جستجوی استاد راهنمای دانشگاهی

Fabian Fagerholmمشاهده پروفایل

استادیار

• Software Engineering
• Human-Computer Interaction
• Agile Development
• +۳ مورد دیگر

Fabian Fagerholm is an Assistant Professor in the Department of Computer Science at Aalto University. His work bridges software engineering, human-computer interaction, and empirical research methodologies. He actively participates in research groups such as Software and Service Engineering (SSE) and Human-Computer Interaction and Design (HCID). Fagerholm's research explores: Continuous experimentation in software development Developer cognition and mental models Agile methodologies and team dynamics Low-code platforms and end-user programming Software engineering education and pedagogy His publications reflect a strong empirical focus, with recurring themes of human factors in technical systems and educational innovation. He has received notable awards including: Journal of Systems and Software Best Paper Award (2018) EUROMICRO SEAA Distinguished Paper Award (2017) Teacher of the Year (2013) Nokia Foundation Scholarship (2013) Fagerholm contributes to software engineering infrastructure through tools for experimentation and boundary artifacts, enhancing collaboration in distributed teams.

Kwantae Kimمشاهده پروفایل

استادیار

• Power-Efficient Analog/Mixed-Signal Integrated Circuits
• Biomedical Sensor Systems
• Neuromorphic Sensor ICs
• +۳ مورد دیگر

Kwantae Kim is an Assistant Professor at the Department of Electronics and Nanoengineering within Aalto University's School of Electrical Engineering . He leads the Tiny Systems and Circuits (TSirc) Group , focusing on power-efficient analog/mixed-signal ICs for biomedical and neuromorphic sensor systems. IEEE Senior Member (2025) Collaborates with institutions across Europe, Asia, and America Specializes in ultra-low-power AI-embedded IoT platforms His research emphasizes Tiny, Sensory, Intelligent, and Wireless IoT systems through: Development of energy-efficient IC architectures Democratizing access to advanced chip design Hardware-software co-design for edge computing Recent publications highlight innovations in: Spoken-language-understanding SoCs Temporal-sparsity-aware keyword spotting Open-source silicon frameworks Awards include: 2025 IEEE Senior Member 2023 Best Poster Award (AICAS) 2019 Samsung HumanTech Silver Award Research partnerships span: Prof. Tobi Delbruck (UZH/ETH Zurich) Prof. Hoi-Jun Yoo (KAIST) Prof. Shih-Chii Liu (UZH) Prof. Sohmyung Ha (NYU Abu Dhabi)

• Video Coding
• Machine Learning
• FPGA Acceleration
• +۳ مورد دیگر

Jarno Vanne is a Professor at the Department of Computing Sciences, Faculty of Information Technology and Communication Sciences at Tampere University. His research focuses on video coding standards, real-time systems, and hardware acceleration, particularly in the context of FPGA implementations and open-source tools. He leads projects involving VVC (Versatile Video Coding), V-PCC (Volumetric Video Coding), and HEVC (High Efficiency Video Coding), with an emphasis on efficiency, low latency, and machine learning integration. Key research interests include point cloud compression, saliency-guided encoding, parallelization schemes, and real-time video communication protocols. His work often addresses challenges in multi-party video streaming, embedded systems, and encryption mechanisms for privacy protection. He has contributed to open-source projects like the UVG dataset, Kvazaar encoder, and CiThruS simulation frameworks. Recent publications highlight advancements in VVC intra encoding optimizations, machine learning-driven partitioning schemes, and FPGA-accelerated solutions for edge computing. His research bridges theoretical video coding algorithms with practical implementations, aiming to improve compression efficiency while maintaining real-time performance.

• Industrial Automation
• Control Systems
• Machine Learning Applications
• +۵ 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.

Matti Tedreمشاهده پروفایل

استاد

• Computer Science Education
• ICT4D
• Social Studies of Computer Science
• +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.

• Video Coding
• Machine Learning in Video Compression
• Energy-Efficient Encoding
• +۳ مورد دیگر

Alexandre Mercat is an Assistant Professor in the Department of Computer Engineering at Tampere University, within the Faculty of Information Technology and Communication Sciences. His research focuses on video coding, energy-efficient encoding, and real-time multimedia systems. He leads projects on open-source video encoders and standards, including contributions to HEVC, VVC, and V-PCC technologies. His work emphasizes machine learning integration, low-power hardware optimizations, and scalable distributed encoding frameworks. Key technical interests include improving video compression efficiency through algorithmic innovations, developing open-source tools like the UVG dataset and Kvazaar encoder, and addressing challenges in volumetric video communication and 3D point cloud encoding. His research spans theoretical algorithm design to practical implementations, with applications in virtual reality, live streaming, and edge computing. Recent projects include real-time saliency-guided video coding frameworks, energy reduction techniques for HDR streaming, and multi-layer VVC coding schemes for hybrid machine-human consumption. He also explores FPGA acceleration and parallelization strategies for distributed video encoding systems. No scientific awards are explicitly mentioned in the provided texts. While no formal advisees are listed, his research group likely involves students through open-source development and collaborative projects. His work integrates closely with industry standards bodies and open-source communities, emphasizing reproducible evaluation frameworks and end-to-end software tools.

