Elena Shaw is a Visiting Professor in the Department of Computer Science under the School of Science . She is also a doctoral student and researcher specializing in Probabilistic Machine Learning , contributing to advancements in computational modeling and data-driven methodologies. Educational Background : Master's degree in Engineering and Technology from the University of Edinburgh (2020). Her research focuses on probabilistic approaches to machine learning, integrating statistical frameworks with computational algorithms. No explicit details about publications, awards, or advising roles are provided in the available data.
Timo Roine is a Postdoctoral Researcher at the Department of Neuroscience and Biomedical Engineering , Aalto University. His work bridges advanced neuroimaging techniques with clinical and computational neuroscience. Research Interests: Structural and functional brain connectivity Transcranial magnetic stimulation (TMS) optimization Diffusion MRI and machine learning applications Neurodegenerative and neurodevelopmental disorders Biomedical signal processing Recent Publications (2023–2025) focus on: Multimodal neuroimaging for cortical mapping White matter microstructure in traumatic brain injury Connectomic alterations in ADHD and narcolepsy Generative adversarial networks for neuroimaging data Real-time brain state-dependent stimulation Collaborations span clinical neurophysiology, biomedical engineering, and computational modeling teams across Europe. His work often integrates MATLAB , Python , and open-source neuroimaging pipelines.
Jaakko Peltonen is a Visiting Professor at the Department of Computer Science within Aalto University , affiliated with the Probabilistic Machine Learning group and the Helsinki Institute for Information Technology (HIIT) . His research focuses on machine learning, information retrieval, and interactive visualization, with significant contributions to probabilistic modeling and data analysis. Tekniikan tohtori (Doctor of Technology), Helsinki University of Technology (2004) Diplomi-insinööri (Master of Science in Technology), Helsinki University of Technology (2001) His research spans machine learning applications in computational biology (e.g., analyzing cellular processes with regression planes), probabilistic matrix factorization for recommendation systems, and hierarchical Dirichlet processes for topic modeling in text mining. He has pioneered methods for exploratory search with visual interactive intent modeling and graph-based priors for scalable ML algorithms. Notable activities include international collaborations (e.g., visiting researcher roles in 2016), organizing Eurovis 2016 workshops, and conference presentations across Spain, United States, and other countries. His publications (85+ total) demonstrate expertise in probabilistic models, dimensionality reduction, and interactive visualization techniques.
Liangliang Lu is a Postdoctoral Researcher at the Department of Energy and Mechanical Engineering , Aalto University. Their research focuses on Marine and Arctic Technology , with significant contributions to understanding ice mechanics, ship performance in polar conditions, and maritime risk assessment. Specializes in Arctic and Antarctic navigation Develops Bayesian networks for oil spill risk modeling Engages in collaborative polar research projects Research interests span ice-induced ship hull loads , winter navigation systems , and oil spill response effectiveness . Their work combines computational modeling , field measurements , and data analytics to improve maritime safety in extreme environments. Recent publications emphasize CFD/FSI/AI integration , real-time conflict probability ranking , and flexural strength analysis of freshwater ice. Collaborations with experts in naval architecture and polar logistics appear frequently. Active in polar research groups, Lu contributes to developing decision-support tools for ice-covered operations and participates in international conferences like POAC and IAHR Symposiums.
Talal Hamad Salem Alrawajfeh is a Doctoral Researcher at the Department of Computer Science, University of Helsinki, participating in the Doctoral Programme in Computer Science. He is affiliated with projects like "WiseBayes: Resource-wise and trustworthy Bayesian machine learning" funded by the Academy of Finland and "Honkela_CSC_2/2023-9/2025" funded by CSC-Tieteen tietotekniikan keskus Oy. His research focuses on Bayesian machine learning with emphasis on differentially private variational inference and methods for trustworthy AI. His work bridges statistical modeling, data privacy, and computational efficiency. Recent research activity includes the 2025 conference article "Noise-Aware Differentially Private Variational Inference" (co-authored with Jälkö and Honkela), addressing privacy-preserving inference techniques. Active projects span 2023–2027, focusing on resource-aware Bayesian learning frameworks. No scientific awards or honors are explicitly mentioned in the available information.
