جست‌وجوی استاد و پژوهشگر

Patrick Rinkeمشاهده پروفایل

استاد مدعو

• Theoretical Physics
• Material Science
• Computational Science
• +۱ مورد دیگر

Patrick Rinke serves as an Adjunct Professor in the Department of Applied Physics at Aalto University, Finland. His research bridges theoretical physics, materials science, and computational methodologies with a strong focus on machine learning applications. His computational work spans electronic structure theory, materials design, and atmospheric chemistry. Rinke's research integrates Bayesian optimization, active learning, and high-throughput computational screening to accelerate materials discovery, particularly in hybrid perovskites, catalysts, and biomaterials. Recent work demonstrates machine learning's transformative potential in predicting molecular properties, optimizing materials functionality, and solving complex physical chemistry problems. His scientific contributions have been recognized with multiple awards: Thesis Prize from the Institute of Physics (2003) DFG Research Scholarship (2007-2009) Outstanding Postdoctoral Achievement Award (2009) Outstanding Referee of Physical Review Letters (2014) August-Wilhelm Scheer Visiting Professorship (2017)

Simo Särkkäمشاهده پروفایل

استاد

• Machine Learning
• Signal Processing
• Bayesian Filtering
• +۷ مورد دیگر

Professor Simo Särkkä holds a position in Sensor Informatics and Medical Technology at the Department of Electrical Engineering and Automation (EEA), Aalto University. His research focuses on multi-sensor data processing, Bayesian filtering, machine learning, and their applications in medical technology, brain imaging, and inverse problems. He leads research groups including the Helsinki Institute for Information Technology (HIIT) and Sensor Informatics and Medical Technology. His work bridges theoretical advancements in probabilistic methods with practical implementations in healthcare and engineering. Key research interests include Gaussian processes, stochastic differential equations, quantum machine learning, and signal processing. He has contributed to advancements in algorithms for nonlinear state-space models, parallel computing techniques, and medical imaging technologies such as scatter correction in CT scans. His methodologies are applied across domains like autonomous systems, robotics, and bioengineering. Notable publications span topics like quantum-assisted Gaussian regression, physics-informed machine learning for industrial processes, and parallel-in-time numerical methods. His work emphasizes computational efficiency and robustness in high-dimensional and real-time systems.

Mohamed Noureldinمشاهده پروفایل

استادیار

• Structural Engineering
• Artificial Intelligence in Civil Engineering
• Structural Health Monitoring
• +۵ مورد دیگر

Mohamed Noureldin is an Assistant Professor at the Department of Civil Engineering, Aalto University , Finland, with prior academic roles at Sungkyunkwan University, South Korea (2015–2022). His expertise lies in integrating Artificial Intelligence (AI) with Structural Health Monitoring (SHM) , Structural Digital Twin , Predictive Maintenance , and Seismic Retrofitting . Research Focus : AI-powered sustainable structural design, smart retrofitting, predictive maintenance, structural material innovation, and next-generation performance-based seismic/wind design. Industrial Experience : 20+ years in offshore/onshore structural engineering (Hyundai Heavy Industries, Samsung Engineering, Arab-Swiss Engineering Company, Zuhair Fayez Partnership). Teaching : Courses in structural analysis, seismic design, dynamics, and reinforced concrete at Aalto and Sungkyunkwan Universities. Laboratory : Leads the Structural Design AI Lab (SDAI), focusing on AI-driven resilient infrastructure. Contact : mohamed.noureldin@aalto.fi , +358504544861. His publications explore cutting-edge applications of AI, ML, and DL in seismic retrofitting, structural durability, soil stabilization, and hybrid damping systems. Collaborative work emphasizes life-cycle cost assessment and augmented reality for predictive maintenance.

