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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

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

Sebastian Szyller serves as an Assistant Professor in the Department of Computer Science at Aalto University, Finland, where he is a core member of Adjunct Professor N. Asokan's research group. His academic role centers on advancing security frameworks for machine learning systems through rigorous theoretical and empirical research. His research expertise spans machine learning security , adversarial machine learning , and model ownership verification , with particular emphasis on defense mechanisms against model extraction attacks, unintended interactions among security measures, and intellectual property protection for AI models. Key contributions address the tension between model confidentiality and practical deployment requirements in real-world systems. Analysis of his 2019-2024 publications reveals an evolving research trajectory from foundational model extraction defenses (e.g., PRADA, 2019) to sophisticated analyses of defense interactions (SoK, 2024) and ownership resolution frameworks (False Claims, 2024). His work consistently bridges theoretical security principles with practical ML deployment challenges, establishing critical benchmarks for model protection. Dr. Szyller actively contributes to premier security venues including IEEE Symposium on Security and Privacy, USENIX Security Symposium, and AAAI conferences. His collaborations within Asokan's research group demonstrate strong interdisciplinary engagement in securing machine learning ecosystems, with recent work expanding into multimedia security and eCommerce fraud detection applications.

• Machine Learning Security
• Adversarial Machine Learning
• Model Ownership
• +3 more

Magnus Westerlund is a Senior Lecturer in Information Technology and Director of the Laboratory for Trustworthy AI at Arcada University of Applied Sciences in Helsinki, Finland. His industry background spans telecom and information management, and he holds a doctoral degree in Information Systems from Åbo Akademi University. He actively contributes to the Z-Inspection® network, focusing on ethical AI implementation and governance. Westerlund’s research emphasizes trustworthy AI, cybersecurity, and distributed systems. Key areas include AI regulatory compliance (e.g., EU AI Act), healthcare AI applications, blockchain security, and IoT edge solutions. His work bridges academia and industry, such as the Valohai-CSC collaboration for machine learning infrastructure in Finnish academia. His publications highlight practical AI assessment methods, ethical AI integration, and decentralized technologies. Notable contributions include frameworks for sustainable AI development, privacy-preserving autonomous systems, and smart contract-based IoT security protocols. Westerlund also explores educational innovations, such as integrating large language models (LLMs) into coding education. His research consistently addresses real-world challenges like pandemic-era healthcare AI, edge computing for IoT, and cybersecurity in autonomous systems.

• Trustworthy AI
• Cybersecurity
• IT Security
• +6 more

Anton Akusok is a Part-time Lecturer in the Big Data Analytics Master's program at Arcada University of Applied Sciences. He holds a BSc in IT from Moscow (2011), MSc in ML and Data from Aalto University (2014), and a DSc in ML from the University of Iowa, USA (2016). His research focuses on Extreme Learning Machines (ELM), hardware acceleration for ML on mobile devices, and real-time geospatial predictions. He has developed libraries like HPELM and Scikit-ELM, and created the HaSuRiski app for acid sulfate soil prediction in Finland. Research Interests: ELM applications in environmental modeling, federated learning security, mobile edge computing, and geospatial visualization. Key projects include real-time mapping apps with iOS integration and open-source ML tools. Publications (2021-2024) highlight work on federated learning privacy, acid sulfate soil detection, signature verification, and distributed ELM algorithms.

• Machine Learning
• Big Data Analytics
• Mobile Edge Computing
• +3 more

Mickaël Bettinelli is an Associate Professor at Université Savoie Mont Blanc, affiliated with the LISTIC laboratory. He holds a PhD in Computer Science (Distributed Artificial Intelligence) from Université Grenoble Alpes and previously worked as a postdoctoral researcher at the University of Oulu's Center for Ubiquitous Computing under the EU Horizon 2020 Fractal project. Current research focuses on federated learning and distributed systems resilience Developed DonatelloPyzza, an educational Python gridworld game Active reviewer for journals/conferences including IEEE Transactions and AAMAS His research bridges federated learning, multi-agent systems, and collective intelligence, emphasizing societal impact through sustainable technological solutions and science popularization via his French YouTube channel DonatelloPyzza . Publications span topics like decentralized decision-making and bias mitigation frameworks. Scientific Awards Prix 'Coup de coeur', Concours Conter et Rencontrer les Sciences 2024 Best Paper Award at JFSMA 2021

• Federated Learning
• Distributed Intelligence
• Complex Systems
• +3 more

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.

• Statistical Signal Processing
• Wireless Communications
• Multisensor Systems
• +4 more

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.

