جست‌وجوی استاد و پژوهشگر

Petteri Kaskiمشاهده پروفایل

دانشیار

• Theoretical Computer Science
• Algorithm Design
• Exact and Parameterized Algorithms
• +۵ مورد دیگر

Petteri Kaski is an Associate Professor at the Department of Computer Science, School of Science, Aalto University , and a member of the Helsinki Institute for Information Technology (HIIT) . His research focuses on theoretical computer science, particularly in algorithm design, exact and parameterized algorithms, algebraic algorithms, and combinatorics. Doctoral Degree in Engineering and Technology, Helsinki University of Technology (2005) Licentiate Degree in Engineering and Technology, Helsinki University of Technology (2002) Master's Degree in Engineering and Technology, Helsinki University of Technology (2001) His recent work explores tensor scaling, Johnson-Lindenstrauss transforms, Hamiltonian cycles, and computational complexity, with contributions to polynomial-time algorithms, finite field computations, and combinatorial optimization. Notable awards include the Best Paper Award at ICALP 2017 , an ERC Starting Grant (2014) , and the Kirkman Medal (2007) . He has served on scientific committees for conferences like STACS 2025 and ICALP 2024 , and collaborated with institutions such as the IT University of Copenhagen and Universität Regensburg . Key Research Areas : Theoretical Computer Science, Algorithm Design, Exact Algorithms, Algebraic Computation, Graph Theory, Combinatorics

• Machine Learning
• Statistical Computing
• Mathematical Modeling
• +۵ مورد دیگر

Juho Kanniainen is a Professor of Computing Sciences at Tampere University (Finland), leading the Financial Computing and Data Analytics research group. He has directed large EU projects like HPCFinance and BigDataFinance, securing €7.5M in funding. He heads the international MSc program in Computing Sciences at Tampere University. His research focuses on machine learning, statistical computing, and mathematical modeling applied to time-series analysis, graphs, and financial markets (e.g., limit order books, information cascades). He has published in top journals including IEEE Transactions on Neural Networks and Learning Systems, Pattern Recognition, and the Review of Finance. He has supervised/co-supervised 10 PhD students and organized conferences. His work bridges computational methods with finance, healthcare, and sustainability, exemplified by recent projects in emergency department crowding prediction and circular bioeconomy logistics.

Tuomas Virtanenمشاهده پروفایل

استاد

• Audio signal processing
• Machine listening
• Sound source separation
• +۵ مورد دیگر

Tuomas Virtanen is a Professor at Tampere University's Signal Processing Research Centre within the Faculty of Information Technology and Communication Sciences. His primary affiliation is the Computing Sciences department, where he leads the Audio Research Group . His work focuses on computational analysis of audio signals, machine listening, and acoustic scene understanding. Research Interests: Audio signal processing, content analysis of audio, sound source separation, acoustic scene classification, speech processing (including noise-robust ASR and speaker recognition), and machine learning techniques such as deep neural networks and statistical modeling. His group develops methods for sound event detection, localization, and tracking in complex acoustic environments. Key Contributions: Leading the DCASE (Detection and Classification of Acoustic Scenes and Events) challenges, developing open-source datasets like TUT Acoustic Scenes and STARSS, and advancing techniques in audio captioning, privacy-preserving audio processing, and multimodal learning. Notable Projects: Creation of the Audio Research Group , development of the Clotho audio captioning dataset, and pioneering work in zero-shot audio classification using semantic embeddings. His research bridges signal processing fundamentals with modern machine learning paradigms.

• Machine Learning
• Computer Science
• Artificial Intelligence
• +۳ مورد دیگر

Amauri Holanda De Souza Junior is a Postdoctoral Researcher affiliated with the Department of Computer Science , focusing on Probabilistic Machine Learning . His work bridges theoretical advancements with practical applications in graph-based models. Active research areas include Graph Neural Networks , Persistent Homology , and Simulation-based Inference . His recent publications highlight innovations in: Topological data analysis for graph representations Robust statistical methods under model misspecification Scalable Bayesian inference frameworks Equivariant architectures for graph learning

