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Tarja KokkolaView profile

Researcher

• Neurodegenerative Diseases
• Alzheimer's Disease Biomarkers
• Type 2 Diabetes
• +6 more

Tarja Kokkola, PhD, docent, serves as Research Coordinator at the Institute of Clinical Medicine within the School of Medicine at the University of Eastern Finland. She manages quality systems for the Clinical Research Centre, including two ISO 15189-accredited diagnostic laboratories, and contributes to development, validation, and quality assurance of clinical chemistry methodologies. Her research spans multiple critical areas in biomedical science, with particular emphasis on biomarkers for neurodegenerative diseases (especially Alzheimer's disease), type 2 diabetes mechanisms, and sirtuin-related pathways. She has developed expertise in cerebrospinal fluid diagnostics, metabolic phenotyping, and multi-omics integration approaches. Her work bridges clinical laboratory practice with translational research through extensive collaboration in major European research initiatives. Analysis of her 15 most recent publications (2023-2025) reveals a strong focus on Alzheimer's disease biomarkers (6 articles), type 2 diabetes mechanisms (5 articles), and methodological innovations in multi-omics data analysis (4 articles). Her research demonstrates consistent methodological rigor with emphasis on clinical applicability, particularly in differential diagnosis of neurodegenerative conditions and stratification of metabolic disease subtypes. As an active collaborator in the IMI DIRECT consortium and FinnGen project, she contributes to large-scale European efforts to understand disease heterogeneity and develop precision medicine approaches. Her publications consistently appear in high-impact journals across neurology, diabetes research, and analytical methodology fields. Dr. Kokkola also provides lectures and training in quality management, auditing, and risk assessment, supporting the next generation of clinical researchers. Her leadership in maintaining ISO 15189 accreditation for diagnostic laboratories demonstrates commitment to research quality standards essential for translational medicine.

• Computer Science
• Machine Learning
• Differential Privacy
• +2 more

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.

• Machine Learning
• Artificial Intelligence
• Statistics
• +3 more

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.

• Internet of Things
• 6G Software Architecture
• Machine Learning Systems
• +5 more

Abhishek Kumar is a Researcher at the Center for Ubiquitous Computing, part of the Faculty of Information Technology and Electrical Engineering at the University of Oulu. He holds a PhD from the Department of Computer Science at the University of Helsinki (2018–2022). His research focuses on software-hardware co-integration in areas such as IoT, 6G, machine learning systems, energy efficiency, and environmental sensing, with a strong emphasis on trustworthy computing and UN Sustainable Development Goals (SDGs) like clean energy and sustainable cities. Teaching responsibilities include courses such as 521290S Distributed Systems (5 ECTS, 2025), High Performance Computing: Programming Parallel Supercomputers (3 ECTS, 2025), and Network Analysis and Graph Machine Learning (2 ECTS, 2025). Past courses include IoT and distributed systems modules from 2022 onward. He actively supervises 1 PhD student, 11 Master’s thesis students, and 4 independent studies, with prior supervision of 3 Bachelor’s, 4 Master’s, and 10 Advanced Practical Training students. Research collaborations span VTT, Ericsson Research, Alif Semiconductor, Aalto University, and University of Helsinki. He advises Soundxvision Oy, a Helsinki XR Center startup developing input modalities for XR headsets. Funding sources include the Finnish-American Research & Innovation Accelerator (FARIA), Indian-Finnish Consortia (FICORE), Nokia Foundation, and Walter Ahlström Foundation. His work bridges IoT, 6G, and AI to address environmental and societal challenges, leveraging software-defined systems and distributed computing paradigms. Recent publications explore metaverse integration, edge AI, and cybersecurity in emerging networks.

• Bayesian Statistics
• Probabilistic Programming
• Machine Learning
• +4 more

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.

• Probabilistic Machine Learning
• Causal Inference
• Optimization Tasks
• +3 more

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.

Samuel KaskiView profile

Professor

• Machine Learning
• Probabilistic Modelling
• Bayesian Inference
• +5 more

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.

• Reinforcement Learning
• Neural Networks
• Sequential Learning
• +11 more

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.

• Pharmacology
• Drug Response Prediction
• Machine Learning in Healthcare
• +3 more

Betul Güvenç Paltun is a researcher and doctoral student at the School of Science, focusing on computational approaches in pharmacology and drug response prediction. Her work integrates machine learning with biological data to advance precision medicine and drug development. Education: Master's degree in Engineering and Technology from Bogazici University (2016). Research interests include drug combination therapies, integrative data analysis, and applying Bayesian methods to predict drug responses. Her publications emphasize machine learning frameworks for optimizing drug treatments and understanding cellular responses to therapeutics.

