جست‌وجوی استاد و پژوهشگر

Lasse Lensuمشاهده پروفایل

استاد

• Computer Vision
• Pattern Recognition
• Machine Learning
• +۵ مورد دیگر

Lasse Lensu is a Professor of Machine Vision and Data Analysis at Lappeenranta-Lahti University of Technology LUT (LUT University), Finland. He is affiliated with the Department of Computational Engineering within the LUT School of Engineering Sciences. His research spans multiple domains including computer vision, pattern recognition, and machine learning with applications in marine biology, medical imaging, and spectroscopy. Prof. Lensu received his M.Sc. degree in data communications in 1991, followed by Lic.Sc. and D.Sc. degrees in computer science in 2001 and 2002 respectively, all from LUT. His academic journey has been deeply rooted in the same institution where he now serves as a professor. His primary research interests focus on computer vision and pattern recognition with machine learning approaches. Specifically, he has made significant contributions to plankton recognition using deep learning techniques, medical image analysis particularly in retinal imaging, and signal processing in Raman and CARS spectroscopies. Prof. Lensu has also contributed to energy forecasting and battery state-of-charge prediction using advanced machine learning models. Analysis of his recent publications reveals a strong trend toward applying computer vision and deep learning to specialized domains. His work spans from fundamental research in machine learning techniques to practical applications in marine biology (plankton recognition), medical diagnostics (retinal imaging), and chemical analysis (spectroscopy). A significant portion of his recent work focuses on addressing challenges in open-set recognition, domain adaptation, and uncertainty quantification in deep learning models. Prof. Lensu is an active member of the Computer Vision and Pattern Recognition Laboratory at LUT. He has been instrumental in technology transfer initiatives, contributing to the creation of multiple spin-off companies from the university. His research has practical implications across several industries including environmental monitoring, healthcare diagnostics, and energy management.

• Machine Learning
• Medical Imaging
• Computer Science
• +۳ مورد دیگر

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 .

• Biosynthetic Materials
• Protein Engineering
• Microbial Biotechnology
• +۳ مورد دیگر

Markus B. Linder is a Professor in the Department of Bioproducts and Biosystems at Aalto University, specializing in biosynthetic materials with properties inspired by natural biological systems such as silk proteins, barnacle adhesives, and extracellular matrices. His work focuses on genetic engineering of proteins for microbial material production and assembly optimization. Affiliation: Aalto University Department: Bioproducts and Biosystems His research explores creating sustainable alternatives to petrochemical materials using biological systems, including living materials with programmable growth, adaptive capabilities, and self-repair. Current projects investigate protein phase separation, hydrogel mechanics, and bioinspired manufacturing processes. Recent publications highlight advancements in artificial spider silk production (2025), laser-controlled drug partitioning in protein condensates (2024), and quorum sensing exploration in prokaryotes (2024). These works span materials science, biophysics, and microbiology, emphasizing sustainable design and functional adaptability. Scientific Awards 1998: VTT Prize for Scientific Excellence 2015: Leading Edge-Lecturer

• Human-in-the-Loop
• Machine Learning
• Predictive Maintenance
• +۴ مورد دیگر

Alexander Nikitin is a Postdoctoral Researcher in the Department of Computer Science at Aalto University, working within the Probabilistic Machine Learning group under Professor P. Marttinen. His research integrates human expertise with machine learning systems to solve complex real-world problems, particularly in predictive maintenance and uncertainty quantification. He holds a Master's degree in Engineering and Technology from the State University Higher School of Economics, awarded in 2019, and completed his doctoral thesis at Aalto University in 2024. Nikitin's work centers on Human-in-the-Loop systems where human feedback enhances AI performance, predictive maintenance using graph-based models for industrial applications, and uncertainty quantification in large language models. His approach combines probabilistic methods with deep learning to address challenges in spatiotemporal data analysis, domain adaptation, and non-separable systems. Recent publications demonstrate his focus on practical implementations where machine learning directly impacts operational efficiency. Analysis of his 2021-2025 publications reveals a clear trajectory toward industrial AI applications, with increasing emphasis on human-AI collaboration frameworks. His work bridges theoretical machine learning advances (like kernel entropy methods for LLMs) with concrete use cases in workstation monitoring and synthetic time series generation, frequently appearing in top-tier venues like NeurIPS and KDD. He actively contributes to major research initiatives including the FCAI Flagship 2 project (2022-2026), which develops next-generation AI systems through collaboration between Aalto University, the University of Helsinki, and industry partners like Elisa. His earlier work on error log analysis for predictive maintenance (2020-2023) established foundations for current human-in-the-loop approaches. Nikitin operates within Aalto's Probabilistic Machine Learning ecosystem and the Finnish Center for Artificial Intelligence (FCAI), where he collaborates with leading researchers including Sami Kaski and P. Marttinen on projects spanning generative modeling, workstation maintenance, and large-scale AI deployment.

