جست‌وجوی استاد و پژوهشگر

• Social Philosophy
• Collective Responsibility
• AI Ethics
• +۳ مورد دیگر

Pekka Mäkelä is a Research Coordinator and Vice-Director at the Helsinki Institute for Social Sciences and Humanities (HSSH), University of Helsinki. He holds a permanent position in the Department of Social and Moral Philosophy, Faculty of Social Sciences, and has led major projects like Robophilosophy , AI ethics , and Trust research . His career spans roles as a Researcher, Adjunct teacher, and organizer of international workshops. Research Focus: Social ontology Collective moral responsibility Human-robot interaction ethics Trust and social capital Group action theory Publication Trends: Recent work (2023-2025) addresses AI ethics, robotic accountability, and institutional responsibility frameworks, often through collective intentionality lenses. Scientific Awards: Finnish Cultural Foundation grants (2001-2003) Advising: Supervised PhD candidates including Säde Hormio and Miguel Garcia, with ongoing mentorship of pro graduate theses since 2005. Labs/Teams: Core member of HSSH, TINT Centre, and RADAR research units focusing on AI ethics and collective responsibility.

• DFT (Density Functional Theory)
• SPM (Scanning Probe Microscopy)
• Surface Science
• +۶ مورد دیگر

Ondrej Krejci is a researcher in the Department of Applied Physics at Aalto University, specializing in computational and theoretical approaches to surface science, scanning probe microscopy, and materials discovery. His work bridges advanced simulation techniques with experimental validation. Research interests include: Density Functional Theory (DFT) Scanning Probe Microscopy (SPM) simulations Molecular adsorption on surfaces X-ray spectroscopy simulations Condensed matter physics Atomic and molecular physics Recent publications highlight trends in machine learning for catalyst discovery, probe-particle model innovations, and on-surface synthesis of novel carbon allotropes and frameworks. Collaborative work spans computational modeling, experimental validation, and instrumentation development.

Pedro Nardelliمشاهده پروفایل

استاد

• Cyber-physical Systems
• IoT in Energy Systems
• Smart Grids
• +۵ مورد دیگر

Pedro Nardelli is a Full Professor (tenured) of IoT in Energy Systems at the LUT School of Energy Systems, Lappeenranta University of Technology (Finland). He holds a double doctoral degree in electrical engineering from the University of Campinas (Brazil) and communications engineering from the University of Oulu (Finland). As a Docent in Information Processing and Communications Strategies for Energy Systems, he leads research in cyber-physical systems, smart grids, and 6G-enabled energy networks. Research Interests : His work focuses on integrating IoT, AI, and communication technologies into energy systems. Key areas include cyber-physical systems, UAV-enabled networks, sustainable energy management, and cybersecurity in critical infrastructure. He emphasizes interdisciplinary approaches to address challenges in the green-digital transition. Projects & Leadership : He is Principal Investigator for projects such as 'Energy-Conscious Operation: Network Efficiency for Wireless Sustainability' (2024–2026) and coordinates the Finnish-Brazilian AI and 5G training program. Past roles include leadership in the 'Hydrogen and Carbon Value Chains in Green Electrification' initiative (2021–2024). Publications : Recent work explores topics like hybrid optimal power flow models, UAV-IRS NOMA systems, and energy-centric analysis. His research bridges theoretical frameworks with practical applications, emphasizing sustainability and resilience in energy networks. Labs & Teams : Leads the IoT Solutions group within LUT's MORE SIM research platform, focusing on simulation-driven innovation for energy systems.

• 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.

Niko Mäkitaloمشاهده پروفایل

استادیار

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

Hossam H. H. Mousaمشاهده پروفایل

پژوهشگر

• Electrical Power Engineering
• Renewable Energy Systems
• Power Systems
• +۳ مورد دیگر

Hossam H. H. Mousa is a Doctoral Researcher at Aalto University's Department of Electrical Engineering and Automation, School of Electrical Engineering. He also serves as an Assistant Lecturer at South Valley University's Department of Electrical Engineering since 2020. B.Sc. in Electrical Engineering (2017), South Valley University M.Sc. in Electrical Power and Machines Engineering (2020), South Valley University His research focuses on electrical power engineering, including maximum power point tracking (MPPT) for renewable energy, power systems analysis, energy management, and machine learning applications in grid optimization. He has published extensively on topics like hosting capacity estimation, unbalanced microgrids, and hydrogen storage integration. The 15 most recent articles emphasize modern power systems optimization through machine learning (2025), smart inverter applications in renewable integration (2025), and hydrogen storage's role in cold climate energy management (2025). Earlier works include best practice studies on capacitor allocation (2024) and photovoltaic system controls (2024), earning him the 2024 Best Paper Award in the International Journal of Electrical Power & Energy Systems. Best Paper Award (2024), International Journal of Electrical Power & Energy Systems His scholarly activities span energy conversion, microgrid stability, and applied machine learning, contributing to sustainable energy transition solutions. He has collaborated on international research books addressing distribution network hosting capacity (2025) and future energy systems challenges.

