جست‌وجوی استاد و پژوهشگر

Arno Solinمشاهده پروفایل

دانشیار

• Machine Learning
• Signal Processing
• Sensor Fusion
• +۸ مورد دیگر

Dr. Arno Solin is a tenured Associate Professor in Machine Learning at Aalto University's Department of Computer Science and an Academy of Finland Research Fellow . He leads the Aalto machine learning research group and serves as Director of the Finnish Doctoral Program Network in AI (AI-DOC) . His work bridges probabilistic modeling with practical applications in sensor fusion and real-time inference. ELLIS Scholar (European Laboratory for Learning and Intelligent Systems) Adjunct Professor at Tampere University Member of Young Academy Finland (2021–2025) Research Interests focus on data-efficient machine learning with probabilistic methods for real-time inference and sensor fusion. Key areas include Gaussian processes, diffusion models, stochastic differential equations, and uncertainty quantification in deep learning. His group develops methods that combine structural constraints with adaptive learning for deployment on resource-limited hardware. Publication Trends show consistent output in top venues (NeurIPS, ICML, ICLR, AISTATS) with emphasis on diffusion models , 3D scene reconstruction , and real-time probabilistic modeling . Recent works explore physics-informed learning, heterophily-aware graph models, and compressed representations for world models in reinforcement learning. Scientific Recognition : Awarded AI Researcher of the Year 2024 by AI Finland Teacher of the Year 2023 at Aalto CS ISIF Jean-Pierre Le Cadre Best Paper Award (2018) MLSP Schizophrenia Classification Challenge Winner (2014) NeurIPS/ICML Reviewer Awards Research Leadership includes coordinating Finland's AI Center of Excellence program and directing the Finnish Doctoral Program Network in AI (AI-DOC). He supervises 15+ doctoral/postdoctoral researchers and has spun off Spectacular AI , a company commercializing sensor fusion technology.

• Machine Learning
• Computational Neuroscience
• Artificial Intelligence
• +۷ مورد دیگر

Aapo Hyvärinen is a Professor of Computer Science at the University of Helsinki , affiliated with the Helsinki Institute for Information Technology and the Helsinki Probabilistic Machine Learning Lab . He previously held the position of Professor of Machine Learning at the Gatsby Computational Neuroscience Unit, University College London (2016-2019). Education : Undergraduate Mathematics at University of Helsinki, Vienna, and Paris; Ph.D. in Information Science from Helsinki University of Technology (1997) His research focuses on machine learning and computational neuroscience , particularly: Independent Component Analysis (ICA) Natural Image Statistics Causal Representation Learning Neural Signal Processing Applications to brain imaging (MEG, CryoEM) Recent publications emphasize causal discovery , identifiable machine learning , and nonlinear ICA . Key projects include: VETURI (AI for health) DIGIMIND (AI in mental health) CIFAR grants (2022-2025) Scientific awards : Highly Cited Researcher (2010) He serves as Action Editor for the Journal of Machine Learning Research and Neural Computation , and has held Area Chair roles at NeurIPS, ICML, ICLR, AISTATS, and UAI conferences. His work bridges theoretical machine learning with neuroscience and philosophical implications of artificial intelligence .

• Wireless Communications
• Signal Processing
• 5G/6G Networks
• +۳ مورد دیگر

Mikko Valkama is a Professor at the Department of Communications Engineering , part of the Faculty of Information Technology and Communication Sciences at Tampere University . His research focuses on advanced wireless communication systems, positioning technologies, and integrated sensing and communication (ISAC). He holds an Orcid ID ( 0000-0003-0361-0800 ) and can be reached at mikko.valkama@tuni.fi . Research interests span 5G/6G networks , RF antenna design , deep learning for signal processing , and millimeter-wave systems . He leads projects on positioning algorithms (e.g., mmWave SLAM, NLOS mitigation), ISAC architectures, and hardware-efficient transmitter linearization. Notable contributions include works on DECT-2020 NR standards, phase-based localization, and RIS-assisted systems. In 2025 alone, his group published over 30 articles on topics such as: Antenna array design for Ka-band and wideband applications Machine learning for power amplifier predistortion Bistatic radio SLAM and mmWave mapping Covert transmission and physical-layer security His work bridges theoretical advancements with practical implementations, often validated through experimental setups (e.g., TUJI1 dataset for indoor localization). No scientific awards were explicitly listed in the provided texts.

