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Pekka MarttinenView profile

Associate Professor

• Machine Learning
• Large Language Models
• Reinforcement Learning
• +8 more

Pekka Marttinen is a tenured Associate Professor of Machine Learning at Aalto University, Department of Computer Science, and leads the Machine Learning for Health (Aalto-ML4H) group within the Helsinki Institute for Information Technology HIIT. Education: M.Sc. in Applied Mathematics, University of Helsinki (2004) Ph.D. in Statistics, University of Helsinki (2008) Title of Docent in Information and Computer Science, Aalto University (2015) Research Focus: His methodological work spans large language models, reinforcement learning, deep learning, probabilistic machine learning, and causal inference . These techniques are applied to critical domains of healthcare, bioinformatics, statistical genetics, epidemiology, and personalized medicine . The group develops novel algorithms, theoretical guarantees, and open-source software that enable data-driven discovery and decision-making in medicine and biology. Publication Trends: Across 2022-2024 the lab has concentrated on (i) rigorous causal reasoning over temporal clinical data, (ii) principled uncertainty quantification in LLMs, (iii) representation learning for neural network comparison, and (iv) translational projects that turn raw EHRs into actionable clinical insights. Earlier work integrated high-dimensional genomics with metabolomics and mapped evolutionary forces in bacterial pathogens. Scientific Awards & Recognition: While no specific awards are listed, his sustained publication record in top-tier venues (NeurIPS, ICML, AISTATS, Nature Genetics, PLOS CB) and his role as responsible professor of the Machine-Learning, Data-Science and AI major signify significant peer recognition. Advising & Grants: Prof. Marttinen currently mentors 8 PhD students as primary supervisor and an additional 7 PhD students as co-supervisor. He has already graduated 8 PhDs since 2014. The group is supported through competitive funding including the Finnish Center for Artificial Intelligence (FCAI) doctoral program. Labs & Teams: He directs the Machine Learning for Health (Aalto-ML4H) research group, comprising postdocs Hans Moen, Ti John, Alexander Nikitin, Negar Safinianaini, Linli Zhang, Zhiyuan Li, and the above-mentioned PhD cohort.

• Statistical signal processing
• Robust statistical methods
• Array processing
• +6 more

Esa Ollila serves as Associate Professor in the Department of Signal Processing and Acoustics at Aalto University, Finland, and holds an adjunct professorship in Statistics at the University of Oulu. His academic appointments include Academy of Finland Research Fellow (2010-2015) and prior senior research/lecturing roles at both institutions. His educational background features: M.Sc. in Mathematics, University of Oulu (1998) Ph.D. in Statistics (with honors), University of Jyväskylä (2002) D.Sc.(Tech) in Signal Processing (with honors), Aalto University (2010) Professor Ollila's research centers on statistical signal processing and robust statistical methodologies , with significant contributions to array processing, high-dimensional data analysis, and covariance matrix estimation. His work bridges theoretical statistics with practical applications in radar systems, wireless communications, and big data analytics, emphasizing robustness against outliers and computational efficiency in modern data-intensive environments. Current focus areas include compressed sensing, sparse approximation, and blind source separation techniques. Analysis of his 15 most recent publications (2024-2025) reveals three dominant trends: (1) robust covariance learning for massive random access systems, (2) integrated sensing and communications (ISAC) for 6G networks using advanced beamforming, and (3) geometric approaches to elliptical distributions in statistical inference. His work increasingly incorporates deep learning (GANs, graph neural networks) while maintaining strong foundations in classical signal processing theory. Key recognitions include: Academy of Finland Postdoctoral Fellowship (2004-2007) Academy of Finland Research Fellowship (2010-2015) His research has been supported through prestigious Academy of Finland grants totaling over a decade of continuous funding. Professor Ollila currently leads an active research group at Aalto University, supervising doctoral candidates and collaborating internationally with institutions including Princeton University (where he served as Visiting Post-doctoral Research Associate during 2010-2011). He maintains strong ties with the University of Oulu through his adjunct professorship and has contributed to EURASIP's Special Area Team on Theoretical and Methodological Trends in Signal Processing. The Esa Ollila Research Group focuses on cutting-edge challenges in statistical signal processing, with current projects spanning robust DOA estimation under non-Gaussian noise, covariance matrix learning for massive MIMO systems, and machine learning-enhanced radar-communication integration. The group actively develops open-source tools like the fitHeavyTail R package for heavy-tailed distribution modeling and maintains collaborations with industry partners in wireless communications.

