جستجوی استاد راهنمای دانشگاهی

Alexandru Palerمشاهده پروفایل

دانشیار

• Quantum Computing
• Quantum Software
• Quantum Circuit Compilation
• +۲ مورد دیگر

Alexandru Paler serves as an Associate Professor in the Department of Computer Science at Aalto University, Finland, where he leads research in quantum software development. His work focuses on designing compilers and optimization frameworks for quantum circuits, with emphasis on quantum error correction implementation and fault-tolerant quantum computing systems. Based in Espoo at Konemiehentie 2, he maintains active research collaborations through the university's quantum computing initiatives. Dr. Paler's research spans quantum circuit compilation, quantum error correction (particularly surface codes and QLDPC codes), and quantum software optimization. His team develops high-performance quantum compilers for neutral atom architectures and modular superconducting systems, addressing critical challenges in resource estimation and fault tolerance. The Quantum Operating Systems (QUANTUM) research group he contributes to explores scalable quantum software frameworks that bridge theoretical algorithms with practical hardware constraints, with significant work on graph-state compilation and reinforcement learning for circuit optimization. Analysis of his 15 most recent publications (2023-2025) reveals concentrated efforts in quantum compiler design, error correction scalability, and hardware-aware quantum software. Key trends include machine learning applications for decoder optimization, novel approaches to measurement-free error correction, and queuing theory models for fault-tolerant circuit analysis. His work consistently addresses the practical barriers to large-scale quantum computing through compiler innovations and resource-efficient circuit design. Dr. Paler actively participates in the Quantum Operating Systems (QUANTUM) research group within Aalto's Department of Computer Science, focusing on Algorithms and Theoretical Computer Science. This team develops quantum software infrastructure for next-generation quantum hardware, with current projects including Pandora (ultra-large-scale circuit compilation), quantum circuit caching mechanisms, and standardized cell approaches for neutral atom systems. Their research directly supports the transition from theoretical quantum algorithms to executable, error-resilient quantum programs.

• Laser Scanning
• Photogrammetry
• Remote Sensing
• +۵ مورد دیگر

Matti Tapio Vaaja is an Assistant Professor at Aalto University's Department of Built Environment, specializing in digital photogrammetry and laser scanning technologies. He also holds an Associate Professorship at Shinshu University's Institute of Mountain Science (Japan) focused on laser scanning applications in forestry. His research spans close-range laser scanning, 3D point cloud processing, environmental change detection, and forest mapping. With over 100 publications and significant contributions to geoinformatics, he coordinates the 4D GEO-IT doctoral education network and supervises 10 doctoral theses in photogrammetry and remote sensing. Notable Achievements: National Open Science Prize (2019) Over 2,700 citations with H-index 24 Contributions to 3D urban modeling, biomass estimation, and smart city development Recent publications demonstrate cross-disciplinary integration of UAV photogrammetry, terrestrial laser scanning, and game engine visualization techniques for urban vegetation monitoring and watershed analysis. His work bridges technical advancements with practical applications in environmental science and urban planning.

• Computer Science
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

Matias Turkulainen is a Doctoral Researcher at the Department of Computer Science , Aalto University. His research focuses on advanced 3D reconstruction techniques using smartphones, particularly leveraging Gaussian splatting and geometric priors for indoor environments. Email: matias.turkulainen@aalto.fi | Phone: +358504760565 Research Interests: Computer Vision 3D Reconstruction Gaussian Splatting Geometric Modeling Smartphone-based Sensing Recent Publication Trends (2024-2025): Focus on Gaussian splatting algorithms with applications in indoor room reconstruction, depth/normal priors for meshing, and motion compensation techniques. Key themes include real-time rendering, geometric priors, and dynamic camera motion analysis.

• Bayesian Inference
• Neural Networks
• Statistical Learning
• +۶ مورد دیگر

Jorge Loría is a Postdoctoral Researcher at the Department of Computer Science within Aalto University . His research focuses on Bayesian inference, neural networks, and statistical learning, particularly exploring probabilistic modeling with heavy-tailed distributions and uncertainty quantification. Recent work includes advancements in posterior inference for Bayesian neural networks with unbounded variance priors, kernel methods for deep learning, and applications of α-stable distributions in regression and neural processes. His publications span conferences like ICLR and journals such as Statistics and Computing , with a thematic emphasis on robust statistical models and causal inference frameworks. He has not been explicitly associated with teaching roles, formal student advisement, or scientific prizes. Contact details include the email jorge.loria@aalto.fi .