Otso Ovaskainenمشاهده پروفایل

استاد

• Ecological Modeling
• Metapopulation Biology
• Movement Ecology
• +۴ مورد دیگر

Professor Otso Ovaskainen holds a permanent position at the University of Helsinki , where he serves as Research Director in the Organismal and Evolutionary Biology Research Program. He is also a Visiting Professor at a Norwegian Centre of Excellence since 2014. Supervisor in the Doctoral Programme in Wildlife Biology Established the Research Center for Ecological Change (REC) with 60+ members ERC Starting Grant recipient (2008-2013) Research Interests: Ovaskainen bridges mathematical theory with empirical ecology, focusing on metapopulation dynamics, movement ecology, and statistical community ecology. His work integrates: Ecological theory with data Individual-based modeling Fungal community dynamics Probabilistic taxonomic placement Environmental DNA analysis Multi-species movement modeling Scientific Leadership: He has led major projects including the Finnish Centre of Excellence in Metapopulation Research (2015-2017) and the LIFEPLAN biodiversity inventory project (2020-2026). His HMSC modeling framework remains a benchmark in joint species distribution modeling.

Elena Simona Lohanمشاهده پروفایل

استاد

• Wireless Positioning
• GNSS Algorithms
• Wearable Computing
• +۷ مورد دیگر

Elena Simona Lohan is a Professor at Tampere University's Faculty of Information Technology and Communication Sciences , leading the Signal Processing for Wireless Positioning research group. She also holds a Visiting Professor position at Universitat Autonoma de Barcelona's SPCOMNAV group. Education : MSc in Electrical Engineering (Politehnica University of Bucharest, 1997), DEA in Econometrics (École Polytechnique, Paris, 1998), PhD in Telecommunications (Tampere University of Technology, 2003) Research Focus : GNSS algorithms, interference detection, wearable computing, and 5G positioning convergence. Her work spans signal processing for GNSS (Galileo, GPS, GLONASS, BeiDou), indoor/outdoor localization, and IoT communication. She coordinates the H2020 MSCA European Joint Doctorate A-WEAR and serves as Associate Editor for the Journal of Navigation and IET Radar, Sonar & Navigation . Notable projects include GRAMMAR (Galileo receivers), UJI IndoorLoc Platform , and SP4TE collaborations. She has authored over 250 publications and 6 patents.

Milica Todorovicمشاهده پروفایل

استاد مهمان

• Machine Learning
• Materials Science
• Quantum Mechanics
• +۶ مورد دیگر

Milica Todorovic is a Visiting Professor at Aalto University's Department of Applied Physics , leading machine learning research within the Computational Electronic Structure Theory (CEST) group. Her work bridges quantum mechanical simulations and machine learning algorithms to optimize material functionality, particularly for solar cell components and organic-inorganic interfaces . Research Interests include: Data-driven materials science Active learning for molecular datasets Bayesian optimization in atomic structure prediction Quantum simulations of surfaces and adsorbates Thermodynamic property modeling for atmospheric molecules Article Trends (2017-2025) show focus on machine learning applied to materials science , with subfields spanning bayesian optimization , conformational analysis , and density functional theory . Key collaborations include Patrick Rinke and Hanna Vehkamäki . Activities include organizing workshops like the Young Researcher’s Workshop on Machine Learning for Materials Science (2019) and International Workshop on Machine Learning for Materials Science (2018).

• Sensor Informatics
• Medical Technology
• Machine Learning
• +۴ مورد دیگر

Fatemeh Yaghoobi is a Doctoral Researcher at Aalto University, affiliated with the Department of Electrical Engineering and Automation under the College of Engineering. She actively contributes to Sensor Informatics and Medical Technology research groups. Research Interests: Her work focuses on algorithm development for state estimation in nonlinear systems, leveraging Bayesian statistics and parallel computing. Key areas include probabilistic numerical methods, Kalman smoothers, and optimization techniques for machine learning applications. Publication Trends: Recent research highlights advancements in parallel-in-time computing for ODE solvers, statistical linear regression for state-space models, and iterative Kalman smoother algorithms. These publications reflect interdisciplinary applications in machine learning, signal processing, and computational mathematics. Contact: Email: fatemeh.yaghoobi@aalto.fi

