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Arno SolinView profile

Associate Professor

• Machine Learning
• Signal Processing
• Sensor Fusion
• +8 more

Dr. Arno Solin is a tenured Associate Professor in Machine Learning at Aalto University's Department of Computer Science and an Academy of Finland Research Fellow . He leads the Aalto machine learning research group and serves as Director of the Finnish Doctoral Program Network in AI (AI-DOC) . His work bridges probabilistic modeling with practical applications in sensor fusion and real-time inference. ELLIS Scholar (European Laboratory for Learning and Intelligent Systems) Adjunct Professor at Tampere University Member of Young Academy Finland (2021–2025) Research Interests focus on data-efficient machine learning with probabilistic methods for real-time inference and sensor fusion. Key areas include Gaussian processes, diffusion models, stochastic differential equations, and uncertainty quantification in deep learning. His group develops methods that combine structural constraints with adaptive learning for deployment on resource-limited hardware. Publication Trends show consistent output in top venues (NeurIPS, ICML, ICLR, AISTATS) with emphasis on diffusion models , 3D scene reconstruction , and real-time probabilistic modeling . Recent works explore physics-informed learning, heterophily-aware graph models, and compressed representations for world models in reinforcement learning. Scientific Recognition : Awarded AI Researcher of the Year 2024 by AI Finland Teacher of the Year 2023 at Aalto CS ISIF Jean-Pierre Le Cadre Best Paper Award (2018) MLSP Schizophrenia Classification Challenge Winner (2014) NeurIPS/ICML Reviewer Awards Research Leadership includes coordinating Finland's AI Center of Excellence program and directing the Finnish Doctoral Program Network in AI (AI-DOC). He supervises 15+ doctoral/postdoctoral researchers and has spun off Spectacular AI , a company commercializing sensor fusion technology.

Pekka MarttinenView profile

Associate Professor

• Machine Learning
• Large Language Models
• Reinforcement Learning
• +8 more

Pekka Marttinen is a tenured Associate Professor of Machine Learning at Aalto University, Department of Computer Science, and leads the Machine Learning for Health (Aalto-ML4H) group within the Helsinki Institute for Information Technology HIIT. Education: M.Sc. in Applied Mathematics, University of Helsinki (2004) Ph.D. in Statistics, University of Helsinki (2008) Title of Docent in Information and Computer Science, Aalto University (2015) Research Focus: His methodological work spans large language models, reinforcement learning, deep learning, probabilistic machine learning, and causal inference . These techniques are applied to critical domains of healthcare, bioinformatics, statistical genetics, epidemiology, and personalized medicine . The group develops novel algorithms, theoretical guarantees, and open-source software that enable data-driven discovery and decision-making in medicine and biology. Publication Trends: Across 2022-2024 the lab has concentrated on (i) rigorous causal reasoning over temporal clinical data, (ii) principled uncertainty quantification in LLMs, (iii) representation learning for neural network comparison, and (iv) translational projects that turn raw EHRs into actionable clinical insights. Earlier work integrated high-dimensional genomics with metabolomics and mapped evolutionary forces in bacterial pathogens. Scientific Awards & Recognition: While no specific awards are listed, his sustained publication record in top-tier venues (NeurIPS, ICML, AISTATS, Nature Genetics, PLOS CB) and his role as responsible professor of the Machine-Learning, Data-Science and AI major signify significant peer recognition. Advising & Grants: Prof. Marttinen currently mentors 8 PhD students as primary supervisor and an additional 7 PhD students as co-supervisor. He has already graduated 8 PhDs since 2014. The group is supported through competitive funding including the Finnish Center for Artificial Intelligence (FCAI) doctoral program. Labs & Teams: He directs the Machine Learning for Health (Aalto-ML4H) research group, comprising postdocs Hans Moen, Ti John, Alexander Nikitin, Negar Safinianaini, Linli Zhang, Zhiyuan Li, and the above-mentioned PhD cohort.

