جستجوی استاد راهنمای دانشگاهی

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

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

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

Robert Piche is a Professor at the Computing Sciences Mathematics Research Centre, specializing in advanced signal processing, positioning systems, and sensor fusion. He holds a Doctor of Science (Technology) and Master of Science from the University of Waterloo, Canada (1986 and 1982, respectively). His research focuses on Kalman filters, Global Positioning Systems (GPS), particle filters, and indoor positioning technologies. He has contributed extensively to fields like satellite orbit prediction, non-line-of-sight (NLoS) positioning, and machine learning applications in biomechanics and robotics. Dr. Piche has authored over 230 publications and received recognition through an invitation/ranking in a 2014 competition. He actively participates in academic activities, including conference presentations and peer-review roles. His work bridges theoretical advancements and practical applications, with contributions to autonomous systems, sensor data analysis, and wearable technology. Collaborations span international institutions, reflecting his global impact in engineering and computer science disciplines.

• Kalman Filters
• Global Positioning Systems (GPS)
• Signal Processing
• +۵ مورد دیگر

Kari Lappalainen is an Assistant Professor in the Department of Electrical Engineering at Tampere University, affiliated with the Faculty of Information Technology and Communication Sciences. His research focuses on photovoltaic power systems, energy storage technologies, and renewable energy integration. He leads studies on photovoltaic module aging, parameter identification, and energy storage system optimization for power smoothing and ramp rate control. Key research interests include: Photovoltaic module diagnostics and performance analysis Energy storage system design for hybrid renewable plants Impact of environmental factors (e.g., temperature, cloud cover) on PV efficiency Advanced modeling techniques for photovoltaic systems Recent work emphasizes real-time monitoring of PV degradation via current-voltage curve analysis and optimization of energy storage configurations to mitigate power fluctuations. Over 50 peer-reviewed publications demonstrate sustained contributions to renewable energy systems research. Notably absent are awards or formal advisee listings, though collaboration with institutions like EU PVSEC and frequent conference participation indicate active academic engagement.

• Photovoltaic Power Systems
• Energy Storage Systems
• Renewable Energy
• +۳ مورد دیگر

Heikki Handroos is a Full Professor of Mechanical Engineering at LUT University, leading the Laboratory of Intelligent Machines since 1993. He holds a DSc (Technology) from Tampere University of Technology and has served as Vice-Dean of the Faculty of Technology (2007-2009) and currently chairs the Collegiate Body of LUT University. His research focuses on mechatronics, robotics, control systems, and fluid power, with over 300 publications and 2,400+ citations. He has supervised 34 doctoral theses and 150+ MSc projects, managed R&D projects exceeding €20M, and co-founded four tech startups. His work spans industrial collaborations, digital twin applications, and innovative robotics for nuclear energy (e.g., DEMO reactor maintenance systems). He has held visiting professorships in the U.S., Japan, and Russia, and actively contributes to academic editorial roles and professional societies like ASME and IEEE.

• Mechatronics
• Robotics
• Machine Automation
• +۵ مورد دیگر

Eric Coatanea is a Professor at Tampere University, affiliated with the Automation Technology and Mechanical Engineering department within the Faculty of Engineering and Natural Sciences. His research focuses on manufacturing systems, causal graph networks, multi-disciplinary optimization, and AI integration in engineering design. He holds a BSc from University of West Brittany (1990), MSc from INSA Toulouse (1993), and teaching certification from Ecole Normale Supérieure (1994). Research interests include modeling manufacturing systems, additive manufacturing (e.g., Directed Energy Deposition), causal graphs for decision-making, and systems engineering. Notable awards include the Chevalier des Palmes Académiques (2018) and Marie-Curie Fellowship (2006–2008). Recent publications emphasize optimization algorithms combining AI (e.g., L-ANN-GWO), viscosity effects in 3D printing, and causal graph applications in MDO. His work bridges theoretical frameworks with practical engineering challenges, focusing on early design synthesis and sustainable manufacturing. Commitments include editorial roles (Journal of Integrated Design & Process Science), NSERC Canada advisory, and board memberships (Dynavio Cooperative Oy, Selko Oy). Current projects include LILIAM, ÄVE, and DIGITBrain initiatives.

