جستجوی استاد راهنمای دانشگاهی

Mika Järvinen serves as Associate Professor in the Department of Energy and Mechanical Engineering at Aalto University, leveraging fundamental sciences—physics, thermodynamics, chemistry, and numerical modeling—to address cross-sector energy challenges. His research spans sustainable bioenergy and inter-linked circular economy systems integrating energy, materials, and environmental considerations across biomass, pulp/paper, metallurgy, and waste-to-energy industries. His expertise centers on sustainable bioenergy and circular economy frameworks, utilizing numerical modeling to optimize combustion engineering and metallurgical processes. By maintaining diverse industrial engagement, he ensures research continuity during economic fluctuations while advancing black liquor spraying technologies and energy-material-environment synergies. This approach enables flexible, physics-based solutions for complex industrial energy conversion problems. Professor Järvinen's 2025 publications reveal a cohesive focus on renewable energy systems operating within planetary boundaries, spanning carbon capture (calcium looping), low-temperature heat engines, and comprehensive textbook development. His work integrates solar, wind, bioenergy, and storage technologies into holistic frameworks that balance technical innovation with ecological constraints, emphasizing system-level sustainability over isolated component optimization. His scientific recognition includes: Best Dissertation Award (2002) from Helsinki University of Technology's Department of Machine Technology for research on black liquor droplet conversion Resonate Award (2015) from Caltech University's Resnick Institute As lead of the Energy Conversion and Systems research group, he directs projects bridging fundamental modeling with industrial applications while developing educational resources for next-generation energy engineers. His laboratory work emphasizes experimental validation of numerical models across combustion, metallurgical, and biomass conversion processes.

• Sustainable Bioenergy
• Circular Economy
• Energy Conversion Systems
• +۴ 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)

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

Andres Lucero is Associate Professor of Interaction Design at Aalto University's Department of Art and Media, specializing in human-computer interaction for mobile devices and interactive surfaces. His research integrates human-computer interaction, design methodologies, and playful interfaces. He holds a PhD from Eindhoven University of Technology and has industry experience from Nokia, where he led user-centered design projects as Senior Researcher. Research explores tangible interfaces, social wearables, museum experience design, human-AI collaboration, and cross-cultural co-design approaches. Recent projects investigate conversational agents in design processes, persona generation workflows, and freeform interactive devices. Dr. Lucero received the Honourable Mention Award at MUM 2020 for innovative interaction research. His work advances design methods through first-person perspectives, AI integration, and novel interaction paradigms.

• Interaction Design
• Human-Computer Interaction
• Mobile Interfaces
• +۱ 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.

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

Harri Lähdesmäki is an Associate Professor (tenured) at the Department of Computer Science, Aalto University, where he leads the Computational Systems Biology research group. His work focuses on probabilistic machine learning and deep generative models with applications in biomedicine and molecular biology. Key Research Interests: Probabilistic machine learning, deep generative models, computational biology, bioinformatics, longitudinal data modeling Contact: harri.lahdesmaki@aalto.fi | Konemiehentie 2, 02150 Espoo, Finland His recent publications highlight advancements in: Gaussian process priors for scalable deep generative models Single-cell analysis of immune repertoires in leukemia and diabetes Probabilistic deconvolution methods for RNA-seq data Epigenetic analysis using hidden Markov and mixed models Transformer-based survival prediction and missing data handling Harri’s work integrates mechanistic modeling with Bayesian inference, particularly applied to immunology, cancer biology, and early disease prediction.

• Probabilistic Machine Learning
• Deep Generative Models
• Artificial Intelligence
• +۴ 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.

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

Masood Masoodian is an Associate Professor in the Department of Art and Media at Aalto University, Finland. He leads the Visual Communication Design research group, focusing on interactive visualization for health, energy, and sustainability contexts. Previously, he held roles at the University of Waikato (2000-2016), University of Southern Denmark, and Massey University. Education: Doctoral degree in Other disciplines from the University of Waikato (1999) Research interests include design thinking, visualization of complex data, and creative aging interventions. Notable projects include the EU-funded INT-ACT initiative (2024-2026) addressing intangible cultural heritage. He has received an award for collaborative work on video game ludonarrative analysis (2019). Recent activities include organizing workshops on map-based interfaces, co-creating cultural heritage methods, and delivering public talks on digital design. Supervised two theses and contributed to 131 peer-reviewed outputs, emphasizing human-centered design and sustainability. Grants: Principal investigator for multiple EU projects totaling over 3 years of active funding. Awards: Prize for 'Comedy in the Ludonarrative of Video Games' (2019). Labs/Teams: Visual Communication Design group, collaborating internationally on projects like INT-ACT's cultural heritage mapping.

