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Jouni Partanen is a Professor at Aalto University's Department of Energy and Mechanical Engineering within the College of Engineering. His research focuses on advanced production technologies including Additive Manufacturing (3D-Printing), modern laser processing, and micromachining. Research Group: Materiaaleista tuotteiksi Specialization: Integration of AI in manufacturing processes Sustainability emphasis: Biochar-reinforced materials and carbon footprint reduction His work spans from fundamental material behavior analysis to industrial applications, particularly in metal additive manufacturing and composite fabrication. Recent research explores corrosion resistance in lattice structures and multiscale photopolymerization techniques. Publications highlight interdisciplinary approaches combining mechanical engineering with biomedical applications (e.g., patient-specific implants) and environmental health studies on industrial 3D printing emissions.

• Mechanical Engineering
• Additive Manufacturing
• 3D-Printing
• +2 more

Jan Akmal is an Assistant Professor at Aalto University, holding dual affiliations in the Department of Energy and Mechanical Engineering and the Materials to Products group. His research specializes in additive manufacturing (AM), focusing on defect detection, smart materials, and 4D printing applications. He leads the AIM-Zero project (2023–2026), exploring AI-driven zero-defect AM processes. Akmal has received the Aalto Doctoral Incentive Scholarship (2023) and an Honorary Award (2023). He serves on editorial boards for Frontiers in Manufacturing Technology and Frontiers in Mechanical Engineering , and chairs the Finnish Rapid Prototyping Association (FIRPA). Key research areas include AI-based defect detection in metal AM, self-sensing components, and hybrid materials for dynamic displays. He collaborates globally on topics like optical tomography in powder bed fusion and medical AM applications. His work addresses sustainability, industrial adoption of AM, and legal frameworks for military logistics. Akmal has authored 24 publications and contributed to datasets on AM inaccuracies and defect classification, emphasizing practical applications and industry integration.

• Additive Manufacturing
• 3D Printing Technology
• Defect Detection
• +5 more

Pedro Vilaça is a **Professor and Head of the Department of Energy and Mechanical Engineering** at **Aalto University's School of Engineering**, Finland. Previously, he worked at the Instituto Superior Técnico (Técnico), University of Lisbon, Portugal (1995–2013). His research focuses on **welding technology**, **solid-state manufacturing**, **non-destructive testing (NDT)**, **hydrogen-related materials science**, and **materials safety**, with applications in energy and aeronautics sectors. He leads R&D teams and has collaborated globally, contributing to 142+ publications (h-index 32 via Scopus). **Research Interests**: Advanced welding techniques (e.g., friction stir welding), hydrogen embrittlement in steels, supercapacitor materials, and smart composites. He has pioneered methods for **zero-material-loss welding** and **self-sensing metallic materials**. **Key Projects**: Led initiatives like **THEWFuelCells** (fuel cell welding innovations) and **EARLY/Vilaca** (hydrogen damage assessment). His work aligns with **UN Sustainable Development Goals**, emphasizing renewable energy storage and industrial sustainability. **Awards**: 2011 Eng. Cruz Azevedo Award for outstanding research in Mecânica Experimental. **Collaborations**: Active in international networks, including the International Institute of Welding and European research consortia. He has organized conferences, reviewed patents, and advised doctoral students globally. **Recent Articles**: Focus on corrosion-resistant materials, piezoelectric composites, and hydrogen-induced failure in steels. His 2023–2025 work emphasizes energy storage innovations and advanced joining technologies. **Grants**: Principal investigator for projects funded by Business Finland, EU EIT, and Academy of Finland. **Labs/Teams**: Oversees Aalto’s mechanical engineering research teams and collaborates with institutions like Helmholtz-Zentrum Geesthacht.

• Welding Technology
• Solid-State Manufacturing
• Non-Destructive Testing (NDT)
• +6 more

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.

• Computer Vision
• Machine Learning
• Deep Learning
• +6 more

Kenichiro Mizohata is a University Researcher at the Department of Physics, University of Helsinki, and serves as a Supervisor for the Doctoral Programme in Materials Research and Nanosciences. His research focuses on materials physics, ion beam analysis, and the development of advanced materials for energy and environmental applications. Key interests include thin film deposition techniques (e.g., atomic layer deposition), high-entropy alloys, and radiation effects on materials. He is a core participant in the Eurofusion HerHEA project (2024–2025), collaborating with experts in materials science and nuclear engineering. His work spans interdisciplinary collaborations, addressing challenges in sustainable materials, nuclear technology, and environmental science. Publications emphasize experimental and computational studies on material microstructure, defect dynamics, and surface engineering. Mizohata’s research outputs (166+ publications) reflect expertise in materials characterization, with recent trends in nanomaterials synthesis, irradiation-resistant alloys, and sustainable recycling technologies. His contributions advance both fundamental understanding and applied solutions in materials science.

