Robert Millar is a Senior University Lecturer at Aalto University's Department of Electrical Engineering and Automation, within the School of Electrical Engineering. His research focuses on power systems, high voltage engineering, and renewable energy integration. He is affiliated with the Power Systems and High Voltage Engineering research group. Key research interests include distribution network planning, smart grid technologies, and reliability engineering. His recent work addresses challenges in modernizing distribution systems to accommodate high renewable penetration, optimizing switch placement for reliability, and modeling nuclear power plant electrical safety. Notable contributions include frameworks for probabilistic hosting capacity assessment and MILP-based network optimization models. Millar has collaborated extensively on projects funded by initiatives like SAFIR (Nuclear Power Plant Safety Research). His teaching includes pandemic-proofing electricity distribution courses and developing engineering pedagogy for future competencies. He holds a Ph.D. in electrical engineering and has advised numerous interdisciplinary projects in energy systems.
Kalle Alaluusua is a Doctoral Researcher at the Department of Mathematics and Systems Analysis within the School of Science at Aalto University. His research focuses on network analysis, hypergraph clustering, and Bayesian methods for multilayer networks. He has contributed to advancements in stochastic block models and similarity matrix techniques. Education details are not explicitly listed, but his doctoral work aligns with the university's mathematical statistics and data science programs. His recent publications include work on multilayer hypergraph clustering and Bayesian community recovery in networks, presented at conferences like WAW and IEEE International Symposium on Information Theory. He is affiliated with the Mathematical Statistics and Data Science research group and can be contacted via email . His ORCID identifier is 0009-0009-9136-5649 .
Eero Saksman is a Professor at the Department of Mathematics and Statistics , University of Helsinki . He is affiliated with the Faculty of Science and serves as a supervisor for the Doctoral Programme in Mathematics and Statistics . His research focuses on Mathematics , Statistics and probability , with specific expertise in Geometric Analysis , Partial Differential Equations , and Gaussian Multiplicative Chaos . Research Outputs : Active in 2025 with studies on stochastic pressure equations with log-correlated Gaussian coefficients, quasiconformal mappings in Triebel-Lizorkin spaces, Nevanlinna measures structure, and interdisciplinary projects like the Centre of Excellence in Randomness and Structures (FiRST) . Academic Collaborations : Involved in international collaborations with institutions such as Kings College London , EPFL , and University of Geneva . Scientific Awards : Recipient of the Lorenz Lindelöf Prize (2013) Magnus Ehrnrooth Prize (2018) Nevanlinna Prize for best dissertation (1995) Väisälä Prize for Mathematics (2007) Academic Activities : Regularly participates in conferences like the Barcelona Analysis Conference , Modern Aspects of Complex Analysis , and serves on doctoral evaluation committees.
Tomi Janhunen is a Professor in Computing Sciences at Tampere University, specializing in knowledge representation, automated reasoning, and logic programming. He previously served as Adjunct Professor at Aalto University (2019–2024) and maintains a Doctor of Science (Tech.) degree. His research spans answer set programming, satisfiability checking, optimization, and distributed computation. PhD, Aalto University (Doctor of Science (Tech.)) Adjunct Professor of Computer Science (Aalto University, 2019–2024) His research focuses on Answer Set Programming (modularity, verification, optimization), Satisfiability Modulo Theories , Nonmonotonic Logics , and Computational Complexity . He integrates logic programming into real-world applications like preventive maintenance scheduling and AI security systems. Recent work includes translating logic programs into integer programming, developing probabilistic reasoning systems (Plingo), and creating interpretable classifiers for tabular data. His publications emphasize stable model semantics , optimization techniques , and constraint networks . He supervises M.Sc., Lic.Sc., and Ph.D. theses and has completed pedagogical studies. Janhunen actively reviews for journals like Artificial Intelligence Journal and ACM Transactions on Computational Logic .
Luigi Acerbi is an Associate Professor at the Department of Computer Science, University of Helsinki, leading the Machine and Human Intelligence research group. He is affiliated with the Finnish Center for Artificial Intelligence (FCAI) and ELLIS (European Laboratory for Learning and Intelligent Systems). His research focuses on probabilistic machine learning, statistical inference methods (e.g., amortized and surrogate-based approaches), and computational and cognitive neuroscience, including Bayesian models of perception and resource-constrained rationality. Previously, he held postdoctoral positions at the University of Geneva and New York University. He earned his PhD from the Doctoral Training Centre in Computational Neuroscience at the University of Edinburgh, working with Sethu Vijayakumar and Daniel Wolpert. His work includes developing open-source tools like BADS (Bayesian Adaptive Direct Search) and VBMC (Variational Bayesian Monte Carlo), widely used for optimization and Bayesian inference in MATLAB/Python. He actively contributes to the academic community through teaching (e.g., BAMB! 2022 summer school tutorials on model fitting) and software development (GitHub repositories for optimization, inference, and AI tools like Athanor). His research bridges machine learning, neuroscience, and cognitive science, emphasizing robust and efficient statistical methods.
