جست‌وجوی استاد و پژوهشگر

Arno Solinمشاهده پروفایل

دانشیار

• Machine Learning
• Signal Processing
• Sensor Fusion
• +۸ مورد دیگر

Dr. Arno Solin is a tenured Associate Professor in Machine Learning at Aalto University's Department of Computer Science and an Academy of Finland Research Fellow . He leads the Aalto machine learning research group and serves as Director of the Finnish Doctoral Program Network in AI (AI-DOC) . His work bridges probabilistic modeling with practical applications in sensor fusion and real-time inference. ELLIS Scholar (European Laboratory for Learning and Intelligent Systems) Adjunct Professor at Tampere University Member of Young Academy Finland (2021–2025) Research Interests focus on data-efficient machine learning with probabilistic methods for real-time inference and sensor fusion. Key areas include Gaussian processes, diffusion models, stochastic differential equations, and uncertainty quantification in deep learning. His group develops methods that combine structural constraints with adaptive learning for deployment on resource-limited hardware. Publication Trends show consistent output in top venues (NeurIPS, ICML, ICLR, AISTATS) with emphasis on diffusion models , 3D scene reconstruction , and real-time probabilistic modeling . Recent works explore physics-informed learning, heterophily-aware graph models, and compressed representations for world models in reinforcement learning. Scientific Recognition : Awarded AI Researcher of the Year 2024 by AI Finland Teacher of the Year 2023 at Aalto CS ISIF Jean-Pierre Le Cadre Best Paper Award (2018) MLSP Schizophrenia Classification Challenge Winner (2014) NeurIPS/ICML Reviewer Awards Research Leadership includes coordinating Finland's AI Center of Excellence program and directing the Finnish Doctoral Program Network in AI (AI-DOC). He supervises 15+ doctoral/postdoctoral researchers and has spun off Spectacular AI , a company commercializing sensor fusion technology.

• Computer Vision
• Machine Learning
• Deep Learning
• +۶ مورد دیگر

Esa Rahtu is a Professor in the Department of Computer Science at Aalto University, Finland. His research focuses on computer vision, machine learning, and deep learning applications. He leads projects in image coding, neural networks, 3D reconstruction, object pose estimation, and anomaly detection. Rahtu has contributed to over 98 research outputs since 2017, with recent work emphasizing Gaussian splatting for SLAM, neural radiance fields, and hybrid video codecs for human-machine compatibility. His expertise spans visual-inertial odometry (e.g., ADVIO dataset), LiDAR-based place recognition, and manufacturing quality control systems. Key areas include: 3D scene reconstruction using Gaussian splatting techniques Deep learning models for anomaly detection in industrial processes Hybrid video codecs optimizing human perception and machine processing Multi-sensor fusion for robotic navigation and indoor mapping Notable datasets include ADVIO for visual-inertial odometry and FIORD for 3D reconstruction benchmarking. His research aligns with UN SDG 9 (Industry, Innovation & Infrastructure) and SDG 4 (Quality Education) through advancements in smart manufacturing and educational technology. Rahtu has received continuous research funding, including a grant period from April to June 2018. His work emphasizes practical applications, collaborating on real-world challenges like paper manufacturing quality control and smartphone-based 3D reconstruction.

• Video Coding
• Machine Learning
• FPGA Acceleration
• +۳ مورد دیگر

Jarno Vanne is a Professor at the Department of Computing Sciences, Faculty of Information Technology and Communication Sciences at Tampere University. His research focuses on video coding standards, real-time systems, and hardware acceleration, particularly in the context of FPGA implementations and open-source tools. He leads projects involving VVC (Versatile Video Coding), V-PCC (Volumetric Video Coding), and HEVC (High Efficiency Video Coding), with an emphasis on efficiency, low latency, and machine learning integration. Key research interests include point cloud compression, saliency-guided encoding, parallelization schemes, and real-time video communication protocols. His work often addresses challenges in multi-party video streaming, embedded systems, and encryption mechanisms for privacy protection. He has contributed to open-source projects like the UVG dataset, Kvazaar encoder, and CiThruS simulation frameworks. Recent publications highlight advancements in VVC intra encoding optimizations, machine learning-driven partitioning schemes, and FPGA-accelerated solutions for edge computing. His research bridges theoretical video coding algorithms with practical implementations, aiming to improve compression efficiency while maintaining real-time performance.

• Sound Art
• Acoustic Design
• Sonic Arts
• +۳ مورد دیگر

Ricardo Atienza Badel is an Associate Professor in the Department of Art and Media at Aalto University, Finland, specializing in the intersection of sound, space, and media practices. His research portfolio includes: Sound Art and Experimental Composition Acoustic Design for Emergency Vehicles Geolocated Sound Installations Spatial Audio and Auralization Phenomenological Sound Studies Media Art and Intermedia Practices Recent publications (2021-2025) reveal a consistent investigation into how sound shapes human experience across physical environments—from ambulance sirens to GPS-based artworks—and theoretical frameworks connecting sound, space, and embodiment. His collaborative work bridges artistic innovation with technical acoustic research, particularly in emergency vehicle sound design and immersive sonic environments.

