Janne Heikkilä is a Professor at the Faculty of Information Technology and Electrical Engineering, University of Oulu, Finland. With over 30 years of experience in computer vision and machine learning, he leads the Center for Machine Vision and Signal Analysis (CMVS) and has contributed extensively to both theoretical and applied research. Research Interests: 3D computer vision, biomedical image analysis, computational photography, and deep learning. Scientific Leadership: IAPR Fellow, Senior IEEE Member, and former President of the Pattern Recognition Society of Finland. His work spans computer vision, radiotherapy planning, and biomedical imaging, with over 200 publications and 14,000 citations. He has secured funding from prestigious organizations like the Academy of Finland and Business Finland. His recent research focuses on debiasing AI models, 6D object pose estimation, and radiotherapy dose prediction. Scientific Awards: IAPR Fellow Senior Member of IEEE
Gökhan Alcan is an Assistant Professor in Robotics and Machine Learning at the Automation Technology and Mechanical Engineering Unit of Tampere University, Finland. He leads the Advanced Learning, Control and AutomatioN (ALCAN) Research Group, focusing on safe model predictive control, constrained optimal control theory, reinforcement learning, and their applications to dynamical systems. His research addresses challenges in robotic manipulation, safe navigation, and human-robot collaboration through projects like the Aurora initiative on automated and connected machines. Education: B.Sc., M.Sc., and Ph.D. in Mechatronics Engineering from Sabanci University (2008–2019). Postdoctoral research at Sabanci University (2019) and Aalto University (2020–2024). Research Interests: Robotics, control theory, system identification, autonomous systems, and machine learning applied to robotic manipulation, autonomous vehicles, and safety-critical systems. Notable work includes trajectory optimization for hybrid systems, magnetic manipulation for medical applications, and sim-to-real gap analysis in cloth manipulation. Awards: Third Place in Aalto Open Science Award 2023, Elginkan Foundation Technology Award (2016), and multiple scholarships. Advised Ph.D. student David Blanco Mulero, who defended his thesis on robotic manipulation of deformable objects. Labs/Teams: ALCAN Research Group, former roles in Aalto University's Intelligent Robotics Group and Sabanci University's Control, Vision, and Robotics (CVR) Group.
Antti H. Niemi is a Professor and Dean at the University of Oulu 's Faculty of Technology , specializing in computational solid and structural mechanics. His research focuses on advanced numerical methods for engineering analysis and design. Research areas include computational mechanics, structural engineering, and metamaterials Develops innovative finite element methods for thin-body problems Current projects address snow structures, timber building envelopes, and machine learning applications in mechanical systems His recent work emphasizes discontinuous Petrov-Galerkin (DPG) methods for plates and shells, with applications in civil and mechanical engineering. Publications cover: Snow and ice vaults (2024) Machine learning for steel beam capacity prediction (2024) Hygrothermal analysis of timber structures (2024) DPG formulation for Reissner-Mindlin plates (2023) Shell element benchmarking (2018-2022)
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.
Farhad Javanmardi is a Researcher at Aalto University within the Department of Information and Communications Engineering. His work focuses on applying advanced computational methods to speech and biomedical signal analysis. Research Interests: Speech processing, voice disorders, machine learning, deep learning, biomedical signal processing, computational linguistics, and health informatics. His recent research trends emphasize the use of transformer-based models and wav2vec2 for robust detection of heart failure and voice pathologies in telephony environments. He investigates database-independent approaches, severity classification, and data augmentation techniques to improve model generalizability. Publications appear in journals like Speech Communication and Computer Speech and Language , as well as conferences including ICASSP and INTERSPEECH .
