جست‌وجوی استاد و پژوهشگر

Robert Picheمشاهده پروفایل

استاد

• Kalman Filters
• Global Positioning Systems (GPS)
• Signal Processing
• +۵ مورد دیگر

Robert Piche is a Professor at the Computing Sciences Mathematics Research Centre, specializing in advanced signal processing, positioning systems, and sensor fusion. He holds a Doctor of Science (Technology) and Master of Science from the University of Waterloo, Canada (1986 and 1982, respectively). His research focuses on Kalman filters, Global Positioning Systems (GPS), particle filters, and indoor positioning technologies. He has contributed extensively to fields like satellite orbit prediction, non-line-of-sight (NLoS) positioning, and machine learning applications in biomechanics and robotics. Dr. Piche has authored over 230 publications and received recognition through an invitation/ranking in a 2014 competition. He actively participates in academic activities, including conference presentations and peer-review roles. His work bridges theoretical advancements and practical applications, with contributions to autonomous systems, sensor data analysis, and wearable technology. Collaborations span international institutions, reflecting his global impact in engineering and computer science disciplines.

• Ecology
• Evolutionary Biology
• Population Genetics
• +۳ مورد دیگر

Kalle Matias Leppälä is a postdoctoral researcher at the University of Helsinki's Faculty of Biological and Environmental Sciences, affiliated with the Organismal and Evolutionary Biology Research Programme. His work focuses on population genetics, statistical modeling, and evolutionary biology through computational approaches. Recent research includes Bayesian Gaussian process models for variance component estimation, generalized D-statistic methods for admixture analysis, and mathematical formulations for odds ratio calculations. His computational work intersects with ecology and genomics. He participates in the TreeBio Center of Excellence (2024–2026), funded by the Research Council of Finland, exploring tree biology and population dynamics. Current projects emphasize genetic admixture patterns in Arctic populations and statistical methodology development.

Jenni Nikuمشاهده پروفایل

مدرس ارشد

• Multivariate Statistical Modeling
• Latent Variable Models
• Statistical Method Development
• +۳ مورد دیگر

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.

Petteri Kaskiمشاهده پروفایل

دانشیار

• Theoretical Computer Science
• Algorithm Design
• Exact and Parameterized Algorithms
• +۵ مورد دیگر

Petteri Kaski is an Associate Professor at the Department of Computer Science, School of Science, Aalto University , and a member of the Helsinki Institute for Information Technology (HIIT) . His research focuses on theoretical computer science, particularly in algorithm design, exact and parameterized algorithms, algebraic algorithms, and combinatorics. Doctoral Degree in Engineering and Technology, Helsinki University of Technology (2005) Licentiate Degree in Engineering and Technology, Helsinki University of Technology (2002) Master's Degree in Engineering and Technology, Helsinki University of Technology (2001) His recent work explores tensor scaling, Johnson-Lindenstrauss transforms, Hamiltonian cycles, and computational complexity, with contributions to polynomial-time algorithms, finite field computations, and combinatorial optimization. Notable awards include the Best Paper Award at ICALP 2017 , an ERC Starting Grant (2014) , and the Kirkman Medal (2007) . He has served on scientific committees for conferences like STACS 2025 and ICALP 2024 , and collaborated with institutions such as the IT University of Copenhagen and Universität Regensburg . Key Research Areas : Theoretical Computer Science, Algorithm Design, Exact Algorithms, Algebraic Computation, Graph Theory, Combinatorics

• Linear Statistical Models
• Covariance Analysis
• Biostatistics
• +۳ مورد دیگر

Jarkko Isotalo is a University Lecturer in the Department of Computing Sciences at Tampere University, Finland. His affiliation is within the Faculty of Information Technology and Communication Sciences, focusing on theoretical and applied statistical research. His research interests span linear statistical models , covariance analysis , biostatistics , and computational statistics . Notably, he explores topics like the Best Linear Unbiased Estimator (BLUE), mixed models, and their applications in clinical trials and ecological studies. Recent work includes studies on permissible covariance structures in linear models, treatment outcomes for gambling disorder (using randomized controlled trials), and partitioning of forest CO2 emissions. His articles often emphasize theoretical foundations while addressing practical implications in health and environmental sciences. Isotalo has contributed to methodological advancements in statistical modeling without explicitly mentioned awards or grants. His research often involves interdisciplinary collaborations, bridging mathematics, computing, and applied sciences.