Claudio RoncoliView profile

Visiting Professor

• Transportation Engineering
• Traffic Control Systems
• Urban Planning
• +3 more

Claudio Roncoli is a Visiting Professor in the Department of Built Environment at Aalto University, specializing in Transportation Engineering and Traffic Control Systems. His research focuses on urban mobility optimization, traffic simulation, and sustainable transportation solutions. He holds a Doctoral degree from Università degli Studi di Genova (2013). **Education**: PhD in Other (2013), Università degli Studi di Genova. **Research Interests**: His work addresses challenges in traffic management, emission reduction, and smart urban systems. Key areas include dynamic traffic modeling, autonomous vehicle integration, and data-driven policy design aligned with UN Sustainable Development Goals. **Projects**: Leads major initiatives like the ACUMEN Horizon (AI-driven traffic management) and SMARTEDGE (low-code programming for edge intelligence). These projects aim to enhance urban mobility efficiency, safety, and sustainability through advanced technologies. **Awards**: Recipient of the Best Paper Award (2023) at an international conference. **Grants & Activities**: Principal investigator on EU-funded projects (e.g., Horizon Europe). Engages in conferences, workshops, and media outreach, including public talks on 'What's modeling got to do with planning?' (2023) and media features in outlets like Environmental Research Letters . **Collaborations**: Active in international networks, contributing to interdisciplinary studies on emissions, vehicle trajectory reconstruction, and urban data analysis. Supervised 5 theses, advancing next-gen transport solutions.

• Structural Engineering
• Civil Engineering
• Finite Element Analysis
• +3 more

Youqi Zhang is a Visiting Professor in the Department of Civil Engineering at Aalto University, leading the SHM-TWIN project on bridge state awareness. Their expertise includes structural health monitoring, finite element analysis, and computer vision applications in civil engineering. Zhang holds a Doctoral degree in Engineering and Technology from Kitami Institute of Technology (2019). Research focuses on damage detection in bridges and tunnels, finite element modeling, and digital twin technologies. Key contributions include integrating computer vision with phase field damage analysis and developing real-time structural monitoring systems. Over 27 publications (2016–2025) span topics like seismic event detection, 3D geometry reconstruction, and machine learning-based damage identification. Education: Doctoral degree, Engineering and Technology, Kitami Institute of Technology (2019) Awards: 3rd Best Performance Award (2017) Excellent Presentation Awards (2016, 2017) Japanese Government Scholarship (2016–2019) Mint Award (2018) Grants include the SHM-TWIN project (2021–2024), focusing on bridge structural awareness systems. Media coverage highlights advancements in cloud computing for infrastructure models and vibration-based monitoring techniques. Collaboration networks span global institutions, emphasizing structural dynamics and computational methods.

Ville HautamäkiView profile

Associate Professor

• Data Science
• Statistical Inference
• Deep Learning
• +3 more

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.

Lasse LaursonView profile

Professor

• Domain Walls
• Dislocation Dynamics
• Barkhausen Noise
• +3 more

Lasse Laurson is a Professor in the field of Physics, specializing in interdisciplinary research at the intersection of condensed matter physics, materials science, and computational modeling. His work focuses on understanding complex phenomena such as domain wall dynamics in ferromagnetic materials, dislocation interactions in crystalline solids, and the application of machine learning to predict material behavior. He actively collaborates on projects involving advanced microscopy techniques like Lorentz microscopy and micromagnetic simulations to study magnetic noise phenomena and their underlying mechanisms. Research Interests: Laurson's core interests include domain wall interactions with dislocations, depinning transitions in elastic interfaces, Barkhausen noise analysis, and developing predictive models for plastic deformation using machine learning. His recent work explores Bayesian optimization in alloy design and the critical behavior of avalanche distributions in disordered systems. Key Research Trends: His publications from 2023-2025 reveal a strong focus on combining experimental and computational methods to study magnetic materials and crystal plasticity. He has made significant contributions to understanding how disorder influences domain wall motion and how machine learning can improve predictive capabilities in materials science. Recent efforts emphasize optimizing material properties via advanced simulation techniques and analyzing deformation mechanisms at nanoscale. Collaborations: Laurson collaborates internationally, with notable work involving researchers from Serbia (Spasojević, Marinković) and Finland (Kaappa, Vippola). His research group has developed novel methodologies for studying magnetic noise in thin films and dislocation dynamics in polycrystalline systems. Labs/Teams: While not explicitly named, his research activities suggest involvement in advanced microscopy facilities and computational materials science teams focusing on magnetic and structural analysis.

• Simultaneous Localization and Mapping
• Millimeter Wave Engineering
• Received Signal Strength
• +3 more

Ossi Kaltiokallio is a Senior Research Fellow in Electrical Engineering, specializing in cutting-edge research at the intersection of robotics, wireless communications, and signal processing. His work primarily focuses on Simultaneous Localization and Mapping (SLAM) , millimeter-wave technology, and device-free localization frameworks. Collaborations with leading researchers across institutions highlight his contributions to advancing wireless sensing methodologies. Expertise in SLAM algorithms and bistatic radio applications Pioneering research in double-bounce signal utilization for obstructed environments Recipient of the prestigious Jean-Pierre Le Cadre Award (2021) His recent publications in IEEE Transactions on Robotics and EuCAP conferences demonstrate innovative approaches to multi-hypotheses filtering, graph-based optimization, and Gaussian process modeling. These works align with advancements in wireless sensing for robotics and IoT applications. 2025: Key contributions to batch SLAM frameworks and bistatic mapping techniques 2024: Development of multi-hypotheses importance densities and double-bounce signal analysis in mmWave systems Ossi's research also extends to creating open datasets for 60 GHz indoor sensing, ensuring reproducibility and community-driven progress in wireless localization technologies. Scientific Awards Jean-Pierre Le Cadre Award (2021) - Recognized for excellence in radio SLAM research

• 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 .