جست‌وجوی استاد و پژوهشگر

• Neuroscience
• Computer Science
• Medical Imaging
• +۳ مورد دیگر

Susanne Merz is a Researcher affiliated with the School of Science , focusing on interdisciplinary studies at the intersection of neuroscience and computational methods. Her work leverages advanced statistical and artificial intelligence techniques to investigate brain dynamics and cognitive disorders. Her research interests include: Neuroscience Machine Learning Medical Imaging Biostatistics Cognitive Psychology Neuroinformatics Recent publications highlight her contributions to analyzing magnetoencephalography (MEG) data for neural fingerprinting and developing AI-driven tools for dementia risk assessment in mild cognitive impairment (MCI). Collaborations span international institutions, such as the AI-Mind project funded by the EU H2020 Framework program. Her work integrates computational modeling with clinical applications, aiming to improve early detection of neurodegenerative diseases. Scientific contributions: 2024 : Bayesian reduced rank regression and neural fingerprints in MEG 2024 : AI-Mind protocol for brain connectivity screening in MCI Projects: AI-Mind (2021–2026), EU H2020 Framework program Her research employs magnetoencephalography, functional connectivity, and AI to advance understanding of brain networks and their implications for cognitive health.

• Graph Neural Networks
• Machine Learning
• Artificial Intelligence
• +۴ مورد دیگر

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.

• Computer Science
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

Petri Myllymäki is a Director at Helsinki Institute for Information Technology (HIIT) , Computer Science Adjunct Professors , and Finnish Center for Artificial Intelligence (FCAI) within Aalto University's School of Science, Department of Computer Science . His research focuses on Bayesian networks, information discovery, and interactive intent modeling . Active in ACM Transactions on Information Systems and SIGIR Conference publications Recipient of 2016 CPHC/BCS Academy of Computing Distinguished Dissertation runner-up and 2017 Kone Foundation Rajapinta award Collaborated with institutions like Stockholm University and researchers including Giulio Jacucci and Samuel Kaski His 15 most recent publications (2025-2015) span decision-making under conflicting objectives, machine learning applications in social science, Bayesian network structure learning algorithms , and cognitive modeling for exploratory search . Key technical subfields include hashing techniques, two-dimensional representation, computational efficiency, and user experience optimization . Scientific awards : 2016 CPHC/BCS Academy of Computing Distinguished Dissertation runner-up 2017 Kone Foundation Rajapinta award As an information discovery expert , he has contributed to open-access research through Interactive Visualization and Scinet frameworks, impacting Sustainable Development Goals related to digital innovation and knowledge dissemination.

• Behavioral Science
• Health Psychology
• Education Psychology
• +۳ مورد دیگر

Juho Polet is a Postdoctoral Researcher in the Department of Psychology at the University of Jyväskylä, affiliated with the Faculty of Education and Psychology. He is part of the Centre of Excellence for Learning Dynamics and Intervention Research (InterLearn), focusing on motivation theories and behavior change interventions. His work bridges academic research with practical applications, particularly in public health and educational contexts. Research Interests: Integration of motivation theories to explain lifestyle changes and behavior modification. Impact of social influences, including friendship dynamics and peer pressure, on adolescent emotions and academic motivation. Design and evaluation of web-based interventions targeting physical activity promotion in parent-child dyads. Applications of theoretical models (e.g., Self-Determination Theory) in diverse health behaviors. Collaborations with private sector to translate research into real-world solutions. Key Research Contributions: Juho has published extensively on topics like the protective role of physical activity against dementia, trans-contextual models of leisure-time activity, and the psychosocial factors influencing adolescent health behaviors. His work emphasizes practical interventions while advancing theoretical understanding in behavioral science. Affiliations: Centre of Excellence for Learning Dynamics and Intervention Research (InterLearn) InterLearn – Research team of psychology (focusing on child learning and mental well-being)

• Biostatistics
• Public Health
• Nutrition Science
• +۴ مورد دیگر

Jaakko Nevalainen is a Professor of Biostatistics and Head of the Health Sciences Unit at the University of Tampere , Faculty of Social Sciences. His work integrates computational methods and statistical theory to address health-related research questions in nutrition, diabetes, prostate cancer, and cardiovascular disease. He leads the LoCard research consortium, developing computational methods to study food consumption and health sustainability. His research has received significant funding from the Academy of Finland and Horizon Europe, resulting in over 160 peer-reviewed publications in top journals like Statistics in Medicine , Diabetologia , and JAMA . Key research areas include biostatistical method development, health economics, and applied epidemiology. His teams collaborate with institutions like the University of Helsinki and participate in population studies such as ProScreen (prostate cancer screening) and DIPP (type 1 diabetes). Major projects focus on food purchasing patterns, obesity-related health outcomes, and sustainable dietary transitions. He has secured major grants including Academy of Finland funding for the ProScreen trial and EU Horizon grants for LoCard. His interdisciplinary approach bridges statistical innovation with real-world health challenges, emphasizing translational research and data-driven policy recommendations.