Sami Äyrämö is an Associate Professor at the Faculty of Information Technology , University of Jyväskylä. His research bridges machine learning and health science , focusing on innovative applications in biomechanics , medical imaging , and exercise physiology . Specializes in automated scoring systems for medical diagnostics Pioneer in domain-specific transfer learning for healthcare data Develops synthetic data for wellbeing sector innovation His work spans colorectal cancer tissue analysis , ACL injury risk modeling , and dementia detection from speech , with recent studies applying cluster analysis and deep learning to sports biomechanics challenges. Current projects include the WellbeingDataLab initiative for synthetic exercise data, and collaborations with the Computational Data Science Research Group on spectral imaging and health analytics.
Vikas Kumar Garg is an Assistant Professor at Aalto University specializing in Quantum Computing and Artificial Intelligence (AI)/Machine Learning (ML)/Deep Learning (DL), with research spanning theoretical and applied domains including Computational Biology, Healthcare (protein and drug design), Material Synthesis, Cybersecurity, Energy systems, E-commerce, Wireless networks, Blockchain, IoT, Computer Vision, and Natural Language Processing. His work focuses on human-assisted drug discovery , generative models , and learning under uncertainty , intersecting quantum AI with optimization and game theory. Key application areas include Biopharma, Retail, Manufacturing Automation, and FinTech through his industry collaborations. Professor Garg maintains extensive academic-industrial partnerships with leading researchers: Tommi Jaakkola (PhD supervisor, MIT) Cynthia Rudin (SM supervisor, Duke University) Raquel Urtasun (MS supervisor, University of Toronto/Waabi) Regina Barzilay and Stefanie Jegelka (MIT) Microsoft Research teams (Adam Kalai, David Alvarez-Melis, Ofer Dekel) CMU (Steven Wu), Caltech/Hebrew University (Katrina Ligett) Amazon Research (Inderjit Dhillon), Facebook AI (Lin Xiao) University of Cambridge (Roberto Cipolla), University of Chicago (Risi Kondor) As Co-Founder and Chief Scientist of YaiYai Oy, he drives quantum-AI solutions for global clients across Biopharma, Energy, and Gaming sectors while maintaining active academic research.
Juho Leinonen is an Academy Research Fellow at Aalto University's Department of Computer Science, Finland, specializing in AI-enhanced computing education. His work focuses on leveraging large language models (LLMs) to transform programming instruction through personalized learning analytics and educational technology. Education Background: PhD in Computer Science, University of Helsinki (2019) Docent (Adjunct Professor) in Computer Science, University of Helsinki Postdoctoral research at The University of Auckland, Aalto University, and University of Helsinki Research Focus: Leinonen's work centers on three interconnected pillars: (1) developing fine-grained learning analytics to decode student programming behavior; (2) applying LLMs to create adaptive educational tools for diverse learners; and (3) implementing learnersourcing strategies for scalable resource generation. His research particularly addresses challenges in multilingual programming education and responsible AI integration, with emphasis on non-native English speakers and novice programmers. Publication Trends: Recent publications (2024-2025) reveal a concentrated exploration of generative AI in computing education, with 85% focused on LLM applications. Key themes include synthetic data generation for educational research, multilingual prompting systems, and ethical frameworks for AI feedback. His work demonstrates both practical implementations (e.g., autocompletion quizzes) and critical analyses of AI limitations in educational contexts. Awards & Recognition: ACE2024 Best Paper Award for LLM-generated worked examples study UKICER 2023 Best Paper Award for achievement goals research ACE 2023 Best Practitioner Paper ICER 2022 Best Paper Award for programming exercise generation SIGCSE TS 2022 Best Paper in Computing Education Research ACE 2021 Best Paper Award for contextualized problem descriptions Research Leadership: As principal investigator of the Academy of Finland-funded project 'Advanced Student Modeling and Tailored LLMs for Personalized Learning', Leinonen supervises PhD students and postdocs while leading international collaborations with institutions including The University of Auckland and University of Helsinki. His grant portfolio focuses on ethical AI deployment in education and cross-cultural computing pedagogy. Collaborative Networks: He maintains active partnerships with leading computing education researchers like Paul Denny (Auckland), Arto Hellas (Aalto), and Andrew Luxton-Reilly (Auckland), evidenced by 90% co-authored publications. His work appears consistently in top venues including ACM SIGCSE, ICER, and ACE conferences.
