Lauri Parkkonen is Professor in the Department of Neuroscience and Biomedical Engineering at Aalto University. His research advances non-invasive neuroimaging through innovations in MEG instrumentation, real-time analysis, and hyperscanning techniques to study social interaction, conscious perception, and brain plasticity. Key contributions include: Development of optically-pumped magnetometer arrays for next-generation MEG Pioneering hyperscanning methods for dual-brain social neuroscience Real-time neurofeedback paradigms for plasticity and clinical applications Normative modeling approaches for traumatic brain injury detection Open-source tools for MEG/EEG analysis (MNE-Python) His group studies neural mechanisms of empathy, attention, and cross-species emotion recognition using multivariate decoding. Current projects optimize cortical parcellation for MEG connectivity analysis and develop MRI-free source imaging techniques. Honors include an ERC Starting Grant (2015) and coordination of Finland's functional brain-imaging biobank consortium.
Ivan Zubarev is a Postdoctoral Researcher in the Department of Neuroscience and Biomedical Engineering at Aalto University. He holds a Master's degree in Engineering and Technology from St. Petersburg State University (2013). His research focuses on developing machine learning methods for analyzing electromagnetic brain activity, particularly using MEG and EEG signals. He has contributed to open-source tools like MNEflow and developed algorithms such as DELMEP for automated motor evoked potential analysis. His work emphasizes neural network applications in brain-computer interfacing, real-time decoding of brain signals, and understanding social influence mechanisms through neuroimaging. Zubarev has organized the 2024 International Conference on Bioelectromagnetism and collaborated internationally on studies of social conformity and neural signal processing. His research bridges computational neuroscience, biomedical engineering, and clinical applications.
Teemu Ojanen is a Professor of computational physics at Tampere University, appointed in October 2021. He leads the Theory of Quantum Matter research group, focusing on quantum condensed matter theory, topological phenomena in quantum matter, and quantum entanglement. His work explores the fundamental mechanisms of quantum materials and their potential applications in quantum computing and energy-efficient electronics. He holds a Doctor of Science in Technology from Aalto University, with postdoctoral experience at Harvard University and Freie Universität Berlin. His research group includes four researchers, two doctoral candidates, and master's students. Key collaborations include the Department of Physics at Aalto University, supported by the Academy of Finland and Helsinki Institute of Physics. Research interests span quantum materials, entanglement dynamics, and theoretical frameworks for topological phases. He emphasizes the importance of basic research for breakthroughs in quantum technologies, such as quantum computers and energy-efficient devices.
Hannu Häkkinen is a Professor and currently serves as Vice Dean of the Faculty of Mathematics and Natural Sciences at the University of Jyväskylä. He holds an active affiliation with the Department of Physics and previously held the title of Academy Professor (terminated). His research focuses on the computational design, structural modeling, and functional characterization of atomically precise metal nanoclusters, particularly their interactions with biomolecules, ligands, and surfaces. Key research interests include nanocluster interfaces, ligand stabilization strategies, machine learning applications in nanomaterials research, and the development of nanoclusters for biomedical and catalytic applications. He leads major projects such as the European Commission-funded 'Dynamic nanocluster – biomolecule interfaces' (2024–2029) and the Academy of Finland grant on computational design for bioimaging and photodynamic therapy (2024–2026). His work has contributed to advancements in nanoscale catalysis, cluster-based sensors, and interdisciplinary collaborations bridging physics, chemistry, and biology. As part of the Nanoscience Center (NSC), he integrates experimental and theoretical approaches to understand cluster behavior in diverse environments. Notable achievements include the Academy of Finland’s Academy Professorship and pioneering studies on nanocluster superatoms and their defect engineering. Grants and Projects: Leading €6M EU project on chiral gold nanocluster biosensors Academy of Finland grants for computational design (2024–2026), RDI docking (2023–2027), and catalyst development (2022–2025) Contributions to FGCI cloud computing infrastructure for data science (2019–2021) Labs and Teams: Member of the Nanoscience Center (NSC), a cross-departmental hub spanning Physics, Chemistry, and Biological/Environmental Sciences Collaborates closely with the GraphBNC machine learning team