Riku Linnaمشاهده پروفایل

مدرس ارشد

• Dynamical systems
• Stochastic processes
• Nonlinear dynamics
• +۳ مورد دیگر

Riku Linna serves as a Senior University Lecturer in the Department of Computer Science at Aalto University, contactable via riku.linna@aalto.fi and +358505124356. His research spans: Dynamical systems Stochastic processes Nonlinear dynamics Computational physics Statistical physics Machine learning methods Analysis of his 25+ publications (1996-2020) reveals a dominant focus on polymer dynamics in biophysical contexts, particularly translocation/ejection mechanisms in viral capsids and semiflexible polymers. Recent work integrates machine learning with dynamical systems for brain state modeling using Kuramoto oscillators, while earlier research examines thermal conduction in nanoscale systems and DNA elasticity. His methodological approach consistently combines computational physics with statistical mechanics to solve complex biophysical problems. No scientific awards were documented in the source material. While his faculty role implies teaching responsibilities, no student advisement records or research grants were specified in the provided text.

Hannu Aronenمشاهده پروفایل

استاد

• Medical Imaging
• Magnetic Resonance Imaging (MRI)
• Neuroimaging
• +۳ مورد دیگر

Hannu Aronen is a Professor and Chair of Radiology at the University of Turku since April 2006, and Chief Radiologist at Turku University Hospital since January 2009. He holds degrees including an M.D. from the University of Helsinki (1983), M.Sc. (Eng) from Helsinki University of Technology (1988), and a Doctor of Medical Sciences (1990). He became a Docent in Radiology (1995) and Functional Medical Imaging (1998). His research focuses on magnetic resonance imaging (MRI) , particularly in neuroimaging and prostate cancer diagnostics. Notable contributions include advancements in perfusion/diffusion MRI for stroke and glioma characterization, and the development of MRI protocols for prostate cancer detection using machine learning. He has led the Functional Brain Imaging Unit (2002–2007) as part of the Helsinki Brain Research Centre, funded by the Academy of Finland. Research Highlights: His work spans MRI methodological innovations, such as susceptibility contrast perfusion and fMRI analysis, and collaborations with institutions like Harvard Medical School and the Turku PET Centre. Over 800 prostate MRI scans have been analyzed in his projects, leading to 17 peer-reviewed articles on prostate cancer imaging accuracy. Current research emphasizes biparametric MRI protocols and AI-driven analysis for tumor detection. Grants & Collaborations: His work has been supported by the Academy of Finland and industry partnerships. Key collaborations include projects with Prof. Leif Østergaard (Aarhus University) on perfusion MRI and Prof. Synnöve Carlson on brain network imaging. Recent studies involve the Turku PET Centre and multinational trials in prostate cancer diagnostics.

• Structural Engineering
• Seismic Safety
• Artificial Intelligence
• +۳ مورد دیگر

Khurram Shabbir is a Researcher and Doctoral Student at Aalto University's School of Engineering , affiliated with the Department of Civil Engineering and the Structures – Structural Engineering, Mechanics and Computation group. His research focuses on integrating artificial intelligence and advanced computational methods into structural engineering, particularly in seismic assessment, disaster management, and infrastructure safety. He has contributed to projects involving data-driven modeling for building retrofitting and UAV-based intelligent mobility solutions. Key research interests include: Seismic performance evaluation using machine learning AI applications in structural design and safety Disaster management through early warning systems Accelerated bridge construction techniques His recent publications emphasize interdisciplinary approaches combining civil engineering principles with computer science and statistics. Notably, he has explored neural radiance fields for structural analysis and bootstrap methods for prediction intervals in building performance.

• Antennas
• Wireless Sensors
• Antenna Measurements
• +۵ مورد دیگر

Professor Ville Viikari is a leading academic in antenna engineering and wireless systems at Aalto University's Department of Electronics and Nanoengineering. His work focuses on advanced antenna designs for mobile devices, 5G/6G networks, RF energy harvesting, and radar systems. He is affiliated with the Aalto University School of Electrical Engineering. Research interests include antenna arrays, coupled systems, and measurement techniques with applications in RFID, backscattering, and IoT. His innovations span frequency-reconfigurable antennas, shared-aperture designs, and millimeter-wave systems. Awards: IEEE Sensors Council Early Career GOLD Award (2010) Aalto Doctoral Thesis Award (2022) Young Researcher Award (2014) Key Projects: Zero Power Sensor Network (2010–2013) as project manager Development of 3D-printed antenna arrays and CMOS circulators Recent Work: Advances in Ka-band Vivaldi arrays and E-band transmitarrays AI-driven antenna optimization using swarm intelligence Wideband MIMO antennas for full-screen mobile devices His research group collaborates on cutting-edge projects blending antenna physics with emerging technologies like 6G and AI-driven design methodologies.

