جست‌وجوی استاد و پژوهشگر

• 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.

Ertugrul Basarمشاهده پروفایل

استاد

• Wireless Systems
• 6G and Beyond Wireless Networks
• Multi-Antenna Systems
• +۸ مورد دیگر

Prof. Ertugrul Basar holds the position of Professor of Wireless Systems at the Department of Electrical Engineering, Tampere University, Finland. He is also a member of the Tampere Wireless Research Centre and previously served as founding director of the Communications Research and Innovation Laboratory (CoreLab) at Koç University (2018–2025). His academic career includes faculty roles at Koç University (2018–2025) and Istanbul Technical University (2009–2018), alongside visiting positions at Princeton University and Ruhr University Bochum. He earned his B.S. (High Honors) from Istanbul University, and M.Sc. and Ph.D. from Istanbul Technical University. His research focuses on 6G and beyond wireless systems, reconfigurable intelligent surfaces (RIS), index modulation, physical layer security, and deep learning for communications. He has authored over 190 journal papers, holds 17 patents, and been recognized with prestigious awards including IEEE Fellow (2023), TÜBA Outstanding Young Scientist Award (2017), and multiple best paper awards. He has supervised 9 Ph.D. and 20 M.Sc. students, and served on editorial boards of IEEE Transactions on Communications and other journals. Prof. Basar’s work has been impactful, with over 18,000 citations and an h-index of 55. His contributions span theoretical advancements and practical implementations, including pioneering RIS technologies and waveform designs. He is a Fellow of AAIA and AIIA, and an IEEE Communications Society Distinguished Lecturer (2024–2025).

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.

• 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

Sonja Kivinenمشاهده پروفایل

عضو هیئت علمی

• Climate change impacts on Arctic ecosystems
• Remote sensing applications in biodiversity mapping
• Geoinformatics and environmental data analysis
• +۳ مورد دیگر

Sonja Kivinen is a University Lecturer in Geoinformatics at the Department of Geographical and Historical Studies, Faculty of Social Sciences and Business Studies, University of Eastern Finland. Her research focuses on the intersection of climate change impacts, remote sensing applications, and sustainable resource management in Arctic environments. University Lecturer in Geoinformatics Department of Geographical and Historical Studies Faculty of Social Sciences and Business Studies University of Eastern Finland Research Interests Climate change effects on Arctic ecosystems Remote sensing for biodiversity mapping Environmental justice in natural resource management Post-mining land use strategies Integration of practitioner knowledge with scientific data Geospatial analysis of environmental patterns Key Publication Trends Her recent publications (2017-2024) demonstrate expertise in combining remote sensing with ecological monitoring to address issues like biodiversity conservation in boreal forests, reindeer husbandry adaptation to changing climatic conditions, and mining conflict resolution . She frequently collaborates with multidisciplinary teams across Finland and Europe. Projects Kivinen contributes to research groups including CEMMS (Research Center for Mining, Minerals and Society) and Digital Geosciences , both active since 2020. Her work often addresses policy implications through reports for the University of Eastern Finland and Finnish Environment Institute.

Mario Di Francescoمشاهده پروفایل

استاد

• Wireless Networking
• Mobile and Ubiquitous Computing
• Internet of Things
• +۴ مورد دیگر

Mario Di Francesco is a Professor at the Department of Computer Science, Aalto University. He holds the position of Professor in Computing Systems (ComputingSystems) Research Area and leads multiple research projects in networking and IoT. Previously, he was a research associate and adjunct faculty at the University of Texas at Arlington (2012–2014), and completed his PhD in Information Engineering at the University of Pisa under Prof. Giuseppe Anastasi. He also served as a research fellow at Scuola Superiore Sant'Anna in 2009. Research Interests : His work focuses on wireless networking, mobile and ubiquitous computing, and the Internet of Things (IoT). Key areas include edge computing, fog networks, virtual/augmented reality, and AI-driven resource management. His research contributes to UN Sustainable Development Goals related to innovation and infrastructure (SDG 9), sustainable cities (SDG 11), and industry/infrastructure development (SDG 9). Publications : His recent work spans topics like private virtual reality conversations, cloud-based network measurement frameworks (CLAIM), microservice connectivity analysis, and edge-DNN optimization. These publications reflect a trend toward integrating AI with distributed systems for real-time, low-latency applications in IoT and edge computing. Awards : Recipient of the Google Internet of Things Technology Research Award (2016) and MobileHCI Honorable Mention (2020). Grants & Projects : CRUISE (2023–2025): Cross-system architecture for autonomous wireless networks using lifelong machine learning. MeXICO (2020–2024): Mobile cross-reality through immersive computing. ASPECT (2016–2018): Spectrum utilization for mobile video. Labs & Teams : Leads research groups focused on edge computing, IoT systems, and immersive technologies. Collaborates with international partners on projects like fog radio networks and autonomous system design.

