Find Academic Supervisors

• Robot Vision
• Robot Learning
• Computer Vision
• +5 more

Joni Kämäräinen is a Professor of Signal Processing at Tampere University’s Computing Sciences department under the Faculty of Information Technology and Communication Sciences. He leads the Vision Group and has a tenure-track career at Tampere University since 2012, achieving full professorship in 2020. Prior, he conducted postdoctoral research at the University of Surrey’s Center of Vision, Speech and Signal Processing and held a faculty position at LUT University. His research spans robot vision and robot learning , focusing on computer vision and machine learning applications. Key applied projects address industrial collaboration with Huawei, Nokia Technologies, and others, emphasizing robust visual perception, autonomous systems, and depth sensor integration. Notable work includes long-term visual place recognition, RGB-D tracking, and color constancy solutions. Recent publications (2023–2025) reflect expertise in digital twins for industrial manipulators, probabilistic subgoal modeling, depth-aware video deblurring, and LiDAR-place recognition datasets. His team’s outputs bridge robotics, AI, and signal processing, with interdisciplinary impacts in healthcare and autonomous navigation. Teaching responsibilities include graduate and doctoral colloquia, and the textbook Koneoppimisen perusteet (2023) . Funding sources include the Academy of Finland, EU Horizon 2020, Business Finland, Huawei, and Nokia Technologies.

• Artificial Intelligence
• BIM Collaboration
• Decentralized systems
• +9 more

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.

Rupesh VyasView profile

Associate Professor

• Information Design
• Interface Design
• Interaction Design
• +5 more

Dr. Rupesh Vyas is an Associate Professor at Aalto University , affiliated with the School of Arts, Design and Architecture and the Department of Art and Media . With expertise in information design, data visualization, and interface design, he bridges design principles with scientific data representation across disciplines including genetics, urban planning, and public health. Research Interests : Vyas specializes in creating intuitive visual systems for complex data, focusing on genetic research, smart city infrastructure, and public service design. His work applies wayfinding metaphors and interactive tools to make genomic data, urban mobility patterns, and cultural information more accessible. Scientific Awards and Recognition : Best Smart City Information Design and Signage for Gujarat Financial and Technology City (2015) Best Cultural Design award by Design Expo, Faculty Summit (2013) Publications Trends : His recent articles emphasize interdisciplinary applications of visualization techniques, spanning genetic research (2024), Finnish genome mapping (2023), and smart city systems (2021). Key themes include network analysis, equality in design, and biomedical data representation. Academic Contributions : Vyas has developed tools like LAVAA for disease association visualization, created interactive platforms for Finland’s genetic ancestry, and designed wayfinding systems for urban data. His work impacts both academic research and real-world applications in healthcare, genetics, and city planning.

Sándor Kisfaludi-BakView profile

Assistant Professor

• Algorithms
• Computational Geometry
• Geometric Optimization
• +4 more

Sándor Kisfaludi-Bak is an Assistant Professor in the Department of Computer Science at Aalto University, specializing in theoretical computer science with a focus on computational geometry. He develops algorithms for geometric problems involving points, curves, shapes, and spatial networks. His research interests include Algorithm design for geometric optimization Computational geometry fundamentals Spatial network analysis Hyperbolic and planar graph algorithms Parameterized complexity in geometric contexts Recent publications demonstrate expertise in Traveling Salesman Problem optimization, Steiner network construction, and hyperbolic graph analysis. Key research trends span computational geometry, graph theory, and algorithmic complexity in spatial domains. He has no listed scientific awards in the provided materials. No information about student advising or organizational affiliations beyond Aalto University was found.

• Probabilistic Machine Learning
• Probabilistic Circuits
• Probabilistic Programming
• +2 more

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.

Antti Ylä-JääskiView profile

Professor

• Mobile Cloud Computing
• Distributed Systems
• Pervasive Computing
• +5 more

Antti Ylä-Jääski is a tenured Professor at Aalto University's Department of Computer Science within the School of Science. He holds roles such as former Vice Head of Department (teaching) and leads the Distributed and Mobile Computing Research Group. His research focuses on distributed mobile computing systems, including mobile cloud computing, edge/fog computing, and energy-efficient communication. He has authored over 135 publications and secured funding from institutions like the Academy of Finland and Business Finland. Education: Doctoral degree in Engineering from Hochschule für Technik Zürich (1993), Master's from Helsinki University of Technology (1986) Research interests span mobile cloud computing, distributed systems, and IoT. Recent work includes vehicular fog computing for 5G, real-time gaze prediction in VR, and edge-assisted video streaming. Awards include multiple Best Paper Awards and teaching accolades, such as the 2009 award for best teaching in the School of Science and Technology. He supervises graduate studies and has advised notable students like Teemu Kämäräinen (2019 Dissertation Award) and Ilya Nikolaevskiy (2018 Award). His projects include 5G@II (2017-2019) and PraNA (2016-2018), focusing on network analytics and programmable systems. Labs/Teams: Leader of the Distributed and Mobile Computing Group, member of Helsinki Institute for Information Technology (HIIT), and Nokia Center for Advanced Research Deputy Director.