Indrė Žliobaitėمشاهده پروفایل

استاد

• Data Science
• Machine Learning
• Life Science Informatics
• +۳ مورد دیگر

Indrė Žliobaitė is a Professor at the Department of Computer Science, Faculty of Science, University of Helsinki, and holds a Docent title in the same department. She maintains dual affiliations with the Department of Geosciences and Geography and serves as supervisor for both the Doctoral Programme in Geosciences and Doctoral Programme in Computer Science. Her research integrates data science and machine learning with earth sciences, focusing on evolutionary paleontology, macroecology, and macroevolution. She develops computational frameworks for analyzing fossil records, modeling species longevity, and reconstructing paleoenvironments, with specific applications in habitat suitability prediction and ecometric inference. Her work bridges algorithmic innovation with paleontological data interpretation. Recent publications reveal a cohesive trajectory where machine learning techniques—particularly probabilistic modeling and Bayesian approaches—are systematically applied to paleontological questions. This includes modeling fossil mammal community dynamics, inferring historical climate impacts on species distribution, and establishing quantitative laws for macroevolutionary processes, demonstrating how computational methods can unlock patterns in deep-time ecological data. Žliobaitė actively supervises doctoral candidates through two university programmes and leads the Academy of Finland project "Macroevolution of ecological relationships" (2023-2027). She collaborates internationally with the Turkana Basin Institute, Staatliches Museum Naturkunde Stuttgart, and Institut Catala de Paleontologia, and manages the New and Old Worlds Mammal Fossil Database (NOW), a critical infrastructure for paleontological research.

Elio Nushiمشاهده پروفایل

پژوهشگر

• Data Science
• Machine Learning
• Algorithms
• +۳ مورد دیگر

Elio Nushi is a Doctoral Researcher at the University of Helsinki , affiliated with the Faculty of Veterinary Medicine's Department of Food Hygiene and Environmental Health. His academic journey includes M.Sc. and B.Sc. degrees in Computer Science with focus on Data Science and Machine Learning. B.Sc., University of New York Tirana (2017) M.Sc., Åbo Akademi University (2020) Ph.D. candidate, University of Helsinki (2025 expected) Nushi develops probabilistic models for time series analysis of multi-omics data in precision medicine. His research integrates network science with computational biology to identify personalized drug combinations, particularly in multiple myeloma studies. Current work involves microbiome modeling and legacy transcriptomic data reanalysis using Bayesian methods. Recent publications highlight his contributions to Bayesian time annotation techniques and network modeling approaches for biomedical applications. He has taught Data Analysis with Python for three consecutive years at the University of Helsinki while maintaining active research in mathematical applications to biological systems.

Yifan Zhuمشاهده پروفایل

پژوهشگر

• Machine Learning
• Optimization
• Human-Computer Interaction
• +۴ مورد دیگر

Yifan Zhu is a Doctoral Researcher in the Department of Computer Science at Aalto University, affiliated with the School of Science. His research focuses on interactive personalization of classifiers, multi-objective optimization, and explainable AI systems. He has contributed to the field through publications such as Interactive Personalization of Classifiers for Explainability using Multi-Objective Bayesian Optimization (2023). Zhu's research interests revolve around optimizing machine learning models through multi-objective Bayesian optimization, particularly in the context of user-centered systems. He explores how interactive personalization can enhance explainability of classifiers, ensuring that AI systems are both accurate and interpretable for end-users. His work bridges computational methods with human factors, aiming to develop models that balance technical performance with user needs. His recent work emphasizes the integration of human-in-the-loop methodologies with optimization techniques in AI. The 2023 publications highlight advancements in explainable machine learning and Bayesian optimization for classifier personalization, while earlier work in 2018 explored crowd sensing applications in transportation safety.