Ricardo A. Correiaمشاهده پروفایل

پژوهشگر

• Biodiversity Conservation
• Conservation Biology
• Environmental Science
• +۵ مورد دیگر

Ricardo A. Correia is a Grant-Aided Researcher at the Department of Geosciences and Geography, University of Helsinki, with a Docentship in Helsinki Lab of Interdisciplinary Conservation Science. His work bridges biodiversity conservation, digital data science, and human-nature relationships. PhD in Environmental Sciences (2015) from University of East Anglia MSc in Conservation Biology (2008) from University of Lisbon BSc in Environmental Biology (2007) from University of Lisbon His research focuses on biocultural conservation , invasive species dynamics , and digital data applications (social media, Google Trends) for ecological monitoring. He explores the intersection of mental health and biodiversity through One Health frameworks and advocates for equitable conservation policies using culturomics and AI. Notable scientific outputs include 2025 publications on conservation flagships, biophobias, and AI applications. His 2024 work analyzed charisma in conservation, political vulnerability of parks, and social media sentiment mapping. Best Poster Award (2009) Editorial Board, Journal of Biogeography (2020–) Editor, Conservation Biology Journal (2020) Correia leads projects like the 2022–2022 Biocultural Approach for Neglected Biodiversity (Finnish Academy) and contributes to global biodiversity frameworks through social media analysis and digital monitoring tools like gtrendsAPI .

Yanda Taoمشاهده پروفایل

پژوهشگر

• Machine Learning Systems
• Distributed Deep Learning
• Automatic Parallelism
• +۲ 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.

• Probabilistic Machine Learning
• Probabilistic Circuits
• Probabilistic Programming
• +۲ مورد دیگر

Martin Trapp is an Academy Postdoctoral Researcher in the Department of Computer Science at Aalto University, specializing in probabilistic machine learning. He is affiliated with Professor Arno Solin's research group, focusing on advancing tractable probabilistic models for real-world applications. His research centers on Probabilistic Circuits , Probabilistic Programming , and Bayesian Nonparametrics , with emphasis on hardware-efficient implementations for edge devices and multimodal systems. Key interests include uncertainty quantification in deep learning, neurosymbolic AI integration, and medical imaging applications. His work bridges theoretical foundations with practical deployment constraints, particularly in resource-limited environments. Analysis of his 15 most recent publications (2022-2025) reveals three dominant trends: (1) hardware-aware probabilistic inference for TinyML applications, (2) scalable Bayesian methods using bitstring representations and probabilistic programming, and (3) multimodal robustness in vision-language systems and medical imaging. His contributions span from theoretical circuit representations to real-world implementations in mammography analysis and vision-language models. Trapp secured a HIIT short-term project grant (November 2022) for "Positive Semi-Definite Circuits" under the Department of Computer Science. No formal advising relationships are documented in available sources. He actively collaborates with researchers including Arno Solin, Rui Li, and Marcus Klasson across institutions like Aalto University and the Helsinki Institute for Information Technology. As a core member of Aalto's Probabilistic Machine Learning group, he contributes to advancing probabilistic AI methodologies with applications in healthcare, edge computing, and multimodal reasoning. His current work emphasizes deployable probabilistic systems that maintain rigorous uncertainty quantification while meeting hardware constraints.

• Machine Learning
• Object-Centric Learning
• Deep Learning
• +۵ مورد دیگر

Rongzhen Zhao is a Doctoral Researcher at Aalto University's Department of Electrical Engineering and Automation, affiliated with the Robot Learning research group. Their work focuses on machine learning and object-centric learning, with recent publications in top-tier conferences like ECML and ICLR. Research interests include: Object-Centric Learning Slot Attention Mechanisms Spatiotemporal Processing Neural Network Architecture Design Brain-inspired AI IoT Middleware Development Recent trends in their publications highlight advancements in attention mechanisms, discrete representations for object recognition, and multi-scale fusion techniques. They also explore bio-inspired approaches for temporal data analysis and IoT systems. Research groups: Robot Learning Contact: Email: rongzhen.zhao@aalto.fi

Ekaterina Nepovinnykhمشاهده پروفایل

پژوهشگر

• Machine vision
• Pattern recognition
• Computer vision
• +۳ مورد دیگر

Ekaterina Nepovinnykh is a Postdoctoral Researcher at the LUT School of Engineering Sciences in the Computational Engineering department. Her research focuses on machine vision and pattern recognition , particularly for ecological applications. Research Interests: She specializes in developing computer vision algorithms for animal re-identification, leveraging deep learning and feature aggregation techniques to study biodiversity specimens and endangered species like the Saimaa ringed seal. Publication Trends: Her recent work (2022-2025) addresses pelage pattern unwrapping, geometric similarity analysis, and deep feature aggregation. These studies bridge machine learning , ecological monitoring , and conservation biology , with a focus on scalable solutions for wildlife tracking.

