جستجوی استاد راهنمای دانشگاهی

Petteri Nurmi is a Professor of Computer Science at the University of Helsinki, affiliated with the Department of Computer Science and the Helsinki Institute of Sustainability Science (HELSUS). His research focuses on IoT systems, environmental monitoring, AI-driven solutions, and sustainable computing. He leads projects such as the NordForsk-funded initiative (2024-2028) and the Team Finland Knowledge programme (2024-2026), emphasizing large-scale IoT deployments and quantum computing integration. Key research interests include drone-based air quality monitoring, low-cost sensor networks, and AI applications in environmental science. Nurmi has published extensively in top venues like IEEE IoT Journal and ACM workshops. His work bridges technical innovation with societal challenges, such as urban pollution reduction and sustainable resource management. He supervises doctoral students in the Computer Science program and collaborates internationally on projects like underwater plastic detection (SEAGULL) and smart city infrastructure. Nurmi’s contributions to edge computing and pervasive sensing have been recognized through grants totaling over €2M. His lab develops tools for data-intensive systems, including thermal imaging for energy efficiency analysis and AI-driven sensor fusion frameworks.

• Environmental Monitoring
• Internet of Things (IoT)
• Artificial Intelligence (AI)
• +۷ مورد دیگر

Valeriy Vyatkin is a Professor at the Department of Electrical Engineering and Automation, Aalto University. His research focuses on advancing industrial automation, control systems, and their integration with emerging technologies like machine learning and digital twins. He specializes in standards such as IEC 61499, addressing interoperability, formal verification, and performance optimization in distributed automation systems. Key research interests include: Physics-informed machine learning for industrial processes (e.g., steel rolling, reservoir engineering) Formal methods for control system validation and safety-critical applications Development of adaptive automation frameworks for Industry 5.0 challenges, including human-robot collaboration and energy systems Interoperability between legacy and modern industrial standards (OPAS, OPC UA) Recent work emphasizes real-time simulation, FPGA-based control prototyping, and AI-driven solutions for energy efficiency and sustainability in manufacturing, horticulture, and process industries. Publications span topics like robotic walker design, probabilistic model checking, and decentralized learning management systems. He collaborates on EU and industry-funded projects, focusing on digital twin implementation, edge computing, and virtual commissioning. His team develops tools for automated code generation, system migration, and anomaly detection in complex industrial settings.

• Industrial Automation
• Control Systems
• Machine Learning Applications
• +۵ مورد دیگر

Matti Tedre is a Professor at the School of Computing, Faculty of Science, Forestry and Technology, University of Eastern Finland. His research focuses on computer science education, ICT4D, social studies of computer science, and the history and philosophy of computer science. He leads the Technologies for Learning and Development research group and is involved in the Generation AI project (2022–2028), exploring AI education for security mindset development. His work bridges theory and practice, emphasizing educational technology, AI literacy, and participatory design. Recent projects include developing low-cost AI kits for novice learners and co-designing ML-driven apps with children. He collaborates globally on topics like K-12 computing education, data agency, and ethical AI integration in classrooms. Key contributions include studies on scaffolding in ML education, children’s understanding of algorithmic biases, and the role of generative AI in creative learning. His research also addresses challenges in Tanzanian ICT adoption, including financial management systems for informal groups and timetabling software for higher education institutions. Tedre’s interdisciplinary approach spans computer science, education, and sociology, with a focus on democratizing AI access and fostering critical digital literacy among youth and educators.

• Computer Science Education
• ICT4D
• Social Studies of Computer Science
• +۵ مورد دیگر

Pekka Abrahamsson is a Professor at the Faculty of Information Technology and Communication Sciences at Tampere University , Finland. He actively contributes to research in Software Engineering , Artificial Intelligence , and AI Ethics , with recent work focusing on generative AI, multi-agent systems, and ethical software design. Published over 42 research outputs (2016–2025) Editorial roles in multiple international conferences (2016, 2019, 2022–2024) His research emphasizes practical applications of AI in software development, including tools like ChatGPT for full-stack coding, multi-agent systems for requirements engineering, and frameworks for AI ethics in software practices. He also explores challenges in continuous software engineering and quantum computing architecture. Key publication trends (2024–2025) include: Agile methodologies enhanced by AI Ethical alignment in AI systems Autonomous software development platforms AI tool adoption in programming education Quantum software architecture reviews Technical debt in embedded systems Awards and recognitions : PlumX Metrics highlight 1 scientific prize (unspecified) High readership on platforms like Mendeley (up to 211 readers) Multiple citations in Scopus (up to 53 citations for quantum computing work) Grants and collaborations include global studies on work-from-home impacts, AI tool usage in programming courses, and projects like CodePori for autonomous development. His work influences policy and industry practices, particularly in AI ethics and multi-robot systems.