Pauli MurtoView profile

Professor

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

Sabine GrafView profile

Professor

• Educational Technology
• User Adaptive Systems
• Learning Analytics
• +7 more

Dr. Sabine Graf is a Full Professor at the School of Computing and Information Systems, Athabasca University. She holds a PhD in Computing and Information Systems from Vienna University of Technology (2007) and has been a faculty member since 2009. Her research focuses on user adaptive systems, learning/academic analytics, personalization, and game-based learning, with over $2.3M in external funding and 130+ peer-reviewed publications (cited 8,200+ times). She leads the OMEGA+ educational game project and the User Adaptive Systems (UAS) research cluster. Education: PhD in Computing and Information Systems, Vienna University of Technology, 2007 MSc in Computing and Information Systems, University of Vienna, 2003 Research Interests: Dr. Graf specializes in making learning systems more intelligent through adaptive interfaces, AI-driven recommendations, and data analytics. Her work bridges educational technology, artificial intelligence, and collaborative learning. Recent projects include developing OMEGA+, analyzing student behavior in online courses, and enhancing adaptive learning systems with context-aware features. Grants & Funding: NSERC Discovery Grant ($205,000), 2020 CFI John R. Evans Leaders Fund ($164,573), 2020 AU IDEA Lab Grant ($5,000), 2021 Multiple Mitacs Globalink Internships, NSERC awards, and AU-funded projects. Advising & Team: Dr. Graf has mentored over 30 students, including PhD, MSc, and postdoctoral fellows. Notable advisees include Moustafa Mahmoud (NSERC Scholar) and Mohammad Belghis-Zadeh (3MT Competition winner). The research team collaborates globally, with members from Brazil, Taiwan, Spain, and beyond. Labs & Initiatives: The Academic Analytics Tool (AAT) project and the OMEGA+ game platform are central to her work. The UAS cluster connects researchers worldwide to advance adaptive systems research.

Visa KoivunenView profile

Professor

• Statistical Signal Processing
• Wireless Communications
• Multisensor Systems
• +4 more

Visa Koivunen is a Distinguished Professor of Signal Processing at Aalto University (since 1999), with positions as Academy Professor (2010) and Aalto Distinguished Professor (2020). He holds an honorary D.Sc. (Tech.) from the University of Oulu and has held visiting roles at Princeton University, the University of Pennsylvania, and EPFL. His research focuses on statistical signal processing, wireless communications, radar systems, and integrated sensing and communications (ISAC). He has published over 490 papers, including award-winning works, and advised 31 doctoral theses. Key roles include leadership in conferences (e.g., Asilomar 2018 General Chair) and technical committees (IEEE SPS). Recognitions include the EURASIP Technical Achievement Award (2015), IEEE Signal Processing Society Best Paper Awards (2007, 2017), and EURASIP Fellow status (2020). He co-chairs NATO panels on cognitive radars and ISAC. His research interests span signal processing fundamentals and applications in radar, communications, and machine learning. Recent work emphasizes ISAC, reinforcement learning for resource allocation, and causal inference in federated systems. He has pioneered waveform design techniques using GANs and Bayesian methods for spatial signal analysis. Educations: D.Sc. (Tech.) with honors from the University of Oulu (1994), Primus Doctor Award (1989-1994). Awards: IEEE Fellow, EURASIP Fellow, Member of Academia Europaea. Service: Associate Editor for IEEE Transactions, Chair of IEEE SPAWC and Asilomar conferences. His work bridges theory and practice, addressing challenges in radar-communication coexistence, energy-efficient edge computing, and secure distributed inference. He has delivered over 50 invited talks globally and actively contributes to NATO initiatives on cognitive radar systems.

• Machine Learning
• Artificial Intelligence
• Stochastic Differential Equations
• +3 more

Prakhar Verma is a Doctoral Researcher at Aalto University, specializing in machine learning and probabilistic modeling. His work focuses on Gaussian processes, stochastic differential equations, and their applications in sequential learning and Bayesian optimization. Research Interests: Probabilistic modeling with Gaussian processes Scalable inference for stochastic differential equations Bayesian optimization and active learning techniques Sequential learning with memory-based approaches Mathematical modeling of continuous-time systems Computational statistics for high-dimensional data Collaborations: He collaborates with researchers like Arno Solin, Vincent Adam, Paul E. Chang, Ti John, and Victor Picheny. His publications span conferences such as NeurIPS, ICML, and AISTATS.

• Educational Psychology
• Self-Regulated Learning
• Collaborative Learning
• +5 more

