of Computing, Atlantic Technological University. Her research focuses on machine learning applications in wireless sensor networks, IoT systems, cybersecurity, and environmental monitoring. She contributes to UN Sustainable Development Goals through projects addressing environmental sustainability and health. Her expertise includes federated learning for malware detection in healthcare IoT, deep learning optimization for sensor networks, explainable AI for water quality prediction, and ensemble methods for human activity recognition. Recent work highlights include: Developing graph neural networks for IoT-based healthcare cybersecurity Accelerating environmental monitoring via deep learning in uncontrolled sensor networks Deploying SHAP-based XAI for water quality prediction interpretability No scientific awards explicitly listed. Research output includes 45 publications across journals and conferences, with recent contributions in Results in Engineering and IEEE proceedings. Active collaborations span international institutions focusing on AI-driven environmental and healthcare solutions.

Peter Stuckeyمشاهده پروفایل

استاد

• Constraint Programming
• Discrete Optimization
• Programming Languages
• +۳ مورد دیگر

Peter Stuckey is a Professor in the Faculty of Information Technology at Monash University and project leader at Data61 CSIRO laboratory. His pioneering research focuses on constraint programming and discrete optimization. He holds B.Sc and Ph.D degrees in Computer Science from Monash University. Research interests include discrete optimization, programming languages, constraint solving algorithms, bioinformatics, and constraint-based graphics. His work extends to applications in databases, timetabling, and system security, with industry collaborations including Oracle and Rio Tinto. Recent publications focus on optimization techniques for transport routing, explainable AI, multi-agent systems, and signal processing. Awards include the Google Australia Eureka Prize and Woodward Medal.

Lena A. Jägerمشاهده پروفایل

دانشیار

• Computational Linguistics
• Eye Tracking
• Machine Learning
• +۵ مورد دیگر

Lena A. Jäger is an Associate Professor in Digital Linguistics at the School for Transdisciplinary Studies, University of Potsdam. Her research focuses on computational linguistics, eye tracking, and machine learning applications in language processing. She leads the Digital Linguistics group, supervising 11 PhD students and contributing to interdisciplinary projects like the MultiplEYE corpus initiative. Her work bridges cognitive science and AI, with key contributions to eye movement analysis, biometric identification, and LLM interpretability. Her research interests span eye tracking in reading, linguistic predictability modeling, and the alignment of language models with human cognition. Notable achievements include developing the pymovements toolkit for eye movement data processing and pioneering studies on detecting alcohol inebriation via gaze analysis. Awards include best paper honors at ETRA 2023 and 2024. Recent work explores generative models (ScanDL) for synthetic eye movement generation, cross-lingual reading behavior analysis, and fairness in biometric systems. She advises on AI ethics through the DSI initiative and collaborates internationally on projects like the MECO corpus.

• Human-Centered AI
• Psychology and Technology
• Machine Learning
• +۵ مورد دیگر

Chao Zhang is an Assistant Professor in Human-Centered AI at Eindhoven University of Technology (TU/e), specializing in the intersection of psychology and technology. He holds an MSc and PhD from TU/e, with a focus on using data-driven methods and psychological theories to design AI systems that positively influence human behavior. His research spans behavior change interventions, AI-driven health persuasion, and the social implications of AI systems. He coordinates the HUMAN-AI Alliance program and co-organized workshops on human-centered AI. Education: MSc (Human-Technology Interaction, TU/e), PhD (Intelligent Systems for Lifestyle Behavior Change, TU/e). Research interests include machine learning applications in psychology, computational modeling for behavior change, and ethical AI design. Awards include a 2021 PhD Thesis Award nomination. Key collaborations include work with Utrecht University and projects like ENFIELD (European Lighthouse for Trustworthy AI). He teaches courses on behavioral research methods and human-AI interaction. His lab explores topics like trust in XAI systems and human-robot bonding mechanisms.

