Maija Lampu is a Lecturer specializing in Information and Knowledge Management, contributing to UN Sustainable Development Goals related to education. Her expertise spans Public Sector Management, IT Department Operations, and Enterprise Architecture. She holds a Master of Science (Technology) in Industrial Management (2017) and a Bachelor of Science (Technology) in Industrial Management (2016). Her research focuses on AI applications in public organizations, cybersecurity resilience in healthcare, and digital transformation strategies. Recent work includes examining AI's role in enhancing public sector performance and preparing healthcare systems against cyberattacks. She has published widely on topics like collaborative learning in online environments and organizational resilience frameworks. Dr. Lampu actively reviews for conferences such as the Hawaii International Conference on System Sciences and the European Conference on Information Systems. Her work emphasizes practical solutions for public sector challenges, with a strong focus on cybersecurity and technological adoption in critical infrastructure sectors.
Robert Heikkilä is a Doctoral Researcher in Automation Technology and Mechanical Engineering and a Doctoral Student within the Doctoral Programme in Engineering Sciences, actively engaged in robotics and industrial automation research as evidenced by his 2023 publication output. His core research interests include Robotics , Autonomous Vehicles , Path Planning , Industrial Automation , Mechanical Engineering , and Optimization , with a specialized focus on developing communication frameworks and distributed optimization techniques for industrial vehicle systems. His sole documented research output from 2023 centers on distributed path planning for industrial vehicles, reflecting a concentrated effort to enhance coordination and efficiency in automated industrial logistics through algorithmic optimization and novel communication architectures.
Lucie Klus is a Postdoctoral Researcher in Electrical Engineering actively advancing indoor positioning systems through innovative wireless signal processing and machine learning techniques. Her work bridges theoretical algorithms with practical applications for indoor localization, focusing on fingerprinting methodologies and real-world dataset optimization. Core research spans Indoor Positioning (100% fingerprint relevance), Wearable Device integration (46%), Radio Map analysis (42%), and K-means clustering (35%) Recent breakthroughs include dynamic localization using Intersection over Union metrics and multidimensional compression of positioning datasets via EWOk framework Key publications demonstrate strong synergy between Wi-Fi fingerprinting, deep learning interpretation, and quality-of-service optimization in constrained environments Her 18 research outputs (2019-2024) reveal consistent focus on solving data sparsity challenges through autoencoders, extreme learning machines, and novel radio map compression techniques, with increasing emphasis on multi-device compatibility and measurement density.
Majid Haghparast serves as an Associate Professor in the Faculty of Information Technology at the University of Jyväskylä, Finland, with a sabbatical research fellowship at Johannes Kepler University (Linz, Austria) from April 2017 to January 2018. His research spans Software Engineering, Quantum Computing, and High Performance Computing, with concentrated expertise in Quantum Algorithms, Reversible Computation, Quantum dot Cellular Automata, and Multiple-Valued Logic. He investigates fault-tolerant quantum systems, ternary logic implementations, and quantum software development frameworks, bridging theoretical quantum principles with practical nanoscale computing applications. Recent publications (2024-2025) reveal dominant trends in quantum circuit optimization, reversible logic design for ternary/qutrit systems, and quantum software tool development. Key contributions include efficient quantum hardware components (comparators, multiplexers, flip-flops) using quantum-dot cellular automata, alongside advancements in quantum communication protocols and algorithm simulation tools. Professor Haghparast has supervised over 10 PhD and 150 MSc theses. He leads the SeQuSoS project (Securing the Quantum Software Stack) and DEQSE (Developer Experience in Iterative Quantum Software Engineering), addressing quantum software security and programming usability challenges. He co-leads the Empirical Software Engineering Research group and heads the Quantum Information and Computation Team, which actively explores quantum computational boundaries through projects like SeQuSoS and DEQSE.
Joonas Hämäläinen serves as a Postdoctoral Researcher at the Faculty of Information Technology, University of Jyväskylä. His academic work bridges theoretical machine learning with practical applications in software engineering and computational chemistry, focusing on distance-based methodologies and the Minimal Learning Machine framework. His primary research domains include Machine Learning (particularly minimal learning machines and multi-label classification), Software Engineering (empirical studies and defect prediction), Artificial Intelligence (regression and clustering algorithms), and Computer Science (scalable data processing). This interdisciplinary approach enables novel solutions for complex problems like atomic force prediction in nanomaterials and software quality assurance. Analysis of his 2020-2024 publications reveals a consistent trajectory in advancing distance-based learning techniques. His work demonstrates increasing sophistication in adapting Minimal Learning Machine variants to multi-target regression, feature selection, and nanoscale simulations, with growing emphasis on theoretical validation alongside empirical testing across computational chemistry and software engineering contexts. Hämäläinen actively contributes to two key research groups: the Empirical Software Engineering Research group focusing on evidence-based software development practices, and the Quantum Information and Computation Team which targets quantum computing advancements through interdisciplinary collaboration.