Visa Koivunenمشاهده پروفایل

استاد

• Statistical Signal Processing
• Wireless Communications
• Multisensor Systems
• +۴ مورد دیگر

Visa Koivunen is a Distinguished Professor of Signal Processing at Aalto University (since 1999), with positions as Academy Professor (2010) and Aalto Distinguished Professor (2020). He holds an honorary D.Sc. (Tech.) from the University of Oulu and has held visiting roles at Princeton University, the University of Pennsylvania, and EPFL. His research focuses on statistical signal processing, wireless communications, radar systems, and integrated sensing and communications (ISAC). He has published over 490 papers, including award-winning works, and advised 31 doctoral theses. Key roles include leadership in conferences (e.g., Asilomar 2018 General Chair) and technical committees (IEEE SPS). Recognitions include the EURASIP Technical Achievement Award (2015), IEEE Signal Processing Society Best Paper Awards (2007, 2017), and EURASIP Fellow status (2020). He co-chairs NATO panels on cognitive radars and ISAC. His research interests span signal processing fundamentals and applications in radar, communications, and machine learning. Recent work emphasizes ISAC, reinforcement learning for resource allocation, and causal inference in federated systems. He has pioneered waveform design techniques using GANs and Bayesian methods for spatial signal analysis. Educations: D.Sc. (Tech.) with honors from the University of Oulu (1994), Primus Doctor Award (1989-1994). Awards: IEEE Fellow, EURASIP Fellow, Member of Academia Europaea. Service: Associate Editor for IEEE Transactions, Chair of IEEE SPAWC and Asilomar conferences. His work bridges theory and practice, addressing challenges in radar-communication coexistence, energy-efficient edge computing, and secure distributed inference. He has delivered over 50 invited talks globally and actively contributes to NATO initiatives on cognitive radar systems.

Miina Rautiainenمشاهده پروفایل

استاد

• Remote Sensing
• Forest Sciences
• Peatland Ecology
• +۵ مورد دیگر

Miina Rautiainen is a Full Professor in Remote Sensing at Aalto University, leading the Remote Sensing Research Team in the Department of Built Environment, School of Engineering. She holds dual professorships in Geoinformatics and the Department of Built Environment. Her research focuses on remote sensing and spectroscopy of forests and peatlands, with emphasis on albedo dynamics, vegetation structure, and climate change impacts. She has led major projects including the ERC Consolidator Grant (2018-2024) and multiple Research Council of Finland grants. Education: Doctoral degree in Forest Sciences from the University of Helsinki (2005). Completed university pedagogy studies in the Faculty of Behavioural Sciences, University of Helsinki. Research interests span remote sensing methodologies, forest ecology, peatland monitoring, and spectral analysis. Key projects include PEATSPEC (spectral characterization of boreal peatlands), ARTISDIG (digital twins for forest biodiversity), and BOREALITY (albedo-climate linkages). Awards include ERC funding and leadership in Finnish Society for Forest Sciences (2017-2020). Publications emphasize spectral modeling, LiDAR applications, and climate-relevant vegetation dynamics. Supervised 8 doctoral theses including work on LiDAR-based biomass estimation, peatland rehabilitation, and wolverine habitat analysis. Team members include researchers like Aarne Hovi, Jussi Juola, and Iuliia Burdun. Labs/Teams: Remote Sensing Research Team at Aalto, collaborating with VTT, Natural Resources Institute Finland, and international partners. Instrumentation includes spectral measurement devices, terrestrial LiDAR, and lab setups for leaf analysis.

• Information Theory
• Coding Theory
• Discrete Mathematics
• +۳ مورد دیگر

Charul Rajput is a Research Fellow at Aalto University's Department of Mathematics and Systems Analysis, School of Science. Their research focuses on Information Theory, Coding Theory, Discrete Mathematics, and Algebra, with a particular emphasis on caching systems and network optimization. Recent work includes advancements in hierarchical coded caching, hotplug models, and error probability analysis in communication channels. Publications span topics like function-correcting codes, private information retrieval, and locally recoverable codes. Rajput's research also intersects with systems analysis, addressing challenges in distributed storage and network efficiency. Research interests include the theoretical foundations of coding and information theory, with applications to modern communication systems. Key contributions address the design of efficient caching schemes and error-correcting codes for high-performance networks. No scientific awards or grants are explicitly mentioned in the provided texts. Rajput is affiliated with the Algebra and Discrete Mathematics research group at Aalto University, contributing to interdisciplinary projects that bridge pure mathematics and practical network systems.