• Probabilistic Machine Learning
• Statistical Inference
• Computational Neuroscience
• +۴ مورد دیگر

Luigi Acerbi is an Associate Professor at the Department of Computer Science, University of Helsinki, leading the Machine and Human Intelligence research group. He is affiliated with the Finnish Center for Artificial Intelligence (FCAI) and ELLIS (European Laboratory for Learning and Intelligent Systems). His research focuses on probabilistic machine learning, statistical inference methods (e.g., amortized and surrogate-based approaches), and computational and cognitive neuroscience, including Bayesian models of perception and resource-constrained rationality. Previously, he held postdoctoral positions at the University of Geneva and New York University. He earned his PhD from the Doctoral Training Centre in Computational Neuroscience at the University of Edinburgh, working with Sethu Vijayakumar and Daniel Wolpert. His work includes developing open-source tools like BADS (Bayesian Adaptive Direct Search) and VBMC (Variational Bayesian Monte Carlo), widely used for optimization and Bayesian inference in MATLAB/Python. He actively contributes to the academic community through teaching (e.g., BAMB! 2022 summer school tutorials on model fitting) and software development (GitHub repositories for optimization, inference, and AI tools like Athanor). His research bridges machine learning, neuroscience, and cognitive science, emphasizing robust and efficient statistical methods.

• Interpretability
• Machine Learning
• Data Mining
• +۴ مورد دیگر

Martino Ciaperoni is a Postdoctoral Researcher and Adjunct Professor in the Department of Computer Science at Aalto University. He is affiliated with the research group of Adj. Prof. Gionis Aris. His primary focus is on advancing interpretable machine learning, algorithm optimization, and data mining techniques. Ciaperoni holds a doctoral degree (Tekn. toht.) in Computer Science from Aalto University (2024) and a Doctoral degree in Engineering and Technology from Università degli Studi di Roma 'La Sapienza' (2019). His research emphasizes interpretability in AI, multi-label classification, and efficient algorithms for speech recognition and Bayesian networks. Recent work includes exploring the Rashomon set of rule-based models, developing low-memory Viterbi decoding algorithms (SIEVE), and solving the Hadamard decomposition problem. His research spans topics like core decomposition in temporal networks and low-rank matrix approximation. Collaborations include international projects in computational linguistics, knowledge discovery, and algorithm design. He has contributed to open-source software, such as the 'Efficient Exploration of the Rashomon Set' codebase (Zenodo). His work balances theoretical rigor with practical applications, aiming to bridge gaps between complex algorithms and real-world problem-solving.

Michael Mathioudakisمشاهده پروفایل

استاد مهمان

• Data Mining
• Social Media Analysis
• Text Mining
• +۴ مورد دیگر

Michael Mathioudakis serves as a Visiting Faculty member in the Department of Computer Science at Aalto University and is affiliated with the Adj. Prof. Gionis Aris research group and the Helsinki Institute for Information Technology (HIIT). His academic work spans multiple institutions and research collaborations focused on data-intensive computing and analysis. His educational background includes a Doctor of Philosophy from the University of Toronto, awarded on June 5, 2013. This foundation has enabled his research contributions across various domains of computer science and data analysis. Mathioudakis's research interests focus on Data Mining , Social Media Analysis , Text Mining , Social Network Analysis , and Bayesian Networks . His work explores the intersection of big data technologies with social phenomena, particularly examining how information flows through social networks and how probabilistic models can be optimized for real-world applications. His fingerprint analysis reveals significant contributions to Random Walk algorithms (100%), Social Network analysis (83%), Bayesian Networks (66%), and Big Data applications (33%). His publication record shows a consistent output from 2015-2022 with 22 total research outputs, including conference proceedings and journal articles. His work demonstrates a clear trajectory from foundational data mining research toward increasingly complex applications in social media analysis and urban informatics, with notable contributions to efficient algorithms for Bayesian network processing and social media content exposure systems. Mathioudakis has collaborated extensively with researchers across multiple institutions, as evidenced by his co-authorship on papers involving teams from various international organizations. His work contributes to UN Sustainable Development Goals, particularly in areas related to data-driven approaches to societal challenges.

• Quantitative Modeling
• Bayesian Statistics
• Spatial Analytics
• +۲ مورد دیگر

Henri Wallen is a Researcher at the Arctic Centre, University of Lapland, Finland. His work integrates quantitative modeling, Bayesian statistics, and spatial analytics to study Arctic socio-ecological systems, particularly reindeer herding and climate impacts. Research interests include landcover changes, justice in Arctic governance, and machine learning applications. Key projects involve analyzing historical and modern reindeer management, invasive species impacts, and mining-related social conflicts. His interdisciplinary approach bridges ethnography, archaeology, and policy analysis. Recent publications focus on reindeer osteometry, cultural practices in tourism, and legal reforms. Trends highlight climate adaptation, community-based research, and justice frameworks for sustainable Arctic development. Wallen contributes to EU projects like CHARTER and JustNorth, emphasizing participatory governance.

