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)

Arno Solinمشاهده پروفایل

دانشیار

• Machine Learning
• Signal Processing
• Sensor Fusion
• +۸ مورد دیگر

Dr. Arno Solin is a tenured Associate Professor in Machine Learning at Aalto University's Department of Computer Science and an Academy of Finland Research Fellow . He leads the Aalto machine learning research group and serves as Director of the Finnish Doctoral Program Network in AI (AI-DOC) . His work bridges probabilistic modeling with practical applications in sensor fusion and real-time inference. ELLIS Scholar (European Laboratory for Learning and Intelligent Systems) Adjunct Professor at Tampere University Member of Young Academy Finland (2021–2025) Research Interests focus on data-efficient machine learning with probabilistic methods for real-time inference and sensor fusion. Key areas include Gaussian processes, diffusion models, stochastic differential equations, and uncertainty quantification in deep learning. His group develops methods that combine structural constraints with adaptive learning for deployment on resource-limited hardware. Publication Trends show consistent output in top venues (NeurIPS, ICML, ICLR, AISTATS) with emphasis on diffusion models , 3D scene reconstruction , and real-time probabilistic modeling . Recent works explore physics-informed learning, heterophily-aware graph models, and compressed representations for world models in reinforcement learning. Scientific Recognition : Awarded AI Researcher of the Year 2024 by AI Finland Teacher of the Year 2023 at Aalto CS ISIF Jean-Pierre Le Cadre Best Paper Award (2018) MLSP Schizophrenia Classification Challenge Winner (2014) NeurIPS/ICML Reviewer Awards Research Leadership includes coordinating Finland's AI Center of Excellence program and directing the Finnish Doctoral Program Network in AI (AI-DOC). He supervises 15+ doctoral/postdoctoral researchers and has spun off Spectacular AI , a company commercializing sensor fusion technology.

• 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.

• Wireless Communications
• Coding Theory
• Quantum Information Processing
• +۵ مورد دیگر

Olav Tirkkonen serves as a Full Professor in the Department of Communications and Networking at Aalto University, Finland, a position he has held since August 2006. He leads the Communication Theory research group, driving innovation in wireless communication systems. His academic journey includes a distinguished career spanning industry and academia, with significant contributions to 3G, 4G, and 5G technologies. His educational qualifications are: Doctor of Science (Ph.D.) in Theoretical Physics, Helsinki University of Technology, 1994 Master of Science (M.Sc.) in Theoretical Physics, Helsinki University of Technology, 1990 Professor Tirkkonen's research interests are centered on wireless communications, with a focus on physical layer processing, coding theory, and quantum information processing. His group explores advanced topics including 5G and beyond wireless networks (spectrum management, large-scale MIMO, ultra-reliable low-latency communication), network-level interference coordination, collaborative caching, machine learning applications for wireless channel geography, coding on manifolds, and quantum communication systems. This research bridges fundamental theory with practical implementation in next-generation wireless networks. Analysis of his recent publications (2024-2025) indicates a predominant focus on machine learning techniques for wireless channel modeling (channel charting), pilot allocation in MIMO systems, and quantum error correction. His work is instrumental in addressing key challenges in 5G/6G networks, particularly in scenarios demanding ultra-reliability, low latency, and efficient resource utilization. His scientific contributions include: Co-inventor of approximately 80 families of patents and patent applications Co-author of the book "Multiantenna transceiver techniques for 3G and beyond" Throughout his career, Professor Tirkkonen has mentored numerous graduate students and secured substantial research funding from various sources. His industry experience at Nokia Research Center (1999-2010) and visiting position at Cornell University (2016-2017) have enriched his research perspective and fostered strong industry-academia collaborations. The Communication Theory group, under his leadership, maintains active collaborations with leading institutions and companies worldwide, positioning Aalto University at the forefront of wireless communications research.

Teemu Roosمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Data Science
• +۶ مورد دیگر