• Materials Science
• Physics
• Nuclear Physics
• +4 more

Hannu Hyyppä is a Research Director and Project Employee at Aalto University's Department of Built Environment, affiliated with the MeMo research group. He leads the Research Institute of Measuring and Modelling for the Built Environment, focusing on advanced laser scanning, 3D modeling, and geoinformatics. His work spans interdisciplinary collaborations across engineering, geography, and arts, with a strong emphasis on applications in cultural heritage preservation, urban planning, and environmental monitoring. Education: Doctoral degree (D.Sc.) in Engineering and Technology, Helsinki University of Technology (2000) Licentiate degree in Engineering and Technology, Helsinki University of Technology (1989) Master's degree in Engineering and Technology, Helsinki University of Technology (1986) Research Interests: Laser scanning technologies, point cloud utilization in forestry and urban mapping, virtual reality for cultural heritage, and sustainable infrastructure modeling. His expertise includes photogrammetry, geographic information systems (GIS), and decision support systems for environmental management. Recent Contributions: Over 550 publications and 30+ active projects, including the Centre of Excellence in Laser Scanning Research (2014-2019) and the Pointcloud project (2015-2021). His work advances applications in autonomous road inspection, 3D cultural reconstructions, and smart city technologies. Awards: Recipient of the 2019 Kansallinen avoimen tieteen palkinto for innovative open science contributions. Grants & Leadership: Principal Investigator for projects like DICA (Digital Cultural Heritage) and ToToRo (Automatic Road Inspection). Active in organizing workshops and international conferences on 3D technologies and laser scanning. Labs/Teams: Oversees the MeMo group and collaborates with national organizations like the Finnish Geospatial Research Institute. Develops tools for real-time 3D mapping and virtual environments.

• Laser Scanning
• 3D Modeling
• Geoinformatics
• +5 more

Kari Rummukainen is a Professor at the Department of Physics, Faculty of Science, University of Helsinki. His research focuses on theoretical particle physics and cosmology , particularly using computational methods . He leads the Computational Field Theory research group . Research keywords include: Physical sciences High Energy Physics Cosmology Lattice Gauge Theory Scientific awards: Finnish Academy of Sciences and Letters: 2008 Vaisala prize (awarded in Dec 2006) Current projects: Avaruuden ja kosmologian tutkimus (2024–2030) - University of Helsinki Funds CoCoS AdG (2024–2029) - European Research Council Particle cosmology and gravitational waves (2024–2027) - Academy of Finland

• Theoretical Particle Physics
• Cosmology
• Lattice Gauge Theory
• +1 more

Tapio Niemi is an Associate Professor (tenure track) in the Department of Physics at Aalto University. He holds a Doctor of Science (Technology) from the former Helsinki University of Technology (now part of Aalto University), awarded in 2002. His research focuses on nanophotonics, plasmonics, quantum dots, and semiconductor materials, with particular expertise in nanofabrication techniques such as block copolymer lithography and nanoimprint lithography. His work contributes to the UN Sustainable Development Goals through advancements in solar cell technology and environmental sensor development. Key research areas include the design of optical devices like microdisk lasers, guided-mode resonance gratings, and plasmonic sensors. He has pioneered studies on ZnO nanowire functionalization and nonlinear optical microscopy, with applications in biomedical imaging and environmental sensing. His interdisciplinary approach integrates nanotechnology, photonics, and materials science to address challenges in energy, health, and sustainability. Education: Doctor of Science (Technology), Helsinki University of Technology (2002) Supervision: Advised doctoral student Tommi Isoniemi on carbon nanotubes and graphene in plasmonics (2017–2021) Labs/Teams: Collaborations include the NanoScience Center (University of Jyväskylä) and Aalto’s nanophotonics research groups Recent work emphasizes high-Q resonators, Purcell-enhanced quantum dot light sources, and UV-curable polymer stamp lithography. His publications span journals like Advanced Optical Materials and Applied Physics Letters , with a focus on practical applications in photonics and nanotechnology.

• Nanophotonics
• Plasmonics
• Quantum Dots
• +5 more

Niko Siltala is a University Instructor at Tampere University's Department of Automation Technology and Mechanical Engineering. He holds a Doctor of Science (Technology) in Mechanical Engineering (2016) and a Master of Science (Technology) in Automation Engineering (2001). His research focuses on production system design, reconfiguration, robotics, and virtual reality applications in safety training. He has contributed to the development of semantic rules for capability matchmaking, which supports rapid system design and reconfiguration. Key research areas include: Manufacturing automation and system adaptability Human-robot collaboration and safety protocols Virtual reality-based training solutions Formal resource descriptions for modular systems His work aligns with UN Sustainable Development Goals, particularly in advancing quality education (SDG 4) through innovative robotics education tools. He has received the Distinguished Committee Service Award (2004) and contributed to grants such as the K.F. ja Maria Dunderbergin testamenttisäätiö (2014). He has collaborated on projects like the D-BEST methodology for pilot lines and the ODIN project for scalable production systems. His activities include organizing conferences, presenting at international forums, and developing web-based tools for manufacturing system planning.