• Computing Systems
• Computing Education Research
• Educational Technology

Dr. Otto Seppälä is a Lecturer at Aalto University's Department of Computer Science. His research focuses on computing education research and educational technology, particularly in programming education, algorithm visualization, and student learning challenges. He actively contributes to curriculum development and pedagogical innovation in computer science education. Research Areas: Computing Systems, Computing Education Research, Educational Technology Academic Affiliation: Aalto University, Department of Computer Science His work explores the intersection of software engineering principles and educational methodologies, emphasizing interactive learning tools and adaptive assessment strategies. Publications highlight his contributions to understanding student struggles with algorithms, detecting prerequisite knowledge gaps, and advancing conference-based educational research. He received the prestigious Aalto High 5 Award for Education for pioneering the university's first MOOC initiative.

Jussi Rintanenمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Automated Planning
• Constraint-Based Search
• +۳ مورد دیگر

Jussi Rintanen is an Associate Professor at the Department of Computer Science , Aalto University . He is an internationally leading researcher in constraint-based planning and decision-making , with a focus on applying AI technologies to automating software production and synthesis of intelligent systems . Helsinki University of Technology (Doctoral Degree, 1997) Albert-Ludwigs-University Freiburg (Venia Legendi, 2005) National ICT Australia / Australian National University (Academic Positions) His research spans automated planning , SAT solving , temporal planning , and constraint-based search methods . Recent work highlights symmetry-breaking constraints , partial observability planning , and acyclicity encodings in SAT frameworks. The 15 most recent articles reflect trends in AI planning , graph algorithms , and formal verification , with applications in software synthesis , temporal reasoning , and uncertainty handling . His methodologies integrate integer programming , SMT encodings , and propositional logic .

Zhisong Liuمشاهده پروفایل

دانشیار

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Zhisong Liu serves as Associate Professor in Computational Engineering at LUT School of Engineering Sciences (Finland) since August 2023 while concurrently holding a Principal Research Engineer position at Dell Technologies (Shanghai) since February 2022. His academic journey includes postdoctoral research at École Polytechnique (Paris) and research roles at Caritas Institute of Higher Education and The Hong Kong Polytechnic University. Education PhD in Electronic and Information Engineering, The Hong Kong Polytechnic University (2016-2020) MSc in Electronic and Information Engineering, The Hong Kong Polytechnic University (2014-2016) Research Expertise Prof. Liu's work centers on computer vision and machine learning with specialization in image/video enhancement techniques. His signature contributions include See360 panoramic view interpolation, hybrid face super-resolution models combining deep learning with random forests, and low-light image enhancement networks. Current research extends into physics-informed neural networks for atmospheric chemistry and environmental monitoring systems. Publication Analysis His 15 most recent publications (2020-2025) reveal three dominant research thrusts: (1) multimodal learning for artistic style transfer and video understanding, (2) medical/environmental imaging applications using deep priors and spatio-temporal modeling, and (3) novel architectures for 3D vision and point cloud processing. Over 60% of recent work integrates domain-specific physics with neural networks, particularly evident in CT reconstruction and atmospheric chemical modeling. Professional Trajectory Associate Professor, Computational Engineering, LUT (2023-present) Principal Research Engineer, OCTO, Dell Technologies (2022-present) Postdoctoral Researcher, Computing Science, École Polytechnique (2020-2021) Research Associate, ECE, Caritas Institute (2019-2020)

Markku Reunanenمشاهده پروفایل

مدرس ارشد

• History of Computing
• Digital Culture
• Digital Cultural Heritage
• +۶ مورد دیگر

Markku Reunanen, PhD, is a Senior University Lecturer at Aalto University's Department of Art and Media within Aalto ARTS. He also holds the title of docent in digital culture at the University of Turku, where he teaches programming, academic writing, research skills, and virtual reality. Education: Reunanen holds a PhD and has been awarded the title of docent in digital culture by the University of Turku. Research Interests: His research spans the history of computing, digital culture, digital cultural heritage, videogames, and media. Specific areas include: History of Home Computing (1980s) Demoscene and Computer Art Digital Cultural Heritage Preservation Comparative Studies of Computer Adoption Social Aspects of Gaming History Media Archaeology Publication Trends: Recent publications (2020-2025) focus on the cultural history of marginal home computers, the demoscene, and digital heritage projects. Key themes include the social impact of early computing, comparative international studies of home computer use, and virtual reconstructions of historical sites such as the Finnish Pavilion at the 1900 Paris World Fair. Advising and Grants: The provided information does not specify any advisees or grant funding. Collaborations: Reunanen collaborates with researchers including Lily Diaz-Kommonen on digital cultural heritage projects. He has contributed to initiatives like the Pelikonepeijoonit project for preserving Finnish gaming history and the reconstruction of historical exhibitions.