• Computer Science
• Artificial Intelligence
• Cybersecurity
• +۵ مورد دیگر

Kaushik Roy is an academic researcher affiliated with multiple institutions including North Carolina State University, Purdue University, and North Carolina A&T State University. His primary focus is on computer science, cybersecurity, and AI-driven solutions for real-world challenges. He has extensive experience in machine learning applications for intrusion detection, deepfake detection, biometrics, and federated learning frameworks. His work often bridges theoretical advancements with practical implementations in domains like IoT security, healthcare analytics, and cyber-physical systems. Affiliations: North Carolina State University, Purdue University, North Carolina A&T State University, and others Research Interests: Cybersecurity, Deep Learning, Biometric Authentication, Federated Learning, and AI Ethics He has co-authored over 105 publications, focusing on topics like anomaly-based intrusion detection, malware traffic classification, and privacy-preserving techniques. His recent work emphasizes federated learning architectures for healthcare and transportation IoT systems. Roy collaborates with industry partners to translate research into deployable solutions, particularly in edge computing and sensor networks.

• mHealth
• IoT and Wearables
• Artificial Intelligence
• +۴ مورد دیگر

Dr. Drishty Sobnath is an Assistant Professor in Computer Science at the School of Mathematical & Computer Sciences, Heriot-Watt University. Previously, she held roles as Senior Lecturer and Programme Leader at Solent University, overseeing BSc Computer Science, BSc Software Engineering, and MSc Computer Engineering programs. She earned her PhD in Software Engineering (mHealth) from Kingston University, London. Her research focuses on mHealth, IoT, and AI-driven solutions for healthcare management, including projects like the EU-funded WELCOME initiative for COPD patients and the CANAdvice cancer treatment app. She is also a co-investigator on the Office for Students' AI/Data Science scholarship project addressing workforce digital skills gaps. Research interests span AI in healthcare diagnostics, smart city technologies, and inclusive education strategies. She teaches Data Science and AI at undergraduate/postgraduate levels, contributing to UN Sustainable Development Goals related to health and education equity. Notable achievements include Best Paper Awards (2018, 2019) and a 2021 teaching award nomination. Her work integrates interdisciplinary approaches, combining technical innovation with societal impact through projects like the DAP framework for data-driven daily living and AI-driven PPE monitoring systems. Grants and collaborations include EU FP7 funding, industry partnerships on smart city solutions, and initiatives promoting diversity in STEM education. She advises on postgraduate conversion course design and has authored over 20 peer-reviewed articles, focusing on healthcare tech, computer vision applications, and learning analytics.

Andria Nicolaouمشاهده پروفایل

دانشگاهی

• Biomedical Engineering
• Biomedical Imaging
• Biosignal Processing Systems
• +۳ مورد دیگر

Dr. Andria Nicolaou is an Associate Lecturer at the University of Central Lancashire Cyprus (UCLan Cyprus), specializing in Data Analytics. She holds a PhD in Computer Science (University of Cyprus, 2024), an MSc in Molecular and Applied Physiology (National and Kapodistrian University of Athens, 2019), and a BSc in Computer Engineering with a Biomedical minor (University of Cyprus, 2017). She is also a Research Associate at CYENS Centre of Excellence and has previous roles as a Teaching Assistant at the University of Cyprus and a Research Assistant at the Biomedical Research Foundation of the Academy of Athens. Her research focuses on Biomedical Engineering, particularly in Biomedical Imaging and Biosignal Processing Systems, alongside Explainable Artificial Intelligence (XAI) in healthcare applications. She has published extensively on topics such as MRI analysis in multiple sclerosis (MS) and XAI models for sepsis prediction. Notable awards include the Best Paper Award at the 2023 IEEE EMBS International Conference on Biomedical and Health Informatics and recognition as a finalist in the 2024 She STEMs Symposium for Early-Stage Greek Woman Scientists. Dr. Nicolaou teaches the Exploratory Data Analysis module in the MSc Data Analytics program at UCLan Cyprus. Her work bridges clinical data with advanced AI techniques, emphasizing ethical and interpretable solutions for healthcare challenges. She is a member of the IEEE and the IEEE Engineering in Medicine and Biology Society (EMBS).