Pekka ToivanenView profile

Professor

• Artificial Intelligence
• Healthcare Systems
• IoT and Smart Grids
• +3 more

Pekka Toivanen is a Professor at the School of Computing, Faculty of Science, Forestry and Technology at the University of Eastern Finland. His research focuses on artificial intelligence, healthcare systems, IoT, cybersecurity, and machine learning. He has contributed extensively to fields like medical image analysis, smart grids, and robotics, with over 100 peer-reviewed publications. His work bridges theoretical advancements and practical applications, such as improving healthcare diagnostics via AI, enhancing IoT security, and optimizing energy management in microgrids. Research Interests: Dr. Toivanen’s primary areas include AI-driven healthcare solutions, cybersecurity for IoT devices, deep learning applications in medical imaging, and energy-efficient smart grid systems. He has pioneered methods for tuberculosis risk prediction and developed novel encryption algorithms for Bluetooth security. Publications: His recent work emphasizes AI in healthcare (e.g., tuberculosis prediction models) and energy systems (e.g., reinforcement learning for microgrid management). He also explores vulnerabilities in wireless technologies like Bluetooth and ZigBee, proposing robust countermeasures. Grants/Teams: Leads projects on AI distribution platforms for healthcare and secure IoT architectures. Collaborates with interdisciplinary teams in medical and engineering domains.

Ashwin NatarajanView profile

Researcher

• Nanotechnology
• Nucleic Acid Nanostructures
• DNA Origami
• +4 more

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 .

• Computer Vision
• Machine Learning
• 3D Reconstruction
• +5 more

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
• +2 more

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
• +7 more

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
• +3 more

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 KubjasView profile

Associate Professor

• Algebraic Statistics
• Nonlinear Algebra
• Matrix and Tensor Decompositions
• +5 more

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.

• Risk Analysis
• Financial Market Forecasting
• Quantitative Modelling
• +7 more

Dr Tiejun Ma is an Associate Professor at the University of Southampton , specializing in risk analysis and decision-making. His research integrates quantitative modelling with real-time big data analytics to address financial market forecasting challenges. He has secured over £4 million in research grants across 30 projects funded by EPSRC, ESRC, Innovate UK, and industry partners like Huawei. Member of the Centre for Risk Research Member of CORMSIS (Centre for Operational Research, Management Science and Information Systems) Member of the Centre for Digital Finance Dr Ma's research spans cryptocurrencies, behavioral economics, data anonymization, and financial risk management. His work combines applied data science, mathematical modelling, and behavioral analysis to tackle complex uncertain environments. Key projects include: Huawei-funded Failure Detection Algorithm for SDN/NFV EPSRC-funded Modelling the Wisdom of the Crowd Modelling Complex Uncertain Environments with E.V. Analytics Ltd His publications appear in leading journals like the European Journal of Operational Research , Quantitative Finance , and Journal of Computer Science and Technology . He supervises PhD students in Business Studies & Management.

Aleksanteri Mikulus SladekView profile

Researcher

• Machine Learning
• Probabilistic Modeling
• Bayesian Inference
• +2 more

Aleksanteri Mikulus Sladek serves as a Doctoral Researcher in the Department of Computer Science at Aalto University, Finland, operating under Professor Arno Solin's research group. His institutional affiliation includes the T313 research unit with postal operations based in Espoo. Sladek's research centers on advanced machine learning methodologies, specializing in probabilistic modeling and Bayesian inference frameworks. His work pioneers novel representation techniques including bitstring encoding for approximate inference and algebraic approaches to mixture modeling. He actively contributes to scalable learning algorithms through theoretical innovations in probabilistic graphical models and representation learning architectures. Analysis of his recent publications reveals a distinct trajectory toward efficient computational representations in machine learning, with dual emphases on theoretical rigor and practical implementation. His 2025 work on bitstring representations demonstrates breakthroughs in Bayesian computation, while the 2024 ICLR publication establishes new paradigms for mixture model learning through squaring operations. Sladek operates within Professor Arno Solin's research ecosystem at Aalto University, focusing on probabilistic machine learning systems. The group maintains active participation in premier venues including ICLR and Proceedings of Machine Learning Research, with emphasis on bridging statistical theory with artificial intelligence applications.