Marko Hinkkanenمشاهده پروفایل

استاد

• Electric Drives
• Power Electronics
• Control Systems
• +۴ مورد دیگر

Marko Hinkkanen is a Professor at Aalto University's Department of Electrical Engineering and Automation, affiliated with the School of Electrical Engineering. His research focuses on electric drives, power electronics, and control systems, with a strong emphasis on sensorless control, grid converters, and motor drives. He has received numerous awards, including the IEEE Fellow distinction and multiple best paper awards for contributions to sensorless control and grid integration. Research interests span advanced control algorithms, power converter design, and renewable energy systems. His work addresses challenges in grid stability, motor drives under weak grid conditions, and energy-efficient control strategies. Notable achievements include innovations in bearingless motor systems and grid-forming converter control frameworks. His awards reflect impactful contributions: the Aalto ELEC Supervisor Award (2023) highlights exceptional mentorship, while the IEEE Fellow (2023) and numerous paper awards underscore technical excellence. Key publications include advancements in grid-forming control, sensorless techniques, and electromagnetic damping for aircraft systems. He has advised doctoral students such as F. M. Mahafugur Rahman and Hafiz Asad Ali Awan, whose theses won top awards. His labs and collaborations focus on cutting-edge topics like virtual air gap reactors and six-phase machine dynamics.

• Machine Learning
• Computational Neuroscience
• Artificial Intelligence
• +۷ مورد دیگر

Aapo Hyvärinen is a Professor of Computer Science at the University of Helsinki , affiliated with the Helsinki Institute for Information Technology and the Helsinki Probabilistic Machine Learning Lab . He previously held the position of Professor of Machine Learning at the Gatsby Computational Neuroscience Unit, University College London (2016-2019). Education : Undergraduate Mathematics at University of Helsinki, Vienna, and Paris; Ph.D. in Information Science from Helsinki University of Technology (1997) His research focuses on machine learning and computational neuroscience , particularly: Independent Component Analysis (ICA) Natural Image Statistics Causal Representation Learning Neural Signal Processing Applications to brain imaging (MEG, CryoEM) Recent publications emphasize causal discovery , identifiable machine learning , and nonlinear ICA . Key projects include: VETURI (AI for health) DIGIMIND (AI in mental health) CIFAR grants (2022-2025) Scientific awards : Highly Cited Researcher (2010) He serves as Action Editor for the Journal of Machine Learning Research and Neural Computation , and has held Area Chair roles at NeurIPS, ICML, ICLR, AISTATS, and UAI conferences. His work bridges theoretical machine learning with neuroscience and philosophical implications of artificial intelligence .

• Information Discovery
• Bayesian Networks
• Information Retrieval
• +۵ مورد دیگر

Petri Myllymäki serves as Director of multiple prominent research entities at Aalto University's School of Science, including the Helsinki Institute for Information Technology (HIIT), the Department of Computer Science, the Finnish Center for Artificial Intelligence (FCAI), and his own research group. His leadership positions demonstrate significant influence in the Finnish and international AI research community. Dr. Myllymäki's research focuses on information discovery, Bayesian networks, information retrieval, and machine learning. His work bridges theoretical computer science with practical applications, particularly in developing systems that enhance how humans interact with information spaces. His research fingerprint prominently features Information Discovery (100%), Bayesian Networks (50%), Hashing (50%), Approximation Algorithms (50%), Information Retrieval (50%), structure learning (50%), Search Engine technology (37%), and Task Performance (33%). His recent publications (2015-2025) reveal a consistent trajectory in interactive information discovery systems, with increasing applications in diverse domains including decision science, public health, and social media analysis. Notably, his 2024-2025 work shows expansion into cognitive burden in decision-making, health outcomes research using machine learning, and political social media analysis – demonstrating how his core AI methodologies are being applied to address complex societal challenges. Recognized at AI Finland Gala 2024 for contribution reaching over 1.4 million users Featured in multiple media outlets including X (formerly Twitter) and Bluesky Work referenced in patents and Wikipedia pages Dr. Myllymäki actively hosts academic visitors and participates in collaborative research activities. His work contributes to UN Sustainable Development Goals through applications in information access and AI development. The Myllymäki Petri group (HIIT) serves as a hub for interdisciplinary research at the intersection of artificial intelligence, human-computer interaction, and information systems.

