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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.

• Bayesian Statistics
• Machine Learning
• Statistical Modeling
• +3 more

Paul-Christian Burkner is a researcher in the Department of Computer Science at Aalto University. His work focuses on Bayesian statistical methods, computational modeling, and probabilistic programming. He collaborates with Professor Aki Vehtari's research group and has published extensively on topics like model sensitivity, spatiotemporal analysis, and variable selection techniques. His research interests include: Bayesian inference and model comparison Computational statistics Probabilistic programming Machine learning algorithms Statistical modeling in social sciences Neuroimaging data analysis Recent publications demonstrate expertise in simulation-based calibration, spatiotemporal modeling, and Gaussian process approximations. Collaborations span psychology, neuroscience, and machine learning domains. Contact: ext-paul-christian.burkner@aalto.fi

• Machine Learning
• Artificial Intelligence
• Neural Networks
• +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.

• Bayesian Statistics
• Reliability Engineering
• Risk Analysis
• +4 more

Ahmad BahooToroody is an Academy Research Fellow at Aalto University’s Department of Energy and Mechanical Engineering, specializing in Bayesian statistics, reliability engineering, and risk analysis for autonomous maritime systems. He is actively involved with the Marine and Arctic Technology research group and leads projects that integrate machine learning with safety-critical applications in the maritime and offshore sectors. Research Interests Bayesian statistical methods for reliability and risk modeling Machine learning and deep learning for anomaly detection in autonomous systems Safety assessment of offshore installations and maritime operations Prognostic health management of marine renewable energy systems Human factors and expert judgment in sociotechnical maritime systems His work often incorporates advanced techniques such as Gaussian processes, LSTM-based neural networks, and dynamic Bayesian networks to address uncertainties in complex engineering systems operating in harsh marine environments. Publication Trends Across 2022–2025, BahooToroody’s publications reveal a strong trajectory toward integrating data-driven models with physics-based simulations. Dominant themes include real-time risk monitoring of autonomous ships, failure prognosis for unattended machinery, and safety assessment frameworks for offshore structures. His collaborative outputs span high-impact journals such as Reliability Engineering & System Safety and Safety Science , underscoring his leadership in maritime safety analytics. Research Groups & Labs Marine and Arctic Technology Research Group, Aalto University Academy Research Fellow network within the Department of Energy and Mechanical Engineering Doctoral Supervision & Grants While specific grant details are not listed, his role as Doctoral Candidate Supervisor since 2020 indicates active mentorship of PhD researchers. He is also the Principal Investigator on projects funded under the Academy of Finland Fellowship scheme, supporting next-generation maritime risk analytics.

• DFT (Density Functional Theory)
• SPM (Scanning Probe Microscopy)
• Surface Science
• +6 more

Ondrej Krejci is a researcher in the Department of Applied Physics at Aalto University, specializing in computational and theoretical approaches to surface science, scanning probe microscopy, and materials discovery. His work bridges advanced simulation techniques with experimental validation. Research interests include: Density Functional Theory (DFT) Scanning Probe Microscopy (SPM) simulations Molecular adsorption on surfaces X-ray spectroscopy simulations Condensed matter physics Atomic and molecular physics Recent publications highlight trends in machine learning for catalyst discovery, probe-particle model innovations, and on-surface synthesis of novel carbon allotropes and frameworks. Collaborative work spans computational modeling, experimental validation, and instrumentation development.

Ayush BhartiView profile

Research Fellow

• Probabilistic Machine Learning
• Bayesian Statistics
• Simulation-based Inference
• +3 more

