Find Academic Supervisors

• Machine Learning
• Medical Imaging
• Computer Science
• +3 more

Aleksei Tiulpin is a Visiting Professor affiliated with the Department of Electrical Engineering and Automation and Sensor Informatics and Medical Technology . His research focuses on applying machine learning and deep learning to medical imaging and biomedical engineering , particularly for osteoarthritis diagnosis and progression prediction. His work includes automating histopathological grading using micro-CT , multi-modal imaging data fusion , and segmentation of chest X-rays . Recent publications highlight trends in Bayesian decision-making , tidemark segmentation , and clinical decision support systems .

• Neuroscience
• Biomedical Engineering
• Neurotechnology
• +5 more

Risto Ilmoniemi is a Senior Advisor at the Department of Neuroscience and Biomedical Engineering, Aalto University. He has pioneered innovations in magnetoencephalography (MEG) and transcranial magnetic stimulation (TMS), including the minimum-norm estimate, signal-space projection, and navigated TMS (nTMS). He founded Nexstim Plc, commercializing TMS-EEG and nTMS, and currently leads development of multi-locus TMS (mTMS) and hybrid MEG-MRI systems. His work bridges physics, engineering, and clinical neuroscience. Affiliation : Aalto University, Department of Neuroscience and Biomedical Engineering Expertise : Biodesign, Human brain imaging, Electromagnetism, Neurotechnology, Medical Technology His research focuses on bioelectromagnetism , brain connectivity , and mathematical methods for solving inverse problems in brain signal analysis. He develops closed-loop TMS systems and hybrid imaging modalities like MEG-MRI, with applications in neuroscience and clinical diagnostics. His work emphasizes real-time tractography , electromagnetic modeling , and personalized neuromodulation . The 15 most recent articles highlight his leadership in advancing multi-locus TMS , EEG forecasting , and neuroimaging-guided stimulation . Key trends include state-dependent stimulation protocols , hidden Markov modeling of brain dynamics , and open-source tools for closed-loop systems . Scientific Awards : World Technology Award Finalist in Health & Medicine (2013) Fellow of EAMBES (2015) Finnish Academy of Science and Letters (2008) Innovation of the Year Award (2017) Innosuomi 2004 Prize First prize in Medical Technology competition (1997) His career spans founding a neurotech company, inventing brain imaging techniques, and co-developing MEG , TMS , and hybrid systems . He collaborates with the BioMag Laboratory and Aalto Brain Centre , mentoring researchers in neurostimulation and brain modeling .

• Human-in-the-Loop
• Machine Learning
• Predictive Maintenance
• +4 more

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.

Indrė ŽliobaitėView profile

Professor

• Data Science
• Machine Learning
• Life Science Informatics
• +3 more

Indrė Žliobaitė is a Professor at the Department of Computer Science, Faculty of Science, University of Helsinki, and holds a Docent title in the same department. She maintains dual affiliations with the Department of Geosciences and Geography and serves as supervisor for both the Doctoral Programme in Geosciences and Doctoral Programme in Computer Science. Her research integrates data science and machine learning with earth sciences, focusing on evolutionary paleontology, macroecology, and macroevolution. She develops computational frameworks for analyzing fossil records, modeling species longevity, and reconstructing paleoenvironments, with specific applications in habitat suitability prediction and ecometric inference. Her work bridges algorithmic innovation with paleontological data interpretation. Recent publications reveal a cohesive trajectory where machine learning techniques—particularly probabilistic modeling and Bayesian approaches—are systematically applied to paleontological questions. This includes modeling fossil mammal community dynamics, inferring historical climate impacts on species distribution, and establishing quantitative laws for macroevolutionary processes, demonstrating how computational methods can unlock patterns in deep-time ecological data. Žliobaitė actively supervises doctoral candidates through two university programmes and leads the Academy of Finland project "Macroevolution of ecological relationships" (2023-2027). She collaborates internationally with the Turkana Basin Institute, Staatliches Museum Naturkunde Stuttgart, and Institut Catala de Paleontologia, and manages the New and Old Worlds Mammal Fossil Database (NOW), a critical infrastructure for paleontological research.