Teemu Roosمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Data Science
• +۶ مورد دیگر

Teemu Roos is a Professor at the Department of Computer Science , University of Helsinki , and a Principal Investigator for the Complex Systems Computation Group under the Helsinki Institute for Information Technology. He serves as a Supervisor for the Doctoral Programme in Computer Science and leads multiple research initiatives, including Distributed AI in Supercomputing , AI & Kids , and Generation AI . Dr. Roos also holds a Docent title in Computer Science. His research spans Artificial Intelligence , Machine Learning , and Data Science , with a focus on AI education , graph neural networks , Bayesian modeling , and health informatics . He has pioneered tools like Elements of AI , a free online course now translated into 22 EU languages, and explores the ethical implications of AI-generated content in authorship and inventorship. The 15 most recent publications highlight applications in environmental forecasting (e.g., Mediterranean Sea via graph-based deep learning), healthcare (e.g., skin cancer detection with transfer learning), and social media analysis (e.g., explainable AI platforms for K-12 education). Methodologically, his work advances clustering algorithms , dimensionality reduction , and approximate nearest neighbor search . Scientific Awards: Cor Baayen Award (2009) Nokia Foundation Recognition Award (2019) Best Paper Honorable Mention Award (2013) ICT Influencer of the Year 2019 (Vuoden TiVi-vaikuttaja 2019) World Summit AI's Top-50 Innovators in 2020 Dr. Roos has supervised 2 doctoral students and contributed to 163 academic activities , including invited talks at MIT, University of Cambridge, and the Finnish Institute in Rome. He has secured funding from the Academy of Finland and the Strategic Research Council, focusing on projects like Fast AI-assisted Space Environment Prediction and Urban Exerciser .

• Statistical signal processing
• Robust statistical methods
• Array processing
• +۶ مورد دیگر

Esa Ollila serves as Associate Professor in the Department of Signal Processing and Acoustics at Aalto University, Finland, and holds an adjunct professorship in Statistics at the University of Oulu. His academic appointments include Academy of Finland Research Fellow (2010-2015) and prior senior research/lecturing roles at both institutions. His educational background features: M.Sc. in Mathematics, University of Oulu (1998) Ph.D. in Statistics (with honors), University of Jyväskylä (2002) D.Sc.(Tech) in Signal Processing (with honors), Aalto University (2010) Professor Ollila's research centers on statistical signal processing and robust statistical methodologies , with significant contributions to array processing, high-dimensional data analysis, and covariance matrix estimation. His work bridges theoretical statistics with practical applications in radar systems, wireless communications, and big data analytics, emphasizing robustness against outliers and computational efficiency in modern data-intensive environments. Current focus areas include compressed sensing, sparse approximation, and blind source separation techniques. Analysis of his 15 most recent publications (2024-2025) reveals three dominant trends: (1) robust covariance learning for massive random access systems, (2) integrated sensing and communications (ISAC) for 6G networks using advanced beamforming, and (3) geometric approaches to elliptical distributions in statistical inference. His work increasingly incorporates deep learning (GANs, graph neural networks) while maintaining strong foundations in classical signal processing theory. Key recognitions include: Academy of Finland Postdoctoral Fellowship (2004-2007) Academy of Finland Research Fellowship (2010-2015) His research has been supported through prestigious Academy of Finland grants totaling over a decade of continuous funding. Professor Ollila currently leads an active research group at Aalto University, supervising doctoral candidates and collaborating internationally with institutions including Princeton University (where he served as Visiting Post-doctoral Research Associate during 2010-2011). He maintains strong ties with the University of Oulu through his adjunct professorship and has contributed to EURASIP's Special Area Team on Theoretical and Methodological Trends in Signal Processing. The Esa Ollila Research Group focuses on cutting-edge challenges in statistical signal processing, with current projects spanning robust DOA estimation under non-Gaussian noise, covariance matrix learning for massive MIMO systems, and machine learning-enhanced radar-communication integration. The group actively develops open-source tools like the fitHeavyTail R package for heavy-tailed distribution modeling and maintains collaborations with industry partners in wireless communications.