Ayush Bharti is an Academy Research Fellow (Research Fellow) in the Department of Computer Science at Aalto University, Finland. His primary affiliations include the Probabilistic Machine Learning research group and the Academy Professorship led by Samuel Kaski . His research centers on probabilistic machine learning with dual focus areas: advancing simulation-based inference methodologies and applying statistical techniques to stochastic radio channel modeling. Key interests include Bayesian statistics, robustness under model misspecification, handling missing data, and developing efficient approximate Bayesian computation frameworks. His work bridges theoretical machine learning with practical wireless communication challenges. Analysis of his 15 most recent publications (2021-2025) reveals a consistent trajectory in simulation-based inference innovation, featuring neural processes, diffusion models, and multilevel architectures. Approximately 60% of his work targets general statistical challenges (e.g., robustness, cost-awareness), while 40% addresses radio channel modeling applications. Notable trends include integration of domain expertise in inference loops and development of context-aware optimization techniques. No information is available regarding student supervision or research grants. Bharti operates within Aalto University's Probabilistic Machine Learning ecosystem, contributing to the Academy Professorship project under Samuel Kaski. This collaborative environment focuses on developing scalable inference algorithms for complex real-world systems, with particular emphasis on uncertainty quantification in simulation-driven scientific discovery.

Sammie KattView profile

Researcher

• Bayesian Reinforcement Learning
• Partially Observable Markov Decision Processes
• Robotics
• +2 more

Sammie Katt serves as a Postdoctoral Researcher in the Department of Computer Science at Aalto University's School of Science, specializing in Bayesian Reinforcement Learning for robotics and decision-making under uncertainty. Their work addresses critical challenges in partially observable environments through algorithmic innovation and practical implementations. Dr. Katt's research centers on Bayesian approaches to reinforcement learning, with deep expertise in Partially Observable Markov Decision Processes (POMDPs). They develop scalable algorithms for uncertainty quantification, robot navigation, and real-time decision-making, bridging theoretical advances with robotic applications. Key contributions include BADDr for adaptive POMDP solutions and gym-gridverse for simulation benchmarking. Analysis of Katt's 14 publications (2012-2023) reveals three dominant trends: (1) Bayesian methods for efficient POMDP solving using Monte Carlo tree search and deep learning, (2) Robotics applications in motion prediction, target search, and scene reconstruction, and (3) Framework development for reproducible RL research. Their work consistently emphasizes computational efficiency and real-world applicability in uncertain environments.

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

Amauri Holanda De Souza Junior is a Postdoctoral Researcher affiliated with the Department of Computer Science , focusing on Probabilistic Machine Learning . His work bridges theoretical advancements with practical applications in graph-based models. Active research areas include Graph Neural Networks , Persistent Homology , and Simulation-based Inference . His recent publications highlight innovations in: Topological data analysis for graph representations Robust statistical methods under model misspecification Scalable Bayesian inference frameworks Equivariant architectures for graph learning

Otso OvaskainenView profile

Professor

• Ecological Modeling
• Metapopulation Biology
• Movement Ecology
• +4 more

Professor Otso Ovaskainen holds a permanent position at the University of Helsinki , where he serves as Research Director in the Organismal and Evolutionary Biology Research Program. He is also a Visiting Professor at a Norwegian Centre of Excellence since 2014. Supervisor in the Doctoral Programme in Wildlife Biology Established the Research Center for Ecological Change (REC) with 60+ members ERC Starting Grant recipient (2008-2013) Research Interests: Ovaskainen bridges mathematical theory with empirical ecology, focusing on metapopulation dynamics, movement ecology, and statistical community ecology. His work integrates: Ecological theory with data Individual-based modeling Fungal community dynamics Probabilistic taxonomic placement Environmental DNA analysis Multi-species movement modeling Scientific Leadership: He has led major projects including the Finnish Centre of Excellence in Metapopulation Research (2015-2017) and the LIFEPLAN biodiversity inventory project (2020-2026). His HMSC modeling framework remains a benchmark in joint species distribution modeling.

Petri YlikoskiView profile

Professor

• Philosophy of Social Sciences
• Sociology
• Social Mechanisms
• +3 more

Petri Ylikoski is a Professor in Science and Technology Studies (Sociology) at the University of Helsinki's Department of Social Research and a Visiting Professor at Linköping University's Institute for Analytical Sociology. His research integrates philosophy, sociology, and computational methods to address foundational questions in social science, particularly focusing on mechanisms of explanation, micro-macro relations, and methodological individualism. His work spans several interconnected domains: Philosophy of Social Sciences : Theoretical frameworks for social explanation, causality, and model-based reasoning. Social Mechanisms : Analysis of causal processes in social phenomena, from norms to institutions. Interdisciplinary Methodologies : Agent-based simulation, machine learning applications, and epistemology of modeling. Ylikoski's publications consistently explore the interplay between individual agency and social structures, with emphases on mechanistic explanations, theoretical models, and critiques of reductionism. Recent trends include collaborations bridging philosophy with computational social science, such as studies on AI interpretation and agent-based simulations of social dynamics. No awards, grants, or student advising details are documented in available sources. Similarly, specific labs or teams are not mentioned, though his collaborative projects (e.g., with Juho Pääkkönen and Jaakko Kuorikoski) highlight a network-oriented research approach.

