جستجوی استاد راهنمای دانشگاهی

Eddy Vaassen is Full Professor of Accountancy at Tilburg University's School of Economics and Management (TiSEM), Department of Accountancy. As a Registered Accountant (RA) with a PhD from Maastricht University, he maintains affiliations with Erasmus University Rotterdam, serves as Scientific Advisor at BDO, and holds leadership positions in the Association for Information Systems and CEA. His professional education initiatives extend to major organizations including KLM, ING, Rabobank, and the Dutch Tax Department. His research encompasses: Internal control theory and auditing methodologies Decision support systems for audit professionals IT control frameworks and blockchain integration Contract auditing and compliance verification Continuous monitoring systems and process mining He actively contributes to academic discourse through editorial roles for leading accounting journals including Journal of Information Systems and Journal of Emerging Technologies in Accounting . Recent publications demonstrate strong focus on auditing innovations (process mining, experimental auditing approaches) and accounting information systems development, frequently addressing the intersection of technology, control frameworks, and professional judgment. Professor Vaassen supervises doctoral candidates researching audit innovations, ERP systems, and internal control frameworks. Through his professional education initiatives, he has developed training programs for financial institutions and government agencies across the Netherlands.

• Accounting Information Systems
• Internal Control Theory
• Auditing Methodologies
• +۴ مورد دیگر

Dr. Mojtaba Rostami Kandroodi is a University Lecturer at Tilburg University's Tilburg School of Humanities and Digital Sciences (TSHD) within the Department of Intelligent Systems. His academic profile demonstrates active engagement in both teaching and research, with courses including Machine Learning, Mathematics for Premasters DSS, and supervision of Master's theses through the Data Science in Action program. Dr. Rostami Kandroodi's research centers on the intersection of cognitive neuroscience, computational modeling, and psychiatric disorders. His work investigates how motivational biases influence decision-making processes, with particular focus on reinforcement learning mechanisms in both healthy individuals and those with psychiatric conditions. He examines how neurochemical factors like dopamine and substances such as LSD modulate learning processes, seeking to understand the neurocognitive basis of conditions including depression, anxiety, and addiction. His research employs sophisticated computational models to analyze behavioral data from probabilistic reversal learning tasks, revealing nuanced insights into reward-punishment asymmetries and cognitive control mechanisms. Analysis of his publication record shows a consistent focus on reinforcement learning dynamics across multiple contexts. His work spans from theoretical computational models of asymmetric updating in volatile environments to clinical investigations of learning biases in psychiatric populations and pharmacological studies examining how substances alter learning processes. This research trajectory demonstrates both methodological sophistication and clinical relevance, bridging computational neuroscience with practical applications for understanding and potentially treating psychiatric disorders. Dr. Rostami Kandroodi maintains active collaborations with researchers across institutions, as evidenced by his co-authorship with prominent figures like Hanneke den Ouden. His teaching responsibilities in Machine Learning and data science methodologies complement his research focus, creating a cohesive academic profile centered on quantitative approaches to understanding cognition and behavior.

• Reinforcement learning
• Motivational biases
• Decision-making processes
• +۵ مورد دیگر

Pengcheng Liu is an Associate Professor in the Department of Computer Science at the University of York, holding this position since January 2020. He maintains active memberships in IEEE, IEEE Robotics and Automation Society (RAS), IEEE Control Systems Society (CSS), and the International Federation of Automatic Control (IFAC), while serving on the IEEE Technical Committee for Bio Robotics, Soft Robotics, Robot Learning, and Safety, Security and Rescue Robotics. His research spans robotics, machine learning, automatic control, and optimization, with specialization in humanoid robotics, rehabilitation systems, agricultural applications, and human-computer interaction. Key focus areas include developing lightweight neural networks for embedded agricultural systems, bionic-companionship frameworks for service robots, EMG-controlled rehabilitation devices, and precise control of robotic manipulators using ROS/Gazebo. His work consistently bridges theoretical control systems with practical implementations in healthcare and precision agriculture. Analysis of his publication trends reveals strong emphasis on applying machine learning to real-world robotics challenges, particularly in resource-constrained environments (e.g., agricultural robotics with efficient neural networks) and human-centered applications (e.g., rehabilitation gloves and brain-computer interfaces). Recent work demonstrates increasing integration of computer vision with control systems for autonomous operation. His notable scientific awards include: Global Peer Review Awards from Web of Science (2019) Outstanding Contribution Awards from Elsevier (2017) Dr. Liu has secured and managed research funding through major programs including EPSRC, Newton Fund, Innovate UK, Horizon 2020, Erasmus Mundus, FP7-PEOPLE, and NSFC. He serves as a regular reviewer for EPSRC, NIHR, and NSFC grant panels while reviewing for over 30 flagship journals and conferences in robotics, AI, and control systems. His editorial roles include Associate Editor for IEEE Access and PeerJ Computer Science, where he has edited 17 publications. Though specific lab affiliations aren't detailed, his research in agricultural robotics, rehabilitation systems, and humanoid platforms suggests active collaboration with York's robotics and AI research groups, particularly in developing practical implementations of control algorithms and machine learning models for real-world deployment.