• Sensor Informatics
• Medical Technology
• Machine Learning
• +۴ مورد دیگر

Fatemeh Yaghoobi is a Doctoral Researcher at Aalto University, affiliated with the Department of Electrical Engineering and Automation under the College of Engineering. She actively contributes to Sensor Informatics and Medical Technology research groups. Research Interests: Her work focuses on algorithm development for state estimation in nonlinear systems, leveraging Bayesian statistics and parallel computing. Key areas include probabilistic numerical methods, Kalman smoothers, and optimization techniques for machine learning applications. Publication Trends: Recent research highlights advancements in parallel-in-time computing for ODE solvers, statistical linear regression for state-space models, and iterative Kalman smoother algorithms. These publications reflect interdisciplinary applications in machine learning, signal processing, and computational mathematics. Contact: Email: fatemeh.yaghoobi@aalto.fi

Mikko Saloمشاهده پروفایل

استاد

• Inverse problems for partial differential equations
• Geometric inverse problems
• Mathematical analysis
• +۹ مورد دیگر

Mikko Salo is a Professor at the Department of Mathematics and Statistics in the Faculty of Mathematics and Science at the University of Jyväskylä . His research focuses on mathematical analysis , geometry , and applications in inverse problems for partial differential equations. Research Themes : Inverse problems for PDEs (medical imaging), geometric inverse problems (seismic imaging), microlocal analysis, tensor tomography, and non-scattering phenomena. Awards : Recipient of an ERC Starting Grant and an ERC Consolidator Grant . Journals : Managing Editor of Inverse Problems and Imaging , and Editor for Annales Fennici Mathematici , Journal de l'École Polytechnique , and SIAM Journal on Mathematical Analysis . Collaborations : Part of the Finnish Centre of Excellence in Inverse Modelling and Imaging and the Flagship of Advanced Mathematics for Sensing, Imaging and Modelling (FAME) . His recent publications emphasize nonlinear PDEs , fractional calculus , and fixed-angle scattering , with methodological advances in Carleman estimates and microlocal analysis . Scientific contributions include geometric inverse problems on Riemannian manifolds and exponential instability in inverse problems. Teaching spans advanced topics like Fourier analysis , complex analysis , and analysis on manifolds , with lecture notes on inverse problems, function spaces, and microlocal analysis.

Nuutti Hyvönenمشاهده پروفایل

استاد

• Inverse Problems
• Electrical Impedance Tomography
• Numerical Analysis
• +۳ مورد دیگر

Nuutti Hyvönen is a Professor and Head of Department at the Department of Mathematics and Systems Analysis, Aalto University, School of Science. His research focuses on inverse problems, electrical impedance tomography, and numerical analysis with applications in biomedical imaging and engineering. He holds a Doctor of Science (Technology) degree and has extensive experience in developing mathematical methods for imaging and reconstruction algorithms. His work emphasizes improving the accuracy and robustness of tomographic techniques, particularly in handling modeling errors and electrode configurations. Key research areas include: Inverse problems for partial differential equations Electrical impedance tomography (EIT) and its biomedical applications Bayesian experimental design and uncertainty quantification Numerical methods for nonlinear imaging problems Recent publications highlight advancements in: Optimal electrode positioning Series reversion methods in Calderón problems Feasibility of EIT for monitoring intracerebral hemorrhages Edge-enhancing reconstruction algorithms He has contributed to over 79 peer-reviewed articles, with notable work on monotonicity-based reconstruction methods and stochastic Galerkin finite element approaches. His research bridges mathematical theory and practical applications in medical imaging, engineering, and computational science.