Ricardo Henriques Correiaمشاهده پروفایل

استادیار

• Biodiversity Conservation
• Human-Nature Interactions
• Ecosystem Services
• +۳ مورد دیگر

Ricardo Henriques Correia is an Assistant Professor at the Biodiversity Unit of the University of Turku, PhD. His research focuses on biodiversity conservation, human-nature interactions, ecosystem services, and digital methods in ecological studies. He actively develops computational tools like gtrendsAPI (an R package for Google Trends integration) and contributes to conservation culturomics, which leverages online data to assess public engagement with environmental issues. His work spans multiple continents, including analyses of conservation priorities in sub-Saharan Africa, Brazil, and Europe. He collaborates with interdisciplinary teams to explore topics such as species charisma in academic literature, political vulnerability of protected areas, and invasive species detection using machine learning. Correia also investigates the cultural and psychological dimensions of human-nature relationships, linking biodiversity awareness to societal well-being. Ricardo’s research emphasizes leveraging digital platforms (e.g., social media, citizen science) to address conservation challenges. He has published extensively in high-impact journals like Proceedings of the Royal Society B , PLoS Biology , and Biological Conservation , often focusing on data-driven approaches to biodiversity monitoring and policy evaluation. His contributions include pioneering the use of culturomics to quantify species salience and public interest in conservation efforts. He is affiliated with the BISONS Lab , an interdisciplinary research group at the University of Turku. While no personal scientific awards are explicitly listed, his work has been recognized in innovation awards from the Journal of Biogeography (2022) for advancing methodological approaches in conservation geography. Ricardo’s grants and advising activities are not detailed in the provided texts, but his research portfolio indicates sustained support for projects involving open-access publishing, stakeholder collaboration, and digital data integration. His work bridges ecology, technology, and social sciences to create actionable insights for global conservation strategies.

• Artificial Intelligence
• Machine Learning
• Multi-Agent Systems
• +۳ مورد دیگر

Zhiyuan Li is a Postdoctoral Researcher in the Department of Electrical Engineering and Automation . His research focuses on multi-agent systems, reinforcement learning, and optimization algorithms. He explores topics such as policy factorization, backpropagation methods, and cooperative learning strategies in autonomous systems. Key research interests include multi-agent coordination, search space exploration, and formal analysis of reinforcement learning algorithms. His work addresses challenges like local optima avoidance and gradient optimization in distributed environments. Recent publications highlight advancements in multi-agent policy gradients, correlated policy factorization, and adaptive skill synthesis. These contributions aim to improve efficiency and scalability in collaborative machine learning systems. Zhiyuan has collaborated on projects involving robot learning and resource allocation optimization, reflecting his interdisciplinary approach to AI research.

Pekka Toivanenمشاهده پروفایل

استاد

• Artificial Intelligence
• Healthcare Systems
• IoT and Smart Grids
• +۳ مورد دیگر

Pekka Toivanen is a Professor at the School of Computing, Faculty of Science, Forestry and Technology at the University of Eastern Finland. His research focuses on artificial intelligence, healthcare systems, IoT, cybersecurity, and machine learning. He has contributed extensively to fields like medical image analysis, smart grids, and robotics, with over 100 peer-reviewed publications. His work bridges theoretical advancements and practical applications, such as improving healthcare diagnostics via AI, enhancing IoT security, and optimizing energy management in microgrids. Research Interests: Dr. Toivanen’s primary areas include AI-driven healthcare solutions, cybersecurity for IoT devices, deep learning applications in medical imaging, and energy-efficient smart grid systems. He has pioneered methods for tuberculosis risk prediction and developed novel encryption algorithms for Bluetooth security. Publications: His recent work emphasizes AI in healthcare (e.g., tuberculosis prediction models) and energy systems (e.g., reinforcement learning for microgrid management). He also explores vulnerabilities in wireless technologies like Bluetooth and ZigBee, proposing robust countermeasures. Grants/Teams: Leads projects on AI distribution platforms for healthcare and secure IoT architectures. Collaborates with interdisciplinary teams in medical and engineering domains.