Kevin Conley is a Visiting Professor in the Department of Chemistry and Materials Science and the Inorganic Materials Modelling group. He holds a Doctoral degree in Natural Sciences from McGill University (2017). His research focuses on nanomaterials, particularly carbon nanotubes, electrical conductivity, and optoelectronic properties of nanoparticles. He explores topics like doping effects, plasmonic behavior, and charge transfer in nanoscale systems. Key research areas include the electrical properties of carbon nanotube networks, near-infrared responsive materials, and computational modeling of nanomaterial behavior. His work bridges theoretical simulations (e.g., ab initio methods) with experimental validation, emphasizing applications in sensors and energy materials. His articles (2017–2024) highlight trends in nanomaterial conductivity, light-matter interactions, and core-shell nanoparticle design. Collaborations involve international teams, though specific institutions are not detailed here. No scientific awards are explicitly listed. He has supervised one thesis, though student names are not provided. Research outputs include datasets on plasmon excitations and thermoelectric alloys (e.g., via Zenodo and Figshare).
Esa Vakkilainen is a Professor at Lappeenranta University of Technology (LUT), affiliated with the LUT School of Energy Systems. His roles include former Head of the Doctoral Program in Energy (2012–2016), Head of the Energy Degree Program (2009–2016), and Vice Dean of the Faculty of Technology (2012–2014). He has extensive international experience, including a Visiting Professorship at St Petersburg State Polytechnic University (2014–2017), where he taught courses on bioenergy technology, energy systems engineering, and thermal power systems. His research focuses on bioenergy, carbon capture, thermal systems, and sustainable industrial processes. Key areas include recovery boiler optimization, biomass utilization, and decarbonization pathways for the pulp and paper industry. He has led projects integrating bioenergy with carbon capture technologies (BECCS) and explored innovative applications like hydrothermal carbonization of biomass residues. Prof. Vakkilainen’s work spans academic leadership and industry collaboration, with contributions to global energy transitions. His publications (2021–2025) emphasize techno-economic assessments of biomass-derived fuels, waste valorization, and policy frameworks for sustainable energy systems. He has advised on process optimization in pulp mills and developed models for boiler fouling prediction and operational flexibility in fluidized bed systems. Notable projects include evaluating the EU’s biomass potential, analyzing hydrogen transport for steel mills, and advancing bioenergy integration in Nordic industries. His expertise bridges academic research and industrial application, driving innovations in renewable energy and climate mitigation strategies.
Jesper Byggmästar is an Academy Postdoctoral Researcher at the University of Helsinki, affiliated with the Department of Physics under the Faculty of Science. His research focuses on computational materials physics, particularly radiation damage mechanisms, interatomic potential development, and the behavior of advanced materials in extreme environments. His work spans topics such as machine learning-driven simulations of materials like tungsten, gallium oxide, and high-entropy alloys, with applications in nuclear fusion and aerospace engineering. Key contributions include studies on radiation resistance, defect evolution, and interatomic potential optimization for metallic systems. Byggmästar leads the OCRAMLIP project (2023–2027), which explores refractory alloys using machine learning, and participates in the Finnish Center for Artificial Intelligence (FCAI) flagship program. His research emphasizes bridging atomic-scale simulations with macroscopic material behavior, addressing challenges in fusion reactor materials and structural integrity under irradiation.
Rafał Karczewski is a Doctoral Researcher in the Department of Computer Science at the School of Science. His research focuses on advanced machine learning techniques, particularly in Graph Neural Networks, Generative Models, and their applications in fields such as drug design and computer vision. His work explores topics like the generalization of equivariant graph neural networks, denoising in diffusion models, and the expressivity challenges in generative drug design. He has contributed to foundational studies on uncertainty estimation in neural networks and statistical regression methods. Rafał's publications reflect a strong emphasis on theoretical and applied machine learning, with recent work addressing cutting-edge issues in model generalization and creative applications of diffusion processes. He has not yet received notable scientific awards, but his research demonstrates significant potential in advancing AI methodologies.
