جستجوی استاد راهنمای دانشگاهی

Simo Särkkäمشاهده پروفایل

استاد

• Machine Learning
• Signal Processing
• Bayesian Filtering
• +۷ مورد دیگر

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 Eloمشاهده پروفایل

استاد

• Computational Biomedicine
• Medical Bioinformatics
• Data Science
• +۷ مورد دیگر

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 Honkelaمشاهده پروفایل

استاد

• Privacy-preserving machine learning
• Differential Privacy
• Bayesian Machine Learning
• +۳ مورد دیگر

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.

Harri Lähdesmäkiمشاهده پروفایل

دانشیار

• Probabilistic Machine Learning
• Deep Generative Models
• Artificial Intelligence
• +۴ مورد دیگر

Harri Lähdesmäki is an Associate Professor (tenured) at the Department of Computer Science, Aalto University, where he leads the Computational Systems Biology research group. His work focuses on probabilistic machine learning and deep generative models with applications in biomedicine and molecular biology. Key Research Interests: Probabilistic machine learning, deep generative models, computational biology, bioinformatics, longitudinal data modeling Contact: harri.lahdesmaki@aalto.fi | Konemiehentie 2, 02150 Espoo, Finland His recent publications highlight advancements in: Gaussian process priors for scalable deep generative models Single-cell analysis of immune repertoires in leukemia and diabetes Probabilistic deconvolution methods for RNA-seq data Epigenetic analysis using hidden Markov and mixed models Transformer-based survival prediction and missing data handling Harri’s work integrates mechanistic modeling with Bayesian inference, particularly applied to immunology, cancer biology, and early disease prediction.

• Machine Learning
• Computational Neuroscience
• Artificial Intelligence
• +۷ مورد دیگر

Aapo Hyvärinen is a Professor of Computer Science at the University of Helsinki , affiliated with the Helsinki Institute for Information Technology and the Helsinki Probabilistic Machine Learning Lab . He previously held the position of Professor of Machine Learning at the Gatsby Computational Neuroscience Unit, University College London (2016-2019). Education : Undergraduate Mathematics at University of Helsinki, Vienna, and Paris; Ph.D. in Information Science from Helsinki University of Technology (1997) His research focuses on machine learning and computational neuroscience , particularly: Independent Component Analysis (ICA) Natural Image Statistics Causal Representation Learning Neural Signal Processing Applications to brain imaging (MEG, CryoEM) Recent publications emphasize causal discovery , identifiable machine learning , and nonlinear ICA . Key projects include: VETURI (AI for health) DIGIMIND (AI in mental health) CIFAR grants (2022-2025) Scientific awards : Highly Cited Researcher (2010) He serves as Action Editor for the Journal of Machine Learning Research and Neural Computation , and has held Area Chair roles at NeurIPS, ICML, ICLR, AISTATS, and UAI conferences. His work bridges theoretical machine learning with neuroscience and philosophical implications of artificial intelligence .

Markus Heinonenمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Probabilistic Modeling
• Bayesian Inference
• +۵ مورد دیگر

Markus Heinonen is an Academy Research Fellow at Aalto University's Department of Computer Science within the School of Science. His academic position is tied to Harri Lähdesmäki's Professorship, focusing on probabilistic machine learning. He holds a Doctoral degree in Engineering and Technology from the University of Helsinki (2013). His research integrates probabilistic modeling , deep learning , and differential equations , with applications in computational biology, drug discovery, and biophysics. Key themes include Gaussian processes, Bayesian inference, generative models, and their use in understanding complex biological systems like immune cell behavior (e.g., T cell receptor analysis in aplastic anemia) and molecular design. He leads major projects such as the Deep Learning with Differential Equations initiative (2020–2025), exploring continuous-time models and physics-informed neural networks. His work bridges theory and application, evidenced by collaborations in diffusion models , optimal transport , and single-cell analysis . Publications span over 65 peer-reviewed outputs, with recent emphases on robust neural network training, multi-target molecular prediction, and interpretable drug design frameworks. His research contributes to UN Sustainable Development Goal 3 (Good Health) through advancements in disease modeling and therapeutic development. He has undertaken visiting research roles at the University of California, San Francisco (2017) and Telecom ParisTech (2013–2014). Media highlights include recognition for work on TCR-epitope prediction and AI-driven enzyme engineering.

Masood Masoodianمشاهده پروفایل

دانشیار

• Interactive Visualization
• Design Thinking
• Health Informatics
• +۳ مورد دیگر

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 Marttinenمشاهده پروفایل

دانشیار

• Machine Learning
• Large Language Models
• Reinforcement Learning
• +۸ مورد دیگر

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
• +۳ مورد دیگر

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

• Statistical signal processing
• Robust statistical methods
• Array processing
• +۶ مورد دیگر