Pia Frickerمشاهده پروفایل

دانشیار

• Computational Methodologies
• Urban Design
• Landscape Architecture
• +۸ مورد دیگر

Prof. Pia Fricker is an Associate Professor and Vice Head of the Department of Architecture at Aalto University's School of Arts, Design and Architecture in Finland. She holds the Professorship of Computational Methodologies in Landscape Architecture and Urbanism, directing the Urban Studies and Planning Programme. Her research integrates urban design, landscape architecture, and digital design culture, focusing on data-driven methods, immersive environments, and adaptive urban development. Collaborations include ETH Zurich, Singapore University of Technology and Design, and Hafencity University Hamburg. Key projects include the Metaversity and Future Smart Cities initiatives. Fricker has led over 80 publications and exhibitions globally, including at the Venice Biennale and National Design Centre Singapore. She is an editorial board member for the Journal of Digital Landscape Architecture and peer reviewer for multiple journals. Awards include the Digital Landscape Architecture Award (2018) and DLA Scientific Merit Award (2021). Her teaching emphasizes computational pedagogy and digital innovation in design education. Education: PhD in Architecture (ETH Zurich, 2021) Postgraduate in Didactics (ETH Zurich, 2011) MAS in Computer Aided Architectural Design (ETH Zurich, 2003) MSc Arch in Urban Design & Landscape Architecture (Technical University of Karlsruhe, 2001) Research Interests: Computational design, parametric modeling, mixed reality, climate-adaptive ecosystems, generative AI, and sustainable urban development. Her work bridges emerging technologies with ecological and urban challenges, emphasizing interdisciplinary collaboration. Grants & Projects: Metaversity (2023–2025, Principal Investigator) Future Smart Cities Sasakawa (2023–2024, Principal Investigator) ABRA (2020–2023, Project Member) Awards: DLA Awards (2018, 2021) DLA Review Committee Awards (2020–2022) Exhibition Recognitions (Venice Biennale, National Design Centre Singapore) Labs/Teams: Leads the Urban Studies and Planning Programme and collaborates with interdisciplinary teams on projects like the RAILCORRIDOR Singapore initiative. Active in digital twin development and AI-driven design tools.

Eric Coataneaمشاهده پروفایل

استاد

• Manufacturing Systems
• Additive Manufacturing
• Causal Graphs
• +۵ مورد دیگر

Eric Coatanea is a Professor at Tampere University, affiliated with the Automation Technology and Mechanical Engineering department within the Faculty of Engineering and Natural Sciences. His research focuses on manufacturing systems, causal graph networks, multi-disciplinary optimization, and AI integration in engineering design. He holds a BSc from University of West Brittany (1990), MSc from INSA Toulouse (1993), and teaching certification from Ecole Normale Supérieure (1994). Research interests include modeling manufacturing systems, additive manufacturing (e.g., Directed Energy Deposition), causal graphs for decision-making, and systems engineering. Notable awards include the Chevalier des Palmes Académiques (2018) and Marie-Curie Fellowship (2006–2008). Recent publications emphasize optimization algorithms combining AI (e.g., L-ANN-GWO), viscosity effects in 3D printing, and causal graph applications in MDO. His work bridges theoretical frameworks with practical engineering challenges, focusing on early design synthesis and sustainable manufacturing. Commitments include editorial roles (Journal of Integrated Design & Process Science), NSERC Canada advisory, and board memberships (Dynavio Cooperative Oy, Selko Oy). Current projects include LILIAM, ÄVE, and DIGITBrain initiatives.