• Geropsychology
• Wellbeing and health
• Innovation expertise
• +۹ مورد دیگر

Tuomas Leisti is a Lecturer and Doctor of Psychology (PsT) at Metropolia University of Applied Sciences, affiliated with the Wellbeing Team 3. His professional roles include teaching psychology and research methods, supervising Master's theses, and contributing to RDI (Research, Development, Innovation) projects. He holds certifications as a licensed psychologist and professional teacher (AmO), with proficiency in Finnish (native), English (excellent), Swedish (average), and German (basic). Leisti's research focuses on geropsychology, wellbeing and health, innovation expertise, cognitive psychology, and the philosophy of mind. His doctoral work explored the role of consciousness in decision-making, with prior experience in human-technology interaction. He is particularly interested in decision-making processes, problem-solving, and the metacognitive skills underlying these activities. Key projects include the Wellbeing Hybridist initiative (2021–2023) and the pan-European COMHOM project (2025–2028), which addresses homelessness through data-driven solutions. His research outputs span over two decades, emphasizing subjective image quality evaluation, cognitive modeling, and quantitative methodologies. Leisti's work integrates empirical and philosophical approaches, addressing topics such as qualia, decision heuristics, and the interplay between conscious thought and judgment. Despite no explicitly listed awards, his extensive publication record reflects sustained contributions to psychological and applied research fields.

• Neuroscience
• Computer Science
• Medical Imaging
• +۳ مورد دیگر

Susanne Merz is a Researcher affiliated with the School of Science , focusing on interdisciplinary studies at the intersection of neuroscience and computational methods. Her work leverages advanced statistical and artificial intelligence techniques to investigate brain dynamics and cognitive disorders. Her research interests include: Neuroscience Machine Learning Medical Imaging Biostatistics Cognitive Psychology Neuroinformatics Recent publications highlight her contributions to analyzing magnetoencephalography (MEG) data for neural fingerprinting and developing AI-driven tools for dementia risk assessment in mild cognitive impairment (MCI). Collaborations span international institutions, such as the AI-Mind project funded by the EU H2020 Framework program. Her work integrates computational modeling with clinical applications, aiming to improve early detection of neurodegenerative diseases. Scientific contributions: 2024 : Bayesian reduced rank regression and neural fingerprints in MEG 2024 : AI-Mind protocol for brain connectivity screening in MCI Projects: AI-Mind (2021–2026), EU H2020 Framework program Her research employs magnetoencephalography, functional connectivity, and AI to advance understanding of brain networks and their implications for cognitive health.

• Graph Neural Networks
• Machine Learning
• Artificial Intelligence
• +۴ مورد دیگر

Rafał Karczewski is a Doctoral Researcher in the Department of Computer Science at the School of Science. His research focuses on advanced machine learning techniques, particularly in Graph Neural Networks, Generative Models, and their applications in fields such as drug design and computer vision. His work explores topics like the generalization of equivariant graph neural networks, denoising in diffusion models, and the expressivity challenges in generative drug design. He has contributed to foundational studies on uncertainty estimation in neural networks and statistical regression methods. Rafał's publications reflect a strong emphasis on theoretical and applied machine learning, with recent work addressing cutting-edge issues in model generalization and creative applications of diffusion processes. He has not yet received notable scientific awards, but his research demonstrates significant potential in advancing AI methodologies.

• Robotics
• Machine Learning
• Artificial Intelligence
• +۷ مورد دیگر

Nguyen Tran is a Doctoral Researcher affiliated with the School of Business and the Department of Information and Service Management . Their work focuses on advancing robotic grasping and manipulation techniques through data-driven and physics-based approaches. Research Interests: Robotics, Soft Robotics, Machine Learning, Human-Robot Interaction, Haptics, and physical property modeling for robotic systems. Recent Work: Developments in deformable object manipulation, 6-DOF grasp sampling, and multi-fingered hand designs inspired by human anatomy. Technical Contributions: Novel algorithms like SPONGE for contact prediction, force control systems for safe grasping, and simulation-based quality metrics for deformable object interactions.