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Mika Järvinen serves as Associate Professor in the Department of Energy and Mechanical Engineering at Aalto University, leveraging fundamental sciences—physics, thermodynamics, chemistry, and numerical modeling—to address cross-sector energy challenges. His research spans sustainable bioenergy and inter-linked circular economy systems integrating energy, materials, and environmental considerations across biomass, pulp/paper, metallurgy, and waste-to-energy industries. His expertise centers on sustainable bioenergy and circular economy frameworks, utilizing numerical modeling to optimize combustion engineering and metallurgical processes. By maintaining diverse industrial engagement, he ensures research continuity during economic fluctuations while advancing black liquor spraying technologies and energy-material-environment synergies. This approach enables flexible, physics-based solutions for complex industrial energy conversion problems. Professor Järvinen's 2025 publications reveal a cohesive focus on renewable energy systems operating within planetary boundaries, spanning carbon capture (calcium looping), low-temperature heat engines, and comprehensive textbook development. His work integrates solar, wind, bioenergy, and storage technologies into holistic frameworks that balance technical innovation with ecological constraints, emphasizing system-level sustainability over isolated component optimization. His scientific recognition includes: Best Dissertation Award (2002) from Helsinki University of Technology's Department of Machine Technology for research on black liquor droplet conversion Resonate Award (2015) from Caltech University's Resnick Institute As lead of the Energy Conversion and Systems research group, he directs projects bridging fundamental modeling with industrial applications while developing educational resources for next-generation energy engineers. His laboratory work emphasizes experimental validation of numerical models across combustion, metallurgical, and biomass conversion processes.

• Sustainable Bioenergy
• Circular Economy
• Energy Conversion Systems
• +4 more

Professor Simo Särkkä holds a position in Sensor Informatics and Medical Technology at the Department of Electrical Engineering and Automation (EEA), Aalto University. His research focuses on multi-sensor data processing, Bayesian filtering, machine learning, and their applications in medical technology, brain imaging, and inverse problems. He leads research groups including the Helsinki Institute for Information Technology (HIIT) and Sensor Informatics and Medical Technology. His work bridges theoretical advancements in probabilistic methods with practical implementations in healthcare and engineering. Key research interests include Gaussian processes, stochastic differential equations, quantum machine learning, and signal processing. He has contributed to advancements in algorithms for nonlinear state-space models, parallel computing techniques, and medical imaging technologies such as scatter correction in CT scans. His methodologies are applied across domains like autonomous systems, robotics, and bioengineering. Notable publications span topics like quantum-assisted Gaussian regression, physics-informed machine learning for industrial processes, and parallel-in-time numerical methods. His work emphasizes computational efficiency and robustness in high-dimensional and real-time systems.

• Machine Learning
• Signal Processing
• Bayesian Filtering
• +7 more

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
• Signal Processing
• Sensor Fusion
• +8 more

Jouni Partanen is a Professor at Aalto University's Department of Energy and Mechanical Engineering within the College of Engineering. His research focuses on advanced production technologies including Additive Manufacturing (3D-Printing), modern laser processing, and micromachining. Research Group: Materiaaleista tuotteiksi Specialization: Integration of AI in manufacturing processes Sustainability emphasis: Biochar-reinforced materials and carbon footprint reduction His work spans from fundamental material behavior analysis to industrial applications, particularly in metal additive manufacturing and composite fabrication. Recent research explores corrosion resistance in lattice structures and multiscale photopolymerization techniques. Publications highlight interdisciplinary approaches combining mechanical engineering with biomedical applications (e.g., patient-specific implants) and environmental health studies on industrial 3D printing emissions.

• Mechanical Engineering
• Additive Manufacturing
• 3D-Printing
• +2 more

Mohamed Noureldin is an Assistant Professor at the Department of Civil Engineering, Aalto University , Finland, with prior academic roles at Sungkyunkwan University, South Korea (2015–2022). His expertise lies in integrating Artificial Intelligence (AI) with Structural Health Monitoring (SHM) , Structural Digital Twin , Predictive Maintenance , and Seismic Retrofitting . Research Focus : AI-powered sustainable structural design, smart retrofitting, predictive maintenance, structural material innovation, and next-generation performance-based seismic/wind design. Industrial Experience : 20+ years in offshore/onshore structural engineering (Hyundai Heavy Industries, Samsung Engineering, Arab-Swiss Engineering Company, Zuhair Fayez Partnership). Teaching : Courses in structural analysis, seismic design, dynamics, and reinforced concrete at Aalto and Sungkyunkwan Universities. Laboratory : Leads the Structural Design AI Lab (SDAI), focusing on AI-driven resilient infrastructure. Contact : mohamed.noureldin@aalto.fi , +358504544861. His publications explore cutting-edge applications of AI, ML, and DL in seismic retrofitting, structural durability, soil stabilization, and hybrid damping systems. Collaborative work emphasizes life-cycle cost assessment and augmented reality for predictive maintenance.