Garg Vikasمشاهده پروفایل

استادیار

• Quantum Computing
• Artificial Intelligence
• Machine Learning
• +۸ مورد دیگر

Professor Garg Vikas holds the position of Assistant Professor in the Department of Computer Science at the School of Science. His research spans quantum computing, artificial intelligence, and machine learning with applications in computational biology, healthcare, and drug design. He leads the HEALED/Garg project focused on human-steered machine learning for drug discovery and collaborates with institutions like MIT and industry partners. He co-founded YaiYai Oy, providing AI/ML solutions to global sectors. Education: PhD from MIT CSAIL under Tommi Jaakkola, with postdoctoral and industry experience at Amazon, Microsoft, and IBM. His work aligns with UN SDGs, particularly in healthcare and sustainable energy. Research interests include graph neural networks, generative models, and quantum AI. Recent projects involve climate modeling via physics-informed neural ODEs and optimizing quantum circuits using graph autoencoders. Key collaborations include MIT’s MLPDS Consortium and the Finnish Center for Artificial Intelligence. He supervises doctoral researchers like Yogesh Verma and postdocs such as Kogkalidis.

Simon Puglisiمشاهده پروفایل

استاد

• Algorithms
• Bioinformatics
• Data Compression
• +۳ مورد دیگر

Simon Puglisi is a Professor at the University of Helsinki's Department of Computer Science, within the Faculty of Science. His research focuses on algorithms, bioinformatics, data compression, and string processing, with notable contributions to genomic data analysis and efficient indexing techniques. He holds the Alberto Apostolico Best Paper Award (2021) and leads the WILL # CHAIR # BOSSA project (2025–2029). His work includes scalable k-mer indexing tools like Themisto and advancements in Lempel-Ziv compression and suffix tree algorithms. He frequently collaborates internationally, participates in editorial roles for journals like the ACM Journal of Experimental Algorithmics , and contributes to conferences such as the International Symposium on Combinatorial Pattern Matching. Research Interests: Algorithm design for string processing and bioinformatics Efficient data structures for genomic data Compression techniques (e.g., Lempel-Ziv, Burrows-Wheeler) Dynamic and space-efficient algorithms Grants & Projects: WILL # CHAIR # BOSSA (2025–2029) Ongoing collaborations with institutions like the University of Melbourne and King's College London

• Software Engineering
• Cyber-Physical Systems
• Digital Ecosystems
• +۳ مورد دیگر

Tero Päivärinta is a Professor at the University of Oulu, Faculty of Information Technology and Electrical Engineering. His work focuses on software engineering, digital ecosystems, and cybersecurity. He specializes in empirical studies of software systems, digital twins, and autonomous driving technologies. His research explores hybrid intelligence systems, data-centric decision-making, and governance of collective ambidexterity in digital initiatives. Education: PhD holder with extensive experience in academic and industry collaborations. Key research domains include cyber-physical systems, DevSecOps automation, and IT governance in public sectors. He co-leads projects such as the NUVE Lab’s vehicle testing frameworks and contributes to initiatives like the Software-Defined Vehicle project. Research highlights include advancing knowledge graphs for manufacturing, cybersecurity compliance in DevOps pipelines, and evaluating data-driven decisions. His articles emphasize interoperability challenges, adaptive systems design, and sustainable digital transformation in public utilities. Professional contributions include organizing the TKTP Annual Symposium and co-editing volumes celebrating academic peers like Markku Oivo. His work bridges theoretical software engineering with practical applications in mobility, governance, and industrial systems.

Bogdan Iancuمشاهده پروفایل

مدرس

• Artificial Intelligence
• Computer Vision
• Maritime Object Detection
• +۵ مورد دیگر

