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Simo SärkkäView profile

Professor

• Machine Learning
• Signal Processing
• Bayesian Filtering
• +7 more

Professor Simo Särkkä holds a position in Sensor Informatics and Medical Technology at the Department of Electrical Engineering and Automation (EEA), Aalto University. His research focuses on multi-sensor data processing, Bayesian filtering, machine learning, and their applications in medical technology, brain imaging, and inverse problems. He leads research groups including the Helsinki Institute for Information Technology (HIIT) and Sensor Informatics and Medical Technology. His work bridges theoretical advancements in probabilistic methods with practical implementations in healthcare and engineering. Key research interests include Gaussian processes, stochastic differential equations, quantum machine learning, and signal processing. He has contributed to advancements in algorithms for nonlinear state-space models, parallel computing techniques, and medical imaging technologies such as scatter correction in CT scans. His methodologies are applied across domains like autonomous systems, robotics, and bioengineering. Notable publications span topics like quantum-assisted Gaussian regression, physics-informed machine learning for industrial processes, and parallel-in-time numerical methods. His work emphasizes computational efficiency and robustness in high-dimensional and real-time systems.

Laura EloView profile

Professor

• Computational Biomedicine
• Medical Bioinformatics
• Data Science
• +7 more

Laura Elo serves as Professor of Computational Medicine and Head of the Medical Bioinformatics Centre at the University of Turku, Finland. She concurrently holds the position of Research Director at Turku Bioscience Centre and acts as InFLAMES Flagship Contact, driving interdisciplinary biomedical research initiatives. Her academic foundation includes a PhD in Applied Mathematics (2007) and Adjunct Professorship in Biomathematics (2011), establishing her quantitative expertise before transitioning into biomedical applications. Her research program focuses on transforming molecular and clinical datasets through statistical modeling and advanced machine learning . Key thrusts include robust computational tools for proteome/epigenome analysis, AI-driven digital health diagnostics, and computational systems immunology for immune-mediated diseases. This work directly addresses challenges in reproducibility and scalability of high-throughput biotechnology data. Analysis of her recent publications reveals dominant themes in type 1 diabetes biomarker discovery , multi-omics integration , and immune system modeling , with strong emphasis on clinical translation through collaborations with experimental and medical teams. Her scientific recognition includes: JDRF Career Development Award Professor Elo actively trains MSc/PhD students and postdoctoral fellows while leading major research initiatives including ERC grants. Her teaching portfolio spans Bioinformatics Journal Club, AI in Diagnostics, and Systems Biology courses. The Elo Lab (https://elolab.utu.fi) operates as a hub for computational biomedicine, developing open-source tools like CellRomeR while maintaining close ties with Turku Bioscience Centre's experimental facilities for validating computational predictions in immunology and metabolic disease contexts.

Antti HonkelaView profile

Professor

• Privacy-preserving machine learning
• Differential Privacy
• Bayesian Machine Learning
• +3 more

Antti Honkela is a Professor of Data Science at the University of Helsinki's Department of Computer Science, within the Faculty of Science. He also serves as the Coordinating Professor for the Privacy-preserving and Secure AI Research Programme at the Finnish Center for Artificial Intelligence (FCAI), and as Deputy Director of the Master's Programme in Data Science. His roles include membership in the Health and Social Data Permit Authority (Findata) and as an Action Editor for Transactions on Machine Learning Research. Honkela's research focuses on privacy-preserving machine learning, differential privacy, Bayesian methods, and their applications in computational biology and healthcare. He leads projects such as the European Lighthouse in Secure and Safe AI (ELSA) and the Data Literacy for Responsible Decision-making initiative. His work emphasizes developing robust frameworks for privacy-aware AI, including differentially private synthetic data and federated learning. Honkela has advised numerous PhD and Master's students, and his contributions span theoretical advancements and practical implementations, such as the D3p Python package for differentially private probabilistic programming. Key contributions include advancements in Bayesian inference from synthetic data, privacy accounting mechanisms, and computational methods for genomic epidemiology. His interdisciplinary approach bridges machine learning, statistics, and healthcare, addressing challenges in data privacy and secure AI deployment.