Armin Wehrfritzمشاهده پروفایل

استادیار

• Computational Fluid Dynamics (CFD)
• Fluid Mechanics
• Turbulence
• +۵ مورد دیگر

Armin Wehrfritz is an Assistant Professor in the Department of Mechanical Engineering at the University of Turku. His research focuses on high-fidelity numerical simulations of multiphase and chemically reacting flows, particularly involving low-carbon fuels like hydrogen. He holds a Master's degree from the University of Kaiserslautern and a Ph.D. from Aalto University. During his doctoral studies, he was a visiting researcher at Eindhoven University of Technology, and later worked as a Research Associate at the University of New South Wales in Sydney. His expertise spans computational fluid dynamics (CFD), turbulence modeling, combustion physics, and high-performance computing (HPC), with a growing interest in machine learning applications for numerical methods. Education: Doctoral Degree: Aalto University, Finland Master's Degree: University of Kaiserslautern, Germany Research interests include: Direct numerical simulation (DNS) and large-eddy simulation (LES) of combustion processes Hydrogen integration in compression-ignition engines Development of advanced micro-mixing models for transported PDF methods Data-driven approaches for optimizing combustion systems His recent work emphasizes reducing carbon emissions through hydrogen-diesel dual-fuel systems and improving simulation accuracy for engine-relevant conditions. Collaborations have involved institutions in the Netherlands, Australia, and the U.S., focusing on topics like plasma-ignited hydrogen jets and NOx reduction mechanisms.

• System-on-Chip (SoC) Design
• FPGA-Based High-Level Synthesis
• Real-Time Embedded Systems
• +۵ مورد دیگر

Timo Hämäläinen is a Professor of Computer Engineering at Tampere University, leading the Unit of Computing Sciences within the Faculty of Information Technology and Communication Sciences. He is a core member of the System-on-Chip research group and the Computer Engineering Team, actively contributing to the System-on-Chip Hub initiative. His research focuses on System-on-Chip (SoC) design methodologies, FPGA-based high-level synthesis, real-time embedded systems, and open-source hardware-software co-design frameworks like RISC-V and HEVC encoding. He has pioneered work on agile SoC development processes and validation techniques for large-scale hardware systems. Key research areas include: High-level synthesis (HLS) optimization for FPGAs Real-time processor architectures and context-switching latency reduction Formal verification of IP-XACT-compliant SoC designs Resilient RISC-V MPSoC implementations Hardware-accelerated algorithms for signal processing and networking His recent publications (2023-2025) emphasize agile SoC development frameworks, hardware-software co-design for real-time systems, and validation methodologies for large-scale embedded systems. He has contributed to open-source projects like Kvazaar HEVC encoder and the Kactus2 IP-XACT toolchain. Academic advising includes students such as Santéri Mäki-Äijö (formal verification), Arto Oinonen (RISC-V tooling), and Sakari Lahti (processor modeling). His work integrates academic research with industrial collaboration through frameworks like the Fault-slip-Through quality assessment methodology.

Damian Kedzioraمشاهده پروفایل

دانشیار

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

Shola Oyedejiمشاهده پروفایل

پژوهشگر

• Sustainable Software Development
• Agile Methodologies
• Green ICT
• +۳ مورد دیگر

Shola Oyedeji is a post-doctoral researcher at the Software Engineering department of LUT School of Engineering Sciences . His work bridges software engineering and sustainability , focusing on agile methodologies , green ICT , and digital ethics . Research Focus Sustainable software development practices Climate justice in higher education Quality assurance for low-code/no-code platforms ICT procurement sustainability Security-usability alignment patterns Design science for sustainability frameworks Scientific Contributions His publications span topics like agile sustainability integration , sustainable digital transformation , and ICT environmental impact , appearing in journals such as ACM Transactions on Software Engineering and Journal of Cleaner Production . Collaborations He has collaborated internationally with researchers from institutions including LUT University , University of La Laguna , and KTH Royal Institute of Technology .

• AI Education
• Social Media Literacy
• Gamification in K-12 Education
• +۳ مورد دیگر

Nicolas Pope is a Senior Researcher at the School of Computing, University of Eastern Finland. He actively contributes to AI education research, focusing on social media literacy, gamification in K-12 education, and pedagogical frameworks that cultivate data agency among children. His work spans projects like Generation AI (2017-present) and Technologies for Learning and Development (since 2017). nicolas.pope@uef.fi | +358 50 528 8449 University of Eastern Finland, School of Computing His research addresses critical gaps in children's understanding of data traces, profiling, and algorithmic biases in digital platforms. He develops interactive classroom games and no-code tools to demystify social media mechanics and generative AI for young learners. Recent work explores mixed reality remote learning and interdisciplinary approaches to increase diversity in computer science education. Key publications (2023-2025) include studies on gamified AI education, social media literacy frameworks, and bias-awareness tools for text-to-image generative AI. His projects often involve collaboration with Finnish schools and international research teams. Pope's work bridges technical AI concepts with pedagogical strategies to foster critical thinking about data-driven technologies among children.

