جست‌وجوی استاد و پژوهشگر

• Electrical Engineering
• Power Systems
• Power Quality
• +۵ مورد دیگر

Pertti Pakonen is a University Lecturer at Tampere University's Faculty of Information Technology and Communication Sciences, Department of Electrical Engineering. His research focuses on power quality, partial discharge analysis, high voltage systems, and smart grid technologies. He has contributed extensively to understanding grid stability, electromagnetic compatibility, and renewable energy integration. Key research areas include partial discharge diagnostics in cables and transformers, power quality monitoring in distributed networks, and the impact of emerging technologies like electric vehicle charging stations and LED lighting on grid performance. His work emphasizes data-driven methods for fault detection, predictive maintenance, and grid optimization. Recent publications highlight advancements in HVDC/HVAC grid qualification, machine learning for load control detection, and synchronization challenges in power quality data. His research bridges theoretical analysis with practical applications in industrial and urban electrical systems. Mr. Pakonen's work has addressed challenges in rural grid management, including reserve power solutions and cabling practices. He collaborates on interdisciplinary projects involving energy economics, smart metering systems, and grid-industry interactions.

Jukka Huhtamäkiمشاهده پروفایل

پژوهشگر ارشد

• Social Media Analysis
• Digital Innovation Ecosystems
• Network Visualization
• +۳ مورد دیگر

Jukka Huhtamäki is a Senior Research Fellow at the Department of Information and Knowledge Management, Tampere University. His work focuses on social media analytics, innovation ecosystems, and platform dynamics. He holds a Doctor of Science (Technology) from Tampere University (2016) and a Master of Science (Technology) in Information Technology (2005). Research interests include analyzing social media data for understanding hybrid media events, developing computational methods for team ethnography, and examining platform governance. His work contributes to UN Sustainable Development Goals related to education and sustainable innovation ecosystems. Recent publications explore topics such as AI-driven grading systems, duplicate post classification frameworks (INCEPT), and the digital afterlife of violent events. He has organized workshops on HR x AI and served as a reviewer for journals like Information Communication and Society . Awards: Best Paper Award (2013) Elaine Lawrence Best Paper Award (2012) Kunniamaininta for educational project success (2008) Grants: Multiple short-term grants (2016-2018) His work bridges computational methods with sociological perspectives, emphasizing interdisciplinary approaches to digital phenomena.

• Artificial Intelligence
• Software Development
• Large Language Models
• +۴ مورد دیگر

Kai-Kristian Kemell is a Research Fellow in the Computing Sciences department, focusing on interdisciplinary research at the intersection of Artificial Intelligence, Software Engineering, and Ethical AI. His work explores practical applications of Large Language Models (LLMs), autonomous agents in software development, and regulatory frameworks like the EU AI Act. Research Interests : His primary areas include AI-driven software development tools, ethical implementation of AI systems, multi-agent systems for code refactoring, and systematic literature reviews on trustworthiness in LLMs. He investigates how LLMs can automate tasks like microservice generation from API definitions and assist early-stage software startups through prompt engineering. Collaborations : Active collaborations include projects with researchers like Prof. Petri Abrahamsson, focusing on topics such as RAG systems, AI ethics governance, and hybrid work practices in software engineering. His work bridges theoretical AI concepts with practical industrial applications. Publications : His 2025 publications emphasize autonomous software development agents, EU AI Act critiques, and ethical alignment strategies for LLM-based systems. Earlier work (2023–2024) addresses AI ethics implementation via user stories, hybrid work impacts, and MLOps challenges. Awards : No specific scientific awards mentioned in the provided texts. Grants & Labs : No explicit details on grants or affiliated labs, but his research frequently involves collaborative projects with industry and academic partners.