• Video Coding
• Machine Learning in Video Compression
• Energy-Efficient Encoding
• +۳ مورد دیگر

Alexandre Mercat is an Assistant Professor in the Department of Computer Engineering at Tampere University, within the Faculty of Information Technology and Communication Sciences. His research focuses on video coding, energy-efficient encoding, and real-time multimedia systems. He leads projects on open-source video encoders and standards, including contributions to HEVC, VVC, and V-PCC technologies. His work emphasizes machine learning integration, low-power hardware optimizations, and scalable distributed encoding frameworks. Key technical interests include improving video compression efficiency through algorithmic innovations, developing open-source tools like the UVG dataset and Kvazaar encoder, and addressing challenges in volumetric video communication and 3D point cloud encoding. His research spans theoretical algorithm design to practical implementations, with applications in virtual reality, live streaming, and edge computing. Recent projects include real-time saliency-guided video coding frameworks, energy reduction techniques for HDR streaming, and multi-layer VVC coding schemes for hybrid machine-human consumption. He also explores FPGA acceleration and parallelization strategies for distributed video encoding systems. No scientific awards are explicitly mentioned in the provided texts. While no formal advisees are listed, his research group likely involves students through open-source development and collaborative projects. His work integrates closely with industry standards bodies and open-source communities, emphasizing reproducible evaluation frameworks and end-to-end software tools.

Sammie Kattمشاهده پروفایل

پژوهشگر

• Bayesian Reinforcement Learning
• Partially Observable Markov Decision Processes
• Robotics
• +۲ مورد دیگر

Sammie Katt serves as a Postdoctoral Researcher in the Department of Computer Science at Aalto University's School of Science, specializing in Bayesian Reinforcement Learning for robotics and decision-making under uncertainty. Their work addresses critical challenges in partially observable environments through algorithmic innovation and practical implementations. Dr. Katt's research centers on Bayesian approaches to reinforcement learning, with deep expertise in Partially Observable Markov Decision Processes (POMDPs). They develop scalable algorithms for uncertainty quantification, robot navigation, and real-time decision-making, bridging theoretical advances with robotic applications. Key contributions include BADDr for adaptive POMDP solutions and gym-gridverse for simulation benchmarking. Analysis of Katt's 14 publications (2012-2023) reveals three dominant trends: (1) Bayesian methods for efficient POMDP solving using Monte Carlo tree search and deep learning, (2) Robotics applications in motion prediction, target search, and scene reconstruction, and (3) Framework development for reproducible RL research. Their work consistently emphasizes computational efficiency and real-world applicability in uncertain environments.

Atanas Gotchevمشاهده پروفایل

استاد

• Immersive Imaging
• 3D and Light Field Imaging
• Image Quality Assessment
• +۲ مورد دیگر

Atanas Gotchev is a Professor of Signal Processing at Tampere University, Finland, where he leads the 3D Media Group and directs the Centre for Immersive Visual Technologies (CIVIT). He also serves as Chair of the Erasmus Mundus Joint Master Programme in Imaging and Deputy Director of the TAU Imaging Research Platform. Gotchev holds degrees from Technical University Sofia (M.Sc. in Electronics and Automation Engineering and M.Sc. in Applied Mathematics), the Bulgarian Academy of Sciences (PhD in Information Technologies), and Tampere University of Technology (D. Tech.). His research focuses on immersive imaging, 3D/light field imaging, computational optics, and image quality assessment. He has coordinated three Marie Sklodowska-Curie networks in light field imaging and serves as Senior Editor for IEEE Transactions on Image Processing and the Journal of Electronic Imaging. Gotchev has organized major conferences like the IEEE International Conference on Image Processing (2026) and chaired workshops on multimedia signal processing. Recent research emphasizes computational cameras, diffractive optics, and end-to-end design for metaoptics. He has a visiting professorship at the University of Utah (2023-2024) supported by Fulbright and Nokia Foundation grants. His work spans datasets like CIVIT and contributions to safety-critical applications in robotics and ITER projects.