Henriikka Vartiainen is a Senior Researcher at the School of Applied Educational Science and Teacher Education within the Philosophical Faculty of the University of Eastern Finland (UEF). Her academic roles include leading the Work Package in the Generation AI project, funded by the Finnish Strategic Research Council. She holds a PhD and is a Docent in Education, with research focused on design-oriented pedagogy, AI education, and technology-enhanced learning. Her work emphasizes participatory design, co-teaching models, and fostering data agency in K-12 students through innovative tools like AI-driven apps and robotics kits. Education & Awards: Vartiainen earned her Doctoral Dissertation Award (2014) from the Finnish Educational Research Association and the Young Researcher Award (2015) from UEF. Her doctoral research explored design-oriented pedagogy, a cornerstone of her subsequent projects. Research Interests: Her projects span AI literacy, boundary-crossing learning, and multi-material pedagogy. Notable contributions include developing educational frameworks for generative AI use, analyzing algorithmic bias in K-12 contexts, and co-designing digital tools for creative learning. She collaborates internationally on initiatives like the OpenBio STEAM and Forest as a Learning Environment projects. Publications & Impact: Over 50 peer-reviewed articles reflect her work in journals like IEEE Transactions on Learning Technologies and International Journal of Education through Art . Her research bridges theoretical insights with practical tools, such as no-code AI platforms and interactive games to teach data traces and profiling. Current Projects: Leadership in the Generation AI project (2022–2028) focuses on AI education for security-minded K-12 learners. She also investigates bioart-making and cross-cultural learning through boundary-crossing projects. Labs & Teams: Active in research groups like OpenBio STEAM and Multidisciplinary Craft Teaching and Learning, she collaborates with institutions globally on topics like digital citizenship and ethical AI use.
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.
Mika Nieminen serves as Senior University Lecturer in the Department of Computer Science at Aalto University's School of Science, specializing in the Human-Computer Interaction and Design (HCID) research area. With a Doctoral degree from Aalto University (2015) and extensive background in engineering and technology, he maintains active research leadership through multiple projects including ARISE: Advanced Realities for Cultural Innovation in Europe (2025-2029). His research focuses on Human-Computer Interaction with particular expertise in Virtual Reality , Participatory Design , and Strategic Usability . His work spans multiple application domains including healthcare systems, educational technologies, and cross-cultural interfaces. His Strategic Usability Research Group (STRATUS) develops frameworks for integrating user-centered approaches into business contexts. Nieminen's publication record shows a clear trajectory toward applying HCI principles to medical education (particularly ophthalmology) and migration support systems. His recent work demonstrates increasing focus on asymmetric VR environments and their impact on presence, collaboration, and user experience. His projects often involve international collaboration, particularly with European partners. As principal investigator for the Lapsus research project (2015-2018), he developed patient experience frameworks for pediatric healthcare settings. His current ARISE project continues this healthcare technology focus while expanding into cultural innovation applications of advanced realities. His academic service includes conference presentations on topics such as crowdsourced translation within refugee information systems and visiting researcher positions at foreign academic institutions, demonstrating commitment to both theoretical and applied aspects of HCI research.
Ondrej Krejci is a researcher in the Department of Applied Physics at Aalto University, specializing in computational and theoretical approaches to surface science, scanning probe microscopy, and materials discovery. His work bridges advanced simulation techniques with experimental validation. Research interests include: Density Functional Theory (DFT) Scanning Probe Microscopy (SPM) simulations Molecular adsorption on surfaces X-ray spectroscopy simulations Condensed matter physics Atomic and molecular physics Recent publications highlight trends in machine learning for catalyst discovery, probe-particle model innovations, and on-surface synthesis of novel carbon allotropes and frameworks. Collaborative work spans computational modeling, experimental validation, and instrumentation development.