Jenni Nikuمشاهده پروفایل

مدرس ارشد

• Multivariate Statistical Modeling
• Latent Variable Models
• Statistical Method Development
• +۳ مورد دیگر

Jenni Niku is a Senior Lecturer at the Department of Mathematics and Statistics, University of Jyväskylä, specializing in multivariate statistical modeling and computational methods for ecological data. She is actively involved in the Predictive Community Ecology Group and leads research projects on latent variable models for complex ecological structures. Research Focus: Latent variable models for community ecology Spatial-temporal pattern analysis Statistical methodology for multivariate data Joint species distribution modeling Computational tools for ecological datasets Recent Publications: Jenni has contributed to advancements in analyzing compositional count data, fungal dispersal dynamics, chronic disease modeling, and peatland restoration. Her work bridges statistical innovation with ecological applications, particularly in handling high-dimensional data. Collaborations: She works extensively with researchers from Biological and Environmental Science departments, focusing on interdisciplinary applications of her statistical models.

Alessandro Fogliمشاهده پروفایل

پژوهشگر

• Distributed Systems
• Data Analytics
• Cluster Resource Management
• +۴ مورد دیگر

Alessandro Fogli is a PhD Student at Imperial College London in the Department of Computing, affiliated with the Large-Scale Data & Systems (LSDS) Group . His research focuses on systems support for data analytics in cloud environments, including distributed systems, resource management, and query processing. Education PhD in Computer Science, 2019–Present, Imperial College London MSc in Computer Science, 2015–2017, Roma Tre University BSc in Computer Science, 2012–2015, Roma Tre University His research spans Distributed Systems , Databases , Data Analytics , and Modern Hardware . Recent work examines chiplet-based processor architectures and runtime mapping systems, with applications in performance optimization and hardware-aware query execution. Scientific Contributions Co-developed CHARM (2025), a runtime mapping system for chiplet heterogeneity Published in VLDB (2024) on OLAP processing for chiplet-based CPUs Contributed to HeatWave at Oracle Labs, improving query offloading to in-memory accelerators

• Robotics
• Control Systems
• Mechatronics
• +۴ مورد دیگر

Dr. Almas Shintemirov is a Research Fellow at Aalto University's Department of Electrical Engineering and Automation, specializing in robotics, control systems, and human-robot interaction. His research focuses on intelligent robotics, with emphasis on Real-time motion prediction for collaborative robots Nonlinear control algorithms for safe human-robot interaction Open-source robotic hardware design Deep learning applications in autonomous systems

Jara Uittoمشاهده پروفایل

استادیار

• Massively Parallel Computation
• Distributed Computing
• Sublinear Computing
• +۳ مورد دیگر

Jara Uitto is an Assistant Professor in the Department of Computer Science. His research focuses on Massively Parallel Computation, Distributed Computing, and Sublinear Computing, with a particular emphasis on graph algorithms and distributed systems. Research Interests: Massively Parallel Computation (MPC) Distributed Algorithms and Symmetry Breaking Graph Theory and Edge Processing Algorithm Design for Sparse Graphs Approximation and Optimization in Streaming Models Key Project: Massively Parallel Algorithms for Large-Scale Graph Problems (2020-2024) , where he led research on optimizing algorithms for distributed and parallel computing environments. Publications highlight contributions to symmetry breaking, parallel coloring, and approximation algorithms in dynamic streams. His work bridges theoretical foundations with practical distributed computing challenges.

Satu-Pia Reinikainenمشاهده پروفایل

استاد

• Multivariate Statistical Analysis
• Process Control
• Spectroscopy
• +۳ مورد دیگر

Satu-Pia Reinikainen is a tenured Professor in Computational Engineering at the Lappeenranta-Lahti University of Technology (LUT) School of Engineering Sciences . With expertise in chemometrics and multivariate analysis, her work bridges statistical modeling, spectroscopy, and environmental monitoring. Research Focus Development of advanced kernel-based methods for process control Application of hyperspectral imaging in material and environmental analysis Microplastic pollution dynamics in aquatic systems Integration of spectroscopic techniques for real-time monitoring Conservation of geological and heritage materials through data-driven approaches Her research combines chemometric algorithms, environmental data analysis, and industrial process monitoring to solve complex analytical challenges.

Jussi Kangasharjuمشاهده پروفایل

استاد

• Edge Computing
• Information-Centric Networking
• Content Distribution
• +۶ مورد دیگر

Jussi Kangasharju is a Professor in the Department of Computer Science at the University of Helsinki and leads the Collaborative Networking research group . He is also a supervisor for the Doctoral Programme in Computer Science and a member of IEEE and ACM . Research Interests: Edge Computing Information-Centric Networking Content Distribution Green Networking Future Internet Development Scientific Awards: Best Paper Award (2015) Winner of 9th Helsinki Science Slam Competition (2016) Best Paper Award (2004) Key Projects: He has led initiatives like ELLIS-instituutti (2025–2032) and University Profiling Funding (2025–2030), focusing on transdisciplinary networks and future Internet sustainability. Academic Visits: Notable visits include Seoul National University (2012) and the International Computer Science Institute (2013), further enriching his collaborative work.