Olli VarisView profile

Professor

• Water Resources Management
• Sustainable Development
• Climate Change Adaptation
• +5 more

Olli Varis is an Aalto Distinguished Professor at Aalto University's Department of Built Environment, specializing in Water and Environmental Engineering. He holds adjunct professorships at Asian Institute of Technology (Thailand) and has served as Vice Dean for Research & Innovation at Aalto School of Engineering (2013–2018). His expertise spans water resources management in developing economies, climate change impacts, and transboundary water governance. Education: Doctoral degree in Engineering (Helsinki University of Technology, 1991), Licentiate (1988), and dual Master's degrees in Agriculture (University of Helsinki, 1986) and Engineering (Helsinki University of Technology, 1984). Research focuses on sustainable development goals (SDGs), particularly water-energy-food nexus dynamics, urbanization impacts, and global river basin vulnerabilities. Key interests include flood early warning systems, migration-environment linkages, and Asian transboundary water challenges. Notable awards include Aalto Excellence Awards (2018, 2021), Best Research Paper 2023, and Environmental Research Letters' Best Paper 2010. Over 350 publications include works on China's water risks, Nepal's flood resilience, and global migration drivers. Active in UNESCO, WMO, and UNU/WIDER. Recent projects address urbanization's environmental pressures, interdisciplinary education challenges, and东南亚数字经济的崛起与挑战 (Vietnam's digital economy challenges). Leads the Water and Environmental Engineering research group, supervising 12 theses. Involved in public outreach,如芬兰赫尔辛基大学的媒体参与案例.

Kimmo VehkalahtiView profile

Senior Lecturer

• Social Data Science
• Social Statistics
• Multivariate Methods
• +3 more

Kimmo Vehkalahti is a Senior University Lecturer and docent at the University of Helsinki, specifically affiliated with the Centre for Social Data Science within the Faculty of Social Sciences. He serves as a Teachers' Academy Supervisor in the doctoral program and maintains an active research profile with numerous publications and projects. His research interests focus on social data science, social statistics, multivariate methods, open data science, and statistical data processing using R programming. Vehkalahti has developed expertise in applying statistical methods to social science research questions and data visualization. His recent publications demonstrate a strong focus on multivariate analysis applications in behavioral sciences, social data visualization, and student well-being research. The publications span diverse areas including statistical methodology, data visualization techniques, and educational interventions. Fellow of the Teachers' Academy (2013) Best Teacher of the City: Kaupungin Paras Opettaja (2010) Good Teacher: Hyvä Opettaja (2009) Magister Bonus - Good Teacher (2003) One of the best teachers of Open University (2017) Vehkalahti actively supervises academic work with 40 thesis supervisions and 10 PhD supervisions documented. He participates in significant research projects including SASE Stress as strength in education (since 2022) and WELLS - Promoting university students' well-being and life-long learning (since 2019). His academic service includes conference participation, peer review, and organizational roles in international statistical organizations.