• Software Engineering
• Artificial Intelligence
• AI Ethics
• +۳ مورد دیگر

Matti Minkkinen is a Docent at the Turku School of Economics (University of Turku) and a Postdoctoral Researcher in Information Systems Science at the Department of Management and Entrepreneurship. His work bridges futures studies with ethics, privacy, and socio-technical systems in digital transformation. Recent roles focus on responsible AI governance and foresight methodologies. University: University of Turku School: Turku School of Economics Department: Department of Management and Entrepreneurship His research explores how digital technologies reshape organizational practices, emphasizing Futures Consciousness as a human capacity. Key themes include responsible AI , privacy protection , and causal layered analysis in scenario planning. Publications highlight ethical governance frameworks and EU policy debates. Recent articles address generative AI ethics , ML system integration , and AI auditing across journals like Communications of the Association for Information Systems and Information and Management . Topics cluster around socio-technical systems, digital ethics, and institutional adaptation to AI. Teaching and editorial roles include co-curating student research collections at Finland Futures Research Centre. No explicit scientific awards are listed, but his work contributes to foresight theory and practice.

• Digital Transformation
• Responsible Artificial Intelligence
• Foresight Methods
• +۴ مورد دیگر

Niko Mäkitalo is an Assistant Professor at the University of Jyväskylä's Faculty of Information Technology and a member of the University Consortium Chydenius. His research focuses on advancing cyber-physical systems through innovative software architectures for AI, IoT, and the Cloud-Edge Continuum, with significant emphasis on hardware and network infrastructures. He teaches and mentors graduate students in these areas. Research Interests: Mäkitalo's work centers on creating intelligent environments via novel software frameworks, engineering methodologies for distributed AI/ML applications, and addressing ethical/privacy challenges in edge computing. He explores the intersection of agile development practices, third-party code management, and industry standards like IDSA/GAIA-X for AI integration. Key Contributions: His recent work includes studies on software ownership dynamics, regulatory-aware AI deployment, and liquid AI systems. He leads the Empirical Software Engineering Research group and collaborates on initiatives like 6GSoft for edge-cloud software solutions. Advising & Grants: Mäkitalo supervises Master's and Ph.D. candidates in software engineering and systems architecture. His projects often involve industry partnerships to address real-world challenges in smart manufacturing, autonomous systems, and networked environments. Labs/Teams: Active in the Empirical Software Engineering Research group, contributing to cross-disciplinary projects at the University of Jyväskylä's tech innovation ecosystem.

• Cyber-Physical Systems
• AI/ML Software Engineering
• IoT Architectures
• +۳ مورد دیگر

Jari Vepsäläinen is an Assistant Professor at Aalto University's Department of Energy and Mechanical Engineering under the College of Engineering. He serves as Director of the Fluid Power group and specializes in mechatronics design, energy efficiency, and generative design methodologies. Research focuses on physics-based modeling for energy recovery AI/ML applications in electromechanical system design Applications in robotics, heavy machinery, and sustainable transportation His recent publications demonstrate expertise in hybrid systems, fluid power optimization, and AI-driven engineering, with a strong emphasis on electrification and efficiency across automotive, maritime, and industrial domains. Key areas: Mechatronics, Energy Systems, Generative Design Technologies: Digital Twins, IoT, Machine Learning Current projects involve thermal energy systems, electric motor optimization, and advanced control algorithms for mobile machinery.