Markus Heinonenمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Probabilistic Modeling
• Bayesian Inference
• +۵ مورد دیگر

Markus Heinonen is an Academy Research Fellow at Aalto University's Department of Computer Science within the School of Science. His academic position is tied to Harri Lähdesmäki's Professorship, focusing on probabilistic machine learning. He holds a Doctoral degree in Engineering and Technology from the University of Helsinki (2013). His research integrates probabilistic modeling , deep learning , and differential equations , with applications in computational biology, drug discovery, and biophysics. Key themes include Gaussian processes, Bayesian inference, generative models, and their use in understanding complex biological systems like immune cell behavior (e.g., T cell receptor analysis in aplastic anemia) and molecular design. He leads major projects such as the Deep Learning with Differential Equations initiative (2020–2025), exploring continuous-time models and physics-informed neural networks. His work bridges theory and application, evidenced by collaborations in diffusion models , optimal transport , and single-cell analysis . Publications span over 65 peer-reviewed outputs, with recent emphases on robust neural network training, multi-target molecular prediction, and interpretable drug design frameworks. His research contributes to UN Sustainable Development Goal 3 (Good Health) through advancements in disease modeling and therapeutic development. He has undertaken visiting research roles at the University of California, San Francisco (2017) and Telecom ParisTech (2013–2014). Media highlights include recognition for work on TCR-epitope prediction and AI-driven enzyme engineering.

• Wireless Communications
• Signal Processing
• 5G/6G Networks
• +۳ مورد دیگر

Mikko Valkama is a Professor at the Department of Communications Engineering , part of the Faculty of Information Technology and Communication Sciences at Tampere University . His research focuses on advanced wireless communication systems, positioning technologies, and integrated sensing and communication (ISAC). He holds an Orcid ID ( 0000-0003-0361-0800 ) and can be reached at mikko.valkama@tuni.fi . Research interests span 5G/6G networks , RF antenna design , deep learning for signal processing , and millimeter-wave systems . He leads projects on positioning algorithms (e.g., mmWave SLAM, NLOS mitigation), ISAC architectures, and hardware-efficient transmitter linearization. Notable contributions include works on DECT-2020 NR standards, phase-based localization, and RIS-assisted systems. In 2025 alone, his group published over 30 articles on topics such as: Antenna array design for Ka-band and wideband applications Machine learning for power amplifier predistortion Bistatic radio SLAM and mmWave mapping Covert transmission and physical-layer security His work bridges theoretical advancements with practical implementations, often validated through experimental setups (e.g., TUJI1 dataset for indoor localization). No scientific awards were explicitly listed in the provided texts.

Simo Hostikkaمشاهده پروفایل

استاد

• Fire Safety Engineering
• Thermal Radiation Heat Transfer
• Solid Pyrolysis Modelling
• +۵ مورد دیگر

Simo Hostikka is a Professor in the Department of Civil Engineering at Aalto University's School of Engineering. His research focuses on fire safety engineering , utilizing numerical fire simulations to address critical challenges in building and infrastructure safety. Key Expertise: Fire Dynamics Simulator (FDS) development, thermal radiation heat transfer, pyrolysis modeling, fire toxicity calculations, and probabilistic risk analysis. Leadership: Supervises advanced fire safety research and contributes to international fire safety standards. Research Trends: Recent publications emphasize fire toxicity modeling , hydrogen fire safety , radiation heat transfer , and fire retardancy of polymeric materials . His work bridges computational methods with real-world fire safety applications. Scientific Awards: Philip Thomas Medal of Excellence (2008, 2005) Sjölin Award (2012) Interflam Trophy (2007) Harmathy Award (2020, 2019) Dean’s Award for Best MSc Thesis (2020) Best Paper in Rakenteiden Mekaniikka (2009) Advising: Supervised Topi Sikanen, who received the Young Talent Award from the International Water Mist Association.