Alexandru Palerمشاهده پروفایل

دانشیار

• Quantum Computing
• Quantum Software
• Quantum Circuit Compilation
• +۲ مورد دیگر

Alexandru Paler serves as an Associate Professor in the Department of Computer Science at Aalto University, Finland, where he leads research in quantum software development. His work focuses on designing compilers and optimization frameworks for quantum circuits, with emphasis on quantum error correction implementation and fault-tolerant quantum computing systems. Based in Espoo at Konemiehentie 2, he maintains active research collaborations through the university's quantum computing initiatives. Dr. Paler's research spans quantum circuit compilation, quantum error correction (particularly surface codes and QLDPC codes), and quantum software optimization. His team develops high-performance quantum compilers for neutral atom architectures and modular superconducting systems, addressing critical challenges in resource estimation and fault tolerance. The Quantum Operating Systems (QUANTUM) research group he contributes to explores scalable quantum software frameworks that bridge theoretical algorithms with practical hardware constraints, with significant work on graph-state compilation and reinforcement learning for circuit optimization. Analysis of his 15 most recent publications (2023-2025) reveals concentrated efforts in quantum compiler design, error correction scalability, and hardware-aware quantum software. Key trends include machine learning applications for decoder optimization, novel approaches to measurement-free error correction, and queuing theory models for fault-tolerant circuit analysis. His work consistently addresses the practical barriers to large-scale quantum computing through compiler innovations and resource-efficient circuit design. Dr. Paler actively participates in the Quantum Operating Systems (QUANTUM) research group within Aalto's Department of Computer Science, focusing on Algorithms and Theoretical Computer Science. This team develops quantum software infrastructure for next-generation quantum hardware, with current projects including Pandora (ultra-large-scale circuit compilation), quantum circuit caching mechanisms, and standardized cell approaches for neutral atom systems. Their research directly supports the transition from theoretical quantum algorithms to executable, error-resilient quantum programs.

• Internet of Things
• 6G Software Architecture
• Machine Learning Systems
• +۵ مورد دیگر

Abhishek Kumar is a Researcher at the Center for Ubiquitous Computing, part of the Faculty of Information Technology and Electrical Engineering at the University of Oulu. He holds a PhD from the Department of Computer Science at the University of Helsinki (2018–2022). His research focuses on software-hardware co-integration in areas such as IoT, 6G, machine learning systems, energy efficiency, and environmental sensing, with a strong emphasis on trustworthy computing and UN Sustainable Development Goals (SDGs) like clean energy and sustainable cities. Teaching responsibilities include courses such as 521290S Distributed Systems (5 ECTS, 2025), High Performance Computing: Programming Parallel Supercomputers (3 ECTS, 2025), and Network Analysis and Graph Machine Learning (2 ECTS, 2025). Past courses include IoT and distributed systems modules from 2022 onward. He actively supervises 1 PhD student, 11 Master’s thesis students, and 4 independent studies, with prior supervision of 3 Bachelor’s, 4 Master’s, and 10 Advanced Practical Training students. Research collaborations span VTT, Ericsson Research, Alif Semiconductor, Aalto University, and University of Helsinki. He advises Soundxvision Oy, a Helsinki XR Center startup developing input modalities for XR headsets. Funding sources include the Finnish-American Research & Innovation Accelerator (FARIA), Indian-Finnish Consortia (FICORE), Nokia Foundation, and Walter Ahlström Foundation. His work bridges IoT, 6G, and AI to address environmental and societal challenges, leveraging software-defined systems and distributed computing paradigms. Recent publications explore metaverse integration, edge AI, and cybersecurity in emerging networks.

• Microelectronics
• Computer Engineering
• Embedded Systems
• +۷ مورد دیگر

Dr. Ari Kulmala serves as Professor of Practice at Tampere University's Faculty of Information Technology and Communication Sciences in the Computing Sciences department. With extensive industry experience in System-on-Chip (SoC) architecture and design, he specializes in ASIC and FPGA development for applications spanning 5G telecommunications, cloud acceleration, machine learning, security, and ultra-low-power mobile chips. His research focuses on System-on-Chip (SoC) design , with particular emphasis on heterogeneous architectures, RISC-V implementations, and hardware-software co-design for autonomous systems. Recent work includes secure SoC development for nano-UAVs, FPGA-based cryptographic acceleration, and agile chip development methodologies. The article collection demonstrates expertise in SoC architecture (10+ years), with evolving focus from foundational network-on-chip analysis (2008-2009) to modern implementations in RISC-V (2023-2024), cloud security acceleration (2019), and VR/4K video processing (2017-2018). Key technical areas include clock domain management, resource sharing, and performance optimization across various domains. As an industry leader in chip development organizations, Kulmala has worked across Telecom infrastructure (5G) Cloud acceleration Machine learning/AI Security hardware Ultra-low-power IoT Mobile chip design His work spans both academic research and practical implementation, including patents and commercial applications.

• 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.