Hanna Järvenoja is a researcher in the Faculty of Education at the University of Oulu, Finland, specializing in educational psychology and learning sciences. Her work focuses on self-regulated, co-regulated, and socially shared regulation of learning, particularly in collaborative and classroom contexts. Her research interests include: Self-Regulated Learning Emotion and Motivation Regulation in Education Collaborative and Cooperative Learning Teacher Education and Student Well-Being Learning in Challenging Educational Contexts Methodological Approaches to Studying Regulatory Processes The analysis of her recent publications reveals a consistent focus on the dynamics of regulation in learning, especially how students and teachers manage motivation, emotions, and strategies in collaborative settings. Her work spans early childhood, teacher education, and science classrooms, employing both qualitative and temporal analytical methods. Notable scientific contributions include: Advancing methodological frameworks for capturing socially shared regulation Investigating emotion regulation in day-care and school environments Exploring how self-regulation mitigates anxiety among student teachers Designing collaborative learning interventions to support adaptive motivation Hanna Järvenoja actively contributes to educational research through co-authorship with leading scholars such as Sanna Järvelä and Piia Näykki. She has advised or collaborated with researchers in teacher education and learning sciences, though formal advisees are not listed. Her work is supported by rigorous empirical studies published in top-tier journals. She is also involved in editorial contributions, particularly through Frontiers in Education. She is associated with research groups focused on learning and regulation, likely within the Learning and Educational Technology Research Unit (LET) at the University of Oulu, contributing to team-based investigations into collaborative learning processes.

• Reinforcement Learning
• Representation Learning
• Continuous Control
• +7 more

Mohammadreza Nakhaei is a Doctoral Researcher in the Department of Electrical Engineering and Automation at the School of Electrical Engineering. His work focuses on advanced reinforcement learning techniques and their applications in dynamic environments. Research interests include: Reinforcement Learning Representation Learning Continuous Control Model-Based Reinforcement Learning Dynamic Environments Context Encoding Recent publications highlight trends in: Codebook World Models Entropy Regularization Offline-to-Online Reinforcement Learning Residual Learning

• Data Mining
• Machine Learning
• Artificial Intelligence
• +4 more

Jyrki Nummenmaa is a Professor at the Faculty of Information Technology and Communication Sciences, Department of Computing Sciences at Tampere University. He holds an ORCID ID (0000-0002-7476-7840) and can be contacted via jyrki.nummenmaa@tuni.fi. His research focuses on data mining, machine learning, natural language processing, and database systems. Key research interests include computational methods for analyzing political language, RDF graph processing, ethical AI security systems, and sequential pattern mining. He has collaborated extensively with researchers such as Peltonen J., Zimina E., and Duan L. Recent publications (2022–2024) emphasize interdisciplinary applications like parliamentary discourse analysis and transparent question-answering systems. He has edited conference proceedings and contributed to over 100 peer-reviewed works since 1992. His work spans theoretical advancements (e.g., fair neighbor embeddings, nonparametric graph embeddings) and applied solutions (e.g., bus delay analysis, health-e-living simulations). Notable projects include TraQuLA for RDF querying and PiHVI for forum posting analysis.

• Machine Learning
• Gaussian Processes
• Bayesian Inference
• +3 more

Paul Chang is a Visiting Professor in the Department of Computer Science at Aalto University, associated with the Professorship Solin A. He holds a Doctor of Technology (Tekn. toht.) degree in Computer Science from Aalto University, awarded in December 2024. His research focuses on machine learning, Gaussian processes, Bayesian inference, and sequential learning, with applications in neural networks and optimization. His work contributes to UN Sustainable Development Goals related to education and innovation through advancements in probabilistic modeling and computational methods. Chang's academic background includes a doctoral thesis titled Rethinking Inference in Gaussian Processes: A Dual Parameterization Approach (2024), exploring novel methods for improving efficiency and scalability in Gaussian process models. His research has been published in leading conferences such as ICML and ICLR, with notable contributions to sequential learning algorithms, Bayesian optimization, and memory-based approaches in Gaussian processes. Key research interests include dual parameterization techniques, sparse representations in neural networks, and the integration of simulation-based inference with diffusion models. His work bridges theoretical advancements in machine learning with practical applications in optimization, active learning, and temporal data analysis. Collaborations span international institutions, reflecting his global academic engagement. Chang's publications highlight advancements in function-space parameterization, memory-augmented Gaussian processes, and amortized probabilistic conditioning. These contributions address challenges in scalable Bayesian methods and sequential decision-making systems. His research emphasizes computational efficiency, robustness, and applicability to real-world problems in machine learning.

• Machine Learning
• Neural Networks
• Stochastic Processes
• +3 more

Ella Tamir is a Visiting Professor at the Department of Computer Science , School of Science , University of Helsinki. She concurrently holds a Doctoral Student position. Her research focuses on stochastic processes, neural networks, and sequential learning. Education: Master's degree in Natural Sciences, University of Helsinki (2017) Bachelor's degree in Natural Sciences, University of Helsinki (2016) Research Interests: She explores stochastic differential equations, function-space representations, and diffusion processes. Her work emphasizes applications in neural networks, data generation, and sequential learning. Recent studies involve diffusion generative models for particle smoothing and sparse parameterization techniques. Research Output: Her recent publications address topics like function-space neural networks, diffusion bridges for time series, and scalable inference methods. These contributions highlight advancements in both theoretical and applied machine learning domains. Grants & Advising: No specific grants or advisees are listed in the provided materials. Labs/Teams: No lab affiliations or team collaborations are explicitly mentioned.