Klaus Nordhausenمشاهده پروفایل

استاد

• Multivariate Statistical Methods
• Spatial Statistics
• Blind Source Separation
• +۴ مورد دیگر

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 .

• Complex fluids
• Statistical Physics
• Fluctuation relations in small systems
• +۱۹ مورد دیگر

Tapio Ala-Nissilä is Professor of Physics at Aalto University School of Science, Finland, where he heads the Multiscale Statistical and Quantum Physics (MSP) group. He simultaneously serves as Head of the Interdisciplinary Centre for Mathematical Modelling and Professor of Applied Mathematics & Theoretical Physics at Loughborough University, UK, and as Adjunct Professor of Physics at Brown University, USA. His research spans statistical physics, quantum mechanics, and soft matter systems with emphasis on computational approaches. Key interests include complex fluids, nanofluids, polymer translocation, quantum thermodynamics, and open quantum systems. He extensively employs molecular dynamics, Monte Carlo methods, and density functional theory to study nanoscale phenomena in biological, material, and quantum contexts. Recent publications (2024-2025) reveal strong interdisciplinary trends across quantum materials, biophysics, and environmental engineering. Work focuses on thermal properties of 2D materials like graphene, protein dynamics in viral systems, novel surface phenomena in liquid-repellent materials, and advanced computational methods for many-body quantum systems. He directs the Multiscale Statistical and Quantum Physics group at Aalto University investigating fundamental quantum and statistical phenomena, and leads the Interdisciplinary Centre for Mathematical Modelling at Loughborough University fostering cross-departmental collaboration in applied mathematics.

• Statistics and Probability

Ersin Yılmaz serves as a Postdoctoral Researcher within the Department of Computer Science at Aalto University, conducting advanced research at the intersection of statistical theory and computational applications. His work contributes to the university's data science initiatives through methodological innovations in regression analysis. His research concentrates on statistical methodology development, particularly for complex data structures involving correlated errors and high-dimensional spaces. Key interests include shrinkage estimation techniques, kernel-based regression frameworks, and robust modeling approaches applicable to machine learning systems. This work bridges theoretical statistics with practical computational implementations in computer science contexts. Analysis of his publication record reveals a focused trajectory in advanced regression methodologies, specifically addressing estimation challenges in partially linear models with dependency structures. His work demonstrates technical rigor in mathematical statistics while maintaining relevance to contemporary data science applications, particularly in improving estimator efficiency under complex error dependencies. Scientific Awards: No scientific awards, fellowships, or honors were documented in the source material. Current information indicates no formal student supervision roles or grant-funded projects. The researcher operates within Aalto University's computer science infrastructure without specific lab or team affiliations mentioned in available records.

Mojtaba Naghdyzadegan Jahromiمشاهده پروفایل

پژوهشگر

• Crop Modeling
• Remote Sensing
• Machine Learning
• +۳ مورد دیگر

Mojtaba Naghdyzadegan Jahromi is a postdoctoral researcher at the University of Oulu in the Hydrosystems Engineering & Management (HEM) research group within the Water, Energy, and Environmental Engineering Research Unit . He holds a Ph.D. in Agriculture-Water Engineering from Shiraz University (2023) and has previously worked at Shiraz University, Ghent University, and Georg-August University of Göttingen. B.Sc. and M.Sc. in Water Engineering , Shiraz University Ph.D. in Agriculture-Water Engineering , Shiraz University His research focuses on crop modeling , remote sensing , and machine learning for agricultural and environmental applications. Recent work includes wheat yield prediction , inland water body depletion analysis , and precision agriculture techniques. Publications span journals like European Journal of Agronomy and Water , book chapters on remote sensing and machine learning, and conferences including EGU and Tropentag. Ph.D. Student Travel Grant (2021), Ministry of Science, Iran Student Travel Grant (2017), ICTP, Italy Tropentag 2015 Grant Award He has contributed to interdisciplinary projects involving Google Earth Engine , MODIS data , and GLEAM evapotranspiration models , with applications in water resource management and climate change impact assessment.