Pekka Marttinenمشاهده پروفایل

دانشیار

• Machine Learning
• Large Language Models
• Reinforcement Learning
• +۸ مورد دیگر

Pekka Marttinen is a tenured Associate Professor of Machine Learning at Aalto University, Department of Computer Science, and leads the Machine Learning for Health (Aalto-ML4H) group within the Helsinki Institute for Information Technology HIIT. Education: M.Sc. in Applied Mathematics, University of Helsinki (2004) Ph.D. in Statistics, University of Helsinki (2008) Title of Docent in Information and Computer Science, Aalto University (2015) Research Focus: His methodological work spans large language models, reinforcement learning, deep learning, probabilistic machine learning, and causal inference . These techniques are applied to critical domains of healthcare, bioinformatics, statistical genetics, epidemiology, and personalized medicine . The group develops novel algorithms, theoretical guarantees, and open-source software that enable data-driven discovery and decision-making in medicine and biology. Publication Trends: Across 2022-2024 the lab has concentrated on (i) rigorous causal reasoning over temporal clinical data, (ii) principled uncertainty quantification in LLMs, (iii) representation learning for neural network comparison, and (iv) translational projects that turn raw EHRs into actionable clinical insights. Earlier work integrated high-dimensional genomics with metabolomics and mapped evolutionary forces in bacterial pathogens. Scientific Awards & Recognition: While no specific awards are listed, his sustained publication record in top-tier venues (NeurIPS, ICML, AISTATS, Nature Genetics, PLOS CB) and his role as responsible professor of the Machine-Learning, Data-Science and AI major signify significant peer recognition. Advising & Grants: Prof. Marttinen currently mentors 8 PhD students as primary supervisor and an additional 7 PhD students as co-supervisor. He has already graduated 8 PhDs since 2014. The group is supported through competitive funding including the Finnish Center for Artificial Intelligence (FCAI) doctoral program. Labs & Teams: He directs the Machine Learning for Health (Aalto-ML4H) research group, comprising postdocs Hans Moen, Ti John, Alexander Nikitin, Negar Safinianaini, Linli Zhang, Zhiyuan Li, and the above-mentioned PhD cohort.

• Structural Engineering
• Fire Safety Engineering
• Materials Science
• +۳ مورد دیگر

Jari Puttonen is a Professor of Structural Engineering at Aalto University's Department of Civil Engineering, School of Engineering. His research focuses on structural analysis, fire safety, materials science, and nuclear infrastructure safety. He has held roles as Principal Investigator in projects related to nuclear waste repository concrete modeling and aging management of NPP infrastructure. He has advised over 20 academic visitors and served in doctoral thesis committees. Education: Doctoral degree (1987), Licentiate (1984), and Master's degree (1979) in Engineering and Technology from Helsinki University of Technology (now part of Aalto University). Research Interests: Steel and composite materials behavior under extreme conditions Fire resistance of structural systems Long-term performance of concrete in nuclear facilities Non-destructive testing of construction materials Seismic resilience of critical infrastructure Awards: Recipient of the Knight, First Class of the Order of the White Rose of Finland (2020), PUUPalkinto 2010, and Schweighofer Prize 2011 for innovative energy facade research. Grants & Projects: Led 13 research projects including PERCO2_2023 (nuclear waste repository modeling) and CONAGE2022 (NPP concrete aging). Active in EU-funded initiatives and industry collaborations. Labs & Teams: Core member of Aalto's Structural Engineering Research Group, collaborating with Chalmers University and Technical University of Munich on advanced materials testing.

• Antenna Design
• Wireless Communication
• RFID Technology
• +۳ مورد دیگر

Jari Holopainen is a Senior Lecturer at the Department of Electronics and Nanoengineering , Aalto University. His work focuses on advanced antenna systems, wireless communication, and RFID technologies, with significant contributions to broadband, tunable, and wearable antenna designs. Current affiliation: Aalto University Academic role: Senior Lecturer Research interests span antenna design for mobile terminals, microwave engineering, and machine learning applications in RF systems. His publications highlight innovations in: Bluetooth antennas for metallic smartwatches and jewelry Wideband and dual-polarized antenna arrays RFID transponders with beam steering Machine learning-driven load optimization 3D-printed and capacitive-coupling antenna structures Wave propagation and scattering analysis Scientific contributions include: 15+ peer-reviewed articles (2025-2020) Collaborations with leading researchers in electromagnetics (e.g., Ville Viikari, Pertti Vainikainen)