• Robotics
• Human-Robot Interaction
• Industrial Automation
• +3 more

Alexandre Angleraud is a Postdoctoral Research Fellow at Tampere University within the Automation Technology and Mechanical Engineering department under the Faculty of Engineering and Natural Sciences. His research focuses on advancing human-robot collaboration, robotic manipulation, and sensor-based systems in industrial and manufacturing environments. Key areas include knowledge-based planning, tactile feedback learning, and cognitive semantics for dynamic task coordination. His work integrates robotics with machine learning techniques, emphasizing agile production systems, soft robotic grippers, and natural language interfaces for hierarchical task instruction. Notable contributions include innovations in multi-label annotation for visual models and exploration-exploitation mechanisms inspired by neural processes. Recent research trends highlight advancements in co-speech gesture interpretation for human-robot interaction, imitation learning frameworks leveraging sequential data, and comparative studies on sim-to-real learning controllers. His studies often bridge theoretical cognitive models with practical industrial automation challenges. No scientific awards or funded grants are explicitly listed in the provided materials. Advising activities and lab affiliations remain unspecified in the current data.

• Machine Learning
• Natural Language Processing
• Data Mining
• +3 more

Jinbin Zhang is a Doctoral Researcher at the Department of Computer Science, Aalto University. Their work intersects machine learning and computational linguistics, focusing on extreme multi-label classification and text analysis. Research Interests : Large Language Models and Zero-shot Learning Extreme Multi-label Classification Historical Text Analysis and Genre Detection Publications : 2025: LLM-based zero-shot tagging 2024: Calibration in extreme multi-label classification 2022: Sequential genre change detection in historical texts

• Machine Learning
• Computer Vision
• Neural Networks
• +3 more

Riccardo Mereu is a Doctoral Researcher affiliated with the Department of Computer Science at Aalto University. His research focuses on machine learning, computer vision, and neural network architectures, particularly addressing challenges in 3D scene reconstruction, sequential learning, and sparse representations. Recent publications highlight his work on uncertainty quantification in deep learning for computer vision tasks, function-space parameterization of neural networks, and sparse modeling techniques for improved computational efficiency. These contributions span conferences like ECCV and ICLR, emphasizing robustness and optimization in visual and sequential data processing. Dr. Mereu's collaborations include researchers from Aalto University, with co-authors such as Arno Solin and Joni Pajarinen. His work bridges theoretical advancements in neural network modeling with practical applications in computer vision and sequential learning frameworks.

• large scale learning
• extreme multi-label classification
• deep learning
• +2 more

Rohit Babbar serves as an Assistant Professor in the Department of Computer Science at Aalto University, Finland, leading a research group dedicated to advancing large-scale machine learning methodologies. His team specializes in tackling computational challenges inherent in extreme classification problems with massive output spaces while ensuring model robustness. His primary research domains encompass large-scale learning systems, extreme multi-label classification architectures, deep learning integration, sequential data processing, and robustness engineering. This work directly addresses industry pain points like computational inefficiency in massive label spaces and model vulnerability to distribution shifts, with applications spanning natural language processing, information retrieval, and recommendation systems. Publication trends reveal a strategic focus on algorithmic innovation for extreme classification, featuring breakthroughs in dynamic sparsity techniques, large language model integration for zero-shot scenarios, and calibration of extreme classifiers. Recent work demonstrates consistent emphasis on computational efficiency through optimized negative sampling, lightweight frameworks like InceptionXML, and specialized metrics for long-tail performance evaluation. Scientific recognition includes: Outstanding Reviewer Award at ACL 2021 Conference (July 2021) for exceptional contributions to computer science peer review As research group leader, Babbar directs collaborative efforts on next-generation classification systems while mentoring emerging scholars in machine learning. His team maintains active partnerships with industry leaders in search and recommendation technologies. The research group operates at the intersection of theoretical machine learning and practical deployment, developing frameworks that balance computational feasibility with predictive accuracy in extreme-scale environments. Current initiatives focus on integrating foundation models with specialized classification architectures while addressing real-world challenges like data sparsity and concept drift.