• Process Systems Engineering
• Production Planning and Scheduling
• Waste Supply Chain Management
• +۳ مورد دیگر

Professor Iiro Harjunkoski is affiliated with Aalto University, School of Chemical Engineering, Department of Chemical Engineering and Metallurgy. His research focuses on advanced optimization techniques in Process Systems Engineering. The top fields of interest for Professor Harjunkoski include: Process Systems Engineering Production Planning and Scheduling Waste Supply Chain Management Mixed Integer Linear and Non-Linear Programming Hybrid Algorithms with Data Analytics Process and Operational Optimization His recent work emphasizes: Energy systems optimization under uncertainty Bike-sharing logistics modeling Machine learning integration in chemical production scheduling Stochastic programming for energy storage operations Surrogate-based optimization techniques Hybrid algorithm development Scientific awards and recognitions: Computers and Chemical Engineering Best Paper Award (2014) Award or recognition for output from Department of Chemical Engineering and Metallurgy (Nov 2015)

• Atmospheric Science
• Climate Science
• Environmental Science
• +۳ مورد دیگر

Tuomo Nieminen is a University Lecturer at the Institute for Atmospheric and Earth System Research (INAR) , University of Helsinki. He serves as Supervisor for the Doctoral Programme in Atmospheric Sciences , focusing on global atmosphere-earth surface feedbacks. Institute for Atmospheric and Earth System Research (INAR), University of Helsinki Doctoral Programme in Atmospheric Sciences His research spans atmospheric particle formation, aerosol dynamics, and climate feedback mechanisms. Key collaborations include the CLOUD experiments at CERN and international partnerships with institutions like the School of Earth and Environment, University of Leeds (2015 visiting researcher). Recent publications analyze machine learning applications for boreal forest carbon exchange, parameterization of particle formation rates, and Himalayan aerosol factory modeling. His work connects sulfuric acid dynamics, biogenic emissions, and aerosol-climate interactions. Academic activities include participation in the NOSA-FAAR Symposium (2015 poster), CLOUD data workshops (2015 attendee), and Centre of Excellence in Atmospheric Science seminars.

Ville Kolehmainenمشاهده پروفایل

استاد

• Computational Inverse Problems
• Uncertainty Quantification
• Medical Imaging
• +۶ مورد دیگر

Ville Kolehmainen is a Professor at the Department of Technical Physics within the Faculty of Science, Forestry and Technology at the University of Eastern Finland (UEF). His research focuses extensively on computational inverse problems and uncertainty quantification , with applications in advanced medical imaging techniques such as X-ray tomography , diffuse tomography , and MRI . He actively collaborates with clinical experts and engineering specialists to develop novel imaging modalities for 3D/4D imaging with sparse data Electrical impedance tomography (EIT) Multi-energy CT reconstruction Thermal tomography Ville Kolehmainen leads the Inverse Problems research group (active since 2010) and contributes to the Neuro-Innovation project (2021-2026). His work integrates Bayesian statistical inversion , parallel level sets , and nonlinear reconstruction methods to address challenges in Medical tomography Optical diffusion tomography Thermal conductivity estimation Atmospheric remote sensing Recent publications highlight his contributions to electrical impedance tomography for hemorrhagic stroke monitoring, dual-modality imaging of two-phase flows, and deep learning post-process corrections in remote sensing. His methodological innovations include model error compensation , sparsity-promoting inversion , and anatomical prior integration in image reconstruction. Collaborations span University of Eastern Finland , University College London , and Harvard Medical School researchers.