• Robotics
• Pose Estimation
• Computer Aided Design
• +۴ مورد دیگر

Kulunu Samarawickrama is a Doctoral Researcher in the Department of Automation Technology and Mechanical Engineering, focusing on advanced robotics and computer vision applications. Their research emphasizes robotic manipulation, pose estimation, and human-robot collaboration in industrial settings. They contribute to the development of synthetic datasets for training robotic systems and sensor-based automation solutions. Research interests include 6D pose estimation, point cloud processing, and vision-based grasping techniques. Their work bridges theoretical advancements with practical industrial applications, such as assembly automation and collaborative robotics. Notable contributions include the 6DAPose dataset for robotic manipulation and studies on sensor-based human-robot collaboration. Their research has been published in conferences like IEEE CASE and ICAR, with peer-reviewed articles in journals like Robotics and Computer-Integrated Manufacturing.

• Robotics
• Machine Learning
• Automation Systems
• +۴ مورد دیگر

Nataliya Strokina is a Researcher in the Computing Sciences department at Lappeenranta University of Technology since 2013. She holds a Doctor of Science (Technology) from the same institution (2013). Her work focuses on robotics, automation systems, and safety engineering, with expertise in radar technology and machine learning applications. Education: Doctor of Science (Technology) in Computing Sciences from Lappeenranta University of Technology (Awarded: 20 Dec 2013). Research Interests: Develops solutions in robotics safety, cyber-physical systems, and radar-based autonomous systems. Key areas include ego-velocity estimation, MIMO radar applications, and reinforcement learning for automation. She also explores regulatory compliance (e.g., Regulation 2023/1230) for heavy machinery safety. Awards/Grants: Received TES grant (01.08.2015–30.09.2015). Collaborations span cyber-physical systems and heavy machinery safety, with contributions to international conferences like SICFP'23. Labs/Teams: Active in interdisciplinary teams focusing on automation and safety in robotics and industrial machinery.

Igor Shevkunovمشاهده پروفایل

پژوهشگر ارشد

• Computational Imaging
• Optical Engineering
• Signal Processing
• +۳ مورد دیگر

Igor Shevkunov is a Senior Research Fellow in the Computing Sciences department. Holding a Doctor of Philosophy degree from Saint-Petersburg State University (2012), his research focuses on computational imaging, phase retrieval, optical engineering, and machine learning applications in imaging systems. He has contributed to over 47 research outputs, including seminal works on lensless phase retrieval, hyperspectral imaging, and deep learning-enhanced microscopy. His grants include projects from 2017–2018, though specifics are brief. Collaborations span global institutions, emphasizing computational methods in optical imaging and holography. Notable datasets include code for hyperspectral phase retrieval algorithms. His work bridges theoretical optics and practical applications in biomedical imaging and advanced optical systems design. Key research areas include noise suppression in imaging, ADMM optimization for hyperspectral data, and meta-optics for miniaturized cameras. Despite no listed students, his contributions to imaging frameworks suggest active mentorship in collaborative projects.

Anssi Laukkarinenمشاهده پروفایل

مدرس

• Residential Building Engineering
• Climate Change
• Indoor Air Quality
• +۴ مورد دیگر

Anssi Laukkarinen is a University Instructor in Civil Engineering and a Doctoral Researcher in the Doctoral Programme in Built Environment. His work focuses on building physics, climate change impacts on residential infrastructure, and indoor environmental quality. He holds a Master of Science (Technology) in Building Construction and Community Development (2015) and a Bachelor of Science (Technology) in the same field (2012). His research explores topics such as hygrothermal performance of building components, energy demand forecasting under climate change, and machine learning applications for indoor temperature prediction. Notable contributions include studies on ventilation effects in schools and daycare centers and validation of heat-air-moisture models. Education: Master of Science (Technology), Building Construction & Community Development, 2015 Bachelor of Science (Technology), Building Construction & Community Development, 2012 Laukkarinen has received the Aarre M. Mattisen Foundation Stipend (2014) and contributed to interdisciplinary projects like the COMBI-project. He engages in media discussions on topics such as climate change impacts on housing and building renovation challenges.