• Plant Biology
• Microbiology
• Virology
• +۷ مورد دیگر

Jarkko Salojärvi is a University Researcher at the University of Helsinki, affiliated with the Viikki Plant Science Centre (ViPS) and the Organismal and Evolutionary Biology Research Program . He serves as a supervisor in the Doctoral Programme in Plant Sciences and contributes to bioinformatics research for molecular biology and genomics. His research spans plant genomics , evolutionary biology , and bioinformatics , with a focus on silver birch , pathogen interactions , and plant stress responses . Recent work includes genomic analyses of Taphrina infections , betulin biosynthesis in fungi, and adaptive landscapes in forest trees. He has published extensively on plant-microbe interactions , genetic diversity , and computational biology , with funded projects such as TreeBio: Centre of Excellence in Tree Biology and Future Forests . His activities include peer review , conference presentations , and thesis supervision .

• Phosphoproteomics
• Proteostasis
• Parkinson's Disease
• +۵ مورد دیگر

Ye Hong is a Project Researcher at Åbo Akademi University's Faculty of Natural Sciences and Technology, affiliated with the Turku Life Sciences Centre and the InFLAMES flagship program focused on immune system innovation ecosystems. Their research bridges computational biology and neurodegenerative disease studies. Key research interests include: Phosphoproteomic analysis of signaling pathways Proteostasis regulation in Parkinson's disease Zebrafish larvae as model organisms for anxiety studies Development of automated bioinformatics pipelines Identification of Parkinson's disease diagnostic biomarkers Functional analysis of LRRK2-G2019S mutations Recent work demonstrates expertise in combining mass spectrometry with computational workflows to analyze protein phosphorylation patterns and translational repression in neurodegenerative contexts. Award highlights: Nordic Life Sciences Days prize (2019)

Maria Ballestar de las Herasمشاهده پروفایل

دانشیار مهمان

• Statistics
• Probability
• Artificial Intelligence
• +۱۲ مورد دیگر

Maria Ballestar de las Heras is a Visiting Associate Professor at LUT University, School of Engineering Sciences, where her work intersects Social Sciences, Statistics, and Artificial Intelligence. She is concurrently an Associate Professor in Applied Economics at Universidad Rey Juan Carlos (URJC), Madrid, Spain, and serves as Head of Analytical Consultants at Google in Spain, demonstrating a strong bridge between academia and industry. Universidad Rey Juan Carlos – Associate Professor, Applied Economics LUT University – Visiting Associate Professor, School of Engineering Sciences Google Spain – Head of Analytical Consultants She earned her B.A. in Statistics from Universidad de Zaragoza, M.Sc. in Marketing and Market Research, and M.A. in Information and Knowledge Society from Universitat Oberta de Catalunya, and her Ph.D. in Applied Economics from Universidad Rey Juan Carlos. Her research focuses on applying data science, Big Data, and AI to real-world problems in customer behavior, digital transformation, public policy evaluation, and sustainability. She investigates how AI can enhance marketing strategies, evaluate educational reforms, predict stock markets, and analyze social media discourse on climate change. Her interdisciplinary approach integrates economics, marketing, and machine learning to generate actionable insights for businesses and policymakers. Her recent publications (2021–2025) reveal a consistent trend in leveraging machine learning and AI for business and social impact, particularly in e-commerce, robotics adoption, and educational policy. She frequently collaborates with researchers such as Jorge Sainz, Ismael Sanz, and Taraneh Shahin. Scientific Contributions: Published over 20 articles in leading journals including Technological Forecasting and Social Change , Journal of Business Research , and IEEE Transactions on Engineering Management . Active peer reviewer for journals like Arabian Journal for Science and Engineering and Journal of Business Research . She advises on data-driven decision-making and digital innovation, though no formal students are listed. She leads research at the intersection of technology and society, contributing to both academic knowledge and industrial practice. Her work is supported by her extensive industry experience across banking, IT, and pharmaceuticals.