Jaakko Peltonenمشاهده پروفایل

استاد مهمان

• Machine Learning
• Information Retrieval
• Dimensionality Reduction
• +۳ مورد دیگر

Jaakko Peltonen is a Visiting Professor at the Department of Computer Science within Aalto University , affiliated with the Probabilistic Machine Learning group and the Helsinki Institute for Information Technology (HIIT) . His research focuses on machine learning, information retrieval, and interactive visualization, with significant contributions to probabilistic modeling and data analysis. Tekniikan tohtori (Doctor of Technology), Helsinki University of Technology (2004) Diplomi-insinööri (Master of Science in Technology), Helsinki University of Technology (2001) His research spans machine learning applications in computational biology (e.g., analyzing cellular processes with regression planes), probabilistic matrix factorization for recommendation systems, and hierarchical Dirichlet processes for topic modeling in text mining. He has pioneered methods for exploratory search with visual interactive intent modeling and graph-based priors for scalable ML algorithms. Notable activities include international collaborations (e.g., visiting researcher roles in 2016), organizing Eurovis 2016 workshops, and conference presentations across Spain, United States, and other countries. His publications (85+ total) demonstrate expertise in probabilistic models, dimensionality reduction, and interactive visualization techniques.

• Microelectronics
• Computer Engineering
• Embedded Systems
• +۷ مورد دیگر

Dr. Ari Kulmala serves as Professor of Practice at Tampere University's Faculty of Information Technology and Communication Sciences in the Computing Sciences department. With extensive industry experience in System-on-Chip (SoC) architecture and design, he specializes in ASIC and FPGA development for applications spanning 5G telecommunications, cloud acceleration, machine learning, security, and ultra-low-power mobile chips. His research focuses on System-on-Chip (SoC) design , with particular emphasis on heterogeneous architectures, RISC-V implementations, and hardware-software co-design for autonomous systems. Recent work includes secure SoC development for nano-UAVs, FPGA-based cryptographic acceleration, and agile chip development methodologies. The article collection demonstrates expertise in SoC architecture (10+ years), with evolving focus from foundational network-on-chip analysis (2008-2009) to modern implementations in RISC-V (2023-2024), cloud security acceleration (2019), and VR/4K video processing (2017-2018). Key technical areas include clock domain management, resource sharing, and performance optimization across various domains. As an industry leader in chip development organizations, Kulmala has worked across Telecom infrastructure (5G) Cloud acceleration Machine learning/AI Security hardware Ultra-low-power IoT Mobile chip design His work spans both academic research and practical implementation, including patents and commercial applications.

• Bayesian Statistics
• Probabilistic Programming
• Machine Learning
• +۴ مورد دیگر

Aki Vehtari is a Professor in the Department of Computer Science at Aalto University’s School of Science. His academic roles include leadership in probabilistic machine learning, Bayesian statistics, and computational inference. He is a Principal Investigator for projects like the Finnish Center for Artificial Intelligence and the Iterative Bayesian Model Building initiative. Vehtari holds a Doctoral degree (2001) and Master’s degree (1997) in Engineering and Technology from Helsinki University of Technology. Research Interests: Vehtari specializes in integrating Bayesian methods with computer science, focusing on workflow optimization, probabilistic programming, Gaussian processes, and model diagnostics. His work emphasizes practical inference techniques and their application to real-world problems like aging analysis and RNA sequencing. Recent Research Trends: Recent publications highlight advancements in Bayesian cross-validation, projection predictive inference, and active learning for molecular data. His work often bridges computational efficiency with statistical rigor, addressing challenges in model selection and uncertainty quantification. Grants & Projects: Leading the Finnish Center for Artificial Intelligence (2023–2026) and the Safe Iterative Bayesian Model Building project (2021–2025). Awards: 2016 De Groot Prize, Youden Award (2020), and recognition for contributions to time series prediction (2004). Advising: Supervises doctoral researchers including Guangzhao Cheng and Lu Cheng, advancing topics in RNA analysis and probabilistic modeling. Labs & Teams: Involved in probabilistic AI initiatives, contributing to tools like ArviZ and Stan, and collaborates globally on projects in computational statistics and machine learning.

• 3D Photogrammetry
• Laser Scanning
• Remote Sensing
• +۴ مورد دیگر

Matti Vaaja serves as Professor of Digital Photogrammetry within the Department of Electronics and Nanoengineering at Aalto University. His research bridges microwave radiometry, remote sensing, and advanced 3D modeling techniques with applications spanning environmental monitoring, urban planning, and geospatial technology development. His academic credentials include a Licentiate degree in Engineering and Technology from Aalto University (awarded May 17, 2010) and a Master's degree from Helsinki University of Technology (awarded May 22, 2006). Vaaja's research program demonstrates significant evolution from foundational work in microwave radiometry of snow-covered lake ice (2014-2018) toward contemporary expertise in UAV photogrammetry, terrestrial laser scanning, and digital twin development. His current investigations focus on integrating multi-sensor remote sensing data for urban vegetation monitoring, 3D city modeling, and infrastructure assessment, with particular emphasis on change detection methodologies and environmental sustainability applications. This work intersects with geospatial engineering, computer vision, and sustainable urban development frameworks. Analysis of his 2022-2025 publications reveals dominant trends in multi-platform sensor fusion (UAV, terrestrial laser scanning, SLAM systems), game engine applications for environmental simulation, and practical implementations of geospatial data in construction and urban management. His research consistently addresses real-world challenges in biomass estimation, lighting analysis, and circular economy practices within built environments. He actively contributes to educational initiatives in geoinformatics at Aalto University and develops applied tools like the EODIE earth observation data extractor, demonstrating commitment to both academic advancement and practical implementation of geospatial technologies aligned with UN Sustainable Development Goals.

