جستجوی استاد راهنمای دانشگاهی

• DFT (Density Functional Theory)
• SPM (Scanning Probe Microscopy)
• Surface Science
• +۶ مورد دیگر

Ondrej Krejci is a researcher in the Department of Applied Physics at Aalto University, specializing in computational and theoretical approaches to surface science, scanning probe microscopy, and materials discovery. His work bridges advanced simulation techniques with experimental validation. Research interests include: Density Functional Theory (DFT) Scanning Probe Microscopy (SPM) simulations Molecular adsorption on surfaces X-ray spectroscopy simulations Condensed matter physics Atomic and molecular physics Recent publications highlight trends in machine learning for catalyst discovery, probe-particle model innovations, and on-surface synthesis of novel carbon allotropes and frameworks. Collaborative work spans computational modeling, experimental validation, and instrumentation development.

• Machine Learning
• Artificial Intelligence
• Stochastic Differential Equations
• +۳ مورد دیگر

Prakhar Verma is a Doctoral Researcher at Aalto University, specializing in machine learning and probabilistic modeling. His work focuses on Gaussian processes, stochastic differential equations, and their applications in sequential learning and Bayesian optimization. Research Interests: Probabilistic modeling with Gaussian processes Scalable inference for stochastic differential equations Bayesian optimization and active learning techniques Sequential learning with memory-based approaches Mathematical modeling of continuous-time systems Computational statistics for high-dimensional data Collaborations: He collaborates with researchers like Arno Solin, Vincent Adam, Paul E. Chang, Ti John, and Victor Picheny. His publications span conferences such as NeurIPS, ICML, and AISTATS.

Ayush Bhartiمشاهده پروفایل

پژوهشگر ارشد

• Probabilistic Machine Learning
• Bayesian Statistics
• Simulation-based Inference
• +۳ مورد دیگر

Ayush Bharti is an Academy Research Fellow (Research Fellow) in the Department of Computer Science at Aalto University, Finland. His primary affiliations include the Probabilistic Machine Learning research group and the Academy Professorship led by Samuel Kaski . His research centers on probabilistic machine learning with dual focus areas: advancing simulation-based inference methodologies and applying statistical techniques to stochastic radio channel modeling. Key interests include Bayesian statistics, robustness under model misspecification, handling missing data, and developing efficient approximate Bayesian computation frameworks. His work bridges theoretical machine learning with practical wireless communication challenges. Analysis of his 15 most recent publications (2021-2025) reveals a consistent trajectory in simulation-based inference innovation, featuring neural processes, diffusion models, and multilevel architectures. Approximately 60% of his work targets general statistical challenges (e.g., robustness, cost-awareness), while 40% addresses radio channel modeling applications. Notable trends include integration of domain expertise in inference loops and development of context-aware optimization techniques. No information is available regarding student supervision or research grants. Bharti operates within Aalto University's Probabilistic Machine Learning ecosystem, contributing to the Academy Professorship project under Samuel Kaski. This collaborative environment focuses on developing scalable inference algorithms for complex real-world systems, with particular emphasis on uncertainty quantification in simulation-driven scientific discovery.

Sammie Kattمشاهده پروفایل

پژوهشگر

• Bayesian Reinforcement Learning
• Partially Observable Markov Decision Processes
• Robotics
• +۲ مورد دیگر

Sammie Katt serves as a Postdoctoral Researcher in the Department of Computer Science at Aalto University's School of Science, specializing in Bayesian Reinforcement Learning for robotics and decision-making under uncertainty. Their work addresses critical challenges in partially observable environments through algorithmic innovation and practical implementations. Dr. Katt's research centers on Bayesian approaches to reinforcement learning, with deep expertise in Partially Observable Markov Decision Processes (POMDPs). They develop scalable algorithms for uncertainty quantification, robot navigation, and real-time decision-making, bridging theoretical advances with robotic applications. Key contributions include BADDr for adaptive POMDP solutions and gym-gridverse for simulation benchmarking. Analysis of Katt's 14 publications (2012-2023) reveals three dominant trends: (1) Bayesian methods for efficient POMDP solving using Monte Carlo tree search and deep learning, (2) Robotics applications in motion prediction, target search, and scene reconstruction, and (3) Framework development for reproducible RL research. Their work consistently emphasizes computational efficiency and real-world applicability in uncertain environments.