• Wireless Networking
• Mobile and Ubiquitous Computing
• Internet of Things
• +4 more

Tuomas Virtanen is a Professor at Tampere University's Signal Processing Research Centre within the Faculty of Information Technology and Communication Sciences. His primary affiliation is the Computing Sciences department, where he leads the Audio Research Group . His work focuses on computational analysis of audio signals, machine listening, and acoustic scene understanding. Research Interests: Audio signal processing, content analysis of audio, sound source separation, acoustic scene classification, speech processing (including noise-robust ASR and speaker recognition), and machine learning techniques such as deep neural networks and statistical modeling. His group develops methods for sound event detection, localization, and tracking in complex acoustic environments. Key Contributions: Leading the DCASE (Detection and Classification of Acoustic Scenes and Events) challenges, developing open-source datasets like TUT Acoustic Scenes and STARSS, and advancing techniques in audio captioning, privacy-preserving audio processing, and multimodal learning. Notable Projects: Creation of the Audio Research Group , development of the Clotho audio captioning dataset, and pioneering work in zero-shot audio classification using semantic embeddings. His research bridges signal processing fundamentals with modern machine learning paradigms.

• Audio signal processing
• Machine listening
• Sound source separation
• +5 more

Pauli Miettinen is a Professor of Data Science at the University of Eastern Finland, affiliated with the School of Computing within the Faculty of Science, Forestry and Technology. His research focuses on data science methodologies, including matrix and tensor decompositions, redescription mining, and social network analysis. Key applications span ecological niche modeling, health data analysis, and parliamentary candidate opinion analysis. He leads the Algorithmic Data Analysis research group and contributed to the Neuro-Innovation project (2021–2026). Recent work includes advancements in differentially private redescription mining and hyperbolic community graph generation. His publications emphasize efficient algorithms for data mining tasks like biclustering and non-negative matrix factorization. Selected achievements include developing the HyGen graph generator and pioneering techniques for interpretable data representation. His research bridges theoretical method development with practical applications in diverse domains.

• Data Science
• Data Mining
• Matrix and Tensor Decompositions
• +4 more

Bo Tan is an Associate Professor in Communications Engineering and Signal Processing at the Department of Electronic and Electrical Engineering, University College London (UCL). He previously held positions as Associate and Assistant Professor at Tampere University, Finland, where he retains a Docent title. His research focuses on radio signal processing, wireless communications, integrated sensing and communication (ISAC), machine learning applications in healthcare, and radar systems. He has coordinated major projects such as ANCHOR (Horizon Europe MSCA Doctoral Networks) and SPHERE-DNA (Finland Research Council), and serves as an Associate Editor for IEEE Wireless Communications Letters. Education: PhD in Engineering, University of Edinburgh (United Kingdom) Postdoctoral Researcher roles at: University College London (Computer Science & Electronic Engineering) University of Bristol (Electrical and Electronic Engineering) Research Interests: Wireless communication systems (5G/6G, THz, mmWave) Radar-communication integration and dual-functional systems Machine learning for signal processing and healthcare Integrated sensing and communication (ISAC) architectures Non-invasive healthcare monitoring via passive sensing Key Projects (2021–2025): ANCHOR (2025–2029): Research Coordination Committee lead SPHERE-DNA (2021–2024): PI focusing on federated learning for eHealth ACCESS (2021–2025): PI on wireless network security DIOR (2022–2027): Coordinator for H2020 MSCA Professional Roles: Vice Chair, IEEE Finland AP/ED/MTT Chapter Reviewer for IEEE/ACM/IET journals Labs/Teams: Leading the SPHERE-DNA initiative on privacy-preserving healthcare systems Active contributor to the IEEE's wireless communication standards

• Wireless Communications
• Signal Processing
• Machine Learning
• +7 more

Markus Perola serves as a University Researcher at the University of Helsinki, holding dual roles as Supervisor for the Doctoral Programme in Biomedicine and the Doctoral Programme in Population Health. He maintains an active external position at THL (Finnish Institute for Health and Welfare) since 2001, demonstrating long-term commitment to national public health research. His research spans Biomedicine , Genetic Epidemiology , and Population Health , with particular focus on Genetic determinants of autoimmune diseases and diabetes Chronobiology and socioeconomic interactions Mitochondrial mechanisms in liver disease Large-scale biobank analytics (FinnGen) Electronic health record mining for clinical insights cutting-edge methodologies in precision medicine and population-scale data integration. Analysis of his 367 publications reveals dominant themes in genomic epidemiology and translational biomedicine , with recent work emphasizing AI-driven risk prediction (particularly for COVID-19 outcomes), mitochondrial pathways in chronic diseases, and fine-scale genetic ancestry mapping. His research increasingly integrates multi-omics data with real-world clinical records. His scientific leadership includes major EU-funded initiatives: B1MGplus (€10,420 funding) EU/PROPHET (€66,681 funding) PRIMUS privacy-preserving meta-learning project GoE: Genome of Europe initiative FinnGen biobank consortium Perola actively supervises doctoral candidates through two university programs while contributing to 34 media engagements that translate complex genomic findings for public understanding. His work demonstrates consistent translational impact from basic genetic mechanisms to clinical applications and public health policy.