• Robotics
• Machine Learning
• Automatic Control
• +۷ مورد دیگر

Paulo Jorge Coelho serves as an Adjunct Professor in the Electrical Engineering Department at the School of Technology and Management, Polytechnic University of Leiria, and as an integrated researcher with the ROBiTECH (Advanced Robotics and Smart Factories) group at INESC Coimbra's Leiria delegation. With over 20 years of academic experience since 2004, he specializes in Microprocessors, Industrial Automation, and Computer Vision instruction. Education: Ph.D. in Informatics (2019), Trás-Os-Montes and Alto Douro University Specialization in Automation and Control (2007), Coimbra University Bachelor of Electrical Engineering (2004), Coimbra University Research Focus: His work bridges industrial automation and computer vision with cutting-edge machine learning applications in biomedical imaging, ambient assisted living, and assistive technologies. Current projects emphasize practical implementations for reducing physical impairments and enhancing healthcare solutions through deep learning frameworks. Publication Trends: Recent work (2024-2025) reveals strong interdisciplinary convergence between healthcare diagnostics (schizophrenia/EEG analysis, perinatal depression prediction) and industrial/computer vision systems (sports analytics, activity recognition). His research consistently leverages sensor fusion and deep learning architectures to solve real-world problems across medical and engineering domains. Professional Engagement: Active member of the Portuguese Engineers Order and Portuguese Association for Pattern Recognition, with significant editorial contributions (89+ edited articles) across AI and computer vision domains. Previously served as course director and Scientific-Pedagogical Committee member for the Master's in Electrical and Electronic Engineering. Research Infrastructure: Operates within ROBiTECH's advanced robotics ecosystem at INESC Coimbra, focusing on smart factory solutions and human-robot interaction systems. His lab environment integrates industrial automation testbeds with biomedical sensor networks for cross-domain innovation.

• Industrial Automation
• Computer Vision
• Machine Learning
• +۴ مورد دیگر

Rainer Mühlhoff is Professor of Ethics of Artificial Intelligence at the University of Osnabrück's Institute of Cognitive Science, with additional affiliation at the Weizenbaum Institute for the Networked Society in Berlin. He leads the Ethics and Critical Theories of Artificial Intelligence research group, focusing on the societal implications of digital technologies through interdisciplinary collaboration between philosophy, media studies, and computer science. His research spans critical analysis of AI's ethical dimensions, including data protection frameworks, predictive privacy violations, and the relationship between algorithmic systems and authoritarian tendencies. Key thematic areas include: Power dynamics in datafication and AI governance Intersectional discrimination in automated decision-making Collective privacy as a structural concern Historical parallels between digital fascism and 20th-century authoritarianism Educational strategies for digital literacy and critical engagement Mühlhoff's recent publications analyze how predictive algorithms enable new forms of population management through insurance discrimination, hiring practices, and immigration control. His work advocates for reimagined regulatory frameworks that address AI's structural power imbalances rather than merely technical fixes. Current research initiatives include the DFG-funded project Predictive Knowledge is Power (2025) examining collective privacy frameworks, and development of school curricula through the Data Ethics Outreach Lab (DEOL) which translates academic research into educational materials for critical digital literacy. Professional activities include regular appearances at major conferences (Chaos Communication Congress, re:publica), media commentary (Deutschlandfunk, ARD), and public engagement through book launches and radio discussions addressing AI's societal impacts.