• Information Visualization
• Visual Analytics
• Multidimensional Data
• +۵ مورد دیگر

Rafael Messias Martins is a Researcher at Linnaeus University, affiliated with the Department of Computer Science and Media Technology within the Faculty of Technology. He holds an MSc in Computer Science from the University of São Paulo and a PhD in Computer Science from the University of Groningen. His primary research focuses on Information Visualization and Visual Analytics, particularly emphasizing Multidimensional Data and Networks. He is a core member of the Information and Software Visualization (ISOVIS) research group and leads multiple ongoing and completed research projects, including InfraVis (a national research infrastructure for data visualization) and initiatives addressing medication risks and carbon mitigation in forestry. His work bridges theoretical advancements in visualization with practical applications in education, healthcare, and environmental science. Education MSc in Computer Science, University of São Paulo, Brazil PhD in Computer Science, University of Groningen, Netherlands Research Interests His research explores the intersection of visualization techniques with complex data analysis, emphasizing: Interactive visual analytics for high-dimensional data Machine learning interpretability through visualization Educational data analytics for K-12 institutions Applications in healthcare (e.g., medication risk prediction) and environmental science (e.g., carbon footprint reduction) Development of national visualization infrastructures (InfraVis) Recent Trends in Articles His recent work emphasizes: Enhancing trust in machine learning models through visual explanations Optimizing visualization tools for educational stakeholders Algorithmic fairness in urban planning simulations Scalable dimensionality reduction techniques for streaming data Grants & Collaborations He has coordinated projects such as IDEAL (interaction design curriculum development), TimberVis (3D timber structure visualization), and seed projects addressing carbon mitigation and medication risks. Collaborations span academic, industrial, and governmental partners in Sweden and internationally. Labs & Teams He leads the ISOVIS group, which develops open-source tools like SBGTool (student grouping analytics) and FeatureEnVi (feature engineering visualization). The group also contributes to InfraVis, a national platform for visualization resources.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۳ مورد دیگر

Asim Roy is a Professor of Information Systems at Arizona State University (ASU), specializing in Artificial Intelligence, Machine Learning, and Neuroscience. He holds a Ph.D. in Operations Research from the University of Texas at Austin and has held visiting roles at Stanford University and Oak Ridge National Laboratory. Education: B.E. in Mechanical Engineering (Calcutta University), M.S. in Operations Research (Case Western Reserve University), Ph.D. in Operations Research (University of Texas at Austin). Research Interests: Focus on Explainable AI (XAI), brain theories (localist representation, grandmother cells), neural networks, and optimization tools. He has pioneered software like IFPS/OPTIMUM and developed novel approaches in computer vision and cognitive systems. Awards & Recognition: Listed in multiple Who's Who publications and recognized for contributions to AI and neuroscience. His theories challenge dominant connectionism frameworks and have been covered by Phys.org. Editorial & Leadership Roles: Founded INNS sections on Autonomous Machine Learning, Explainable AI, and AI, Law, and Regulation. Guest Editor-in-Chief for multiple journals, including Frontiers in Psychology and Neural Networks. Served as conference chair and program co-chair for major events like IJCNN and WCCI. Patents & Impact: Holds three U.S. patents on machine learning applications. His work has saved corporations millions through optimization systems and has been described as 'pioneering' by peers.

• Work and Organizational Psychology
• Human Factors
• AI and Robotics in Human Systems
• +۴ مورد دیگر

Luca Pietrantoni is a Full Professor of Work and Organizational Psychology at the Department of Psychology 'Renzo Canestrari', University of Bologna. He coordinates the 'Human Factors, Risk and Safety' research group and actively participates in EU Horizon projects like SESTOSENSO and TUPLES, focusing on AI and robotics in human systems. His research spans mental health promotion in workplaces, cognitive ergonomics, and sustainable commuting. Education: PhD in Health Psychology (University of Florence, 2000) Postdoctoral fellowship in Social Psychology (University of Parma) Psychology Degree (University of Padua, 1995 with top honors) Research Interests: Human factors in AI/robotics integration Workplace mental health interventions Climate change adaptation strategies Cognitive ergonomics in manufacturing Grants & Projects: Principal Investigator in Horizon Europe projects (SESTOSENSO, TUPLES) Coordinator of H-WORK (mental health in SMEs/public workplaces) EU-funded studies on exoskeletons, coastal safety, and cyclist safety Labs/Teams: Human Factors, Risk and Safety research group.