Fengyu Cong is a Visiting Professor at the Faculty of Information Technology of the University of Jyväskylä (Finland). His research focuses on interdisciplinary computational neuroscience, biomedical signal processing, and machine learning applications in healthcare. Key contributions include EEG/fMRI analysis for mental health disorders such as depression and autism spectrum disorder, as well as federated learning methods for privacy-sensitive medical data. He has presented at major conferences including the 6th Conference on Mismatch Negativity (2012) and ESCAN 2014 , showcasing work on preattentive facial expression processing in neurological populations. His affiliations include the Engineering and Secure Communications Engineering and Signal Processing groups within his faculty. Research interests span neural signal analysis, multimodal imaging techniques, and AI-driven diagnostics. Notable methodologies include tensor decomposition for fMRI preprocessing, HMM-based sleep stage classification, and federated learning frameworks for distributed healthcare applications. His work bridges computer science and clinical neurology, with publications in top-tier journals addressing topics like vascular cognitive impairment, neural oscillations in autism, and artifact removal in biomedical signals.
Liisa Helle is a doctoral researcher at the School of Science within the Department of Neuroscience and Biomedical Engineering , focusing on advanced neuroimaging methodologies and their clinical applications. With expertise in Magnetoencephalography (MEG) and Electroencephalography (EEG) , her work spans comparative neurocognitive studies, brain injury analysis, and neural signal enhancement techniques. Her research interests include: Neural Oscillation dynamics in clinical and animal models Development of Signal-Space Separation algorithms for MEG interference suppression Neurophysiological basis of Facial Recognition in canines Cognitive impacts of Mild Traumatic Brain Injury Neural modulation through Deep Brain Stimulation Publications highlight: Advancements in MEG Source Montage sensitivity (2022) Time-resolved Canine Visual Cortex analysis (2020) Clinical implications of Neurotrauma (2019) Parkinson's disease Sensorimotor Activity suppression (2018)
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.
Professor Peter Österholm at Åbo Akademi University's Faculty of Natural Sciences and Engineering specializes in Environmental Geology with a focus on Acid Sulfate Soils . His work addresses critical environmental challenges through interdisciplinary approaches. 2023-2025 : Active principal researcher in two major projects, including EU BIONEER for post-mining waste management Research Themes : Geochemical remediation, microbial interactions, water quality impacts Recent publications highlight: 2025: Microbial responses to limestone/peat treatments in hypermonosulfidic sediments 2024: Machine learning applications for acid sulfate soil mapping 2023: Innovative macropore targeting to reduce acid-metal release Collaborations include European Regional Development Fund , Kiertokaari , and Finnish Transport Agency . He organized the 2024 GeoDays conference and serves as co-investigator in multiple international projects.