Narayan Puthanmadam Subramaniyamمشاهده پروفایل

استاد مهمان

• Neuroscience
• Biomedical Engineering
• Machine Learning
• +۵ مورد دیگر

Narayan Puthanmadam Subramaniyam is a Visiting Professor at the Department of Neuroscience and Biomedical Engineering, Tampere University of Technology. His research focuses on biomedical signal processing and computational neuroscience, with a strong emphasis on integrating machine learning with neuroimaging techniques. Education: Tekniikan tohtori (Doctor of Science) in Medical Technology from Tampere University of Technology Active in neuroscience and biomedical engineering, Subramaniyam's work explores deep brain stimulation, EEG/MEG signal analysis, and emotion classification using spiking neural networks. His publications span topics like cortical parcellation, functional connectivity, and feature fusion of multimodal data. Research Trends: His recent studies combine machine learning with neurological signal processing, focusing on applications in Parkinson's disease, anesthesia monitoring, and emotion recognition. Key methods include clustering techniques, complexity metrics, and deep learning algorithms. Collaborations: Active in international collaborations, with network links in neuroscience and computer science domains. Publications: Contributions to journals like Scientific Reports , Sensors , and Journal of Neuroscience Methods , addressing challenges in signal processing and brain-inspired computing.

• Numerical Analysis
• Finite Element Methods
• Computational Mathematics
• +۵ مورد دیگر

Harri Hakula is a Senior University Lecturer in the Department of Mathematics and Systems Analysis at Aalto University. His academic qualifications include a Doctoral degree (1997) and Master's degree (1991) in Engineering and Technology from Helsinki University of Technology. Dr. Hakula's research spans numerical analysis, computational mathematics, and finite element methods. His primary focus includes: Advanced finite element techniques for eigenvalue problems and error estimation Stochastic modeling and uncertainty quantification in computational frameworks Conformal mappings and geometric computations Structural analysis of shells and sensitive materials Machine learning integration for parameter uncertainty problems His recent publications demonstrate strong trends in computational mechanics, stochastic analysis, and geometric modeling. The majority of work utilizes finite element methods to solve complex eigenvalue problems, characterize shell structures under uncertainty, and develop machine-learning enhanced computational frameworks. A significant portion explores conformal mappings and their applications in geometric analysis. Dr. Hakula has led significant research projects including the ERC-funded ALEM project (2014-2019) where he served as Principal Investigator. He has supervised at least 5 doctoral theses and maintains active collaborations internationally.

Juha Ala-Laurinahoمشاهده پروفایل

پژوهشگر

• Antenna Engineering
• Millimeter Wave Technology
• Terahertz Imaging
• +۳ مورد دیگر

Juha Ala-Laurinaho is a Researcher in the Department of Electronics and Nanoengineering at Aalto University , specializing in antenna arrays , millimeter-wave technology , and terahertz imaging . He has led projects like TORI (2025–2029) and ADENN (2019–2021), focusing on beamforming and holographic imaging applications. Doctoral Degree (2001), Helsinki University of Technology Licentiate Degree (1998), Helsinki University of Technology Master's Degree (1995), Helsinki University of Technology His research spans terahertz corneal sensing , dielectric spectroscopy , and millimeter-wave 5G antenna arrays . Recent work includes telecentric lens systems and 3D-printed metallic antennas , emphasizing compact designs and scan-loss reduction. Scientific recognition includes the EuRAD Young Engineers Prize (2008) and AMTA Student Paper Supervisor Award (2004) . He has mentored doctoral students like S. Karki (2022) and J. Lamberg (2024), and contributed to over 313 publications.

• Neuroscience
• Biomedical Engineering
• Transcranial Magnetic Stimulation
• +۳ مورد دیگر

Dania Humaidan (Faculty) is affiliated with the Department of Neuroscience and Biomedical Engineering. Her research focuses on the intersection of neuroscience and biomedical engineering, particularly in advancing transcranial magnetic stimulation (TMS) technologies for clinical applications. Her recent work explores... Multi-locus TMS system design for clinical deployment Machine learning integration in EEG-TMS excitability decoding Real-time closed-loop brain state modulation These studies highlight her contributions to... Her research activities involve...