Elio NushiView profile

Researcher

• Data Science
• Machine Learning
• Algorithms
• +3 more

Elio Nushi is a Doctoral Researcher at the University of Helsinki , affiliated with the Faculty of Veterinary Medicine's Department of Food Hygiene and Environmental Health. His academic journey includes M.Sc. and B.Sc. degrees in Computer Science with focus on Data Science and Machine Learning. B.Sc., University of New York Tirana (2017) M.Sc., Åbo Akademi University (2020) Ph.D. candidate, University of Helsinki (2025 expected) Nushi develops probabilistic models for time series analysis of multi-omics data in precision medicine. His research integrates network science with computational biology to identify personalized drug combinations, particularly in multiple myeloma studies. Current work involves microbiome modeling and legacy transcriptomic data reanalysis using Bayesian methods. Recent publications highlight his contributions to Bayesian time annotation techniques and network modeling approaches for biomedical applications. He has taught Data Analysis with Python for three consecutive years at the University of Helsinki while maintaining active research in mathematical applications to biological systems.

• Computer Graphics
• Computer Vision
• Machine Learning
• +5 more

Jaakko Lehtinen is a Professor in the Department of Computer Science at Aalto University, affiliated with the School of Science. His research group focuses on computer graphics, computer vision, and machine learning, particularly in generative modeling, realistic image synthesis, and appearance acquisition. They explore combining machine learning with physical simulators to develop robust and interpretable AI systems. Their work contributes to Visual Computing and Human-Computer Interaction. Lehtinen has supervised doctoral researchers including Erik Härkönen and Tuomas Kynkäänniemi. Notable awards include the Best MSc Thesis Award (2020) and Best PhD Thesis Awards (2017). He has contributed to high-impact publications in venues like NeurIPS and Neural Computation, addressing topics like diffusion models, EEG forecasting, and generative adversarial networks. Key Collaborations: International partnerships in machine learning and visual computing. Media Engagement: Regular commentary on AI trends in Finnish and global media. Professional Activities: Served as a PhD thesis committee member for institutions like UC Berkeley and MIT. His research extends to medical imaging, environmental modeling, and neural rendering, with applications in tomography, audio processing, and dynamic scene generation. Lehtinen’s group actively publishes datasets (e.g., for gradient-domain rendering) and engages in open-source software contributions.

• Probabilistic Machine Learning
• Statistical Inference
• Computational Neuroscience
• +4 more

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.

• Machine Learning
• Reinforcement Learning
• Online Learning
• +3 more

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.

• Interpretability
• Machine Learning
• Data Mining
• +4 more

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.

Michael MathioudakisView profile

Visiting Professor

• Data Mining
• Social Media Analysis
• Text Mining
• +4 more

Michael Mathioudakis serves as a Visiting Faculty member in the Department of Computer Science at Aalto University and is affiliated with the Adj. Prof. Gionis Aris research group and the Helsinki Institute for Information Technology (HIIT). His academic work spans multiple institutions and research collaborations focused on data-intensive computing and analysis. His educational background includes a Doctor of Philosophy from the University of Toronto, awarded on June 5, 2013. This foundation has enabled his research contributions across various domains of computer science and data analysis. Mathioudakis's research interests focus on Data Mining , Social Media Analysis , Text Mining , Social Network Analysis , and Bayesian Networks . His work explores the intersection of big data technologies with social phenomena, particularly examining how information flows through social networks and how probabilistic models can be optimized for real-world applications. His fingerprint analysis reveals significant contributions to Random Walk algorithms (100%), Social Network analysis (83%), Bayesian Networks (66%), and Big Data applications (33%). His publication record shows a consistent output from 2015-2022 with 22 total research outputs, including conference proceedings and journal articles. His work demonstrates a clear trajectory from foundational data mining research toward increasingly complex applications in social media analysis and urban informatics, with notable contributions to efficient algorithms for Bayesian network processing and social media content exposure systems. Mathioudakis has collaborated extensively with researchers across multiple institutions, as evidenced by his co-authorship on papers involving teams from various international organizations. His work contributes to UN Sustainable Development Goals, particularly in areas related to data-driven approaches to societal challenges.