Jarno VanhataloView profile

Professor

• Statistics
• Probability
• Ecology
• +6 more

Professor Jarno Vanhatalo is a Professor of Statistics at the University of Helsinki, serving as vice director of the Research Center for Ecological Change. He is affiliated with both the Faculty of Biological and Environmental Sciences and the Faculty of Science, where he leads the Environmental and Ecological Statistics Group. His work bridges advanced statistical methodology with pressing ecological and environmental challenges. His research focuses on the development and application of statistical models for ecological and environmental data. Key areas include: Bayesian statistics and hierarchical modeling Gaussian processes and spatial statistics Species distribution modeling Ecological risk assessment Climate change impact analysis Biodiversity monitoring and conservation Professor Vanhatalo's publication record shows a consistent focus on integrating sophisticated statistical methods with ecological applications. His recent work demonstrates increasing emphasis on climate change impacts on biodiversity, spatially explicit modeling of ecological processes, and the development of Bayesian methods for uncertainty quantification in environmental predictions. His research spans terrestrial and marine ecosystems, with particular attention to Nordic and Baltic regions. Among his notable contributions are advancements in: Joint species distribution modeling Bayesian calibration of environmental models Spatiotemporal survey design Model-based variance partitioning in ecological studies Statistical approaches to Arctic risk management Professor Vanhatalo has received significant research funding, including an ERC grant for "Predective Understanding of the effects" (2024-2029) and leadership roles in multiple Academy of Finland and EU-funded projects focused on biodiversity, ecological change, and risk management in polar waters. He actively contributes to the academic community through: Supervising doctoral students in Wildlife Biology and Mathematics and Statistics programs Serving on editorial boards for journals including Conservation Biology and Ecology Letters Peer reviewing for numerous statistical and ecological journals Organizing workshops on statistical methods in ecology

Jenni NikuView profile

Senior Lecturer

• Multivariate Statistical Modeling
• Latent Variable Models
• Statistical Method Development
• +3 more

Jenni Niku is a Senior Lecturer at the Department of Mathematics and Statistics, University of Jyväskylä, specializing in multivariate statistical modeling and computational methods for ecological data. She is actively involved in the Predictive Community Ecology Group and leads research projects on latent variable models for complex ecological structures. Research Focus: Latent variable models for community ecology Spatial-temporal pattern analysis Statistical methodology for multivariate data Joint species distribution modeling Computational tools for ecological datasets Recent Publications: Jenni has contributed to advancements in analyzing compositional count data, fungal dispersal dynamics, chronic disease modeling, and peatland restoration. Her work bridges statistical innovation with ecological applications, particularly in handling high-dimensional data. Collaborations: She works extensively with researchers from Biological and Environmental Science departments, focusing on interdisciplinary applications of her statistical models.

Satu-Pia ReinikainenView profile

Professor

• Multivariate Statistical Analysis
• Process Control
• Spectroscopy
• +3 more

Satu-Pia Reinikainen is a tenured Professor in Computational Engineering at the Lappeenranta-Lahti University of Technology (LUT) School of Engineering Sciences . With expertise in chemometrics and multivariate analysis, her work bridges statistical modeling, spectroscopy, and environmental monitoring. Research Focus Development of advanced kernel-based methods for process control Application of hyperspectral imaging in material and environmental analysis Microplastic pollution dynamics in aquatic systems Integration of spectroscopic techniques for real-time monitoring Conservation of geological and heritage materials through data-driven approaches Her research combines chemometric algorithms, environmental data analysis, and industrial process monitoring to solve complex analytical challenges.

• Stochastic Processes
• Mathematical Statistics
• Financial Mathematics
• +3 more

Marko Voutilainen is a University Lecturer in the Department of Accounting and Finance at the University of Turku. His research focuses on stochastic processes, time series analysis, and statistical modeling with applications in finance and mathematical statistics. He has published extensively on topics such as stationary processes, AR models, and generalized Ornstein-Uhlenbeck processes. His work emphasizes theoretical developments in stochastic modeling, including Lamperti transformations, spatial sign autocovariance matrices, and parameter estimation techniques. Recent contributions explore discrete and continuous-time stationary processes, as well as applications of ARCH models in financial contexts. Voutilainen frequently collaborates with researchers like Pauliina Ilmonen, Lauri Viitasaari, and Ciprian Tudor, contributing to peer-reviewed journals like Stochastic Models , Theory of Probability and Mathematical Statistics , and Scandinavian Journal of Statistics . Despite his prolific output, no scientific awards or grants are explicitly mentioned in the provided materials. No doctoral students are listed, though his research likely engages graduate-level collaboration.