• Interactive Visualization
• Design Thinking
• Health Informatics
• +۳ 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.

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

Toni Kotnik is an Associate Professor in the Department of Architecture at Aalto University, Finland. He holds dual expertise in mathematics and architecture, with a Ph.D. from the University of Zurich and a second degree in architecture. His roles include principal of d’HKL , a Zurich-based experimental architecture firm, and adjunct lecturer at institutions like Harvard University and the Bartlett School of Architecture. His research focuses on integrating science, engineering, and computation into architectural design, emphasizing structural innovation and sustainability. Education: Ph.D. in Mathematics (Dr. sc. Nat.), University of Zurich, 1999 Second degree in Architecture Research Interests: Computational design and generative systems Structural aesthetics and neuroaesthetics Sustainable urban and architectural solutions Parametric modeling and material systems Key Contributions: Advances in graphic statics and form-finding Design strategies for high-density urban environments Integration of AI and generative tools in architectural practice Recent Work Trends: Recent articles emphasize computational methods for industrial layout optimization, AI’s role in architecture, and embodied perception in structural design. His work bridges theoretical research with practical applications, such as the Singapore Hawker Centre redesign and experimental structures like hypar-combined shells. Awards: Aalto University Doctoral Thesis Awards (2022) Best Presentation Award (2019) Oscari-Vilamo Award (2023) Advising & Grants: Supervised 4 theses, including recipients of awards like the Metex Award shortlist. Active in international academic networks, serving on conference committees (e.g., International Association for Shell and Spatial Structures) and editorial roles (e.g., Automation in Construction journal). Labs & Teams: Leads the d’HKL studio, collaborating globally on projects like the AA/ETH Pavilion and the Hypercube for Gardens by the Bay. Research focuses on experimental design and material systems.

• Computational Design
• Architectural Engineering
• Structural Design
• +۴ مورد دیگر

Timo Rintamäki is an Associate Professor at the Faculty of Management and Business , Tampere University . His research focuses on customer value creation, service ecosystems, and retail innovation, with particular expertise in co-creation, sharing economy platforms, and digital transformation. Research Themes : Customer Value Propositions, Service Ecosystems, E-Commerce, Co-Creation, Retail Experience Design Key Sectors : Retail, Healthcare, Social Media, Public Services Timo’s recent work explores sustainable value creation through collaborative platforms, analyzing customer behavior in online return processes, and optimizing value orchestration in multi-channel environments. His studies span both academic and practical perspectives, including policy implications in healthcare and innovation in consumer cooperatives. His scholarly contributions include frameworks for service experience management, insights into customer data utilization, and models for value co-creation across digital and physical touchpoints. While no specific scientific awards are listed, his interdisciplinary research bridges service science, marketing, and organizational behavior.

• Customer Value
• Service Science
• Retailing
• +۴ 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

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

Marjo Yliperttula is a Professor at the Department of Pharmaceutical Biosciences, Faculty of Pharmacy, University of Helsinki. She serves as a supervisor in the Doctoral Programmes in Biomedicine, Drug Research, and Materials Research and Nanosciences, with expertise in biomaterials and pharmaceutical technology. Her research focuses on nanofibrillated cellulose (NFC) hydrogels for wound healing and drug delivery, extracellular vesicle (EV) engineering for therapeutic applications, and freeze-drying technologies for biomaterial preservation. Key contributions include NFC-based wound dressings that enhance platelet-rich plasma release (2024), Raman spectroscopy methods for monitoring freeze-drying-induced mutarotation (2024), and tandem chromatography techniques for high-purity EV isolation (2023). Her work bridges pharmaceutical sciences with regenerative medicine, emphasizing translational applications in chronic wound treatment and targeted drug delivery. Recent publications (2022-2025) reveal three dominant trends: (1) Optimization of NFC hydrogels for controlled drug release and tissue regeneration, (2) Advanced characterization of EV phenotypes under hypoxic conditions for improved therapeutic efficacy, and (3) Development of analytical methods (Raman spectroscopy, chromatography) to address manufacturing challenges in biopharmaceuticals. These themes reflect her group's commitment to solving critical problems in biomaterial stability, EV-based delivery, and precision wound care. Professor Yliperttula has supervised 10 doctoral theses, including recent work on NFC for skin substitutes (Elle Koivunotko), freeze-drying of hydrogels (Arto Merivaara), and mesenchymal stromal cells for wound healing (Jasmi Snirvi). She currently leads the Academy of Finland-funded GeneCellNa project (2024-2026) on gene/cell/nanotherapy for chronic diseases and a Finnish Red Cross project (2023-2024) on NFC for blood products, with cumulative project funding spanning 18 initiatives since 2005. She heads the Biopharmaceuticals Group within the Drug Research Program, fostering collaborations across pharmaceutical biosciences, materials science, and clinical medicine to advance next-generation therapeutic platforms.