• Structural Engineering
• Artificial Intelligence in Civil Engineering
• Structural Health Monitoring
• +5 more

Antti Honkela is a Professor of Data Science at the University of Helsinki's Department of Computer Science, within the Faculty of Science. He also serves as the Coordinating Professor for the Privacy-preserving and Secure AI Research Programme at the Finnish Center for Artificial Intelligence (FCAI), and as Deputy Director of the Master's Programme in Data Science. His roles include membership in the Health and Social Data Permit Authority (Findata) and as an Action Editor for Transactions on Machine Learning Research. Honkela's research focuses on privacy-preserving machine learning, differential privacy, Bayesian methods, and their applications in computational biology and healthcare. He leads projects such as the European Lighthouse in Secure and Safe AI (ELSA) and the Data Literacy for Responsible Decision-making initiative. His work emphasizes developing robust frameworks for privacy-aware AI, including differentially private synthetic data and federated learning. Honkela has advised numerous PhD and Master's students, and his contributions span theoretical advancements and practical implementations, such as the D3p Python package for differentially private probabilistic programming. Key contributions include advancements in Bayesian inference from synthetic data, privacy accounting mechanisms, and computational methods for genomic epidemiology. His interdisciplinary approach bridges machine learning, statistics, and healthcare, addressing challenges in data privacy and secure AI deployment.

• Privacy-preserving machine learning
• Differential Privacy
• Bayesian Machine Learning
• +3 more

Harri Lähdesmäki is an Associate Professor (tenured) at the Department of Computer Science, Aalto University, where he leads the Computational Systems Biology research group. His work focuses on probabilistic machine learning and deep generative models with applications in biomedicine and molecular biology. Key Research Interests: Probabilistic machine learning, deep generative models, computational biology, bioinformatics, longitudinal data modeling Contact: harri.lahdesmaki@aalto.fi | Konemiehentie 2, 02150 Espoo, Finland His recent publications highlight advancements in: Gaussian process priors for scalable deep generative models Single-cell analysis of immune repertoires in leukemia and diabetes Probabilistic deconvolution methods for RNA-seq data Epigenetic analysis using hidden Markov and mixed models Transformer-based survival prediction and missing data handling Harri’s work integrates mechanistic modeling with Bayesian inference, particularly applied to immunology, cancer biology, and early disease prediction.

• Probabilistic Machine Learning
• Deep Generative Models
• Artificial Intelligence
• +4 more

Petri Myllymäki serves as Director of multiple prominent research entities at Aalto University's School of Science, including the Helsinki Institute for Information Technology (HIIT), the Department of Computer Science, the Finnish Center for Artificial Intelligence (FCAI), and his own research group. His leadership positions demonstrate significant influence in the Finnish and international AI research community. Dr. Myllymäki's research focuses on information discovery, Bayesian networks, information retrieval, and machine learning. His work bridges theoretical computer science with practical applications, particularly in developing systems that enhance how humans interact with information spaces. His research fingerprint prominently features Information Discovery (100%), Bayesian Networks (50%), Hashing (50%), Approximation Algorithms (50%), Information Retrieval (50%), structure learning (50%), Search Engine technology (37%), and Task Performance (33%). His recent publications (2015-2025) reveal a consistent trajectory in interactive information discovery systems, with increasing applications in diverse domains including decision science, public health, and social media analysis. Notably, his 2024-2025 work shows expansion into cognitive burden in decision-making, health outcomes research using machine learning, and political social media analysis – demonstrating how his core AI methodologies are being applied to address complex societal challenges. Recognized at AI Finland Gala 2024 for contribution reaching over 1.4 million users Featured in multiple media outlets including X (formerly Twitter) and Bluesky Work referenced in patents and Wikipedia pages Dr. Myllymäki actively hosts academic visitors and participates in collaborative research activities. His work contributes to UN Sustainable Development Goals through applications in information access and AI development. The Myllymäki Petri group (HIIT) serves as a hub for interdisciplinary research at the intersection of artificial intelligence, human-computer interaction, and information systems.