Teemu Roos is a Professor at the Department of Computer Science , University of Helsinki , and a Principal Investigator for the Complex Systems Computation Group under the Helsinki Institute for Information Technology. He serves as a Supervisor for the Doctoral Programme in Computer Science and leads multiple research initiatives, including Distributed AI in Supercomputing , AI & Kids , and Generation AI . Dr. Roos also holds a Docent title in Computer Science. His research spans Artificial Intelligence , Machine Learning , and Data Science , with a focus on AI education , graph neural networks , Bayesian modeling , and health informatics . He has pioneered tools like Elements of AI , a free online course now translated into 22 EU languages, and explores the ethical implications of AI-generated content in authorship and inventorship. The 15 most recent publications highlight applications in environmental forecasting (e.g., Mediterranean Sea via graph-based deep learning), healthcare (e.g., skin cancer detection with transfer learning), and social media analysis (e.g., explainable AI platforms for K-12 education). Methodologically, his work advances clustering algorithms , dimensionality reduction , and approximate nearest neighbor search . Scientific Awards: Cor Baayen Award (2009) Nokia Foundation Recognition Award (2019) Best Paper Honorable Mention Award (2013) ICT Influencer of the Year 2019 (Vuoden TiVi-vaikuttaja 2019) World Summit AI's Top-50 Innovators in 2020 Dr. Roos has supervised 2 doctoral students and contributed to 163 academic activities , including invited talks at MIT, University of Cambridge, and the Finnish Institute in Rome. He has secured funding from the Academy of Finland and the Strategic Research Council, focusing on projects like Fast AI-assisted Space Environment Prediction and Urban Exerciser .

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.

• Bayesian Statistics
• Machine Learning
• Statistical Modeling
• +۳ مورد دیگر

Paul-Christian Burkner is a researcher in the Department of Computer Science at Aalto University. His work focuses on Bayesian statistical methods, computational modeling, and probabilistic programming. He collaborates with Professor Aki Vehtari's research group and has published extensively on topics like model sensitivity, spatiotemporal analysis, and variable selection techniques. His research interests include: Bayesian inference and model comparison Computational statistics Probabilistic programming Machine learning algorithms Statistical modeling in social sciences Neuroimaging data analysis Recent publications demonstrate expertise in simulation-based calibration, spatiotemporal modeling, and Gaussian process approximations. Collaborations span psychology, neuroscience, and machine learning domains. Contact: ext-paul-christian.burkner@aalto.fi

• Statistical signal processing
• Robust statistical methods
• Array processing
• +۶ 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.

• Machine Learning
• Bayesian Optimization
• Gaussian Processes
• +۴ مورد دیگر

Ti John is a Research Fellow at Aalto University's Department of Computer Science within the School of Science. He is affiliated with Professor Marttinen's research group and the Probabilistic Machine Learning group led by Professor Samuel Kaski. His work connects with the Finnish Center for Artificial Intelligence (FCAI) and the Helsinki Institute for Information Technology (HIIT). Dr. John's research focuses on machine learning, particularly Bayesian optimization, Gaussian processes, and point process models. His work spans theoretical developments in neural processes and practical applications in healthcare analytics and large language models. He has made significant contributions to equivariant neural processes, causal mediation analysis in healthcare, and interpretability of additive models. His publication record shows consistent output with 17 publications between 2021-2024, including multiple papers at top AI conferences like NeurIPS, ICML, and ICLR. His research demonstrates strong interdisciplinary connections between statistical modeling, artificial intelligence, and healthcare applications. Active reviewer for NeurIPS, ICLR, AISTATS Reviewer for Journal of Machine Learning Research Member of Finnish Center for Artificial Intelligence project Dr. John has been actively contributing to the machine learning community through peer review and conference participation, demonstrating expertise across multiple subfields of artificial intelligence and statistical modeling.

Lasse Heikkinenمشاهده پروفایل

مدرس ارشد

• Physics
• Applied Physics
• Process Tomography
• +۵ 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.

• Quantum Chaos
• Quantum Transport
• Magnetic Field Physics
• +۵ مورد دیگر

Joonas Keski-Rahkonen is a researcher active in quantum physics, nanotechnology, and condensed matter physics. He holds a Master of Science in Physics from the University of Oulu and completed his doctoral thesis at Tampere University. His work focuses on quantum chaos, electronic transport in nanostructures, and magnetic field effects. His research has appeared in journals like Physical Review E , Physical Review B , and Computer Physics Communications . Collaborations span topics such as quantum dots, planckian resistivity, and lattice vibrations. Recent articles analyze anomalous diffusion, bouncing-ball scars, and quantum acoustics. He has no explicitly listed scientific awards or grants in the provided text. While details on current affiliations or part-time status are absent, his academic role is inferred from extensive peer-reviewed publications and software development in his field.