• Biomaterials
• Nanomedicine
• Drug Delivery
• +۵ 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).

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

Jyrki Kajaste is a Lecturer at the Department of Energy and Mechanical Engineering, Aalto University. His academic career focuses on mechanical engineering and energy systems, with a strong emphasis on fluid power and hydraulic technologies. His research bridges theoretical and applied engineering, particularly in sustainable energy solutions and mechatronic systems. Research Focus: Mechanical Engineering, Fluid Power, Sustainable Energy Key Projects: CO2 adsorption heat engines, hydraulic system optimization, energy-efficient machinery Jyrki's publications highlight advancements in hydraulic systems, pneumatic efficiency, and additive manufacturing. His collaborations span international conferences like SICFP and the Baltic Mechatronics Symposium, showcasing interdisciplinary applications of mechanical engineering principles.

• Mechanical Engineering
• Energy Systems
• Hydraulic Engineering
• +۳ مورد دیگر

Esa Ollila serves as Associate Professor in the Department of Signal Processing and Acoustics at Aalto University, Finland, and holds an adjunct professorship in Statistics at the University of Oulu. His academic appointments include Academy of Finland Research Fellow (2010-2015) and prior senior research/lecturing roles at both institutions. His educational background features: M.Sc. in Mathematics, University of Oulu (1998) Ph.D. in Statistics (with honors), University of Jyväskylä (2002) D.Sc.(Tech) in Signal Processing (with honors), Aalto University (2010) Professor Ollila's research centers on statistical signal processing and robust statistical methodologies , with significant contributions to array processing, high-dimensional data analysis, and covariance matrix estimation. His work bridges theoretical statistics with practical applications in radar systems, wireless communications, and big data analytics, emphasizing robustness against outliers and computational efficiency in modern data-intensive environments. Current focus areas include compressed sensing, sparse approximation, and blind source separation techniques. Analysis of his 15 most recent publications (2024-2025) reveals three dominant trends: (1) robust covariance learning for massive random access systems, (2) integrated sensing and communications (ISAC) for 6G networks using advanced beamforming, and (3) geometric approaches to elliptical distributions in statistical inference. His work increasingly incorporates deep learning (GANs, graph neural networks) while maintaining strong foundations in classical signal processing theory. Key recognitions include: Academy of Finland Postdoctoral Fellowship (2004-2007) Academy of Finland Research Fellowship (2010-2015) His research has been supported through prestigious Academy of Finland grants totaling over a decade of continuous funding. Professor Ollila currently leads an active research group at Aalto University, supervising doctoral candidates and collaborating internationally with institutions including Princeton University (where he served as Visiting Post-doctoral Research Associate during 2010-2011). He maintains strong ties with the University of Oulu through his adjunct professorship and has contributed to EURASIP's Special Area Team on Theoretical and Methodological Trends in Signal Processing. The Esa Ollila Research Group focuses on cutting-edge challenges in statistical signal processing, with current projects spanning robust DOA estimation under non-Gaussian noise, covariance matrix learning for massive MIMO systems, and machine learning-enhanced radar-communication integration. The group actively develops open-source tools like the fitHeavyTail R package for heavy-tailed distribution modeling and maintains collaborations with industry partners in wireless communications.

• Statistical signal processing
• Robust statistical methods
• Array processing
• +۶ مورد دیگر