• Information Discovery
• Bayesian Networks
• Information Retrieval
• +5 more

Christian Grönroos is Professor of Marketing at Hanken School of Economics, recognized among the top marketing researchers globally. His pioneering work established service marketing theory and the service logic paradigm. Research focuses on value co-creation frameworks, relationship marketing evolution, and service-driven business model innovation. Publications have transformed understanding of customer management and service productivity. Awards include the Don Schultz Award (2025) and AMS-Parasuraman Best Paper Award. Recent work examines service-informed marketing reform and employee alignment in brand promise co-creation.

• Service Marketing
• Value Co-Creation
• Relationship Marketing
• +2 more

Professor Petri Kuosmanen is affiliated with Aalto University as a faculty member of the School of Engineering and holds a position in the Department of Energy and Mechanical Engineering and the Mechatronics unit. Doctoral Degree in Engineering and Technology, Helsinki University of Technology (2004) Licentiate Degree in Engineering and Technology, Helsinki University of Technology (1992) Master's Degree in Engineering and Technology, Helsinki University of Technology (1988) His research focuses on Industrial Internet and Rotor Dynamics , with significant contributions to elevator systems , hydraulics , and wastewater treatment plant design . Recent work includes optimizing natural frequencies in rotor systems and developing digital twins for engineering education . Key trends in his 2024 publications include: Rotor dynamics and vibration analysis Smart data validation in industrial IoT networks Cold-climate adaptations for wastewater treatment Automated fiber optics coating mechanisms Energy efficiency in aeration systems As Principal Investigator, he leads projects like DRIVE FORWARD (2024–2027) and GOOD (2021–2024), focusing on electrified mobile machinery. He actively collaborates with networks such as DAAAM International (Chair) and European CLUSTER-university network (Member).

• Mechatronics
• Rotor Dynamics
• Industrial Internet
• +7 more

Olav Tirkkonen serves as a Full Professor in the Department of Communications and Networking at Aalto University, Finland, a position he has held since August 2006. He leads the Communication Theory research group, driving innovation in wireless communication systems. His academic journey includes a distinguished career spanning industry and academia, with significant contributions to 3G, 4G, and 5G technologies. His educational qualifications are: Doctor of Science (Ph.D.) in Theoretical Physics, Helsinki University of Technology, 1994 Master of Science (M.Sc.) in Theoretical Physics, Helsinki University of Technology, 1990 Professor Tirkkonen's research interests are centered on wireless communications, with a focus on physical layer processing, coding theory, and quantum information processing. His group explores advanced topics including 5G and beyond wireless networks (spectrum management, large-scale MIMO, ultra-reliable low-latency communication), network-level interference coordination, collaborative caching, machine learning applications for wireless channel geography, coding on manifolds, and quantum communication systems. This research bridges fundamental theory with practical implementation in next-generation wireless networks. Analysis of his recent publications (2024-2025) indicates a predominant focus on machine learning techniques for wireless channel modeling (channel charting), pilot allocation in MIMO systems, and quantum error correction. His work is instrumental in addressing key challenges in 5G/6G networks, particularly in scenarios demanding ultra-reliability, low latency, and efficient resource utilization. His scientific contributions include: Co-inventor of approximately 80 families of patents and patent applications Co-author of the book "Multiantenna transceiver techniques for 3G and beyond" Throughout his career, Professor Tirkkonen has mentored numerous graduate students and secured substantial research funding from various sources. His industry experience at Nokia Research Center (1999-2010) and visiting position at Cornell University (2016-2017) have enriched his research perspective and fostered strong industry-academia collaborations. The Communication Theory group, under his leadership, maintains active collaborations with leading institutions and companies worldwide, positioning Aalto University at the forefront of wireless communications research.

• Wireless Communications
• Coding Theory
• Quantum Information Processing
• +5 more

Leena Järvi is a Professor at the Institute for Atmospheric and Earth System Research (INAR) and Helsinki Institute of Sustainability Science (HELSUS) , University of Helsinki. Her work bridges urban climate science , air pollution , and greenhouse gas dynamics through experimental and theoretical approaches. Research Interests : Urban micrometeorology, carbon sequestration in green spaces, climate mitigation strategies, and air quality modeling. Key Projects : CO-CARBON (Strategic Research Council), GHUGS (Research Council of Finland), and PAUL (EU Horizon 2020). Her recent publications focus on urban CO2 fluxes , carbonyl sulfide as a carbon proxy , and climate impacts of urban vegetation . She has supervised 12 PhD students, 7 postdocs, and 17 undergraduates, while serving on editorial boards and organizing international workshops. Scientific Awards : Timothy Oke Award 2021 (IAUC). Teaching : Courses on Urban Climate and Atmospheric Sciences .