Mahsa Asadi is a Postdoctoral Researcher in the Department of Computer Science, focusing on theoretical and applied aspects of machine learning. Her research emphasizes online learning, collaborative algorithms, and reinforcement learning, with contributions to regret minimization, state-action equivalence in RL, and distributed multitask learning. She explores intersections between statistical theory and algorithmic design, with applications in autonomous systems and multi-agent coordination. Her work spans foundational topics like concentration inequalities and upper confidence bounds, alongside practical implementations such as the CYRUS 2D simulation team and Miss PacMan AI controller. Notable contributions include model-based reinforcement learning frameworks that exploit structural equivalence properties, and collaborative mean estimation algorithms. Research Themes: Online Learning, Reinforcement Learning, Distributed Systems, Statistical Learning Theory Key Contributions: Human-in-the-loop systems, state-action abstraction techniques, regret-optimal algorithms No formal academic awards or grants are listed in the provided information. Her advising and collaborative work focus on advancing algorithmic foundations with practical implementations in robotics and game AI contexts.
Martino Ciaperoni is a Postdoctoral Researcher and Adjunct Professor in the Department of Computer Science at Aalto University. He is affiliated with the research group of Adj. Prof. Gionis Aris. His primary focus is on advancing interpretable machine learning, algorithm optimization, and data mining techniques. Ciaperoni holds a doctoral degree (Tekn. toht.) in Computer Science from Aalto University (2024) and a Doctoral degree in Engineering and Technology from Università degli Studi di Roma 'La Sapienza' (2019). His research emphasizes interpretability in AI, multi-label classification, and efficient algorithms for speech recognition and Bayesian networks. Recent work includes exploring the Rashomon set of rule-based models, developing low-memory Viterbi decoding algorithms (SIEVE), and solving the Hadamard decomposition problem. His research spans topics like core decomposition in temporal networks and low-rank matrix approximation. Collaborations include international projects in computational linguistics, knowledge discovery, and algorithm design. He has contributed to open-source software, such as the 'Efficient Exploration of the Rashomon Set' codebase (Zenodo). His work balances theoretical rigor with practical applications, aiming to bridge gaps between complex algorithms and real-world problem-solving.
Guilhem Sommeria-Klein is a Research Fellow in the Department of Computing at the University of Turku, Finland, holding an Academy of Finland postdoctoral research fellowship focused on probabilistic modeling for microbial ecology. He teaches the Statistical and Probabilistic Programming course and contributes to the Turku Data Science Group under Professor Leo Lahti. His academic training spans disciplines with a Master's degree in Physics and a PhD in Ecology from Toulouse, France, where he conducted theoretical modeling, field DNA sampling, and statistical analysis of ecological communities. Dr. Sommeria-Klein develops probabilistic frameworks to integrate classical ecological theory with microbial data science, advancing understanding of community assembly and dynamics across host-associated microbiota, soil ecosystems, and ocean plankton. His interdisciplinary methodology bridges mechanistic models from evolution with tailored statistical approaches for high-dimensional omic datasets. Recent publications (2021-2025) reveal consistent application of spatial and phylogenetic modeling to microbiome systems, with emphasis on ocean plankton biogeography (Tara Oceans project), gut microbiota evolution in vertebrates, and antibiotic resistome dynamics in human cohorts like FINRISK. No scientific awards are documented in the provided materials. Supported by his Academy of Finland fellowship, he actively collaborates within the Turku Data Science Group on grant-funded research while mentoring through course instruction. His work demonstrates strong integration of theoretical ecology with computational data science across diverse biological scales. His primary research environment is the Turku Data Science Group, with significant prior contributions to the international Tara Oceans consortium for marine microbial studies.
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.
Dr. Yang Lyu serves as a University Researcher in the Department of Geosciences and Geography at the University of Helsinki, actively contributing to the InterEarth RESET project funded by the Academy of Finland (2023-2028). His research profile centers on innovative applications of ambient seismic noise for Earth structure imaging across multiple scales. Dr. Lyu's scientific focus encompasses: Deep Earth structure (mantle transition zone investigations) Sedimentary basin characterization (Vienna Basin case studies) Environmental seismology (soil moisture monitoring) Advanced correlation techniques (single-station cross-component methods) Probabilistic tomography development His work bridges fundamental geophysics and practical environmental monitoring through passive seismic approaches. Analysis of Dr. Lyu's 2025 publications reveals a cohesive research program leveraging ambient noise correlations across diverse settings - from continental-scale US mantle studies to Austrian basin investigations and hydrological applications. This demonstrates exceptional methodological versatility while maintaining technical depth in noise-based imaging. As a key participant in the InterEarth RESET initiative, Dr. Lyu contributes to the University of Helsinki's strategic research profiling in Earth sciences. His collaborative approach is evident in multinational projects involving Austrian geological institutions and US-based mantle research, utilizing both traditional seismic arrays and innovative large-N nodal deployments for high-resolution imaging.