Olli Sotamaaمشاهده پروفایل

استاد

• Game Studies
• Media Studies
• Production Studies
• +۵ مورد دیگر

Olli Sotamaa is a Professor of Game Culture Studies at Tampere University's Faculty of Information Technology and Communication Sciences, Department of Communication Sciences. He leads the Tampere University Game Research Lab alongside Professor Frans Mäyrä and serves as team leader in the Centre of Excellence in Game Culture Studies. Specializes in game cultural phenomena: online communities, fandom, modding, data-driven game development Co-edited Game Production Studies (Amsterdam University Press, 2021) His research focuses on game industry practices, data analytics impact, and player production cultures. Recent work examines game data labor, industry ethics, and digital preservation challenges. Key trends in his 15 most recent articles include: Game data work and algorithmic culture (2023-2025) Game modding and user-generated content (2021-2022) Game development practices (2019-2021) Game preservation and heritage (2020) Industry sustainability and ethics (2021-2025) Scientific awards: ERC-Advanced-Grant project 'Making Sense of Games (MSG)' Labs/teams: Tampere University Game Research Lab Centre of Excellence in Game Culture Studies

• E-Commerce
• Online Retailing
• Consumer Behavior
• +۳ مورد دیگر

Abdallah Houcheimi is a researcher at Åbo Akademi University's School of Business and Economics within the Faculty of Humanities and Social Sciences. His work focuses on critical e-commerce mechanisms and emerging market digitalization challenges. His research expertise centers on online retailing ecosystems , with particular emphasis on: Secure payment system design and consumer trust dynamics Product information architecture for conversion optimization E-commerce adaptation in developing economies like Lebanon His work directly contributes to UN Sustainable Development Goals related to industry innovation and infrastructure. Analysis of his recent publications reveals an evolving research trajectory from context-specific market studies (Lebanon 2022) toward fundamental e-tailing mechanics (global payment systems and product information 2024). This progression demonstrates increasing focus on universal digital commerce frameworks applicable across diverse regulatory and cultural environments. Houcheimi actively publishes in peer-reviewed venues including the Journal of Organizational Computing and Electronic Commerce and WorldCIST proceedings, with multiple open-access publications showing significant download metrics (70+ for payment systems paper). His collaborative work with J. Mezei indicates established research partnerships in information systems.

• Artificial Intelligence
• Data Science
• Computational Creativity
• +۲ مورد دیگر

Hannu Toivonen is a Full Professor of Computer Science at the University of Helsinki, affiliated with the Faculty of Science and the Department of Computer Science. He leads the Discovery Research Group and is part of the Helsinki Institute for Information Technology (HIIT) and the Finnish Center for Artificial Intelligence (FCAI). He obtained his PhD in Computer Science from the University of Helsinki in 1996 and has held his professorship since 2002. His research spans Artificial Intelligence , Data Science , Computational Creativity , and Data Mining , with applications in generative art, automated journalism, and bioinformatics. His research focuses on: Developing AI systems for creative tasks (e.g., poetry, music, and art generation) Cross-lingual natural language processing and unsupervised learning Ethical and societal implications of AI in media and labor Analysis of his 15 most recent publications (2021–2025) reveals a strong emphasis on computational creativity and AI-driven content generation , with interdisciplinary applications in digital humanities, journalism, and biology. Trends include ethical AI frameworks, human-AI collaboration, and innovative evaluation methods for generative systems. Scientific Awards & Honors: Knight, First Class, Order of the White Rose of Finland (2019) Best Applied Research Award, IEEE ICDM (1998) Honorary Member of TKO-äly (2010) Member of Finnish Academy of Science and Letters He has supervised over 20 doctoral theses and secured ~7 MEUR in grants (e.g., EU projects: Embeddia , Newseye ). He leads the Discovery Research Group and collaborates with institutions like VUB (Belgium) as a Francqui International Professor (2025).