Matti PirinenView profile

Professor

• Statistical Genetics
• Population Genetics
• Human Genetics
• +4 more

Matti Pirinen is a Professor at the Department of Mathematics and Statistics, Faculty of Science, University of Helsinki, and holds a Docent title in the same department. He leads the Centre of Excellence in Complex Disease Genetics and contributes to precision medicine initiatives. His affiliations include the Helsinki Institute for Information Technology and supervision roles in the Doctoral Programmes in Mathematics/Statistics and Population Health. Supervision of doctoral and master's theses Visiting researcher at Stanford University (Oct 2018) His research focuses on statistical and population genetics , particularly genome-wide association studies (GWAS), lipidomics, and nicotine metabolism. Collaborations with FinnGen and the Long COVID Host Genetics Initiative highlight applications in public health and disease risk modeling. Recent projects include Genetic risk in Finland: from genetic ba... (Sigrid Jusélius Foundation, 2024–2026) and CoECDG: Center of Excellence in Complex Disease Genetics (Academy of Finland, 2023–2025). Publications (164 total) span topics like smoking-lipidome associations, long COVID genetics, and employment outcomes post-traumatic brain injury. Activities include public lectures (e.g., Sukupolvien Ketjut Tämän Päivän Geenitutkimuksessa , Oct 2024) and oral presentations on multivariate GWAS analysis (Apr 2017).

Pauliina IlmonenView profile

Associate Professor

• Mathematical Statistics
• Probability Theory
• Data Science
• +3 more

Pauliina Ilmonen is an Associate Professor and Vice Dean in the Department of Mathematics and Systems Analysis at Aalto University, within the School of Science. Her research focuses on mathematical statistics, probability theory, and data science, with applications in healthcare and engineering. She holds academic positions in both the Department of Mathematics and Systems Analysis and Statistics and Mathematical Data Science. Her work spans statistical methodology, stochastic processes, and interdisciplinary collaborations in biomedicine. Notable research areas include extreme value analysis, functional data analysis, and the modeling of dependent data. She has contributed to studies on HIV transmission dynamics, prostate cancer genomics, and the statistical analysis of infectious diseases. Dr. Ilmonen’s recent publications (2020–2025) emphasize methodological advancements in statistical inference, computational statistics, and applications in healthcare. She leads projects on predictive modeling, cost-effectiveness analysis in cancer screening, and network traffic prediction. Her findings bridge theoretical statistics with practical challenges in medicine and technology. She advises 8 theses and collaborates on grants exploring mathematical data science, biostatistics, and stochastic systems. Active in academic service, she contributes to curriculum development and promotes ethical use of statistics in research.

Frédéric GuillaumeView profile

Professor

• Evolutionary Biology
• Population Genetics
• Quantitative Genetics
• +4 more

Frédéric Guillaume is a Professor in the Organismal and Evolutionary Biology Research Programme at the University of Helsinki 's Faculty of Biological and Environmental Sciences. His work integrates computational and empirical approaches to study evolutionary processes under environmental change. Education: PhD in Evolutionary Biology (2005) and BSc in Biological Sciences (2001) from Université de Lausanne His research focuses on: Genetic architecture of complex traits Eco-evolutionary responses to climate change Genotype-phenotype mapping and adaptive constraints Forward-time simulation modeling using Nemo software Experimental evolution with Tribolium castaneum Recent publications (2021-2024) span: Quantitative trait analysis through identity-by-descent methods Gene expression evolution under novel environmental pressures Trade-offs between environmental robustness and evolvability Climate change impacts on alpine plant genetics Pleiotropy versus linkage in genetic correlation detection Active in academic service through: Supervision of Master's theses International research visits (ICTS 2024, UCSB 2022) Collaborative projects like WildAP (2023-2025) and MSCA SalmonScales (2023) He maintains the open science Nemo software ecosystem and contributes to initiatives like Peer Community Journal . His work combines theoretical modeling with experimental validation to predict species persistence under global change scenarios.