• Mechatronics Design
• Energy Efficient Systems
• Generative Design
• +۷ مورد دیگر

Yanda Tao is a Researcher at the Department of Computing, Imperial College London, funded by Samsung since March 2024. He graduated with distinction from the MSc Advanced Computing program at Imperial College London (2022-2023) and holds an Ingénieur degree in AI from CentraleSupélec, Université Paris-Saclay (2019-2023). During his master's, he developed HetML , a system for heterogeneity-aware automatic parallel training of deep learning models under the supervision of Professor Peter Pietzuch at LSDS (Large-Scale Data and Systems Group). His research focuses on machine learning systems design for heterogeneous GPU clusters, optimizing cloud infrastructure for deep learning workflows, and improving distributed training efficiency. Technical expertise includes system architecture, parallel computing, and ML pipeline deployment gained through internships at Alstom (Data Scientist) and SAP France (Software Engineer). His work bridges theoretical system design with practical implementation challenges in large-scale ML environments. The HetML project (2023, in preparation) demonstrates his specialization in automatic parallelism techniques and heterogeneity-aware optimization for distributed deep learning. This aligns with his broader interest in scalable machine learning infrastructure and cost-efficient system design for democratizing large-scale AI applications.

• Machine Learning Systems
• Distributed Deep Learning
• Automatic Parallelism
• +۲ مورد دیگر

Hong-Linh Truong is an Associate Professor (tenured) at the School of Science , Aalto University in Finland. She leads the AaltoSEA Group focused on Systems and Services Engineering Analytics. Additionally, she holds an Adjunct Associate Professor position at the Faculty of Informatics, TU Wien, Austria. Her research spans Cloud Computing Internet of Things (IoT) Big Data Analytics Distributed Systems Elastic Computing Context-aware Systems with particular emphasis on engineering analytics for hybrid cloud-IoT systems and service elasticity management. Analysis of her 15 most recent publications reveals focus areas including Edge Computing (5 papers) Blockchain Applications (3 papers) Machine Learning Systems (2 papers) Cyber-Physical Systems (2 papers) Collaborative Computing (2 papers) Resource Orchestration (1 paper) Scientific achievements include Best Poster Award at European Grid Conference 2005 Best Paper Award at CAiSE'12 She actively collaborates with institutions across Europe and Asia, with research applications in both industrial contexts (air traffic management, manufacturing) and public sectors (disaster response, sustainability governance). Her work integrates theoretical foundations with practical implementations, as evidenced by numerous toolset developments like SCALEA-G and IoTCloudSamples.

• Cloud Computing
• Internet of Things (IoT)
• Big Data Analytics
• +۹ مورد دیگر

Nada Sanad is a postdoctoral researcher at the Empirical Software Engineering in Software, Systems, and Services (M3S) research unit at the University of Oulu's Faculty of Information Technology and Electrical Engineering. She earned her Ph.D. (2024) in Information Processing Science from the University of Oulu, focusing on data-driven decisions and human-machine collaboration. She holds a B.Sc. (2011) and M.Sc. (2013) in Business Informatics from the German University in Cairo, Egypt, with a focus on data mining and big data analytics. Her research interests span data-driven decision-making, hybrid intelligence, human-machine collaboration, AI/ML for decision support, big data analytics, and business intelligence. Her work emphasizes ethical and practical applications of AI in decision-making processes, particularly in autonomous systems and manufacturing contexts. Recent publications (2023–2025) highlight trends in evaluating AI-driven decisions, hybrid intelligence frameworks, and data governance in industry. She contributes to projects like OXILATE, integrating AI insights into actionable expertise. Nada Sanad has not been listed as having formal advisees but collaborates actively in interdisciplinary teams at the M3S unit. She maintains profiles on ORCID, SCOPUS, Google Scholar, and ResearchGate.

• data-driven decision-making
• human-machine collaborative rationality
• hybrid intelligence
• +۹ مورد دیگر

Kari Systä is a Professor of Software Engineering at Tampere University's Faculty of Information Technology and Communication Sciences. His career spans academia and industry, including 16 years at Nokia Corporation and current leadership in software engineering research. He teaches courses like Continuous Development and Deployment - DevOps and supervises Master's theses. Research Focus: Data-driven software engineering, web/cloud platforms, IoT architectures, energy systems software, and edge computing. Current Projects: LiquidAI (2023-2025) for IoT-Edge-Cloud ML systems; 6GSoft (2023-2026) for 6G software engineering; Microblock (2021-2023) on blockchain micro-credentials; and DELI (2023-2025) for energy research infrastructure. Research Unit: Leads work at TASE (Tampere Software Engineering Research Group). His expertise bridges software engineering theory and practice, with collaborations across Finnish universities and industry. He advocates for integrating AI/ML into software development workflows and optimizing energy systems through programmable edge-cloud solutions.