• Machine Learning
• Computational Data Analysis
• Neural Networks
• +3 more

Yogesh Verma is a doctoral researcher at Aalto University's Department of Computer Science, specializing in machine learning and computational data analysis. His work bridges theoretical advancements with practical applications in molecular generation, climate forecasting, and topological modeling. Areas of expertise: Computer and information sciences, Computational data analysis Research interests: Focus on physics-informed neural ODEs for climate modeling, graph generation with diffusion models, topological neural networks, and ab initio antibody design. His interdisciplinary approach connects machine learning with computational biology and scientific computing. Scientific awards: Nokia Scholarship for doctoral studies in ICT-related fields Key publications appear in leading venues like ICLR and NeurIPS, spanning topics from molecular design to climate forecasting. Collaborators include Vikas Garg, Markus Heinonen, and Giangiacomo Mercatali.

• Data Mining
• Machine Learning
• Temporal Networks
• +3 more

Nikolaj Tatti is a Visiting Professor at Aalto University's Department of Computer Science , affiliated with the Helsinki Institute for Information Technology (HIIT) and the Adj. Prof. Gionis Aris research group . His work focuses on algorithmic analysis of temporal networks and graph theory. Research Interests: Temporal network analysis Graph decomposition and density Segmentation algorithms Pattern discovery in time-series data Interactive data analysis frameworks Publication Trends (2018-2021): Research spans Temporal Networks (dynamic hierarchies, cascade reconstruction), Graph Theory (density, decomposition), and Data Mining (periodic patterns, event detection). Collaborative work with Aris Gionis and Polina Rozenshtein dominates his publications. Technical Collaborations : Works with multidisciplinary teams including researchers from ACM Transactions on Knowledge Discovery and IEEE conferences.

• Materials Science
• Machine Learning Potentials
• MXenes
• +3 more

Rina Ibragimova is a Postdoctoral Researcher at Aalto University, affiliated with the DAS Group in the Chemistry and Materials domain. Her research focuses on computational materials science, particularly the development and application of machine learning interatomic potentials for modeling hydrocarbons and MXenes. Her work spans surface functionalization, defect analysis, and electronic properties of 2D materials. Recent publications highlight collaborations with leading researchers in the field, emphasizing experiment-driven atomistic modeling and theoretical studies of MXene stability and structure. Key trends in her research include combining spectroscopic data (XPS) with machine learning for precise structure inference, investigating pH-dependent functionalization in MXenes, and exploring electron beam effects on 2D materials. Her publications appear in high-impact journals like ACS Nano , Nature Communications , and Physical Review B .

• 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.

• Optimization Algorithms
• Additive Manufacturing
• Superconductivity
• +3 more

Di Wu is a Research Fellow at the Department of Automation Technology and Mechanical Engineering , part of the Faculty of Engineering and Natural Sciences at Tampere University. Their work focuses on advanced manufacturing technologies, optimization algorithms, and materials science, with significant contributions to thermal modeling in additive manufacturing and superconductivity research. Affiliations : Tampere University (since 2019) Research Interests : Specializes in multi-objective optimization using AI methods, additive manufacturing process modeling, thermal field prediction in metal AM, and superconductivity applications. Their work integrates knowledge-based systems and causal modeling to enhance engineering design processes. Key Research Trends : Recent publications emphasize real-time thermal management in thin-wall metal additive manufacturing, ANN-based optimization for HTS solenoids, and AI-driven approaches to superconductivity research. They also explore functional modeling and dimensional analysis in welding and micro-turbine systems. Collaborations : Active in international collaborations with institutions like American Society of Mechanical Engineers and Springer, focusing on advanced manufacturing and material science challenges.