Esa Ollila serves as Associate Professor in the Department of Signal Processing and Acoustics at Aalto University, Finland, and holds an adjunct professorship in Statistics at the University of Oulu. His academic appointments include Academy of Finland Research Fellow (2010-2015) and prior senior research/lecturing roles at both institutions. His educational background features: M.Sc. in Mathematics, University of Oulu (1998) Ph.D. in Statistics (with honors), University of Jyväskylä (2002) D.Sc.(Tech) in Signal Processing (with honors), Aalto University (2010) Professor Ollila's research centers on statistical signal processing and robust statistical methodologies , with significant contributions to array processing, high-dimensional data analysis, and covariance matrix estimation. His work bridges theoretical statistics with practical applications in radar systems, wireless communications, and big data analytics, emphasizing robustness against outliers and computational efficiency in modern data-intensive environments. Current focus areas include compressed sensing, sparse approximation, and blind source separation techniques. Analysis of his 15 most recent publications (2024-2025) reveals three dominant trends: (1) robust covariance learning for massive random access systems, (2) integrated sensing and communications (ISAC) for 6G networks using advanced beamforming, and (3) geometric approaches to elliptical distributions in statistical inference. His work increasingly incorporates deep learning (GANs, graph neural networks) while maintaining strong foundations in classical signal processing theory. Key recognitions include: Academy of Finland Postdoctoral Fellowship (2004-2007) Academy of Finland Research Fellowship (2010-2015) His research has been supported through prestigious Academy of Finland grants totaling over a decade of continuous funding. Professor Ollila currently leads an active research group at Aalto University, supervising doctoral candidates and collaborating internationally with institutions including Princeton University (where he served as Visiting Post-doctoral Research Associate during 2010-2011). He maintains strong ties with the University of Oulu through his adjunct professorship and has contributed to EURASIP's Special Area Team on Theoretical and Methodological Trends in Signal Processing. The Esa Ollila Research Group focuses on cutting-edge challenges in statistical signal processing, with current projects spanning robust DOA estimation under non-Gaussian noise, covariance matrix learning for massive MIMO systems, and machine learning-enhanced radar-communication integration. The group actively develops open-source tools like the fitHeavyTail R package for heavy-tailed distribution modeling and maintains collaborations with industry partners in wireless communications.

Olli Varisمشاهده پروفایل

استاد

• Water Resources Management
• Sustainable Development
• Climate Change Adaptation
• +۵ مورد دیگر

Olli Varis is an Aalto Distinguished Professor at Aalto University's Department of Built Environment, specializing in Water and Environmental Engineering. He holds adjunct professorships at Asian Institute of Technology (Thailand) and has served as Vice Dean for Research & Innovation at Aalto School of Engineering (2013–2018). His expertise spans water resources management in developing economies, climate change impacts, and transboundary water governance. Education: Doctoral degree in Engineering (Helsinki University of Technology, 1991), Licentiate (1988), and dual Master's degrees in Agriculture (University of Helsinki, 1986) and Engineering (Helsinki University of Technology, 1984). Research focuses on sustainable development goals (SDGs), particularly water-energy-food nexus dynamics, urbanization impacts, and global river basin vulnerabilities. Key interests include flood early warning systems, migration-environment linkages, and Asian transboundary water challenges. Notable awards include Aalto Excellence Awards (2018, 2021), Best Research Paper 2023, and Environmental Research Letters' Best Paper 2010. Over 350 publications include works on China's water risks, Nepal's flood resilience, and global migration drivers. Active in UNESCO, WMO, and UNU/WIDER. Recent projects address urbanization's environmental pressures, interdisciplinary education challenges, and东南亚数字经济的崛起与挑战 (Vietnam's digital economy challenges). Leads the Water and Environmental Engineering research group, supervising 12 theses. Involved in public outreach,如芬兰赫尔辛基大学的媒体参与案例.

Kimmo Vehkalahtiمشاهده پروفایل

مدرس ارشد

• Social Data Science
• Social Statistics
• Multivariate Methods
• +۳ مورد دیگر

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.

• Digital Design and Manufacturing (D2M)
• Sustainable Manufacturing
• Optimization Driven Design
• +۷ مورد دیگر

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.

Markku Karjalainenمشاهده پروفایل

استاد

• Timber construction
• Multi-story wooden buildings
• Sustainable construction
• +۵ مورد دیگر

Markku Karjalainen is a Professor in the Department of Architecture at Tampere University's Faculty of Built Environment. With over 80 research publications spanning from 2016 to 2025, he has established himself as a leading expert in timber construction and wooden building systems in Finland. Professor Karjalainen's research primarily focuses on timber construction , particularly multi-story wooden buildings, dovetail wood construction techniques, and sustainable building practices. His work spans architectural design, structural engineering, fire safety, and environmental impact assessment of wooden structures. He has conducted extensive statistical analyses of Finnish timber residential buildings, examining construction practices from 1995 to the present. His research demonstrates a strong commitment to advancing wooden construction technologies while addressing practical challenges in fire safety, structural performance, and building physics. Analysis of his recent publications (2023-2025) reveals a consistent focus on dovetail construction techniques for mass timber elements, with numerous studies examining structural performance, fire properties, and air permeance. His work bridges theoretical research with practical applications in the construction industry, particularly in Finland where wooden multi-story construction has seen significant growth. Karjalainen's research often involves international collaboration, with studies comparing practices across different countries and examining global perspectives on timber construction. His scholarly output demonstrates a methodical progression from basic statistical analysis of building practices to increasingly sophisticated investigations of specific construction techniques and their performance characteristics. This evolution reflects both his growing expertise and the maturation of timber construction as a field of academic inquiry.

Heikki Remesمشاهده پروفایل

دانشیار

• Marine Engineering
• Structural Engineering
• Fatigue and Fracture Mechanics
• +۳ مورد دیگر

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.