جستجوی استاد راهنمای دانشگاهی

• Data Mining
• Graph Theory
• Computational Biology
• +۲ مورد دیگر

Laura Langohr is a Postdoctoral Researcher at the Finnish Institute of Molecular Medicine (FIMM), University of Helsinki, specializing in computational approaches to biomedical research. Her work bridges computer science and molecular medicine through advanced data analysis techniques. Her core research domains include: Data Mining for pattern extraction in complex datasets Graph Theory applied to biological networks Computational Biology for genetic and physiological modeling Network Analysis of weighted and probabilistic systems Genetics-focused algorithm development Langohr's publication history reveals consistent innovation in subgroup discovery and network algorithms, with emphasis on identifying non-redundant information and representative nodes in biological contexts. Her methodologies directly support molecular medicine applications through computational rigor. Current research funding includes: MetaStem: Academy of Finland Center of Excellence in Stem Cell Metabolism (2023-2025) New insights into leukemia development (Academy of Finland, 2022-2026) iCAN: Digital personalized cancer medicine flagship (Academy of Finland, 2022-2026) Organ transport mechanisms in stem cells (Academy of Finland, 2024-2028) No scientific awards or fellowships are documented in available sources. As a research-focused academic, her contributions center on collaborative project execution rather than formal student supervision. She operates within FIMM's interdisciplinary ecosystem, connecting computational theory with experimental biomedical research.

Victor Bolbotمشاهده پروفایل

پژوهشگر

• Autonomous ships
• Marine systems safety
• Ship propulsion systems
• +۳ مورد دیگر

Victor Bolbot serves as a Postdoctoral Researcher in the Department of Energy and Mechanical Engineering at Aalto University, Finland, affiliated with the Marine and Arctic Technology research group. His work focuses on advancing safety, reliability, and cybersecurity frameworks for autonomous maritime systems through rigorous systems engineering approaches and data-driven methodologies. He maintains active collaborations with international researchers and institutions, evidenced by extensive co-authorship across high-impact publications. Dr. Bolbot's research centers on autonomous ships, marine systems safety, ship propulsion cybersecurity, and risk modeling. He employs systems-theoretic process analysis (STPA), Bayesian networks, and association rule mining to address critical challenges including maritime accident causation, cybersecurity vulnerabilities in dual-fuel engines, safety acceptance criteria for autonomous vessels, and socio-technical implications of maritime automation. His methodological innovations bridge theoretical safety engineering with practical applications in Arctic navigation, inland waterways, and regulatory compliance, emphasizing the integration of cyber-physical risk assessment. Analysis of his 2023-2025 publications reveals three dominant research trajectories: (1) Development of cyber-physical risk frameworks like STPA-Cyber for maritime cybersecurity; (2) Real-time Bayesian modeling for dynamic operations including remote pilotage and ice navigation; and (3) Socio-technical investigations into regulatory frameworks, educational needs, and workforce skill transformations for autonomous shipping. His work consistently addresses the interplay between technological innovation and safety assurance, with growing emphasis on cybersecurity as a critical maritime safety component. As an active member of Aalto University's Marine and Arctic Technology research group, Dr. Bolbot contributes to interdisciplinary projects tackling complex challenges in marine safety engineering, Arctic operations, and sustainable maritime technologies. The group's collaborative environment supports the development of safer, more efficient, and environmentally conscious maritime systems through experimental validation, computational modeling, and industry partnerships.

• Machine Learning
• Computational Data Analysis
• Neural Networks
• +۳ مورد دیگر

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

• Probabilistic Machine Learning
• Probabilistic Circuits
• Probabilistic Programming
• +۲ مورد دیگر