• Production System Design
• Robotics
• Manufacturing Automation
• +3 more

Mika Sillanpää is a Professor at the Department of Applied Physics , Aalto University , and serves as a Professor (Associate Professor) at the Centre of Excellence in Quantum Technology and the Quantum Nanomechanics group. His research focuses on experimental superconducting nanoelectronics at milli-Kelvin temperatures, particularly Josephson junction quantum circuits and microwave optomechanics. Doctoral degree from Helsinki University of Technology (2005) Master's degree from Helsinki University of Technology (1999) Research Interests: Prof. Sillanpää has pioneered low-noise amplification schemes using microwave optomechanics and demonstrated quantum squeezing/entanglement in micromechanical devices. Key projects include GUANTUM (quantum-gravity limits) and MQSens (quantum sensing with mechanical resonators). Scientific Contributions: His work spans quantum back-action evading measurements, noiseless amplification, and TLS detection in resonators. Recent publications highlight Casimir force measurements between superconductors and long-lived electromechanical systems using silicon-carbide membranes. ERC Consolidator Grant (2013) ERC Starting Grant (2009) IUPAP C5 Young Scientist Prize (2011) Väisälä Prize (2015)

• Quantum Technology
• Nanophysics
• Microwave Optomechanics
• +5 more

Aimad el Issaoui is a Doctoral Researcher at Aalto University's T213 Built Environment unit, specializing in geoinformatics and laser scanning technologies. His work focuses on integrating advanced sensing systems with deep learning applications for infrastructure and environmental monitoring. His research spans multiple domains: Infrastructure Analysis : Pavement distress detection, road rut measurement using mobile/terrestrial laser scanning Environmental Monitoring : Forest health assessment, UAV-based canopy mapping, tree water stress analysis 3D Mapping : Semantic point cloud segmentation, ALS/MLS integration, indoor scanning methodologies The publications reveal a consistent focus on improving accuracy and efficiency in spatial data processing across civil engineering and ecological contexts. He has contributed to advancements in: Real-time localization systems Deep learning approaches for point cloud analysis Multiscale forest observation techniques 3D indoor/outdoor environment modeling

• Geoinformatics
• Remote Sensing
• Photogrammetry
• +5 more

Liisa Holm is a Professor at the Institute of Biotechnology, University of Helsinki, and a supervisor in the Doctoral Programme in Integrative Life Science. Her research focuses on computational genomics, structural bioinformatics, and protein function prediction. Research Interests : Computational biology, protein structure analysis, machine learning applications in genomics Key Projects : HiLIFE Grand Challenge (antimicrobial resistance), burn wound infection metagenomics, protein structural aging studies Recent Trends : Holm's work spans protein structure comparison (DALI algorithm), pan-genome analysis of protein crops (faba bean), and aging-related structural changes in proteins. She also contributes to AI-driven advancements in structural biology. Scientific Awards : 2024 Nobel Prize in Chemistry (for AI-driven protein research) Leadership Roles : Project leader for Academy of Finland grants, member of international scientific committees

• Bioinformatics
• Computational Genomics
• Protein Structure Analysis
• +3 more

Tuomas Puttonen is a Researcher at Aalto University, affiliated with the Department of Energy and Mechanical Engineering and the Materials to Products division. His work focuses on additive manufacturing (AM), corrosion of materials, and design complexity in AM processes. He holds a Doctoral Thesis (2023) exploring long-term material behavior in additive manufacturing contexts. His research spans industrial barriers to digital spare parts adoption, lattice structure corrosion analysis, and AI-driven defect detection in metal AM. Key research interests include powder bed fusion technologies, material characterization via X-ray tomography, and sustainable manufacturing practices. Puttonen has contributed to datasets on AM polyamide weathering, lattice structure corrosion, and dimensional accuracy evaluations. Collaborations emphasize cross-industry applications of AM in the machine-building sector. Education: Doctoral Thesis (2023) Key datasets: Aalto 3D prints , Corrosion Test 20 His work aligns with UN Sustainable Development Goals related to sustainable industry and innovation. Research trends emphasize bridging AM technology gaps between academia and industry, particularly in corrosion-resistant designs and AI integration for quality control.

• Additive Manufacturing
• 3D Printing
• Powder Bed Fusion
• +8 more

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.

• Computer Graphics
• Computer Vision
• Machine Vision
• +7 more

Nada El Bouharrouti is a Doctoral Researcher at the Department of Electrical Engineering and Automation, Aalto University. Her work focuses on the intersection of electrical engineering and machine learning for mechanical defects analysis in industrial machinery. School: School of Electrical Engineering Department: Electrical Engineering and Automation Academic Rank: Researcher Research Interests: Driven by electromechanical systems and industrial diagnostics, her research emphasizes: Advanced vibration signal analysis for fault detection Machine learning applications in bearing defect classification Multi-physics simulation of induction machines Time-frequency representation techniques Predictive maintenance of industrial motors Data-driven monitoring system development Publication Trends: Recent articles highlight interdisciplinary approaches combining deep transfer learning, multi-rate signal processing, and 3D mechanical simulations to address bearing faults in induction machines. Collaborative work spans computational electromechanics and industrial automation domains. Contact: Email: nada.elbouharrouti@aalto.fi

• Electrical Engineering
• Machine Learning
• Mechanical Defects Analysis
• +2 more