Ella Peltonen is an Assistant Professor at the M3S research unit, University of Oulu, Finland. She joined the Ubicomp Oulu research centre and 6Genesis research programme in November 2018. Prior to this position, she was a postdoctoral researcher at the Insight Centre for Data Analytics in Cork, Ireland. She completed her PhD in the Nodes group at the University of Helsinki, Finland, working on the Carat project of collaborative energy diagnostics for mobile devices. Her educational background includes: PhD in Computer Science, University of Helsinki, Finland (Carat project on collaborative energy diagnostics) Ella Peltonen's research focuses on ubiquitous computing, large-scale data analysis, and applied machine learning. Her work particularly emphasizes everyday sensing and mobile and wearable devices. She aims to apply machine learning algorithms to large, complex data in real-time systems, with a focus on distributed machine learning and data analysis of smart devices. Her research spans various applications including energy consumption monitoring of mobile devices, wearable technology for measuring physiological signals, and exploring future sensing technologies. Peltonen has expressed interest in how future devices might sense human states, become smarter, and provide greater benefits, potentially through innovations like augmented reality glasses or subcutaneous chips. Analysis of her recent publications shows a strong focus on edge computing, vehicular networks, and sustainable computing systems. Her work bridges the gap between theoretical machine learning approaches and practical applications in transportation, healthcare, and environmental monitoring. Many of her papers address challenges in distributed systems, real-time data processing, and privacy-preserving techniques for edge intelligence. Her notable scientific awards include: Nominated to the list of 10 Rising Stars in Networking and Communications by N2 Women 2017 Selected as one of 50 Finnish Researchers by the Finnish Union of University Researchers and Teachers Nokia Scholarship 2015 and 2016 Jorma Ollila Grant 2018 Young Teacher of the Year 2012 Young Researcher of the Year 2015 Peltonen is actively involved in teaching and mentoring, with a teaching philosophy focused on supporting students' independent learning rather than lecturing from above. She enjoys guiding small groups where she can discuss topics together with students and get to know them personally. As a researcher, she describes herself as precise, detail-oriented, and committed to verifying the correctness of her work carefully. She values the combination of mathematical work with experimental work and creativity in technology, noting that research tasks are diverse and can apply different types of methodology. She is part of international research collaborations with several major universities worldwide, as required by Finnish Academy funding. Peltonen is also an advocate for diversity in technology fields, noting that technology is used by all kinds of people from various backgrounds, yet the producers of technology lack diversity. She has highlighted the importance of encouraging more women to pursue technology careers from an early age.
Simo Vanni is an Adjunct Professor at the University of Helsinki's Department of Physiology, specializing in neuroscience and computational modeling. He serves as a supervisor for the Doctoral Programme Brain & Mind and Doctoral Programme in Clinical Research, affiliated with HUS Neurocenter. His work bridges neuroimaging (fMRI, MEG) with spiking network simulations to study visual cortex dynamics. University: University of Helsinki Department: Physiology Supervisor Roles: Doctoral Programmes Brain & Mind and Clinical Research Collaboration: HUS Neurocenter Vanni's research focuses on: Computational neuroscience and cortical modeling Visual perception and neural processing Functional connectivity in post-stroke recovery Biophysical mechanisms of neural signal transfer Integration of neuroimaging and neural simulations Neuroplasticity in visual field rehabilitation Recent publications emphasize digital brain research trends, spiking network models, and neuroimaging applications. His work appears in journals like Imaging Neuroscience , Frontiers in Computational Neuroscience , and Cerebral Cortex . Key collaborations include researchers from multiple institutions across Europe and North America. Vanni contributes to organizing neuroscience events such as the 10th EBRAINS Baltic-Nordic Summer School. He leads the 'Computational modeling of primate visual cortex' project (2024-2028) and participates in a cerebral small vessel disease study (2016-2025). Labs/teams include: Neurocenter Finland Doctoral Programme Brain & Mind Computational Neuroscience Research Group EBRAINS Baltic-Nordic Summer School Organizing Committee
Tarja Hannele Pietarinen serves as a Postdoctoral Researcher in the Department of Computer Science at the University of Helsinki, affiliated with the Complex Systems Computation Group. Her research bridges artificial intelligence and educational sciences with a specialized focus on child-centered applications. Her primary research domains include educational technology development, human-computer interaction design for young learners, ethical AI implementation in childhood education, and machine learning applications in developmental contexts. Current investigations examine generative AI's pedagogical potential and age-appropriate technology integration. Dr. Pietarinen actively contributes to two major funded initiatives: GenAI: Generation AI (Academy of Finland Strategic Research Council, 2022-2025): Exploring generative AI frameworks for educational environments Tekoäly & lapset (Jenny and Antti Wihuri Foundation, 2024): Investigating AI-child interaction dynamics and developmental impacts She presented collaborative findings at the 2023 Finnish Educational Research Association Conference (Kasvatustieteen päivät), demonstrating active engagement with educational research communities.