Martin Trapp is an Academy Postdoctoral Researcher in the Department of Computer Science at Aalto University, specializing in probabilistic machine learning. He is affiliated with Professor Arno Solin's research group, focusing on advancing tractable probabilistic models for real-world applications. His research centers on Probabilistic Circuits , Probabilistic Programming , and Bayesian Nonparametrics , with emphasis on hardware-efficient implementations for edge devices and multimodal systems. Key interests include uncertainty quantification in deep learning, neurosymbolic AI integration, and medical imaging applications. His work bridges theoretical foundations with practical deployment constraints, particularly in resource-limited environments. Analysis of his 15 most recent publications (2022-2025) reveals three dominant trends: (1) hardware-aware probabilistic inference for TinyML applications, (2) scalable Bayesian methods using bitstring representations and probabilistic programming, and (3) multimodal robustness in vision-language systems and medical imaging. His contributions span from theoretical circuit representations to real-world implementations in mammography analysis and vision-language models. Trapp secured a HIIT short-term project grant (November 2022) for "Positive Semi-Definite Circuits" under the Department of Computer Science. No formal advising relationships are documented in available sources. He actively collaborates with researchers including Arno Solin, Rui Li, and Marcus Klasson across institutions like Aalto University and the Helsinki Institute for Information Technology. As a core member of Aalto's Probabilistic Machine Learning group, he contributes to advancing probabilistic AI methodologies with applications in healthcare, edge computing, and multimodal reasoning. His current work emphasizes deployable probabilistic systems that maintain rigorous uncertainty quantification while meeting hardware constraints.

• Robot Learning
• Object-Centric Learning
• Machine Learning
• +۳ مورد دیگر

Vivienne Huiling Wang is a Postdoctoral Researcher at Aalto University's Department of Electrical Engineering and Automation, specializing in Machine Learning and Robotics. Her research focuses on object-centric learning, hierarchical reinforcement learning, and probabilistic modeling for robotic systems. Robot Learning Object-Centric Learning Probabilistic Subgoal Representations Her recent work (2023-2025) spans object representation techniques, adversarial subgoal generation, and processor performance estimation using machine learning. She contributes to the Robot Learning research group and collaborates with experts in computer vision and AI planning. Current affiliations: Aalto University Department of Electrical Engineering and Automation

• Data Mining
• Machine Learning
• Temporal Networks
• +۳ مورد دیگر

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

Heikki Haarioمشاهده پروفایل

استاد

• Bayesian Statistics
• Parameter Estimation
• Chaotic Dynamical Systems
• +۷ مورد دیگر

Heikki Haario is a Professor in Computational Engineering at the LUT School of Engineering Sciences, LUT University, Lappeenranta. His research focuses on robust Bayesian inference, parameter estimation, and uncertainty quantification in chaotic and stochastic systems. Broad research areas: Bayesian Statistics, Chaotic Dynamical Systems, Gaussian Processes, Machine Learning The 15 most recent publications (2025-2023) demonstrate expertise in computational modeling, data-driven methods, and interdisciplinary applications spanning finance, biology, and engineering. Articles emphasize Bayesian techniques, kernel flows, and neural network integration for solving inverse problems and optimizing predictions in uncertain environments.

• Data Mining
• Machine Learning
• Artificial Intelligence
• +۴ مورد دیگر

Jyrki Nummenmaa is a Professor at the Faculty of Information Technology and Communication Sciences, Department of Computing Sciences at Tampere University. He holds an ORCID ID (0000-0002-7476-7840) and can be contacted via jyrki.nummenmaa@tuni.fi. His research focuses on data mining, machine learning, natural language processing, and database systems. Key research interests include computational methods for analyzing political language, RDF graph processing, ethical AI security systems, and sequential pattern mining. He has collaborated extensively with researchers such as Peltonen J., Zimina E., and Duan L. Recent publications (2022–2024) emphasize interdisciplinary applications like parliamentary discourse analysis and transparent question-answering systems. He has edited conference proceedings and contributed to over 100 peer-reviewed works since 1992. His work spans theoretical advancements (e.g., fair neighbor embeddings, nonparametric graph embeddings) and applied solutions (e.g., bus delay analysis, health-e-living simulations). Notable projects include TraQuLA for RDF querying and PiHVI for forum posting analysis.