• Software Engineering
• Web Engineering
• Edge Computing
• +۷ مورد دیگر

Damian Kedziora serves as an Associate Professor at the Software Engineering Department within LUT School of Engineering Sciences in Lahti, Finland. His academic credentials include a Docentship in Business Process Automation Software (2022) and a Doctoral Degree in Industrial Engineering and Management (2018) from LUT University. With over a decade of industry experience in banking, ICT, and technology consulting, his research bridges practical digital transformation and theoretical software engineering, focusing on Low-code/no-code platforms for process automation Commercial aspects of software ecosystems AI integration in enterprise systems His recent work analyzes 2023-2025 trends in ISO 25010:2023 compliance Hybrid workforce dynamics Intelligent automation maturity Post-pandemic digital strategies Scientific recognition includes Docentship in Business Process Automation Software (2022) Available for consultation at Damian.Kedziora@lut.fi or via +358 44 768 4796. His career combines academic rigor with corporate experience in implementing digital solutions.

• Low-code software development
• Robotic Process Automation (RPA)
• Intelligent Automation
• +۳ مورد دیگر

Linh Truong is an Associate Professor at Aalto University School of Science, Department of Computer Science, where he leads the AaltoSEA Group on Systems and Services Engineering Analytics. He also holds an adjunct position at TU Wien. Research interests: Distributed systems, edge-cloud continuum, IoT analytics, and machine learning infrastructure. Current focus includes resource optimization for real-time ML on edge devices, federated learning marketplaces, and context-aware service policies across edge environments. Publications: Recent work addresses challenges in edge-based ML serving, blockchain-edge integration, and DevOps for IoT microservices. Articles appear in IEEE conferences and journals including Transactions on Services Computing, Cloud Computing, and Internet of Things. Professional service: Regular program committee member for IEEE ICWS, CLOUD, BigData conferences. Editorial board member for Empirical Software Engineering journal. Professor Truong supervises doctoral students in distributed systems and edge intelligence, with research supported by EU and national funding.

• Edge Computing
• Distributed Systems
• IoT Analytics
• +۳ مورد دیگر

Tahir Mohammad is a Senior Lecturer and Docent in Cyber Security Engineering at the University of Turku, Faculty of Technology. Previously, he served as a Senior Lecturer and Program Leader at Sunway University Malaysia, managing a BSc dual degree program with Lancaster University UK. His industry experience includes roles at MIMOS and TM R&D in Malaysia, where he designed spectrum utilization systems and IoT solutions. He holds patents in wireless communication and IoT, and has consulted for startups and educational institutions. His research focuses on cybersecurity, AI-driven wireless networks, IoT security, and resource optimization in 5G/6G systems. Notable contributions include federated machine learning for IoT authentication and blockchain applications. He leads the Cyber Security Engineering Lab and teaches courses on ethical hacking and IoT security. Recent work emphasizes AI explainability in threat detection, smart city resilience, and gamification's impact on security awareness. His publications span threat intelligence, SDN multicast systems, and healthcare IoT integration with 6G. Research projects include optimizing LiDAR-based SLAM algorithms and analyzing distributed ML resource consumption. He collaborates with industry partners on embedded systems security and cloud-based healthcare solutions.

• Cybersecurity
• Artificial Intelligence for Wireless Networks
• Internet of Things (IoT)
• +۴ مورد دیگر

Minna Kivihalme is a Senior Lecturer at Metropolia University of Applied Sciences' School of ICT and Industrial Management. She specializes in data intelligence, AI/ML integration in education, and digital platform development. Her current role includes project management of the Data Intelligence Launchpad - AI & Machine Learning Pilot Program funded by SECLE. Education: Master of Science (University of Helsinki) Licentiate of Science/Technology (Aalto University) Her research focuses on optimizing digital learning environments, applying machine learning in higher education, and implementing innovative teaching methods. She has completed advanced courses in Deep Learning with Python, Digital Platform Economy, and Social Media Analytics at Aalto University. Key Projects: Leading AI/ML pilot program for industry-academia collaboration Developing blended learning frameworks for ICT education Lean Service Creation Facilitation certification (Futurice) Minna holds strong language proficiency in Finnish (native), English (excellent), and intermediate Swedish/French, enabling multilingual academic collaboration.

• Data Intelligence
• AI & Machine Learning
• Digital Learning Environments
• +۷ مورد دیگر