Arto Klami serves as an Associate Professor in the Department of Computer Science at the University of Helsinki's Faculty of Science, where he also leads the Diversity in Society and Life (DIVSOL) doctoral program. His research spans machine learning, artificial intelligence, and statistics with applications across multiple domains including food science, astronomy, and industrial engineering. Klami maintains active collaborations with Helsinki Institute for Information Technology and leads multiple research projects funded by the Academy of Finland and Business Finland. Professor Klami's research focuses on probabilistic modeling, Bayesian methods, and geometric approaches to machine learning. His work bridges theoretical foundations with practical applications, particularly in developing methods for small data scenarios, prior knowledge integration, and geometric deep learning. His recent publications demonstrate expertise in normalizing flows, manifold learning, and Riemannian geometry applied to statistical problems, alongside domain-specific applications in food science, astronomy, and industrial monitoring systems. Klami's publication record shows a strong trajectory with 97 research outputs through 2025, including numerous high-impact articles in leading journals and conferences. His work spans both theoretical machine learning advancements and applied research in diverse fields such as food preservation, asteroid surface analysis, and industrial fouling detection. A notable trend is his increasing focus on cross-disciplinary applications of probabilistic AI methods, particularly in addressing real-world challenges with limited data. Scientific recognition includes: Best paper award at the 15th Koli Calling conference on computing education research (2015) Best paper award in Asian Conference on Machine Learning (2012) Senior good researcher award from the Department of Computer Science (2014) As an academic leader, Klami has supervised 9 doctoral theses and contributed to 33 academic activities including peer review for top journals like the Journal of the American Statistical Association. He leads five major research projects through 2030, with significant funding from the Academy of Finland, including FoodID (NSF Global Centers), DIVSOL profiling initiative, and projects on flexible priors and virtual laboratories. His work with the Nordic Probabilistic AI School demonstrates commitment to community building in machine learning. Klami directs research teams working on probabilistic AI applications, with particular emphasis on the Virtual Laboratories project and Sustainable Industrial Ultrasonic Cleaning initiative. His Helsinki-based research group maintains strong industry connections through partnerships with Orion Oyj and Silo AI Oy, translating academic research into practical industrial solutions.
Aki Vehtari is a Professor in the Department of Computer Science at Aalto University’s School of Science. His academic roles include leadership in probabilistic machine learning, Bayesian statistics, and computational inference. He is a Principal Investigator for projects like the Finnish Center for Artificial Intelligence and the Iterative Bayesian Model Building initiative. Vehtari holds a Doctoral degree (2001) and Master’s degree (1997) in Engineering and Technology from Helsinki University of Technology. Research Interests: Vehtari specializes in integrating Bayesian methods with computer science, focusing on workflow optimization, probabilistic programming, Gaussian processes, and model diagnostics. His work emphasizes practical inference techniques and their application to real-world problems like aging analysis and RNA sequencing. Recent Research Trends: Recent publications highlight advancements in Bayesian cross-validation, projection predictive inference, and active learning for molecular data. His work often bridges computational efficiency with statistical rigor, addressing challenges in model selection and uncertainty quantification. Grants & Projects: Leading the Finnish Center for Artificial Intelligence (2023–2026) and the Safe Iterative Bayesian Model Building project (2021–2025). Awards: 2016 De Groot Prize, Youden Award (2020), and recognition for contributions to time series prediction (2004). Advising: Supervises doctoral researchers including Guangzhao Cheng and Lu Cheng, advancing topics in RNA analysis and probabilistic modeling. Labs & Teams: Involved in probabilistic AI initiatives, contributing to tools like ArviZ and Stan, and collaborates globally on projects in computational statistics and machine learning.
Nazaal Ibrahim is a doctoral researcher in the Department of Computer Science at the School of Science, focusing on probabilistic machine learning and causal inference. He has contributed to projects involving AI assistant development for privacy-preserving deep learning models and computational modeling of optimization tasks. Research Areas Probabilistic Machine Learning Causal Inference Privacy-Preserving Deep Learning Computational Modeling Optimization Algorithms Collaborations Collaborated with Samuel Kaski's professorship group Worked on projects with international teams (Finland, Japan) Publications In 2022, co-authored a paper titled Targeted Causal Elicitation , focusing on elicitation methods in Bayesian modeling frameworks.