• Machine Learning
• Gaussian Processes
• Bayesian Inference
• +۳ مورد دیگر

Paul Chang is a Visiting Professor in the Department of Computer Science at Aalto University, associated with the Professorship Solin A. He holds a Doctor of Technology (Tekn. toht.) degree in Computer Science from Aalto University, awarded in December 2024. His research focuses on machine learning, Gaussian processes, Bayesian inference, and sequential learning, with applications in neural networks and optimization. His work contributes to UN Sustainable Development Goals related to education and innovation through advancements in probabilistic modeling and computational methods. Chang's academic background includes a doctoral thesis titled Rethinking Inference in Gaussian Processes: A Dual Parameterization Approach (2024), exploring novel methods for improving efficiency and scalability in Gaussian process models. His research has been published in leading conferences such as ICML and ICLR, with notable contributions to sequential learning algorithms, Bayesian optimization, and memory-based approaches in Gaussian processes. Key research interests include dual parameterization techniques, sparse representations in neural networks, and the integration of simulation-based inference with diffusion models. His work bridges theoretical advancements in machine learning with practical applications in optimization, active learning, and temporal data analysis. Collaborations span international institutions, reflecting his global academic engagement. Chang's publications highlight advancements in function-space parameterization, memory-augmented Gaussian processes, and amortized probabilistic conditioning. These contributions address challenges in scalable Bayesian methods and sequential decision-making systems. His research emphasizes computational efficiency, robustness, and applicability to real-world problems in machine learning.

• Artificial Intelligence
• Machine Learning
• Multi-Agent Systems
• +۳ مورد دیگر

Zhiyuan Li is a Postdoctoral Researcher in the Department of Electrical Engineering and Automation . His research focuses on multi-agent systems, reinforcement learning, and optimization algorithms. He explores topics such as policy factorization, backpropagation methods, and cooperative learning strategies in autonomous systems. Key research interests include multi-agent coordination, search space exploration, and formal analysis of reinforcement learning algorithms. His work addresses challenges like local optima avoidance and gradient optimization in distributed environments. Recent publications highlight advancements in multi-agent policy gradients, correlated policy factorization, and adaptive skill synthesis. These contributions aim to improve efficiency and scalability in collaborative machine learning systems. Zhiyuan has collaborated on projects involving robot learning and resource allocation optimization, reflecting his interdisciplinary approach to AI research.

Tuomas Välimäkiمشاهده پروفایل

پژوهشگر

• Robotics
• Automation Engineering
• Sensor Calibration
• +۳ مورد دیگر

Tuomas Välimäki is a Doctoral Researcher affiliated with the Faculty of Engineering and Natural Sciences. His research focuses on robotics and automation, with a strong emphasis on sensor calibration, visual navigation, and control systems for hydraulic manipulators. He holds a Master of Science (Technology) in Automation Engineering. Master of Science (Technology), Automation Engineering (2015) Lower-Degree Level Tertiary Education in Technology (2015) His research explores Robotics Engineering , Motion-based Calibration , and Probabilistic Movement Primitives . Key themes include optimizing sensor configurations for autonomous systems, trajectory tracking in hydraulic manipulators, and gaze-direction strategies for visual navigation tasks. His work intersects with Computer Vision , Control Systems , and Mechatronics . Tuomas’s publications highlight advancements in sensor fusion , multi-camera systems , and probabilistic motion planning . These studies focus on improving accuracy and efficiency in robotic perception and control, particularly for mobile platforms and industrial manipulators.

Abdullah Tokmakمشاهده پروفایل

پژوهشگر

• Cyber-physical Systems
• Control Systems
• Artificial Intelligence
• +۳ مورد دیگر

Abdullah Tokmak is a Doctoral Researcher at the Department of Electrical Engineering and Automation, Aalto University (Finland). His research focuses on cyber-physical systems and safety-critical control algorithms. Current affiliation: Aalto University Department: Electrical Engineering and Automation Email: abdullah.tokmak@aalto.fi Research Interests: Safe exploration in reproducing kernel Hilbert spaces Parameter tuning for multi-agent systems Bayesian optimization with safety guarantees Kernel-based control approximation techniques His publications demonstrate expertise in integrating machine learning with control systems through methods like: Nonlinear MPC approximation Safe Bayesian optimization Distributed multi-agent control Kernel-based modeling