Ashwin Natarajanمشاهده پروفایل

پژوهشگر

• Nanotechnology
• Nucleic Acid Nanostructures
• DNA Origami
• +۴ مورد دیگر

Ashwin Natarajan is a doctoral candidate and researcher at the Department of Neuroscience and Biomedical Engineering (Aalto University). His work focuses on DNA/RNA nanostructures, self-assembly, and advanced microscopy techniques. Doctor of Technology (Tekn. toht.), Aalto University (2023) Master's in Engineering and Technology, Anna University (2017) Research spans DNA origami , RNA polyhedra , and nanostructure stability , with applications in biomolecular analysis and transmission electron microscopy . Articles highlight 3D nanofabrication , dynamic DNA systems , and protein-nanoparticle interactions . Received the NBE Best Paper Award (2022) for advances in DNA origami techniques. Collaborated with institutions including Max-Delbrück-Centrum , University of Würzburg , and INSERM U1184 .

Pavel Zemcikمشاهده پروفایل

استاد مهمان

• Computer Graphics
• Computer Vision
• Machine Vision
• +۷ مورد دیگر

Pavel Zemcik serves as a Visiting Professor in the Computational Engineering department at the School of Engineering Sciences, Lappeenranta University of Technology (LUT). His academic work spans multiple domains within computer science with a strong emphasis on visual computing technologies. Dr. Zemcik's research interests encompass a broad spectrum of visual computing disciplines, including computer graphics, computer vision, machine vision, and image processing. His work particularly focuses on light field rendering techniques, 3D display technologies, and advanced wavelet transform applications. He has made significant contributions to GPU acceleration methods for real-time visual processing and has explored applications in both industrial settings and medical imaging. Analysis of his recent publication trends reveals a concentrated research trajectory in light field technologies and 3D display systems over the past five years. His work demonstrates increasing sophistication in handling visual quality metrics, focus management, and compression techniques specifically tailored for 3D displays. The research shows strong interdisciplinary connections between computer graphics, signal processing, and human perception studies. His scholarly output demonstrates consistent productivity across multiple high-impact venues in computer graphics and computer vision. While specific grant information isn't detailed in the available materials, his publication pattern suggests sustained research funding supporting his work in visual computing technologies.

• Artificial Intelligence
• BIM Collaboration
• Decentralized systems
• +۹ مورد دیگر

Seppo Törmä is a Senior Lecturer and Principal Lecturer at Metropolia University of Applied Sciences, specializing in computing and construction. He holds a D.Sc (Tech) in Computer Science and Engineering from Helsinki University of Technology (1997), an M.Sc (Tech) in Electrical Engineering (1988), and a Teacher's pedagogical qualification (AmO) from Haaga-Helia (2022). As the Head of the International Master's Programme in Computing in Construction, he focuses on advancing digitalization in construction through innovative methodologies. His competences include Artificial Intelligence, BIM Collaboration, Decentralized systems, Digital Twin, Information visualization, Interoperability, IoT, Knowledge graphs, Linked Data, Ontologies, Semantic Web, and Sustainable built environment. He leads projects such as RADIAL (digital models for construction), Digipurku (digital solutions for deconstruction), and HereUkraine! (educational support for rebuilding efforts). His research emphasizes semantic interoperability, ontology-based systems, and sustainable construction practices. Notable projects include developing the DRUMBEAT platform for federated building models and exploring peer-to-peer networks for linked building data. His work bridges information technology with construction workflows to enhance efficiency and sustainability. He has no listed scientific awards but has contributed to over 70 publications since 1988, focusing on BIM, Linked Data, and construction digitalization. His advising and grants are not explicitly detailed, but his leadership in academic and applied projects underscores his commitment to advancing the field. He is part of the Sustainable Development in Real Estate Team at Metropolia.

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

Iaroslav Melekhov is a Postdoctoral Researcher at Aalto University's Department of Computer Science, affiliated with the Professorship Kannala Juho. His work focuses on advancing computer vision techniques, particularly in camera relocalization, 3D reconstruction, semantic segmentation, and autonomous systems. He has contributed to high-impact datasets like ECLAIR and developed methods for memory-efficient visual localization and medical image analysis. Melekhov holds a Nokia Foundation Scholarship (2018) and has engaged in collaborative research with institutions such as Wayve Technologies and ETH Zurich. Research Interests: 3D Reconstruction and Gaussian Splatting Camera Relocalization and Visual Localization Semantic Segmentation and Dataset Development Deep Learning for Autonomous Systems Self-Supervised Learning and Feature Descriptors Key Contributions: Co-developed the ECLAIR aerial LiDAR dataset for semantic segmentation. Advanced memory-efficient camera relocalization via differentiable product quantization. Explored medical image segmentation using SAM-generated annotations. Grants & Awards: Nokia Foundation Scholarship, 2018 Labs & Collaborations: Associated with the imedslab research group, contributing to open-source tools like SOLT. Collaborative efforts include projects with Wayve Technologies and ETH Zurich, focusing on autonomous driving and robotics.