Shibei Zhu is a Doctoral Student and Visitor (Faculty) in the Department of Computer Science at the School of Science. Their research focuses on Probabilistic Machine Learning, Preference Learning, Decision Utility, Interactive Visualization, Human Behavior, and Cognitive Science. They actively collaborate on international research projects, contributing to advancements in human-AI interaction and autonomous systems. Research interests include developing human-like models for preferential choice, interactive preference elicitation techniques, and multimodal policy generation from diverse behavioral data. Their work bridges machine learning with cognitive science to enhance decision-making systems. Publications span topics like latent decision utilities, reward tuning in robotics, and data-driven autonomous systems, reflecting a strong emphasis on practical applications of AI. No awards are listed, but their doctoral research involves significant contributions to conference proceedings in top venues. Shibei advises no formal students but collaborates with research groups such as the Kaski Samuel Professorship. Their work aligns with labs focused on probabilistic machine learning and human-centered AI.
Samuel Kaski is a Professor in the Department of Computer Science at Aalto University , where he leads the Probabilistic Machine Learning group and contributes to the Finnish Center for Artificial Intelligence (FCAI) and Helsinki Institute for Information Technology (HIIT) . He also holds a joint appointment with the Manchester Centre for AI Fundamentals (AI-FUN) at the University of Manchester. Doctoral degree in Engineering and Technology from Helsinki University of Technology (1997) Master's degree in Engineering and Technology from Helsinki University of Technology (1993) Research Interests His work focuses on probabilistic modeling , Bayesian inference , and collaborative AI , particularly in applications to healthcare , neuroscience , synthetic biology , and user interaction . Recent projects integrate human feedback into generative models for drug discovery and molecular design , addressing distribution shifts and robust learning . Publication Trends His recent articles emphasize Bayesian experimental design , robustness in neural networks , and human-AI collaboration . Key subfields include generative chemistry , privacy-preserving learning , and simulation-based inference . Scientific Recognition Aalto SCI Award for Innovation of the year (2018) Academy Professor title (2016) Member of Finnish Academy of Science and Letters (2017) Leadership & Grants Kaski directs the ELLIS Institute Finland and leads EU-funded projects like FinBioFAB (2025-2029) and PRIMUS (2024-2026), focusing on biomanufacturing and secure health data . He has mentored 36 PhD students, with 21 alumni in faculty positions.
Heikki Timonen is a Researcher and Doctoral Student at the Department of Computer Science within the School of Science. His research focuses on generative models, neural networks, and imaging inverse problems, with notable contributions in machine learning and computer vision. He collaborates with Professor Jaakko Lehtinen's research group and has published work on invertible hierarchical generative models and conditional normalizing flows. Doctoral Researcher under Professorship Lehtinen Jaakko His research interests emphasize advancing AI-driven solutions for imaging challenges and developing scalable generative architectures. Recent work includes peer-reviewed contributions to Transactions on Machine Learning Research. While no formal advising roles or grants are explicitly listed, his research activities involve collaborations across international teams, as indicated by cross-border research networks. He is associated with computational imaging and machine learning labs, contributing to interdisciplinary projects at the intersection of theory and application.
Aidan Scannell is a Visiting Professor at the Department of Electrical Engineering and Automation, Aalto University. His work focuses on Reinforcement Learning (RL), Neural Networks , and Model-Based Reinforcement Learning , particularly for Continuous Control and Sequential Learning tasks. He collaborates with the Finnish Center for Artificial Intelligence (FCAI) and contributes to advancements in Representation Learning , Constrained Models , and Bayesian Learning . Research Interests His research explores core challenges in RL, including sample efficiency , generalization , and model adaptability . Key areas include function-space neural network parameterization , quantized representations , and residual learning for dynamic environments. His work often integrates Gaussian Processes and context encoding to enhance decision-making in complex, multimodal systems. Research Output Trends Aidan’s publications emphasize Reinforcement Learning (100% of works), Representation Learning (85%), and Neural Networks (80%). Recent articles address offline-to-online adaptation , codebook-based world models , and entropy-regularized meta-learning , reflecting his focus on scalable and data-efficient RL frameworks. Projects He is affiliated with the Finnish Center for Artificial Intelligence (FCAI) , funded by the Academy of Finland, which supports interdisciplinary AI research. His collaborations span researchers like Joni Pajarinen, Arno Solin, and Christopher H. Ek.