Leo Lahtiمشاهده پروفایل

استاد

• Data Science
• Artificial Intelligence
• Machine Learning
• +۱۱ مورد دیگر

Leo Lahti is a Professor in Data Science at the University of Turku, Finland. His research focuses on computational analysis and modeling of complex natural and social systems, with applications in microbiome science, ecological modeling, and computational humanities. He is actively involved in open science initiatives and leads a dynamic research team. University: University of Turku Role: Professor, Data Science Contact: leo.lahti@utu.fi Lahti holds a Doctor of Science (DSc) from Aalto University (2010), where he developed probabilistic machine learning methods for life science data. He conducted postdoctoral research at EBI/Hinxton (UK), Wageningen University (NL), and VIB/KU Leuven (BE), establishing a strong international research profile. His research interests span Data Science, Artificial Intelligence, Machine Learning, Applied Statistics, Probabilistic Models, Microbial Ecology, Computational Humanities, and Open Science . He applies computational and statistical methods to understand complex systems, particularly in microbiome dynamics, ecological resilience, and historical data analysis. His work integrates interdisciplinary approaches, combining data analytics with domain-specific knowledge in biology, medicine, and humanities. The recent publications (2021–2025) reflect a strong focus on gut microbiome analysis , including maternal-infant health, dietary impacts, disease risk (CKD), and methodological advances in differential abundance and phylogenetic diversity. Other themes include ecological modeling (resilience, early warning signals), infectious disease modeling (COVID-19), and open science education (Bioconductor). The keyword trends show deep engagement with microbiology, bioinformatics, and mathematical modeling, with subfields emphasizing reproducibility, host-microbe interactions, and computational methods. Lahti plays a significant role in open science and academic leadership: Vice Chair, Open Science Finland Member, International Science Council Committee on Data (2023–2025) Member, Bioconductor Community Advisory Board Founder, Open Science Working Group, Open Knowledge Finland He is also active in teaching computational and data science, statistical programming, AI, and open science. His research homepage is available at iki.fi/Leo.Lahti, and his full publication list is accessible via ORCID and bibtex.

Gleb Tikhonovمشاهده پروفایل

پژوهشگر

• Ecology
• Evolutionary Biology
• Statistical Modelling
• +۳ مورد دیگر

Gleb Tikhonov is a postdoctoral researcher at the University of Helsinki , affiliated with the Faculty of Biological and Environmental Sciences and the Organismal and Evolutionary Biology Research Programme . He also holds a postdoctoral position at Aalto University in the Department of Computer Science . His work bridges statistical ecology, machine learning, and computational biology. Research Interests : Development of joint species distribution modelling (JSDM) for ecological communities Integration of statistical and machine learning methods High-performance computing implementations Phenological and dispersal pattern analysis Publications demonstrate expertise in applying advanced statistical frameworks to ecological questions, including fungal dispersal mechanisms, microbiome variation, and climate change impacts. His LIFEPLAN project (funded by the European Commission Joint Research Centre) focuses on planetary biodiversity synthesis using big data. Education : PhD in Biological and Environmental Sciences, University of Helsinki (2018) MS in Applied Mathematics, Lomonosov Moscow State University (2014)

Jaakko Suutalaمشاهده پروفایل

دانشیار

• Machine learning
• Signal processing
• Probabilistic modelling
• +۵ مورد دیگر

Jaakko Suutala is an Associate Professor in the Biomimetics and Intelligent Systems Group (BISG) at the Faculty of Information Technology and Electrical Engineering, University of Oulu, Finland. His work focuses on integrating machine learning, statistical signal processing, and data science to advance autonomous systems with applications in health technologies, environment modeling, sensor fusion, and robotics. Research Interests: Machine learning, Signal processing, Probabilistic modelling, Sensor fusion, Data science Industry Role: Co-founder and technical advisor of IndoorAtlas Ltd, an indoor positioning spin-off company Contact: Email: Jaakko.Suutala@oulu.fi | Visiting Address: Erkki Koiso-Kanttilan katu 3 (door E), Oulu | Postal Address: 9BISG, P.O. Box 4500, FI-90014 University of Oulu, Finland Researcher Profiles: ORCID, SCOPUS, Google Scholar, ResearchGate Keywords: Technology, Computer science, Artificial intelligence, Machine learning