• Manufacturing Systems
• Additive Manufacturing
• Causal Graphs
• +۵ مورد دیگر

Heikki Peura is an Associate Professor in the Department of Information and Service Management at Aalto University School of Business. Previously, he served as an Assistant Professor at Imperial College Business School, Imperial College London. His educational background includes: PhD in Management Science and Operations from London Business School (awarded June 30, 2016) MSc in Engineering Physics and Mathematics from Aalto University, specializing in Systems and Operations Research (awarded April 27, 2010) Professor Peura specializes in analytics methods for understanding and improving firms' operational decisions . His research spans several interconnected domains including revenue management, sustainable energy generation, and supply chain management. He applies advanced analytical techniques to solve complex business problems, particularly focusing on optimization methods and decision-making under uncertainty. His work bridges theoretical operations research with practical business applications, making significant contributions to both academic knowledge and industry practice. Analysis of his recent publications reveals a consistent focus on operational decision-making with applications across multiple sectors. His work demonstrates strong methodological rigor in optimization techniques while addressing practical business challenges in energy markets, supply chain dynamics, and revenue management systems. There's a clear evolution in his research from foundational work in pricing and optimization toward more complex applications involving sustainability, risk management, and strategic decision-making in competitive environments. Professor Peura has been actively involved in teaching operations and supply chain analytics as well as revenue management courses at Aalto University. At Imperial College, he taught courses including Data Structures and Algorithms, and contributed to various Master's programs in Business Analytics and Financial Engineering. His research has established significant connections with industry applications, particularly in energy markets and platform businesses. Professor Peura maintains an active research agenda with several working papers addressing contemporary challenges in non-profit fundraising, decarbonization pathways, and healthcare financing.

• Operations Research
• Revenue Management
• Supply Chain Management
• +۳ مورد دیگر

Professor Garg Vikas holds the position of Assistant Professor in the Department of Computer Science at the School of Science. His research spans quantum computing, artificial intelligence, and machine learning with applications in computational biology, healthcare, and drug design. He leads the HEALED/Garg project focused on human-steered machine learning for drug discovery and collaborates with institutions like MIT and industry partners. He co-founded YaiYai Oy, providing AI/ML solutions to global sectors. Education: PhD from MIT CSAIL under Tommi Jaakkola, with postdoctoral and industry experience at Amazon, Microsoft, and IBM. His work aligns with UN SDGs, particularly in healthcare and sustainable energy. Research interests include graph neural networks, generative models, and quantum AI. Recent projects involve climate modeling via physics-informed neural ODEs and optimizing quantum circuits using graph autoencoders. Key collaborations include MIT’s MLPDS Consortium and the Finnish Center for Artificial Intelligence. He supervises doctoral researchers like Yogesh Verma and postdocs such as Kogkalidis.

• Quantum Computing
• Artificial Intelligence
• Machine Learning
• +۸ مورد دیگر

Antti H. Niemi is a Professor and Dean at the University of Oulu 's Faculty of Technology , specializing in computational solid and structural mechanics. His research focuses on advanced numerical methods for engineering analysis and design. Research areas include computational mechanics, structural engineering, and metamaterials Develops innovative finite element methods for thin-body problems Current projects address snow structures, timber building envelopes, and machine learning applications in mechanical systems His recent work emphasizes discontinuous Petrov-Galerkin (DPG) methods for plates and shells, with applications in civil and mechanical engineering. Publications cover: Snow and ice vaults (2024) Machine learning for steel beam capacity prediction (2024) Hygrothermal analysis of timber structures (2024) DPG formulation for Reissner-Mindlin plates (2023) Shell element benchmarking (2018-2022)

• Computational Solid Mechanics
• Structural Engineering
• Civil Engineering
• +۵ مورد دیگر