Jake Muff is a Doctoral Researcher at the University of Jyväskylä , affiliated with the Faculty of Information Technology and the Quantum Information and Computation Team . His research focuses on advancing quantum computing's computational potential and bridging theoretical algorithms with practical hardware implementations. He is actively involved in two major projects: EM4QS: Enhanced Middleware for Quantum Software – Developing middleware solutions to optimize quantum software execution. BEQAH: Between Quantum Algorithms and Hardware – Investigating the interface between quantum algorithms and physical hardware constraints. His research interests span quantum information theory, quantum algorithm design, and the practical deployment of quantum software systems. Despite no listed articles or awards, his work seeks to address foundational challenges in quantum computing scalability and real-world applicability.
Ronja Heikkinen is a Project Researcher at the Faculty of Information Technology, University of Jyväskylä, where she contributes to the Quantum Information and Computation Team. She is currently preparing to commence her doctoral program after prior experience as a research assistant, course assistant, and private-sector software developer. Her research spans quantum computing, software development, user experience, and knowledge management, with emphasis on quantum software engineering and developer-centric tooling. She actively investigates quantum algorithm development frameworks and human-computer interaction in emerging computational paradigms. Her scholarly output centers on quantum software infrastructure, exemplified by the 2025 SoftwareX publication of Quirk-E—a quantum circuit simulator designed to enhance algorithm development workflows—reflecting her focus on practical quantum computing applications. Heikkinen participates in two major research initiatives: SeQuSoS (Securing the Quantum Software Stack) addressing quantum software security, and DEQSE (Developer Experience in Iterative Quantum Software Engineering) optimizing quantum development workflows. The Quantum Information and Computation Team, which she supports, pursues fundamental advances in quantum computational theory and its real-world implementation.
Olli Wiikinkoski is an active researcher in automation and mechanical engineering, with recent contributions to additive manufacturing and sensor technology in 2023 and 2024. Their work focuses on advanced material processing and precision measurement techniques. Research Interests: Additive manufacturing, multilayer structures, laser processing, and sensor integration for industrial applications. Key Trends: Focus on optimizing LWDED (Laser Wire Directed Energy Deposition) processes, improving height/contour detection accuracy, and enhancing material properties using nanoparticles.
Samuli Aalto is a Visiting Professor at the Department of Information and Communications Engineering, Aalto University School of Electrical Engineering. His research focuses on Communication Engineering , particularly in Queueing Theory , Stochastic Scheduling , and Energy-Aware Systems . He has contributed extensively to optimizing resource allocation in wireless networks and server farms. Academic Affiliation : Aalto University, School of Electrical Engineering Research Themes : Optimal scheduling in queueing systems, energy-efficient network management, and stochastic resource allocation Recent Publications highlight work in: Whittle index applications for multi-class queueing systems Energy-aware dispatching in heterogeneous networks Slowdown minimization in single-server queues Age-energy tradeoff models for wireless systems His research involves mathematical modeling of Markovian systems , processor sharing , and size-based scheduling . Projects include optimizing P2P video-on-demand systems, analyzing queue-specific job sizes , and developing energy-performance tradeoff frameworks for modern data centers and 5G networks.
Jaakko Nieminen is a Visiting Faculty member in the Department of Neuroscience and Biomedical Engineering at Aalto University's School of Science. He holds a Doctoral degree in Engineering and Technology from Aalto University (2012) and a Master's degree from Helsinki University of Technology (2008). Doctoral degree, Engineering and Technology, Aalto University (2012) Master's degree, Engineering and Technology, Helsinki University of Technology (2008) His research focuses on transcranial magnetic stimulation (TMS), particularly the development and optimization of multi-locus and multi-coil TMS systems for precise brain stimulation. His work integrates neuroscience, biomedical engineering, and computational modeling to advance neuromodulation techniques. Key interests include electric field orientation, motor evoked potentials, primary motor cortex circuitry, and closed-loop TMS with EEG feedback. Recent publications highlight advancements in multi-locus TMS systems, deep learning for motor evoked potential analysis (DELMEP), and spatiotemporal modulation of brain networks. His research trend emphasizes precision, automation, and integration of neuroimaging and electrophysiological feedback in brain stimulation technologies. Scientific Awards: Innovation of the Year Award (2017) McKinsey-palkinto, McKinsey & Company, USA (2009) McKinsey-palkinto, McKinsey, USA (2008) The Best Graduate Thesis Award (2009) The Best PhD Poster Award (2012) Nieminen has served as Principal Investigator on multiple Academy of Finland-funded projects focused on multi-locus TMS, demonstrating leadership in securing research grants. He has supervised theses and reviewed PhD dissertations, contributing to academic training. His collaborative network spans neuroscience, engineering, and clinical neurophysiology, with extensive publication output and media coverage of his work on making brain stimulation more reliable through algorithmic advancements. He is actively involved in research teams developing next-generation TMS technologies, including software tools like DELMEP and hardware systems for clinical and preclinical applications. His work continues to push the boundaries of non-invasive brain stimulation through engineering innovation and rigorous scientific investigation.