• Structural Engineering
• Mechanics
• Computational Mechanics
• +۲ مورد دیگر

Athanasios Markou serves as a University Lecturer in the Department of Civil Engineering at Aalto University, Finland, affiliated with the Structures research group specializing in Structural Engineering, Mechanics, and Computation. His academic role integrates teaching with experimental and computational research on innovative structural systems, particularly focusing on geometrically nonlinear behavior validated through advanced techniques like high-speed imaging and photogrammetry. His research interests span Structural Engineering, Mechanics, and Computational Methods, with emphasis on bending-active and twisted structures, kinematic pavilions, and chain dynamics. He investigates material reuse in sustainable construction, elastic torsion systems, and base isolation for seismic protection, often blending architectural design with engineering principles to develop efficient structural forms. Analysis of his 2019-2024 publications reveals consistent exploration of nonlinear structural behavior through experimental-computational synergy. Key trends include bending-active beam elements, falling chain dynamics, and torsion-based systems like the 'Zero Gravity' series, frequently resulting in open datasets and physical prototypes. His interdisciplinary collaborations emphasize sustainable material use and kinematic innovation in architectural applications. Within Aalto University's Structures research group, Markou contributes to cutting-edge work on structural morphology and computational mechanics. The group pioneers bending-active elements and sustainable construction methodologies, utilizing photogrammetry and high-speed imaging for data-driven validation while advancing material-efficient design paradigms through physical prototyping.

• Generative Adversarial Networks
• Image Quality
• Diffusion Model
• +۷ مورد دیگر

Miika Aittala is a Visiting Professor at the Department of Computer Science, Aalto University School of Science. His research focuses on Generative Adversarial Networks (GANs) , Diffusion Models , and Image Quality optimization. Doctor of Science, Media Technology, Aalto University School of Science (2016) Diplomi-insinööri, Tietotekniikan koulutusohjelma, Mediatekniikka, Aalto-yliopisto, Perustieteiden korkeakoulu (2010) Tekniikan kandidaatti, Tietotekniikan koulutusohjelma, Mediatekniikka, Teknillinen korkeakoulu (2008) His work primarily addresses Diffusion Model training dynamics, Image Synthesis techniques, and Surface Reflectance modeling for photorealistic rendering. Recent publications highlight advancements in Neural Networks for image generation and AI-driven texture mapping . Notable trends in his research include: Development of facilitated diffusion frameworks for improved sample quality Hybrid generator architectures for controllable face synthesis Data-driven approaches to MIP mapping and pixel filtering Scientific recognition includes: Best PhD Thesis Annual Award (2017) Best PhD Thesis Award (2017) Aittala's collaborations span institutions like NVIDIA and teams focused on Generative AI , with media coverage in AI ethics and synthetic image detection.

• Computer Graphics
• Real-Time Rendering
• Parallel Computing
• +۳ مورد دیگر

Erwan Leria is a Doctoral Researcher in the Department of Information Technology at Mid-Sweden University (2021–2025). His research focuses on real-time rendering techniques, multi-GPU computing, and light field technologies. Key interests include optimizing path tracing algorithms for high-performance graphics processing units (GPUs), developing scalable open-source rendering solutions, and advancing real-time applications in virtual/augmented reality environments. His work emphasizes interdisciplinary collaboration, with contributions to projects like the Tauray open-source path tracer for stereo and light field displays. Research topics span multi-source spatial reprojection, denoising techniques, and parallel computing architectures. Publications highlight trends in next-generation rendering systems, particularly in balancing computational efficiency with visual fidelity. Collaborations involve international teams exploring emerging technologies for immersive display systems.

Kaie Kubjasمشاهده پروفایل

دانشیار

• Algebraic Statistics
• Nonlinear Algebra
• Matrix and Tensor Decompositions
• +۵ مورد دیگر

Kaie Kubjas is an Associate Professor at Aalto University in the Department of Mathematics and Systems Analysis, School of Science. Since 2024, she has held a tenured position, following a tenure-track role from 2017–2024. She earned her PhD in Mathematics at Freie Universität Berlin (2013) under Professors Christian Haase and Klaus Altmann, with postdoctoral research at institutions including the Max Planck Institute and MIT. Her research focuses on applied nonlinear algebra, algebraic statistics, and their applications in biology (e.g., phylogenetics and 3D genome reconstruction), as well as matrix/tensor decompositions. She has organized major events like the European Women in Mathematics General Meeting 2022 and the 2025 workshop on Algebraic Statistics and Multistate Models. Kubjas serves on editorial boards of journals like SIAM Journal on Applied Algebra and Geometry and Annales Fennici Mathematici . Recent work includes advances in log-concave maximum likelihood estimation, 3D genome reconstruction, and structured matrix decompositions. Her students, such as Olga Kuznetsova (Second Place MEGA 2021 Poster Award winner), have contributed to these areas. She regularly contributes to seminars like the Algebra and Discrete Mathematics at Aalto, fostering interdisciplinary collaboration.