• Digital Transition
• Human Factors
• Safety and Risk Management
• +۶ مورد دیگر

Marco De Angelis is a Senior Assistant Professor (fixed-term) at the Department of Psychology 'Renzo Canestrari', University of Bologna. His research focuses on digital transition, human factors, occupational safety, and mental health at work. He leads the H-WORK project, a EU-funded initiative promoting mental health in SMEs and public workplaces through multilevel interventions. He is also involved in projects like Digi-B-Well, addressing digital wellbeing and SME digitalization. Education: PhD in Psychology (2019, University of Bologna), Master's in Work and Organizational Psychology (2013), and Bachelor's in Psychology (2011, Sapienza University of Rome). Professional experience includes roles in the Modena Local Health Authority (2013–2014) and the CE.TRANS research group (2014–2015). Research interests emphasize digital interventions for mental health, AI integration in workplaces, safety systems design, and compassionate healthcare organizations. He coordinates the LAV.OR.I.O. Research Lab, exploring work-related stress, organizational innovation, and human factors. Teaching activities span international programs in Work-Organizational Psychology, Chemical Innovation, and International Development. He supervises student theses on digital mental health, AI at work, and Industry 5.0 cognitive workload. Membership includes editorial boards for the International Journal of Environmental Research and Public Health , Human Factors and Ergonomics Society - Europe, and associations like EAWOP, SIOP, and EAOHP. He contributes to over 100 peer-reviewed articles and conference proceedings, focusing on workplace safety, technology acceptance, and mental health policies.

Andreas Holck Høeg-Petersenمشاهده پروفایل

پژوهشگر ارشد

• Computer Science
• Reinforcement Learning
• Explainable AI
• +۴ مورد دیگر

Andreas Holck Høeg-Petersen is a PhD fellow at the Department of Computer Science, Aalborg University, specializing in Reinforcement Learning (RL) , Explainable AI (XAI) , and Cyber-Physical Systems . His research focuses on integrating formal verification techniques with RL to enhance transparency and safety in autonomous systems. Fields of Interest : Reinforcement Learning, Explainable AI, Formal Verification, Autonomous Driving, Cyber-Physical Systems, Machine Learning. Email : ahhp@cs.aau.dk Research Trends : Andreas works on bridging formal methods with AI, particularly in applications like autonomous vehicles and environmental systems (e.g., reducing combined sewer overflows). His work emphasizes causality enforcement and model-predictive control in RL frameworks. Collaborations : He collaborates with researchers across Denmark and internationally, including supervisors Kim G. Larsen , Amir Ploeger , and Andrzej Wasowski , contributing to interdisciplinary projects in AI, formal verification, and environmental engineering.

Sławomir Nowaczykمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Data Mining
• +۷ مورد دیگر

Sławomir Nowaczyk is a Professor at Halmstad University affiliated with the School of Information Technology . His research focuses on Artificial Intelligence , Machine Learning , and Data Mining , particularly for Streaming Big Data and Self-aware Systems . Current research areas: XAI, diagnostics, knowledge representation, and interdisciplinary AI applications Teaching: Artificial Intelligence , Data Mining , and Operating Systems His work emphasizes data-driven fault detection in distributed systems, with applications in Intelligent Vehicles and Healthcare . He develops adaptive interestingness metrics for real-time data analysis and promotes resource-bounded AI for practical environments. Recent publications reflect expertise in hybrid modeling (physics + data-driven), graph neural networks , and cross-modal diagnostic systems . Collaborations span automotive and smart grid industries through projects like VPMS , FuelFEET , and EVE . PhD Supervision : Current: Yuantao Fan, Pablo del Moral, Ece Calikus, Awais Ashfaq, Shiraz Farouq, Alexander Galozy, Kunru Chen, Ghaith Taghiyar, Zahra Taghiyar Past: Rune Prytz (Volvo), Iulian Carpatorea, Hassan Nemati Projects: ReDi2Service, VPMS, InnoMerge, FuelFEET, EIS-IGS Smart Grids, Science without Borders, In4Uptime, ARISE, IMedA, SeMI, HEALTH, BIDAF, EVE