• Statistical Machine Learning
• Exploratory Data Analysis
• Data Science
• +4 more

Jaakko Peltonen is a Professor of Statistics and Data Analysis at Tampere University, Faculty of Information Technology and Communication Sciences, Department of Computing Sciences. He leads the Statistical Machine Learning and Exploratory Data Analysis (SMiLE) research group and is affiliated with the Tampere Research Center in Information and Systems and the Academy of Finland Center of Excellence in Game Culture Studies. His research focuses on statistical machine learning, exploratory data analysis, and their applications in diverse domains including healthcare, social media analytics, gaming, and political discourse analysis. Notable projects include developing algorithms for transparent AI systems (e.g., TraQuLA, Flow of Trust framework) and analyzing online communities through platforms like Twitch and Steam. Recent work emphasizes ethical AI integration in education and healthcare, predictive modeling for emergency department management, and game studies involving player behavior analysis. Peltonen collaborates across disciplines, leveraging text mining, graph theory, and visualization techniques to address complex societal challenges. Key contributions span methodological advancements (e.g., constrained non-negative matrix factorization) and interdisciplinary applications in policy analysis, digital humanities, and health informatics. His work bridges technical innovation with real-world societal impact through collaborative research initiatives.

• Computer Science
• Artificial Intelligence
• Machine Learning
• +3 more

Petri Myllymäki is a Director at Helsinki Institute for Information Technology (HIIT) , Computer Science Adjunct Professors , and Finnish Center for Artificial Intelligence (FCAI) within Aalto University's School of Science, Department of Computer Science . His research focuses on Bayesian networks, information discovery, and interactive intent modeling . Active in ACM Transactions on Information Systems and SIGIR Conference publications Recipient of 2016 CPHC/BCS Academy of Computing Distinguished Dissertation runner-up and 2017 Kone Foundation Rajapinta award Collaborated with institutions like Stockholm University and researchers including Giulio Jacucci and Samuel Kaski His 15 most recent publications (2025-2015) span decision-making under conflicting objectives, machine learning applications in social science, Bayesian network structure learning algorithms , and cognitive modeling for exploratory search . Key technical subfields include hashing techniques, two-dimensional representation, computational efficiency, and user experience optimization . Scientific awards : 2016 CPHC/BCS Academy of Computing Distinguished Dissertation runner-up 2017 Kone Foundation Rajapinta award As an information discovery expert , he has contributed to open-access research through Interactive Visualization and Scinet frameworks, impacting Sustainable Development Goals related to digital innovation and knowledge dissemination.

Jari TurunenView profile

Lecturer

• Sensitivity Analysis
• Radionuclide Transport Modeling
• Environmental Safety Assessment
• +4 more

Jari Turunen is a University Lecturer in Computing Sciences at Tampere University. His expertise spans signal processing, environmental safety analysis, and machine learning applications. He holds a Doctor of Science (Technology) in Information Technology (2003) and a Vocational Teacher qualification (2016) from Tampere University of Applied Sciences. His research focuses on sensitivity analysis of radionuclide transport models, environmental risk assessment, and 3D data economy applications. Notable contributions include neural network surrogate models for geochemical process emulation and studies on post-glacial land uplift in Fennoscandia. Turunen has supervised multiple master's theses in data warehouse methodology and telecom revenue analysis. He actively reviews for journals like Journal of Urban Planning and Development and collaborates internationally on topics such as radioactive waste safety analysis and industrial data utilization.

• Demography
• Fertility Studies
• Population Economics
• +4 more

Julia Ingrid Sofia Hellstrand is a Postdoctoral Researcher at the Centre for Social Data Science (CSDS) and Helsinki Institute for Demography and Population Health, Faculty of Social Sciences, University of Helsinki. Her research focuses on demographic dynamics with emphasis on fertility trends, human capital investment, and economic sustainability in Nordic contexts. Her primary research interests include: Computational fertility forecasting and scenario modeling Drivers of fertility decline (educational attainment, economic uncertainty, partnership formation) Human capital-fertility interrelationships Nordic comparative demography Long-term socioeconomic implications of demographic change Dr. Hellstrand's recent publications (2023-2025) demonstrate methodological evolution from descriptive cohort analysis to integrated computational frameworks. Her work now combines probabilistic forecasting of Finnish total fertility rate (TFR) with economic sustainability modeling, examining how educational pathways and partnership dynamics influence demographic trajectories. Key contributions include calibrated TFR projections to 2070 and analyses of how specific educational fields affect fertility decisions in Finland. She actively participates in the Academy of Finland-funded FLUX project (2024-2027), collaborating with international researchers from the Max Planck Institute and Nordic universities. Her methodological toolkit spans demographic decomposition, microsimulation, and machine learning applications for population forecasting. As a postdoctoral researcher, Dr. Hellstrand contributes to graduate training through research assistantships and co-supervision within the CSDS framework. Her work provides critical evidence for Nordic population policy debates, particularly regarding family formation support measures and long-term economic planning under low fertility conditions.