Klaus NordhausenView profile

Professor

• Multivariate Statistical Methods
• Spatial Statistics
• Blind Source Separation
• +4 more

Klaus Nordhausen is a Professor in the Department of Mathematics and Statistics at the University of Helsinki. His research focuses on multivariate statistical methods, spatial statistics, blind source separation, and high-dimensional data analysis. He actively contributes to computational statistics and machine learning applications in environmental and biomedical domains. Recent work includes advancements in spatio-temporal modeling, invariant coordinate selection, and variational autoencoders for multivariate data. Nordhausen leads the project Signal recovery in noisy spatial data (2024–2028), funded by the Academy of Finland. He is a frequent invited speaker at international conferences and collaborates with researchers globally, particularly in spatial and computational statistics. His publications span statistical methodology, environmental modeling, and machine learning, emphasizing robust techniques for complex data structures. Key contributions include spatial blind source separation algorithms, signal dimension estimation, and novel applications of independent component analysis. Professional activities include organizing workshops and hosting academic visitors, reflecting his role in fostering international collaboration. Nordhausen maintains an active research agenda with over 130+ peer-reviewed outputs across journals like Environmetrics , Neural Networks , and Annals of the Institute of Statistical Mathematics .

Lassi RoininenView profile

Professor

• Statistics and Probability
• Geophysics
• Applied Mathematics
• +5 more

Lassi Roininen is a tenured Professor of Applied Mathematics at LUT University's School of Engineering Sciences, holding this position since September 2022 after serving as Assistant Professor there from 2018 to 2022. He maintains significant adjunct appointments as Associate Professor at University of Oulu, Assistant Professor at Bahir Dar University (Ethiopia), and faculty member at AIMS Rwanda, demonstrating strong international academic engagement. Education: Master of Science (Engineering), Tampere University of Technology Doctorate in Applied Mathematics, University of Oulu (2015) - conducted at Sodankylä Geophysical Observatory His research integrates Statistics, Geophysics, and Applied Mathematics with core expertise in Bayesian inference, uncertainty quantification, and inversion problems. He develops computational frameworks for geophysical imaging, climate modeling, and industrial applications, emphasizing robust statistical methodologies for real-world data challenges. Recent work shows increasing focus on African climate adaptation and medical/industrial tomography. Analysis of his 15 most recent publications reveals dominant trends in Bayesian approaches to climate science (particularly East African adaptation studies), medical/industrial imaging (tomography and fault detection), and geophysical modeling. His work consistently bridges mathematical innovation with practical applications across environmental science, healthcare, and manufacturing sectors. Research support includes Academy of Finland postdoctoral funding. Through AIMS Rwanda, he actively mentors African mathematicians and contributes to capacity building in computational sciences across the continent. His collaborative projects demonstrate commitment to solving region-specific challenges through advanced statistical methods. His work is closely tied to geophysical research networks including Sodankylä Geophysical Observatory, with recent expansions into East African climate resilience initiatives. Current projects integrate multi-instrument atmospheric data with Bayesian frameworks to address pressing environmental challenges in developing regions.

Heikki HaarioView profile

Professor

• Bayesian Statistics
• Parameter Estimation
• Chaotic Dynamical Systems
• +7 more

Heikki Haario is a Professor in Computational Engineering at the LUT School of Engineering Sciences, LUT University, Lappeenranta. His research focuses on robust Bayesian inference, parameter estimation, and uncertainty quantification in chaotic and stochastic systems. Broad research areas: Bayesian Statistics, Chaotic Dynamical Systems, Gaussian Processes, Machine Learning The 15 most recent publications (2025-2023) demonstrate expertise in computational modeling, data-driven methods, and interdisciplinary applications spanning finance, biology, and engineering. Articles emphasize Bayesian techniques, kernel flows, and neural network integration for solving inverse problems and optimizing predictions in uncertain environments.

• Machine Learning
• Signal Processing
• Computer Vision
• +3 more

Zina-Sabrina Duma is a post-doctoral researcher at the Department of Computational Engineering , Lappeenranta University of Technology , affiliated with the School of Engineering Sciences . Her work bridges computational methods with applied engineering challenges. Research Focus : Multivariate statistical modeling for industrial and environmental applications Hyperspectral imaging analysis with kernel-based optimization Advanced data fusion techniques for microscopy and spectroscopy Development of portable sensing systems for contaminant detection Recent publications highlight her contributions to Kernel Flows for image retrieval, Varroa destructor detection in bee colonies, and multisensor integration for plant contamination analysis. Her work demonstrates cross-disciplinary impact in computer science, mechanical engineering, and agricultural technology.