• Ethics of Artificial Intelligence
• Data Protection
• Critical Social Theory
• +۷ مورد دیگر

Jan Pons is a Researcher at the Business Innovation Knowledge Centre of Rotterdam University of Applied Sciences, specializing in the value assessment of digital healthcare and involved in the Digital Business research group. With a background in analytical chemistry and extensive professional experience in business, technology, and healthcare innovation, he focuses on implementing and scaling digital solutions in healthcare settings. His educational background includes training as an analytical chemist at the Van 't Hoff Institute in Rotterdam, along with specialized training in finance, governance, and health innovation. Pons has held various roles including laboratory work, sales and marketing for an international software manufacturer, business plan assessment at KPN Multimedia, and running his own online marketing company which he sold in 2011. Research Interests Jan Pons specializes in the value assessment of healthcare innovations using Health Technology Assessment principles, examining medical, social, ethical and economic implications. His work focuses on implementation challenges in healthcare and welfare organizations, particularly regarding independent living at home. He co-founded the Rotterdam eHealth Agenda foundation in 2019 to promote sustainable connections between healthcare organizations for sharing knowledge about e-health implementation. Since 2022, his primary focus has been on value assessment of healthcare innovations, and in October 2023 he began working as a social domain expert for 'transformation agenda' and 'value assessment' teams at Digizo.nu. His research shows that healthcare innovations often fail because values are insufficiently assessed from all stakeholder perspectives - patients, professionals, healthcare organizations, and financiers. His approach expresses value through qualitative and quantitative indicators to support decision-making and successful implementation. Scientific Contributions Jan Pons has contributed significantly to understanding digital transformation in healthcare and SMEs. His publications examine digital innovation gaps, manufacturing industry automation, smart industry implementation challenges, and SME digitalization strategies. His work with the Responsible AI for SMEs project (2023-2025) investigates how small and medium enterprises can responsibly implement AI technologies. Professional Activities As one of the four driving forces behind the Transitie010 movement, Pons works with regional partnerships strengthened by a network of leaders, experts, and experienced professionals across all sectors of welfare and healthcare. In 2016, he co-founded an online therapy platform for aphasia treatment, and in 2017 co-initiated the Rotterdam eHealth Agenda foundation. Between 2014-2017, he developed the 'Home Technology' arrangement for the municipality in the IJsselmonde area, resulting in a Home Technology Guide for consumers and professionals.

• Digital Healthcare
• Value Assessment of Healthcare Innovations
• e-Health Implementation
• +۴ مورد دیگر

Jeffrey Dellosa serves as a Professor at Caraga State University in the College of Engineering and Geosciences, Butuan, Philippines. His academic career focuses on renewable energy research with particular emphasis on solar photovoltaic systems for rural development applications in the Philippines. He holds a Doctor of Engineering degree specializing in Renewable Energy from Ateneo de Davao University (2019-2023). Education: Doctor of Engineering in Renewable Energy, Ateneo de Davao University (2019-2023) Professor Dellosa's research spans multiple domains within renewable energy engineering, with particular expertise in solar photovoltaics, energy conversion systems, and power generation technologies. His work bridges theoretical research with practical applications for rural electrification and sustainable development. Current research directions include floating solar photovoltaic systems, IoT-based energy monitoring, and renewable energy integration for healthcare facilities. Analysis of his publication record reveals a strong emphasis on practical implementation of renewable energy solutions in the Philippine context, with increasing focus on interdisciplinary approaches combining AI, IoT, and traditional energy engineering. Recent publications demonstrate a shift toward comprehensive system design that addresses both technical and socioeconomic aspects of renewable energy deployment in rural communities. Professor Dellosa leads research in Nelson Jr Enano's Lab and collaborates extensively with regional institutions on renewable energy projects. His work has resulted in 67 publications with significant readership (60,773 reads) and citations (286 citations), demonstrating impactful contributions to the field of renewable energy engineering in Southeast Asia.

• Renewable Energy
• Solar Photovoltaics
• Energy Engineering
• +۲ مورد دیگر