Juho Hirvonen is a Research Fellow at Aalto University's School of Science , Department of Computer Science. His work focuses on distributed algorithms, network computing, and algorithmic game theory. Education: Master's degree in Engineering and Technology from University of Helsinki (2012) Research Interests: Active in Distributed Algorithms , with a focus on locality-sensitive approaches, fault-tolerant systems, and convergence complexity in graphical games. Key subfields include sparse matrix operations, static fast rerouting, and distributed stable matching. Scientific Recognition: Recipient of the Best Paper Award at FOCS 2019 for foundational work in distributed network computing. Projects: Led the Academy of Finland-funded project Towards a Complexity Theory of Distributed Network Computing (2018-2021) Collaborations: Engaged with institutions like Université Paris Diderot and international conferences (OPODIS, SIROCCO)
Joakim Löfgren is a Visiting Professor at the Department of Applied Physics, focusing on interdisciplinary research that bridges machine learning with materials science and biorefinery technologies. His work spans advanced applications in smart textiles, thermally-activated polymer actuators, and sustainable biomass processing. His research interests center on integrating machine learning systems into biorefinery and polymer science to enhance materials design and sustainability. Key areas include e-textiles , conductive polymers , and lignin-carbohydrate complexes . The latest publications highlight a trend in applying data-driven optimization to polymer actuators and biorefinery processes. Notable work involves polypyrrole-grafted yarns for flexible electronics and NMR spectroscopy for biomass characterization.
Patric Östergård is a Professor at Aalto University's Department of Information and Communications Engineering. His research focuses on fundamental problems in discrete mathematics and information theory, utilizing combinatorial algorithms and massive computations to study existence and classification of mathematical structures with applications in ICT. Department: Information and Communications Engineering Institution: Aalto University His key research areas include coding theory, design theory, graph theory, and Shannon theory. He leads a high-performance computing cluster Medusa for computational work. His research is supported by the Academy of Finland's project 'Construction and Classification of Discrete Mathematical Structures' (2015–2019). Notable trends: Steiner triple systems, Hadamard matrices, and error-correcting codes Collaboration: Active in international partnerships, particularly in combinatorics and coding theory Scientific Awards Kirkman Medal (1997) for contributions to combinatorial research Doctor et Professor Honoris Causa from University of Pécs, Hungary (2013) He has supervised 7 doctoral theses and actively participates in academic service through editorial board memberships, conference committee roles, and hosting visiting scholars.
Maryamolsadat Samavaki is a Researcher affiliated with the Computing Sciences Mathematics Research Centre , focusing on computational modeling of cerebrovascular dynamics and neuroelectromagnetism. Her work bridges biomedical engineering and applied mathematics through advanced numerical methods. Key research areas include EEG source localization , transcranial stimulation , and multi-compartment head modeling . Develops spatiotemporal hemodynamic models to analyze microcirculation and blood volume fractions. Applies L1-norm optimization and metaheuristic algorithms for neurostimulation montage design. Recent publications explore cerebral circulation's impact on electrical conductivity and in silico imaging techniques using anatomical atlases . Collaborations emphasize boundary-fitted meshing for subcortical structures in EEG modeling.
Aleksandr Ometov is a Senior Research Fellow in the Department of Electrical Engineering, focusing on cutting-edge research in wireless communications, Internet of Things (IoT), and extended reality (XR) technologies. His work bridges hardware optimization, network protocols, and applied engineering solutions. Education: Doctor of Science (Technology) in Telecommunications Technology (2018) Master of Science (Technology) in Information Technology (2016) His research explores adaptive computing, terrestrial and non-terrestrial networks, and spurious signal analysis in wireless systems. Recent publications highlight innovations in maritime IoT, XR-assisted surgery, and UWB-Wi-Fi coexistence challenges. Scientific Awards: ECIU Research Mobility Fund (2024) Mobility grant from Finland to Japan, Taiwan or Russia (2022) Nokia Scholarship (2018) Publisher of the Year Award (2022) Recognition of excellent doctoral dissertation (2019) Ometov actively contributes datasets on IoT, localization, and sensor technologies, demonstrating his commitment to open research. His collaborations span global institutions, focusing on 5G/6G, wearable devices, and mission-critical systems.