Sergiy Vorobyov is a Professor at the Department of Signal Processing and Acoustics , Aalto University , Finland. He has held academic and research positions at multiple institutions, including the University of Alberta (Canada), Kharkiv National University of Radio Electronics (Ukraine), RIKEN (Japan), McMaster University (Canada), Duisburg-Essen University and Darmstadt University of Technology (Germany), and Heriot-Watt University (UK). His expertise spans optimization, signal processing, and multi-antenna systems. Dr. Vorobyov holds a Doctoral degree in Natural Sciences from the National Technical University Kharkiv Polytechnical Institute, awarded on January 15, 2002. His research interests focus on optimization and multi-linear algebra applied to signal processing challenges, including statistical and array signal processing, sparse signal processing, estimation and detection theory, and sampling theory. He explores multi-antenna, large-scale, cooperative, and cognitive systems, contributing to advancements in wireless communications and radar engineering. His work aligns with UN Sustainable Development Goals, emphasizing education and innovation. In recent years (2025), his publications emphasize cutting-edge advancements in wireless communications and signal processing. Topics include millimeter-wave MIMO channel estimation, optimization algorithms with momentum-based techniques, vehicular network communications, and robust covariance matrix estimation in challenging noise environments. These contributions highlight his expertise in developing efficient and adaptive methods for modern communication systems. He has received prestigious awards, including: 2004 IEEE Signal Processing Society Best Paper Award 2007 Alberta Ingenuity New Faculty Award 2011 Carl Zeiss Award for teaching and innovative methods 2012 NSERC Discovery Accelerator Award 1st Price Best Paper Award (2015) 1st Price Best Student Paper Award at CAMSAP 2015 As a researcher, Vorobyov has supervised seven theses and led multiple funded projects, such as: AI Based RAN (2023–2025): Scalable AI solutions for 5G/6G networks. MASSIVE AND SPARSE ANTENNA ARRAY PROCESSING FOR MILLIMETERWAVE COMMUNICATIONS (2019–2021): Advanced antenna design and processing techniques. M-CUBE SPA (2017–2021): EU-funded sparse antenna array research. Transmit beamspace for active compressive sensing and communication with multiple waveforms (2016–2020): Radar and MIMO system optimization. He leads the Sergiy Vorobyov Group , focusing on real-time signal processing algorithms and their applications in next-generation wireless systems. His research addresses practical challenges such as efficient channel estimation, robust detection in massive access scenarios, and improving network performance in urban environments.
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.
Jouni Järvinen is a University Lecturer in Software Engineering at LUT University in Lahti, Finland, and a Docent in the Department of Mathematics and Statistics at the University of Turku. He holds a PhD in Mathematics and serves as a Mathematics Consultant at TXODDS. His work bridges theoretical and applied research across multiple domains. Affiliations : LUT School of Engineering Sciences, University of Turku Education : PhD in Mathematics Research Interests lie at the intersection of Rough Set Theory , Lattice Theory , and Non-Classical Logics . He explores algebraic structures like Nelson algebras, Kleene algebras, and Galois connections, applying them to problems in Artificial Intelligence and Bioinformatics . His work often connects abstract mathematical frameworks to practical applications in Information Systems and Machine Learning . Publication Trends reveal a focus on Rough Sets and Algebraic Structures across 51 works (2025–1998). Recent studies (2025–2020) examine multigranular rough sets, pseudocomplemented Kleene algebras, and relational correspondences for fuzzy rough approximations. Earlier works (2015–2007) address topics like Tolerance-Based Rough Sets , Galois Connections , and Natural Language Disambiguation . Peer Review Activities include contributions to journals such as Fuzzy Sets and Systems , International Journal of Approximate Reasoning , and Studia Logica . His research spans Theoretical Computer Science , Mathematical Logic , and Biomedical Data Analysis .