• Urban Climate
• Air Pollution
• Greenhouse Gases
• +7 more

Leena Aarikka-Stenroos is a Professor at Tampere University, leading the Research Centre for Managing Circular Economy (ManCE). She holds a Doctor of Science degree in Economics and Business Administration from Turku School of Economics (2011). Her research focuses on circular economy innovation, business models, sustainability transitions, and value creation mechanisms. She has pioneered studies on catalysts for circular economy ecosystems, B2B customer referencing, and the role of digital technologies in enabling circular value chains. Academic Qualification: PhD in Economics and Business Administration (Turku School of Economics, 2011) Roles: Head of Research Centre for Managing Circular Economy, Tampere University Research Interests: Circular Economy, Sustainability, Business Models, Innovation Management, Value Creation Her work contributes to UN Sustainable Development Goals, particularly addressing climate action and responsible consumption. Key contributions include frameworks for circular business model innovation and stakeholder engagement strategies in sustainability transitions. She has received prestigious awards such as the Alex Gofman Award (2016) and multiple best paper recognitions. Her research spans industries like construction, textiles, and healthcare, emphasizing cross-sector collaboration. Recent articles explore emotional dynamics in innovation, ethical value co-creation, and AI-driven value creation in B2B contexts. She actively collaborates internationally, contributing to conferences and editorial boards. Her work bridges theoretical insights with practical business strategies for environmental sustainability.

• Circular Economy
• Sustainability
• Business Models
• +3 more

Markus Heinonen is an Academy Research Fellow at Aalto University's Department of Computer Science within the School of Science. His academic position is tied to Harri Lähdesmäki's Professorship, focusing on probabilistic machine learning. He holds a Doctoral degree in Engineering and Technology from the University of Helsinki (2013). His research integrates probabilistic modeling , deep learning , and differential equations , with applications in computational biology, drug discovery, and biophysics. Key themes include Gaussian processes, Bayesian inference, generative models, and their use in understanding complex biological systems like immune cell behavior (e.g., T cell receptor analysis in aplastic anemia) and molecular design. He leads major projects such as the Deep Learning with Differential Equations initiative (2020–2025), exploring continuous-time models and physics-informed neural networks. His work bridges theory and application, evidenced by collaborations in diffusion models , optimal transport , and single-cell analysis . Publications span over 65 peer-reviewed outputs, with recent emphases on robust neural network training, multi-target molecular prediction, and interpretable drug design frameworks. His research contributes to UN Sustainable Development Goal 3 (Good Health) through advancements in disease modeling and therapeutic development. He has undertaken visiting research roles at the University of California, San Francisco (2017) and Telecom ParisTech (2013–2014). Media highlights include recognition for work on TCR-epitope prediction and AI-driven enzyme engineering.

• Machine Learning
• Probabilistic Modeling
• Bayesian Inference
• +5 more

Erkki Ikonen is a Professor of Measurement Science and Technology at Aalto University since 1995, with a joint appointment at VTT Technical Research Centre of Finland Ltd since 2005. His roles include membership in the Board of Directors of EURAMET, Chairmanship of the EMPIR Committee (2016–2021), and Vice President Technical of the CIE (2015–2019). He holds M.Sc. and Dr.Sc.Tech. degrees from Helsinki University of Technology (now part of Aalto University). His research focuses on optical measurements, particularly in metrology of complex micro/nanostructures, aerosol analysis, and photovoltaic characterization. He has supervised 35 doctoral students and authored over 190 peer-reviewed publications. Key contributions include advanced reflectometry techniques, field-deployable sensors, and international metrology standards. Education: M.Sc. (Tech.), Helsinki University of Technology, 1982 Dr.Sc.Tech., Helsinki University of Technology, 1988 Awards: Working Community Award 2008 (Helsinki University of Technology) Grants/Advising: 35 supervised doctoral theses; active in international metrology programs like EMPIR. Labs/Teams: Metrology Research Institute at Aalto University.

• Optical Measurements
• Metrology
• Photonics
• +3 more