Masood MasoodianView profile

Associate Professor

• Interactive Visualization
• Design Thinking
• Health Informatics
• +3 more

Masood Masoodian is an Associate Professor in the Department of Art and Media at Aalto University, Finland. He leads the Visual Communication Design research group, focusing on interactive visualization for health, energy, and sustainability contexts. Previously, he held roles at the University of Waikato (2000-2016), University of Southern Denmark, and Massey University. Education: Doctoral degree in Other disciplines from the University of Waikato (1999) Research interests include design thinking, visualization of complex data, and creative aging interventions. Notable projects include the EU-funded INT-ACT initiative (2024-2026) addressing intangible cultural heritage. He has received an award for collaborative work on video game ludonarrative analysis (2019). Recent activities include organizing workshops on map-based interfaces, co-creating cultural heritage methods, and delivering public talks on digital design. Supervised two theses and contributed to 131 peer-reviewed outputs, emphasizing human-centered design and sustainability. Grants: Principal investigator for multiple EU projects totaling over 3 years of active funding. Awards: Prize for 'Comedy in the Ludonarrative of Video Games' (2019). Labs/Teams: Visual Communication Design group, collaborating internationally on projects like INT-ACT's cultural heritage mapping.

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

• Digital Design and Manufacturing (D2M)
• Sustainable Manufacturing
• Optimization Driven Design
• +7 more

Inigo Flores Ituarte is a Research Professor at Tampere University's Faculty of Engineering and Natural Sciences, affiliated with the Automation Technology and Mechanical Engineering department. He leads the Digital Design and Manufacturing (D2M) research lab, focusing on sustainable manufacturing and twin-transition strategies integrating digital and green technologies. His work emphasizes optimization-driven design, additive manufacturing innovations, and AI-driven expert systems to enhance energy efficiency and reduce environmental impacts. Key research pillars include: Pillar 1: Twin-transition in Engineering Design and Manufacturing Processes, addressing sustainable manufacturing and intelligent systems Pillar 2: Development of open D2M systems and Process-Structure-Property-Performance (PSPP) linkages in advanced materials His research explores multi-disciplinary optimization combining model-based simulations and data-driven techniques. Notable contributions include generative AI integration in CAD systems, cognitive manufacturing systems, and cost-effective process monitoring using CNN-based methods. Inigo's work emphasizes environmental sustainability, with a focus on reducing manufacturing's energy consumption (54% of global use) and CO2 emissions. He advocates for interconnected material systems, smart manufacturing processes, and AI-assisted decision-making to achieve cognitive intelligence in industrial operations. His D2M lab's overarching goal is to maximize product/process performance while improving cost-effectiveness and minimizing environmental footprints. Recent projects include railway bogie demonstrators via multi-material deposition and sensor systems leveraging IoT and ChatGPT integration.

Kimmo VehkalahtiView profile

Senior Lecturer

• Social Data Science
• Social Statistics
• Multivariate Methods
• +3 more

Kimmo Vehkalahti is a Senior University Lecturer and docent at the University of Helsinki, specifically affiliated with the Centre for Social Data Science within the Faculty of Social Sciences. He serves as a Teachers' Academy Supervisor in the doctoral program and maintains an active research profile with numerous publications and projects. His research interests focus on social data science, social statistics, multivariate methods, open data science, and statistical data processing using R programming. Vehkalahti has developed expertise in applying statistical methods to social science research questions and data visualization. His recent publications demonstrate a strong focus on multivariate analysis applications in behavioral sciences, social data visualization, and student well-being research. The publications span diverse areas including statistical methodology, data visualization techniques, and educational interventions. Fellow of the Teachers' Academy (2013) Best Teacher of the City: Kaupungin Paras Opettaja (2010) Good Teacher: Hyvä Opettaja (2009) Magister Bonus - Good Teacher (2003) One of the best teachers of Open University (2017) Vehkalahti actively supervises academic work with 40 thesis supervisions and 10 PhD supervisions documented. He participates in significant research projects including SASE Stress as strength in education (since 2022) and WELLS - Promoting university students' well-being and life-long learning (since 2019). His academic service includes conference participation, peer review, and organizational roles in international statistical organizations.

Heikki RemesView profile

Associate Professor

• Marine Engineering
• Structural Engineering
• Fatigue and Fracture Mechanics
• +3 more

Heikki Remes serves as Associate Professor in the Department of Energy and Mechanical Engineering at Aalto University's School of Engineering, where he investigates high-performance steel structures for marine environments with emphasis on lightweight ship designs using advanced materials and manufacturing techniques. His research integrates fundamental fatigue and fracture mechanics with practical structural challenges, spanning from crystal-level material behavior to continuum-scale modeling. Key focus areas include welded joint integrity, additive manufacturing defects, and computational analysis of marine structures under extreme conditions. Recent publications reveal strong trends in fatigue assessment methodologies for complex welded geometries, experimental validation of distortion effects, and AI-enhanced damage prediction systems, reflecting his commitment to bridging theoretical mechanics with shipbuilding applications. Scientific Awards: Aalto Education Impact Award (2018) for establishing Marine Technology study programs SNAME Honorable Mention for 2018 Vice Admiral E. L. Cochrane Award Teaching Award of Aalto School of Engineering (2012) for educational tools No specific student advising or grant information appears in available sources, though his active publication record indicates ongoing research leadership. He contributes significantly to the Marine and Arctic Technology research group, driving projects on structural integrity assessment and advanced manufacturing solutions for next-generation marine vessels.