• Data Intelligence
• AI & Machine Learning
• Digital Learning Environments
• +۷ 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.

• Deep Neural Networks
• Convolutional Neural Networks
• Social Robotics
• +۴ مورد دیگر

Francisco Fernandes Junior is a Postdoctoral Researcher in the Department of Information and Communications Engineering at Aalto University, specializing in AI-driven systems. His work intersects computer science, human-computer interaction, and neural network optimization. His research focuses on Deep Neural Networks (100% fingerprint match), Convolutional Neural Networks (92%), and Social Robotics (90%), with significant contributions to neural architecture design and evolutionary strategies. Current projects explore generative AI in software development and real-time resource allocation frameworks. Recent publications reveal trends toward human-AI collaboration (2025 touchscreen error simulation) and low-code AI platforms (AgentForge), emphasizing practical applications in medical imaging and software engineering. His work contributes to UN Sustainable Development Goals through computational sustainability. ACM CHI 2025 Best Paper Honorable Mention Award (2025) Google Cloud for Researchers Grant (2025) As an active academic contributor, he serves as reviewer for IEEE Transactions on Artificial Intelligence (2024–present) and Neural Computing & Applications (2023), while participating in conference organization including ICAART 2025. His research integrates industry partnerships through cloud computing resources and collaborative AI frameworks.

• Mathematical Logic
• Computational Complexity
• Descriptive Complexity Theory
• +۳ مورد دیگر

Miikka Vilander is an active researcher associated with the Mathematics Research Centre, specializing in mathematical logic, computational complexity, and entropy theory. He holds a Master of Science degree from the University of Tampere (awarded 8 June 2016) and contributes to theoretical computer science through publications on formula size, succinctness, and logical definability. Education: Master of Science, University of Tampere His research focuses on the intersection of logic, entropy, and data interpretability. Key areas include first-order logic complexity, Kolmogorov complexity, and game-theoretic models. Recent work spans 2023–2025, with publications in top venues like STACS and CSL conferences. Vilander has collaborated internationally, including visits to Leibniz-Universität Hannover (2018) and co-authored works with researchers like R. Jaakkola and A. Kuusisto. His contributions emphasize theoretical foundations and practical applications in AI explainability.

• Transcranial Magnetic Stimulation
• Functional Connectivity
• Neuroscience
• +۷ مورد دیگر

Roberto Guidotti is a Lecturer and Visitor Faculty member in the Department of Neuroscience and Biomedical Engineering at Aalto University's School of Science. His research focuses on the intersection of neuroscience and biomedical engineering, particularly in the areas of transcranial magnetic stimulation, brain network analysis, and neuroimaging data processing. Dr. Guidotti's research interests span several key areas in modern neuroscience and biomedical engineering. He specializes in Transcranial Magnetic Stimulation (TMS) , where he investigates state-dependent stimulation protocols and their effects on brain network dynamics. His work in Electroencephalography (EEG) and Magnetoencephalography (MEG) involves developing advanced signal processing techniques to analyze brain connectivity. He has made significant contributions to Hidden Markov Model applications in neuroscience, particularly for studying brain state transitions during neuromodulation. His research also encompasses functional connectivity analysis , cross-frequency coupling , and the development of multi-locus brain stimulation approaches. Analysis of Dr. Guidotti's recent publications reveals a strong focus on integrating control theory with neuromodulation techniques, advancing multi-locus transcranial magnetic stimulation methods, and developing novel analytical approaches for EEG/MEG data. His work demonstrates a consistent trajectory toward more precise and targeted brain stimulation techniques that account for dynamic brain states. Dr. Guidotti actively collaborates with leading researchers in the field, including Risto J. Ilmoniemi, Vittorio Pizzella, and Laura Marzetti, contributing to the advancement of non-invasive brain stimulation technologies and their clinical applications.