• Biomedicine
• Genetic Epidemiology
• Population Health
• +5 more

Jari Lietzen serves as a Postdoctoral Researcher within the Department of Information and Communications Engineering at Aalto University, Finland, actively contributing to the Communication Engineering research group. His work focuses on pioneering ultra-low-power communication solutions for next-generation wireless networks, particularly through backscatter technologies that enable battery-free device operation by harvesting ambient energy. His research spans critical domains including Backscatter Communications for Ambient IoT, Physical Layer Security mechanisms like secret key generation, Visible Light Communication integration, and Quantum-Enhanced Wireless Systems. He investigates thin-film device fabrication using additive manufacturing, polarization conversion techniques, and reconfigurable intelligent surfaces for harmonic beam steering, addressing fundamental challenges in energy efficiency and security for constrained IoT environments. Analysis of his 13 publications (2018-2024) reveals a cohesive research trajectory centered on backscatter communications evolution. Key trends include the shift from foundational quantum backscatter paradigms (2018) toward practical hardware implementations like light-controlled thin-film devices (2024) and multi-antenna integrated systems. His work consistently bridges theoretical advances in physical layer security with experimental validation, demonstrating expertise in Sub-1GHz radio systems, satellite communications security, and hybrid VLC-backscatter architectures for ambient IoT. Within Aalto University's Communication Engineering group, Lietzen collaborates extensively on experimental projects involving prototype development and link budget validation, with strong partnerships including Boxuan Xie, Kalle Ruttik, and Riku Jäntti. His research directly supports emerging 6G technologies through innovations in passive wireless infrastructure and quantum-inspired communication protocols.

• Backscatter Communications
• Wireless Communications
• Internet of Things
• +4 more

Ivy K. Y. Woo is a Doctoral Researcher (academic rank: Researcher) in the Department of Mathematics and Systems Analysis at Aalto University, Finland. She is an active member of the Algebra and Discrete Mathematics research group, collaborating with principal investigators including Russell W.F. Lai and Chris Brzuska on advanced cryptographic protocols. Her research concentrates on Cryptography and Privacy with specialized expertise in lattice-based constructions, anonymous transaction systems, and steganography. Key focus areas include traitor tracing mechanisms, functional encryption schemes, Learning With Errors (LWE) assumptions, and privacy-preserving technologies for blockchain applications. Her methodology integrates theoretical cryptography with practical security implementations, particularly addressing leakage resilience and sustainable anonymity systems. Analysis of her 10 recent publications (2022-2025) reveals dominant research trajectories in lattice-based cryptography (70% of output), with significant contributions to traitor tracing, attribute-based encryption, and obfuscation techniques. Parallel streams explore blockchain privacy (20%) through transaction graph analysis and sustainable ring signatures, plus steganographic methods (10%) for natural data hiding. Her work consistently targets high-impact venues including ASIACRYPT, CRYPTO, and Privacy Enhancing Technologies Symposium. Scientific recognition includes: The Andreas Pfitzmann - PETS Best Student Paper Award Runner Up 2022 for "On Defeating Graph Analysis of Anonymous Transactions" Woo maintains active research partnerships within Aalto's cryptography ecosystem, particularly through the Algebra and Discrete Mathematics group. Her current projects involve lattice-based solutions for multi-authority attribute encryption and leakage-resilient cryptographic primitives, with several 2025 publications indicating ongoing high-productivity collaboration with international research teams.

• Cryptography
• Privacy
• Computer Security
• +2 more

Nikolaj Tatti is a Visiting Professor at Aalto University's Department of Computer Science , affiliated with the Helsinki Institute for Information Technology (HIIT) and the Adj. Prof. Gionis Aris research group . His work focuses on algorithmic analysis of temporal networks and graph theory. Research Interests: Temporal network analysis Graph decomposition and density Segmentation algorithms Pattern discovery in time-series data Interactive data analysis frameworks Publication Trends (2018-2021): Research spans Temporal Networks (dynamic hierarchies, cascade reconstruction), Graph Theory (density, decomposition), and Data Mining (periodic patterns, event detection). Collaborative work with Aris Gionis and Polina Rozenshtein dominates his publications. Technical Collaborations : Works with multidisciplinary teams including researchers from ACM Transactions on Knowledge Discovery and IEEE conferences.

• Data Mining
• Machine Learning
• Temporal Networks
• +3 more