• Artificial Intelligence
• Machine Learning
• Signal Processing
• +۶ مورد دیگر

Moncef Gabbouj is a Professor of Signal Processing at the Department of Computing Sciences, Tampere University, Finland. He holds a PhD from Purdue University and has held academic positions including Academy of Finland Professor (2011–2015) and Head of the Department of Signal Processing (2002–2007). His research focuses on artificial intelligence, machine learning, multimedia signal processing, and nonlinear signal/image processing. He has authored over 800 papers and supervised 64 doctoral and 72 master’s theses, earning accolades such as IEEE Fellow, Finnish Cultural Foundation Award, and TUT Foundation Grand Award. Education: BS (Electrical Engineering, Oklahoma State University, 1985), MS and PhD (Electrical Engineering, Purdue University, 1986–1989). Visiting roles include Hong Kong University of Science and Technology and University of Southern California. Research interests include Big Data analytics, multimedia content analysis, pattern recognition, and video coding. He leads the Artificial Intelligence Research Task Force of the Research Alliance on Autonomous Systems (RAAS) and directs the NSF IUCRC Center for Visual and Decision Informatics (CVDI). Awards highlight contributions to signal processing and AI, including IEEE Fourier Award Committee membership and leadership roles in EURASIP and IEEE. Grants and projects span EU Horizon programs, NSF, and industry collaborations.

Petter Holmeمشاهده پروفایل

استاد

• Network Science
• Complex Systems
• Computational Social Science
• +۳ مورد دیگر

Petter Holme is a Professor at the Department of Computer Science, Aalto University, specializing in network science and complex systems. His research focuses on understanding interconnected systems through network analysis, including topics like epidemic spreading, climate change interactions, and power grid synchronization. He holds a Doctoral degree in Natural Sciences from Umeå University (2004) and has received the prestigious Fellow of the Network Science Society (2024). His work is interdisciplinary, collaborating with social scientists, medical researchers, and engineers. Key areas include temporal networks, computational social science, and data-driven modeling. Education: PhD in Natural Sciences (Umeå University, 2004), Licentiate in Natural Sciences (Umeå University, 2001), Master's in Engineering (Uppsala University, 2000), and Master's in Humanities (Stockholm University, 2000). Research interests span network structure analysis, epidemic models, and social dynamics, with contributions to UN Sustainable Development Goals. He has authored over 116 publications and frequently engages with media on topics like Paxlovid's public health impact and climate change networks.

Jara Uittoمشاهده پروفایل

استادیار

• Massively Parallel Computation
• Distributed Computing
• Sublinear Computing
• +۳ مورد دیگر

Jara Uitto is an Assistant Professor in the Department of Computer Science. His research focuses on Massively Parallel Computation, Distributed Computing, and Sublinear Computing, with a particular emphasis on graph algorithms and distributed systems. Research Interests: Massively Parallel Computation (MPC) Distributed Algorithms and Symmetry Breaking Graph Theory and Edge Processing Algorithm Design for Sparse Graphs Approximation and Optimization in Streaming Models Key Project: Massively Parallel Algorithms for Large-Scale Graph Problems (2020-2024) , where he led research on optimizing algorithms for distributed and parallel computing environments. Publications highlight contributions to symmetry breaking, parallel coloring, and approximation algorithms in dynamic streams. His work bridges theoretical foundations with practical distributed computing challenges.

Hanna Vehkamäkiمشاهده پروفایل

استاد

• Atmospheric Science
• Physical Chemistry
• Computational Chemistry
• +۷ مورد دیگر

Hanna Vehkamäki is a Professor at the Faculty of Science, University of Helsinki , and Vice Dean responsible for well-being, equality, bilingual affairs, and facilities/safety. She leads the Academy of Finland Center of Excellence VILMA (2022-2029) focused on molecular-level atmospheric transformations. Field of Science: Physical Sciences Email: hanna.vehkamaki@helsinki.fi Address: P.O. Box 64, Gustaf Hällströmin katu 2, 00014 Helsinki Her research bridges atmospheric science , physical chemistry , and computational modeling , with a focus on molecular cluster dynamics, ion-induced nucleation, and aerosol-cloud-climate interactions. She develops tools for molecular-level atmospheric simulations and machine learning applications in predicting particle formation. Recent publications highlight her work on APi-ToF mass spectrometer optimization , α-pinene ozonolysis mechanisms , and alkylammonium ion mobility . Her projects include VILMA (Virtual laboratory for molecular-level atmospheric transformations) and Atmospheric Mathematics . Scientific Awards : Finnish Aerosol Research Foundation Distinguished Scientist Award (2014) Magnus Ehrnrooth Foundation award (2010) Suomen Valkoisen Ruusun I luokan ritarimerkki (2022) NOSA Aerosologist Award (2014) University of Helsinki Maikki Friberg award (2015) She supervises Master’s/PhD theses (e.g., hydration layer simulations on K-feldspar) and participates in international conferences (e.g., ISSPIC XVIII, Gordon Research Seminar). Her grants include the Academy of Finland Center of Excellence and the Jane and Aatos Erkko Foundation project RESTART.