Linda Turunen is an Assistant Professor in Statistics, Mathematics, and Data Science at Aalto University's School of Business, Department of Information and Service Management. She holds adjunct professorships (title of docent) in Mathematics at the University of Helsinki (since 2019) and in Applied Mathematics at the University of Vaasa (since 2016). She earned her Doctor of Science (DSc) in Mathematics from Aalto University School of Science in 2014. Her research focuses on sustainability communication, circular business models, and luxury brand strategies, particularly exploring second-hand markets and consumer behavior in fashion industries. Her recent work analyzes systemic challenges in textile industries, pandemic impacts on luxury sectors, and innovative sustainability frameworks like the 'Shades of Green' instrument. She actively supervises theses across bachelor/master/PhD levels, emphasizing data-driven approaches to sustainable consumption patterns.

• Sustainability Communication
• Sustainable Fashion Consumption
• Circular Business Models
• +۳ مورد دیگر

Sergei Khakalo is an Assistant Professor at the Department of Civil Engineering, Aalto University. His research focuses on strain gradient mechanics, computational modeling, and advanced materials. Research Interests: Structural Engineering, Continuum Mechanics, Computational Mechanics, and Nanomaterials. His work addresses multiscale modeling, thermoelasticity, and mechanical behavior of lattice and composite materials. Publication Trends (2024-2017): Research spans strain gradient elasticity, isogeometric analysis, and bio-based nanomaterials. Key themes include microscale plasticity, structural stability, and thermoelastic metamaterials. Scientific Awards: Oskari Vilamo Fund best thesis award (2018) Student prize for Best Doctoral Dissertation (2017)

• Structural Engineering
• Continuum Mechanics
• Computational Mechanics
• +۳ مورد دیگر

Lasse Heikkinen is a Senior Lecturer at the Department of Technical Physics, Faculty of Science, Forestry and Technology, University of Eastern Finland. His work spans applied physics, process tomography, and educational technology, with a focus on innovative teaching methods like flipped classrooms during the pandemic. Current Role: Deputy Head of Department, Senior University Lecturer Research Themes: Electrical impedance tomography for industrial processes, flipped teaching frameworks, and physics education adaptation to remote learning. Recent publications highlight his dual expertise in process tomography (gas-solid flows, pharmaceutical monitoring) and pedagogical innovation (learning analytics, flipped classrooms). He has contributed to educational manuals and toolkits for teacher training. Key Projects: Technology education infrastructure development (2015–2024), pandemic-era teaching adjustments (2022). Collaborations: Active in international process tomography conferences and multidisciplinary teams like the Ameba project.

• Physics
• Applied Physics
• Process Tomography
• +۵ مورد دیگر

Natalia Reen is a Senior Researcher at the Industrial Economics Laboratory within the School of Business at Åbo Akademi University. Her work bridges industrial services, sustainability strategies, and technological innovation, aligning with the UN Sustainable Development Goals (SDGs). Education: Doctoral Degree, Åbo Akademi University, 2014 MBA, Concordia University, Estonia, 2004 Master's Degree in Mathematics and Mechanics, Moscow State University, 1989 Research Interests Natalia focuses on advancing business sustainability through nature-positive and climate-neutral technologies. Her expertise spans industrial services management, pricing strategies for IT and industrial solutions, and fostering collaborative innovation for green transitions. She emphasizes the integration of technological neutrality and value-driven paradigms in her work. Article Trends Her publications reflect a trajectory from pricing optimization in industrial and IT services (2014–2017) to large-scale sustainable maritime innovation (2024). Recent work highlights collaborative ecosystems for decarbonizing cruise ships, aligning with global SDG targets. Labs & Teams Active in the Industrial Economics Laboratory , Natalia engages with interdisciplinary teams on EU-funded projects and public-private partnerships, particularly in sustainable development and IoT architecture via her company, Innotrain OY.

• Business Sustainability
• Industrial Services
• Climate-Neutral Technologies
• +۳ مورد دیگر