Garg VikasView profile

Assistant Professor

• Quantum Computing
• Artificial Intelligence
• Machine Learning
• +8 more

Professor Garg Vikas holds the position of Assistant Professor in the Department of Computer Science at the School of Science. His research spans quantum computing, artificial intelligence, and machine learning with applications in computational biology, healthcare, and drug design. He leads the HEALED/Garg project focused on human-steered machine learning for drug discovery and collaborates with institutions like MIT and industry partners. He co-founded YaiYai Oy, providing AI/ML solutions to global sectors. Education: PhD from MIT CSAIL under Tommi Jaakkola, with postdoctoral and industry experience at Amazon, Microsoft, and IBM. His work aligns with UN SDGs, particularly in healthcare and sustainable energy. Research interests include graph neural networks, generative models, and quantum AI. Recent projects involve climate modeling via physics-informed neural ODEs and optimizing quantum circuits using graph autoencoders. Key collaborations include MIT’s MLPDS Consortium and the Finnish Center for Artificial Intelligence. He supervises doctoral researchers like Yogesh Verma and postdocs such as Kogkalidis.

Sheraz AhmedView profile

Associate Professor

• International Finance
• Asset Pricing
• Factor Investing
• +10 more

Sheraz Ahmed is an Associate Professor (Tenure Track) at LUT Business School, Lappeenranta University of Technology, Finland, where he also serves as Academic Director of the Master’s Degree Program in Strategic Finance and Analytics. He holds a Doctor of Science in Financial Economics from Hanken School of Economics and has been affiliated with LUT since 2011. Doctor of Science, Financial Economics and Economic Statistics, Hanken School of Economics (2004–2009) Master of Science, Computational Finance, Hanken School of Economics (2001–2003) His research focuses on international finance, asset pricing, factor investing, portfolio management, high-frequency trading, corporate governance, and emerging markets . He employs advanced econometric techniques to analyze financial market behavior, market efficiency, liquidity risk, and the impact of governance reforms. His work frequently appears in journals covering finance, accounting, and economics. His recent publications explore topics such as the adaptive market hypothesis in cryptocurrencies, dynamic dependence between ETFs and crude oil, liquidity risk pricing, and financial integration in CIVETS and African markets. His research demonstrates a consistent focus on empirical analysis of financial markets in transitional and emerging economies. Sheraz Ahmed is an active peer reviewer for journals including Applied Economics, Journal of Economic Surveys, and Journal of Forecasting . Member, European Finance Association (2014–present) He has supervised over 80 master’s theses and multiple doctoral students in finance and analytics. He teaches financial theory, valuation of financial securities, and empirical research in finance at the master’s level. He is affiliated with the Department of Finance and Statistics within the School of Business and Management at LUT.

• Plant Genetics
• Berry Crop Breeding
• Molecular Biology
• +5 more

Timo Hytönen is a Professor in the Department of Agricultural Sciences at the University of Helsinki's Faculty of Agriculture and Forestry. He holds a Docentship in the same department and is an integral member of the Viikki Plant Science Centre (ViPS). His academic leadership extends to supervising doctoral students across three major programs: Sustainable Use of Renewable Natural Resources, Integrative Life Science, and Plant Sciences. Additionally, he maintains an external position as Principal Research Scientist at NIAB EMR in Genetic Genomics & Breeding since November 2018. Professor Hytönen's research interests span plant genetics, molecular biology, and crop production sciences with a specialized focus on berry crops, particularly strawberries. His work investigates fundamental plant processes including flowering time regulation, environmental adaptation mechanisms, fruit development, and disease resistance. His research integrates genomic, transcriptomic, and epigenetic approaches to understand how plants respond and adapt to environmental challenges. His extensive publication record (88 publications) reveals a strong focus on woodland strawberry (Fragaria vesca) as a model system for studying economically important traits in berry crops. Recent work demonstrates expertise in QTL mapping, genome-wide association studies, circadian rhythm adaptation, and the genetic basis of climate adaptation. His research shows consistent emphasis on translating basic science into practical applications for crop improvement. Professor Hytönen leads significant research projects including 'Mansikan vihreä vallankumous' (Strawberry Green Revolution) funded by the Academy of Finland and 'ASTROS: How meristems guide plant reproductive development' supported by the Jane and Aatos Erkko Foundation. He is also a Principal Investigator in the Viikki Plant Science Centre (ViPS), a major collaborative research infrastructure. His academic service includes extensive peer review activities (37 manuscript reviews), hosting academic visitors (32 instances), and participation in numerous conferences and seminars. He has provided guidance to graduate students and supervised doctoral candidates, contributing significantly to the training of next-generation plant scientists.