Jouni Helskeمشاهده پروفایل

پژوهشگر

• Bayesian statistics
• Causal inference
• State space models
• +۷ مورد دیگر

Jouni Helske is an Academy Research Fellow in Statistics at the University of Turku, Finland, affiliated with the INVEST Research Flagship Centre. He leads the CAUSALTIME project and is a subconsortium-PI in the PREDLIFE consortium at the University of Jyväskylä. His work bridges statistical methodology and applied research in social sciences and epidemiology. Academy Research Fellow, University of Turku PI, CAUSALTIME Project Subconsortium-PI, PREDLIFE Consortium, University of Jyväskylä Associate Editor, The R Journal and rOpenSci Open Science Ambassador, Open Science Community Turku Education: PhD in Statistics, University of Jyväskylä, Finland (2015) Jouni Helske’s research centers on developing advanced Bayesian methods for causal inference, particularly using complex multivariate time series and panel data. His expertise includes state space models, hidden Markov models, computational statistics, and probabilistic programming. He is deeply involved in statistical software development, especially in the R ecosystem, contributing to open science and reproducible research. His applied work spans sociology, education, public health, and epidemiology, where he analyzes longitudinal and sequential data to understand causal mechanisms and life course trajectories. The recent publications highlight a strong trend in methodological innovation for causal analysis in panel data, spatio-temporal disease modeling, and R package development. His work integrates Bayesian computation with real-world applications, especially in social policy and health, using historical and contemporary data. The focus on dynamic multivariate models and sequence analysis underscores his leadership in modern statistical methodology for complex data. Scientific Awards and Recognition: Academy Research Fellow (prestigious research position funded competitively) Jouni Helske has led and contributed to major research projects such as CAUSALTIME and PREDLIFE, which aim to improve policy decisions through predictive modeling of life trajectories. He mentors and collaborates widely, evidenced by his numerous co-authored publications and software projects. While no formal students are listed, his role as a project leader and software maintainer suggests significant advisory and collaborative activity. He is a key contributor to the open-source statistical community, particularly through rOpenSci and Stan. Labs and Teams: He leads the CAUSALTIME project team and is part of the PREDLIFE research consortium. He is actively involved in the R and Stan developer communities, contributing to state-of-the-art Bayesian computational tools.

Martha Arbayani Zaidanمشاهده پروفایل

استاد مدعو

• Environmental Sensing
• Air Quality Monitoring
• Atmospheric Science
• +۴ مورد دیگر

Dr. Martha Arbayani Zaidan serves as an Academy Research Fellow at the University of Helsinki's Department of Computer Science and holds the prestigious Docent title (equivalent to Adjunct Professor) at the Institute for Atmospheric and Earth System Research (INAR) and Helsinki Institute of Sustainability Science (HELSUS). Her interdisciplinary work bridges computer science, atmospheric physics, and sustainability science to advance environmental sensing technologies. Her educational foundation includes a PhD in Automatic Control and Systems Engineering from the University of Sheffield (2014), where she developed Bayesian approaches for complex system prognostics, complemented by an MSc in Control Systems (2009) and BEng in Electrical Engineering (2007). Dr. Zaidan's research focuses on environmental sensing systems , particularly drone-based air quality monitoring and machine learning applications for atmospheric data analysis. She investigates critical phenomena including black carbon distribution in Asian megaregions, urban-suburban air quality disparities in East Africa, and aerosol size distribution modeling. Her work emphasizes practical applications for sustainability challenges through advanced computational methods. Her scientific contributions have earned significant recognition, including the competitive Academy Research Fellowship from Finland's Academy of Finland (2023) and the HIIT Research Fellowship (2022), reflecting her leadership in environmental AI research. As an active research leader, Dr. Zaidan directs the Academy of Finland project on AI-enhanced sensing technologies and participates in the large-scale InterEarth RESET initiative. She co-founded the annual EnvSys workshop series, fostering international collaboration in environmental sensing for smart cities. Her supervision record includes mentoring graduate students in computational environmental science.