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Aapo Hyvärinen is a Professor of Computer Science at the University of Helsinki , affiliated with the Helsinki Institute for Information Technology and the Helsinki Probabilistic Machine Learning Lab . He previously held the position of Professor of Machine Learning at the Gatsby Computational Neuroscience Unit, University College London (2016-2019). Education : Undergraduate Mathematics at University of Helsinki, Vienna, and Paris; Ph.D. in Information Science from Helsinki University of Technology (1997) His research focuses on machine learning and computational neuroscience , particularly: Independent Component Analysis (ICA) Natural Image Statistics Causal Representation Learning Neural Signal Processing Applications to brain imaging (MEG, CryoEM) Recent publications emphasize causal discovery , identifiable machine learning , and nonlinear ICA . Key projects include: VETURI (AI for health) DIGIMIND (AI in mental health) CIFAR grants (2022-2025) Scientific awards : Highly Cited Researcher (2010) He serves as Action Editor for the Journal of Machine Learning Research and Neural Computation , and has held Area Chair roles at NeurIPS, ICML, ICLR, AISTATS, and UAI conferences. His work bridges theoretical machine learning with neuroscience and philosophical implications of artificial intelligence .

• Machine Learning
• Computational Neuroscience
• Artificial Intelligence
• +7 more

Pauli Murto is a Professor and Head of the Department at Aalto University School of Business, Department of Economics. His research spans microeconomic theory, information economics, and game theory, with a focus on strategic decision-making under uncertainty. Aalto University School of Business, Espoo, Finland Member of Helsinki Graduate School of Economics Research Interests: Dr. Murto's work examines strategic timing in economic decisions, information aggregation in games, auction theory, and investment behavior under uncertainty. His publications address topics like: Common value auctions and affiliated signals Stepwise investment under multi-dimensional uncertainty Equilibrium delay and neighborly coordination Irreversible investment in oligopolistic markets Publications (2002–2024): His research appears in top journals like Review of Economic Studies , Theoretical Economics , Journal of Economic Theory , and RAND Journal of Economics , often collaborating with scholars such as Juuso Välimäki and Chang-Koo Chi. Contact: Available at pauli.murto@aalto.fi or +358 40 353 8174. Office located in Room V308, School of Business building, Aalto University.

• Microeconomic theory
• Information economics
• Game theory
• +3 more

Daolang Huang is a Doctoral Researcher and Student in the Department of Computer Science at the School of Science, affiliated with Professor Samuel Kaski's group. He holds a Bachelor's degree in Engineering and Technology from Jinan University (2020). His research focuses on advanced machine learning techniques, including Bayesian inference, robust statistical modeling, and simulation-based methods. Key areas include experimental design optimization, neural processes, and equivariance in deep learning. Recent work emphasizes decision-aware algorithms and cost-effective simulation frameworks. Huang has collaborated internationally, with publications in top venues like NeurIPS. Despite no listed awards, his work demonstrates significant contributions to probabilistic modeling and optimization. Education : Bachelor's degree in Engineering and Technology, Jinan University (2020) Research Interests : Bayesian methods and amortized inference Robust statistics under model misspecification Continuous control and neural process architectures Optimization algorithms with decision-theoretic foundations Recent Research Trends : His articles (2020–2025) emphasize Bayesian experimental design, preference-based optimization, and equivariant neural networks. Themes include balancing statistical rigor with computational efficiency, particularly in high-dimensional decision-making contexts. Labs/Teams : Active member of Samuel Kaski’s research group, focusing on interdisciplinary applications of machine learning.

• Machine Learning
• Artificial Intelligence
• Neural Networks
• +3 more

Joona Oikarinen is a Postdoctoral Researcher at Aalto University's Department of Mathematics and Systems Analysis within the School of Science. His research focuses on interdisciplinary areas including Mathematical Physics, Quantum Field Theory, and Probability Theory. He contributes to groups studying Nonlinear Partial Differential Equations (NPDE), Conformal Field Theory, and Differential Geometry. Key research interests span Liouville Quantum Field Theory, Gaussian Multiplicative Chaos, and the interplay between stochastic processes and geometric structures. His work often bridges theoretical frameworks with applications in critical phenomena and statistical mechanics. Joona is affiliated with research clusters exploring stress-energy tensors in conformal field theories and the mathematical foundations of two-dimensional quantum systems. His research also intersects numerical analysis and systems analysis, reflecting his department’s multidisciplinary approach.

• Mathematical Physics
• Partial Differential Equations
• Probability Theory
• +5 more

Charul Rajput is a Research Fellow at Aalto University's Department of Mathematics and Systems Analysis, School of Science. Their research focuses on Information Theory, Coding Theory, Discrete Mathematics, and Algebra, with a particular emphasis on caching systems and network optimization. Recent work includes advancements in hierarchical coded caching, hotplug models, and error probability analysis in communication channels. Publications span topics like function-correcting codes, private information retrieval, and locally recoverable codes. Rajput's research also intersects with systems analysis, addressing challenges in distributed storage and network efficiency. Research interests include the theoretical foundations of coding and information theory, with applications to modern communication systems. Key contributions address the design of efficient caching schemes and error-correcting codes for high-performance networks. No scientific awards or grants are explicitly mentioned in the provided texts. Rajput is affiliated with the Algebra and Discrete Mathematics research group at Aalto University, contributing to interdisciplinary projects that bridge pure mathematics and practical network systems.