Arttu Julinمشاهده پروفایل

پژوهشگر

• Photogrammetry
• Terrestrial Laser Scanning
• Change Detection
• +۵ مورد دیگر

Arttu Julin serves as a Postdoctoral Researcher at Aalto University's Department of Built Environment, specializing in geospatial technologies and 3D modeling for urban environments. His research integrates terrestrial laser scanning (TLS), UAV photogrammetry, and virtual reality to address challenges in urban vegetation monitoring and sustainable development, contributing directly to UN Sustainable Development Goals. His research interests focus on photogrammetry , terrestrial laser scanning , and change detection in urban ecosystems. Key areas include 3D point cloud processing for vegetation analysis, multitemporal watershed modeling, and real-time visualization of built environments using game engines. This work bridges geospatial engineering with practical urban planning applications, emphasizing Finland-specific environmental contexts. Recent publications (2024–2025) demonstrate a clear trend toward comparative analysis of remote sensing technologies, particularly UAV photogrammetry versus laser scanning for urban biomass estimation. His work increasingly incorporates multimodal data fusion and real-time visualization , with growing emphasis on practical applications in construction and forest management. Scientific recognition includes: Oskari Vilamo Fund award (2022) Julin actively supervises academic work with one documented thesis completion and participates in five major research projects, including ModyFroad (forest road quality systems) and MIXER (multimodal interaction for vehicles). Funding sources span Academy of Finland strategic grants, EU structural funds, and industry collaborations. He operates within Aalto's MeMo research ecosystem and the Department of Built Environment's digital modeling teams, leveraging interdisciplinary collaborations with forest industry stakeholders and urban planning authorities for real-world implementation of geospatial technologies.

• Atmospheric Science
• Climate Modeling
• Computational Fluid Dynamics
• +۳ مورد دیگر

Johanna Grönqvist is a researcher affiliated with the Faculty of Science and Engineering at Åbo Akademi University, specializing in Atmospheric Science and Climate Modeling. She holds a Doctoral degree from the same university (2018). Her work focuses on Large Eddy Simulation (LES) techniques for studying shallow cumulus clouds, mesoscale variability in climate models, and Python-based tools for atmospheric modeling. She led the project Modellering av nervsignaler som elektromagnetiska vågor (2022), funded by Svenska kulturfonden. Key research areas include computational fluid dynamics, climate model development, and the application of superparameterization methods. She has contributed to open-source software like the Dutch Atmospheric LES Python interface and datasets such as the Cloud Botany LES ensemble visualizations. Her work bridges computational physics, climate science, and software engineering. Grönqvist received the Photonics in Flatland Poster Award (2024) for her contributions to quantum model visualization. She has collaborated on interdisciplinary projects, including swarm robotics simulations and graphene material studies, showcasing her versatility in both atmospheric and condensed matter physics.

Juho Kannalaمشاهده پروفایل

دانشیار

• Computer Vision
• Machine Learning
• 3D Reconstruction
• +۵ مورد دیگر

Juho Kannala is an Associate Professor of Computer Vision at Aalto University and an Adjunct Professor at the University of Oulu. He co-founded Spectacular AI, a company focused on spatial AI and sensor fusion solutions. His primary affiliation is with the Department of Computer Science at Aalto University, where he leads research in computer vision, machine learning, and robotics. Education: PhD in Computer Science, University of Oulu (2010) Master's Thesis: 'Measuring the Shape of Sewer Pipes from Video,' Helsinki University of Technology (2004) Research Interests: Juho's work spans 3D reconstruction , visual localization , deep learning , and robotics applications . His research emphasizes practical systems like real-time visual-inertial odometry (HybVIO) and large-scale datasets (TBPos). He explores interdisciplinary areas such as medical imaging and atomic force microscopy automated analysis. Key Projects: Co-developed the ADVIO dataset for visual-inertial odometry Advanced multi-view stereo techniques with Gaussian splatting Co-founded Spectacular AI for spatial AI solutions Teaching: Responsible for the Computer Vision (CS-E4850) course at Aalto University. Advises on bachelor/master/doctoral thesis topics in computer vision and machine learning.