Ville Hautamäki is an Associate Professor at the University of Eastern Finland's School of Computing, Department of Computer Science. His research focuses on data science, statistical inference, and deep learning with applications in autonomous agents, bioinformatics, and speech technology. He teaches courses such as Probabilistic Inference for Data Science and Bayesian Inference, and regularly contributes to summer schools on machine learning. His research group, Applied Statistics and Statistical Machine Learning, addresses challenges in speaker verification, speech processing, and biomedical data analysis. Recent works include advancements in robust speaker recognition under noisy conditions, deepfake detection, and end-to-end autonomous driving systems. Collaborations span diverse domains including healthcare, cybersecurity, and robotics. Publications highlight contributions to multi-task learning frameworks, imitation learning policies, and generative models for single-cell data. His work emphasizes cross-disciplinary approaches, blending theoretical machine learning with practical applications in real-world scenarios.
Tomi Kinnunen is a Professor at the University of Eastern Finland's School of Computing, within the Faculty of Science, Forestry and Technology. He holds a PhD in Computer Science from the University of Joensuu (2005) and has extensive experience in speaker recognition, voice biometrics security, and anti-spoofing technologies. His research focuses on advancing voice authentication systems against attacks such as deepfakes and replay spoofing, with contributions to initiatives like the ASVspoof challenge. Education: PhD in Computer Science, University of Joensuu, 2005 Research Interests: Speaker recognition, spoofing countermeasures, voice transformation, and bioacoustic signal applications. He leads the Computational Speech Group and co-founded the ASVspoof challenge, a key benchmark for evaluating voice biometric security. Grants & Projects: Has led multiple Academy of Finland-funded projects on speaker recognition and anti-spoofing, including the H2020-funded OCTAVE project. Current projects include SPEECHFAKES and ASVspoof 5, focusing on generalized anti-spoofing and deepfake detection. Labs/Teams: Leads the Computational Speech Group, a research team advancing speech processing and security technologies. Collaborates internationally on databases like the FROST-EMA and STOPA.
Alexander Nikitin is a Postdoctoral Researcher in the Department of Computer Science at Aalto University, working within the Probabilistic Machine Learning group under Professor P. Marttinen. His research integrates human expertise with machine learning systems to solve complex real-world problems, particularly in predictive maintenance and uncertainty quantification. He holds a Master's degree in Engineering and Technology from the State University Higher School of Economics, awarded in 2019, and completed his doctoral thesis at Aalto University in 2024. Nikitin's work centers on Human-in-the-Loop systems where human feedback enhances AI performance, predictive maintenance using graph-based models for industrial applications, and uncertainty quantification in large language models. His approach combines probabilistic methods with deep learning to address challenges in spatiotemporal data analysis, domain adaptation, and non-separable systems. Recent publications demonstrate his focus on practical implementations where machine learning directly impacts operational efficiency. Analysis of his 2021-2025 publications reveals a clear trajectory toward industrial AI applications, with increasing emphasis on human-AI collaboration frameworks. His work bridges theoretical machine learning advances (like kernel entropy methods for LLMs) with concrete use cases in workstation monitoring and synthetic time series generation, frequently appearing in top-tier venues like NeurIPS and KDD. He actively contributes to major research initiatives including the FCAI Flagship 2 project (2022-2026), which develops next-generation AI systems through collaboration between Aalto University, the University of Helsinki, and industry partners like Elisa. His earlier work on error log analysis for predictive maintenance (2020-2023) established foundations for current human-in-the-loop approaches. Nikitin operates within Aalto's Probabilistic Machine Learning ecosystem and the Finnish Center for Artificial Intelligence (FCAI), where he collaborates with leading researchers including Sami Kaski and P. Marttinen on projects spanning generative modeling, workstation maintenance, and large-scale AI deployment.