• Information Theory
• Coding Theory
• Discrete Mathematics
• +3 more

Azzam Al-nahari is an Associate Professor in the Department of Electrical Engineering at Ibb University, Yemen, since 2017. He holds a B.Sc. in Electronics and Communications Engineering from the University of Technology, Iraq, and M.Sc. and Ph.D. in Electrical Communications from Menoufia University, Egypt. He has held postdoctoral positions at Lund University, Sweden (2012), University at Buffalo, USA (2014), and is currently a Visiting Scholar at Aalto University, Finland (since 2019). Education: B.Sc., Electronics and Communications Engineering, University of Technology, Iraq M.Sc. & Ph.D., Electrical Communications, Menoufia University, Egypt Research Interests focus on Backscatter Communications , Massive MIMO Systems , Physical Layer Security , Cognitive Radio Networks , and Signal Processing for Wireless Communications . His work explores UAV-assisted systems, energy-efficient protocols, and covert communication techniques. Recent Publications highlight advancements in Ambient IoT , UAV-Backscatter Integration , Reconfigurable Intelligent Surfaces (RIS) , and Secure Beamforming , reflecting trends in low-power networking, AI-driven resource allocation, and wireless security. Affiliation: Research groups in Communication Engineering at Aalto University.

• Backscatter Communications
• Massive MIMO Systems
• Physical Layer Security
• +4 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.

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

Tuomas Orponen is an Associate Professor in the Department of Mathematics and Statistics at the University of Jyväskylä, Faculty of Mathematics and Science. His research focuses on geometric measure theory, harmonic analysis, and fractal geometry, with particular interests in incidence geometry, Fourier analysis, and metric geometry. Orponen is affiliated with the research groups for Nonlinear Partial Differential Equations and Geometric Measure Theory and Harmonic Analysis. His work explores topics such as projections of fractal sets, additive properties of fractal configurations, and geometric incidence problems. He leads projects in approximate incidence geometry, multi-scale incidence geometry, and incidences on fractals. His research trends include advancing understanding of geometric and analytic properties of fractals, exploring connections between incidence geometry and harmonic analysis, and studying the interplay between discrete and continuous geometric structures. Recent articles highlight investigations into radial projections, Kakeya-type sets, and the discretised ABC sum-product problem. Orponen has no listed scientific awards but has contributed to significant collaborative projects, including studies on Heisenberg group geometries and singular integrals. He actively publishes in top journals such as Transactions of the American Mathematical Society and Geometric and Functional Analysis. His research is anchored in two core teams: the Nonlinear PDE group and the Geometric Measure Theory team, reflecting his dual focus on analytical and geometric methodologies.

• Geometric Measure Theory
• Harmonic Analysis
• Fractal Geometry
• +4 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.

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

Paavo Salminen is a faculty member in the Mathematics department at the Faculty of Natural Sciences and Engineering. With a prolific publication record spanning from 1990 to 2025, he has established himself as a prominent researcher in probability theory and stochastic processes. His research interests primarily focus on: Brownian Motion and its variants Optimal Stopping problems Diffusion processes and spider processes Excessive functions and Green kernels Theta functions and their probabilistic aspects Markov processes and their applications Salminen's recent work demonstrates a continued focus on theoretical aspects of stochastic processes with applications in mathematical finance and other fields. His publications show consistent contributions to understanding diffusion processes, optimal stopping problems, and the mathematical properties of Brownian motion variants. The research spans both theoretical developments and potential applications in financial mathematics and other domains requiring sophisticated stochastic modeling techniques. His scientific contributions include: 34 publications documented between 2009-2024 Multiple collaborations with international researchers including Christophe Vignat, Ernesto Mordecki, and Pierre Vallois Hosting academic visitors at his institution in 2022 and 2024 Salminen maintains an active research program, with several publications appearing in 2024 and even some listed for 2025, indicating ongoing productive research activities. His work bridges pure probability theory with potential applications in mathematical finance and other fields requiring advanced stochastic modeling techniques.

• Probability Theory
• Stochastic Processes
• Brownian Motion
• +5 more

Liam Hughes is a Postdoctoral Researcher at Aalto University's Department of Mathematics and Systems Analysis within the School of Science. His research focuses on Quantum Gravity, Mathematical Physics, and Nonlinear Partial Differential Equations. He is affiliated with the Mathematical Physics group and contributes to areas such as Euclidean Geometry and Metric Spaces. His recent work includes a 2024 paper in Electronic Communications in Probability , exploring properties of Liouville quantum gravity metrics. Hughes holds a Research Fellow academic rank and can be contacted via email . Research interests emphasize theoretical frameworks bridging probability theory with geometric structures, particularly in quantum gravity contexts. His work often intersects conformal geometry and stochastic processes, as seen in his analysis of metric spaces in Liouville gravity models.

• Quantum Gravity
• Mathematical Physics
• Nonlinear Partial Differential Equations
• +3 more

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.

• Inverse Problems
• Electrical Impedance Tomography
• Numerical Analysis
• +3 more

Rahinatou Njah is a Doctoral Researcher at Aalto University's School of Science, affiliated with the Department of Mathematics and Systems Analysis and the Algebra and Discrete Mathematics research group. She focuses on cryptography, particularly homomorphic encryption and cryptanalysis, with contributions to post-quantum security and algebraic structures. Education: Pursuing a doctoral degree in Mathematics/Computer Science at Aalto University. Research Interests: Cryptographic protocol design, lattice-based cryptography, security analysis of encryption schemes, and applications of algebraic structures in cryptography. Her work bridges theoretical mathematics and practical security challenges in the quantum era. Publications: Recent work includes studies on efficient polynomial arithmetic in homomorphic encryption systems and classical security analysis of abelian group-based encryption. Her research spans topics like cryptanalysis of cyclotomic structures and post-quantum cryptography verification. Collaborations: Collaborates internationally with researchers in cryptography and algebra, including institutions involved in Women in Numbers Europe initiatives.

• Cryptography
• Homomorphic Encryption
• Cryptanalysis
• +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 .

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

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.

• Statistics and Probability