Bogdan Iancu is a University Lecturer in the Department of Information Technology at the Faculty of Science and Engineering, Åbo Akademi University. He holds a PhD and Docent qualification in Computer Science, with extensive expertise in artificial intelligence and computer vision applications, particularly in the maritime domain. His academic career spans numerous research projects and publications that bridge theoretical AI concepts with practical industry applications. Dr. Iancu's research focuses on AI applications in maritime technology, with special emphasis on object detection systems, security challenges in AI models, and sustainable technological solutions. He has developed benchmark datasets like ABOships and ABOships-PLUS that have become valuable resources for researchers in maritime computer vision. His work addresses critical challenges including adversarial attacks on object detection systems, as evidenced by his 2025 publication on TOG Adversarial Attacks in YOLO Models. The analysis of his recent publications reveals a clear progression from foundational dataset creation to advanced security analysis and neurosymbolic approaches that combine neural networks with symbolic reasoning. His research shows increasing sophistication in addressing real-world challenges in maritime AI systems, with particular attention to robustness, security, and practical implementation. Dr. Iancu actively participates in numerous research projects including EDISS (Engineering of Data-intensive Intelligent Software Systems), SMARTER (Sea4Value Smart Terminals), and DECATRIP (Decarbonizing Transport Corridors). These projects involve collaboration with industry partners across Finland and Europe, focusing on applying AI to solve real-world challenges in maritime transport, digitalization, and sustainability. He has contributed to the academic community through teaching courses in Artificial Intelligence, Data Science, and Graph Algorithms, and through active participation in the Finnish Artificial Intelligence Society. His work aligns with UN Sustainable Development Goals, particularly those related to industry innovation, infrastructure, and climate action through projects like DECATRIP that focus on decarbonizing transport corridors.

William Culhaneمشاهده پروفایل

پژوهشگر ارشد

• Big Data Processing
• Cloud Computing
• Distributed Systems
• +۷ مورد دیگر

William Culhane is a Post-doctoral Research Associate working with the Large-Scale Distributed Systems (LSDS) research group at Imperial College London under Dr. Peter Pietzuch. His research focuses on big data processing in cloud computing environments, with expertise spanning distributed systems architecture, network-aware algorithms, and practical implementation of theoretical models. His academic credentials include: PhD in Computer Science from Purdue University (2015), Thesis: "Optimal 'Big Data' Aggregation Systems – From Theory to Practical Application" Masters in Computer Science from Purdue University (2011) BS in Computer Science and Engineering with Music minor from Ohio State University (2008), Cum Laude with Distinction Culhane's research program bridges theoretical computer science with practical systems implementation. His early work established mathematical models for optimal aggregation overlays, demonstrating how specific properties of aggregation functions guide theoretically optimal overlay construction. This evolved into practical systems like LOOM that implement these models for real-world big data processing. His current research on Meta-Dataflows (MDF) enables users to specify solution spaces of data analysis approaches and automatically discover optimal outcomes without code changes. The SquirrelJoin project addresses network skew in distributed joins by dynamically rerouting traffic to underutilized network segments. His publication record shows consistent progression from theoretical foundations (INFOCOM'15, SIGCOMM'14) to applied systems (VLDB 2017, Middleware'14) with practical impact. His professional trajectory includes significant industry exposure through a Google Techstop internship and research at Qatar University, where he explored anonymized database relations for third-party cloud processing. These experiences informed his approach to balancing theoretical purity with practical constraints in distributed systems design. Culhane has developed numerous open-source tools for the research community including LaTeX macros for consistent document formatting, Linux deployment scripts for distributed system management, and JavaScript interactive graphing utilities. These practical contributions demonstrate his commitment to improving research infrastructure beyond his primary publications.

Petter Holmeمشاهده پروفایل

استاد

• Network Science
• Complex Systems
• Computational Social Science
• +۳ مورد دیگر

Petter Holme is a Professor at the Department of Computer Science, Aalto University, specializing in network science and complex systems. His research focuses on understanding interconnected systems through network analysis, including topics like epidemic spreading, climate change interactions, and power grid synchronization. He holds a Doctoral degree in Natural Sciences from Umeå University (2004) and has received the prestigious Fellow of the Network Science Society (2024). His work is interdisciplinary, collaborating with social scientists, medical researchers, and engineers. Key areas include temporal networks, computational social science, and data-driven modeling. Education: PhD in Natural Sciences (Umeå University, 2004), Licentiate in Natural Sciences (Umeå University, 2001), Master's in Engineering (Uppsala University, 2000), and Master's in Humanities (Stockholm University, 2000). Research interests span network structure analysis, epidemic models, and social dynamics, with contributions to UN Sustainable Development Goals. He has authored over 116 publications and frequently engages with media on topics like Paxlovid's public health impact and climate change networks.

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.

• 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.