Jarno VanhataloView profile

Professor

• Statistics
• Probability
• Ecology
• +6 more

Professor Jarno Vanhatalo is a Professor of Statistics at the University of Helsinki, serving as vice director of the Research Center for Ecological Change. He is affiliated with both the Faculty of Biological and Environmental Sciences and the Faculty of Science, where he leads the Environmental and Ecological Statistics Group. His work bridges advanced statistical methodology with pressing ecological and environmental challenges. His research focuses on the development and application of statistical models for ecological and environmental data. Key areas include: Bayesian statistics and hierarchical modeling Gaussian processes and spatial statistics Species distribution modeling Ecological risk assessment Climate change impact analysis Biodiversity monitoring and conservation Professor Vanhatalo's publication record shows a consistent focus on integrating sophisticated statistical methods with ecological applications. His recent work demonstrates increasing emphasis on climate change impacts on biodiversity, spatially explicit modeling of ecological processes, and the development of Bayesian methods for uncertainty quantification in environmental predictions. His research spans terrestrial and marine ecosystems, with particular attention to Nordic and Baltic regions. Among his notable contributions are advancements in: Joint species distribution modeling Bayesian calibration of environmental models Spatiotemporal survey design Model-based variance partitioning in ecological studies Statistical approaches to Arctic risk management Professor Vanhatalo has received significant research funding, including an ERC grant for "Predective Understanding of the effects" (2024-2029) and leadership roles in multiple Academy of Finland and EU-funded projects focused on biodiversity, ecological change, and risk management in polar waters. He actively contributes to the academic community through: Supervising doctoral students in Wildlife Biology and Mathematics and Statistics programs Serving on editorial boards for journals including Conservation Biology and Ecology Letters Peer reviewing for numerous statistical and ecological journals Organizing workshops on statistical methods in ecology

Aleksi LehikoinenView profile

Associate Professor

• Ornithology
• Population Monitoring
• Climate Change Ecology
• +4 more

Aleksi Lehikoinen is an Associate Professor at the University of Helsinki's Faculty of Biological and Environmental Sciences, holding a docentship and serving as Supervisor in the Doctoral Programme in Sustainable Use of Renewable Natural Resources and the Doctoral Programme in Wildlife Biology. He works in the Zoology Unit as Chief Superintendent and is based at the Biocenter 3 in Viikinkaari, Helsinki. His research focuses on ornithology, climate change impacts on biodiversity, and conservation biology, with extensive field work in Finnish ecosystems. Lehikoinen's research interests center on bird population monitoring, climate change effects on avian communities, and biodiversity conservation. His work examines how climate warming influences species distributions, community composition, and ecosystem functioning, particularly in boreal regions. He investigates the effectiveness of protected areas in mitigating climate change impacts and studies human-wildlife interactions through citizen science initiatives. His research combines long-term monitoring data with advanced statistical approaches to understand ecological patterns and processes. His recent publications (2024-2025) demonstrate a strong focus on climate change ecology, with particular attention to how warming temperatures affect bird communities in boreal regions. The research spans multiple scales from individual species responses to community-level changes, examining thermal niches, distribution shifts, and biodiversity-stability relationships. His work increasingly incorporates interdisciplinary approaches, connecting ecological monitoring with human dimensions of conservation through studies of citizen science and public engagement in bird monitoring. BirdLife Suomen kultainen ansiomerkki (2024) Environmental award of Sophie von Julins society to Hanko Bird Observatory (2010) Helsingin Seudun Lintutieteellisen yhdistyksen Tringan BirdLife -ansiomerkki (2011) Influencer of the year in the Finnish Museum of Natural History (2011) Newcomer of the year 2010 in the Museum of the Natural History (2010) Lehikoinen actively supervises doctoral students in sustainable resource use and wildlife biology programs. He leads multiple significant research projects including 'Lajien kumulatiiviset ja vuorovaikutteiset vasteet ilmastonmuutokseen' (2024-2028) funded by the Academy of Finland, 'HABITRACK RIA' (2024-2027) with the European Commission, and 'Digitaalinen kansalaistiedekeskus Erkko' (2024-2028) funded by the Jane and Aatos Erkko Foundation. His work combines scientific research with practical conservation applications. Lehikoinen is deeply involved in large-scale biodiversity monitoring initiatives, particularly bird population monitoring programs in Finland. He collaborates with the Finnish Museum of Natural History and participates in citizen science projects that engage the public in data collection. His work with the 'Lintuatlas' (Bird Atlas) project represents a significant contribution to understanding bird distribution changes across Finland, combining professional scientific monitoring with volunteer observations.

• 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.