• Maritime Engineering
• Machine Learning
• Risk Analysis
• +۴ مورد دیگر

Mingyang Zhang is a Postdoctoral Researcher at the Department of Energy and Mechanical Engineering , Aalto University, Finland. His research focuses on the intersection of Maritime Engineering and Artificial Intelligence , particularly in Digital Twin Technology , Ship Motion Prediction , and Risk Analysis for Arctic and inland waterway operations. Awarded Best Paper at G-NAOE 2024 Honored with 5 Highly Cited Paper Awards (2021-2024) Active in Marine and Arctic Technology research group His machine learning work addresses ship fuel consumption prediction , collision avoidance , and grounding risk assessment , with applications in autonomous shipping and offshore wind farm monitoring . Key publications span Reliability Engineering , Ocean Engineering , and Marine Hydrodynamics journals. Recent article trends include: Digital Twin applications for shipping decarbonization (2024) Deep learning models for 6-DoF ship motions and focused wave prediction (2023) Big Data Analytics for Arctic navigation risks and inland waterways (2022-2021) Integration of HFACS and fault tree analysis in risk modeling (2019) Scientific achievements include: 2024: Best Paper, G-NAOE Conference 2023: 2 Highly Cited Paper Awards 2022: 2 Highly Cited Paper Awards 2021: Highly Cited Paper Award 2019: Best Paper, 5th International Conference on Transportation Information and Safety Current research at the Marine and Arctic Technology group emphasizes AI-driven maritime safety , real-time risk monitoring , and data-intensive shipping decarbonization projects. Collaborations include Spyros Hirdaris , Pentti Kujala , and Jakub Montewka .

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
• 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.

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

• Probabilistic Machine Learning
• Bayesian Optimization
• Multi-Agent Systems
• +۳ مورد دیگر

Ali Akbar Khoshvishkaie is a Visiting Professor at Aalto University, affiliated with the School of Science and the Department of Computer Science. His research focuses on probabilistic machine learning and Bayesian optimization, particularly in multi-agent systems contexts. Research interests include: Probabilistic Machine Learning Bayesian Optimization Multi-Agent Systems Collaborative Learning He has published recent work on cooperative Bayesian optimization for imperfect agents, contributing to computational intelligence and uncertainty quantification methodologies.

• Deep Learning
• Partial Differential Equations
• Physics-Informed Neural Networks
• +۳ مورد دیگر

Katsiaryna Haitsiukevich is a Visitor (Faculty) and Doctoral Researcher in the Department of Computer Science at Aalto University. Her research focuses on deep learning applications in science and engineering, particularly at the intersection of partial differential equations and neural networks. She holds a doctoral degree (Tekn. toht., tietotekniikka) awarded in May 2025. Her work includes advancing physics-informed deep learning frameworks, improving neural network training through ensemble methods, and leveraging large language models for symbolic regression. Key contributions span dynamical systems analysis, parameter space exploration, and Hamiltonian trajectory learning. Since 2021, she has published six peer-reviewed articles in top conferences like ICANN, IJCNN, and ACL. Notable collaborations include projects with researchers like Prof. P. Marttinen and Prof. A. Ilin. Her research emphasizes practical applications in computational physics, mathematical modeling, and engineering systems.

• Robot Vision
• Robot Learning
• Computer Vision
• +۵ مورد دیگر

Joni Kämäräinen is a Professor of Signal Processing at Tampere University’s Computing Sciences department under the Faculty of Information Technology and Communication Sciences. He leads the Vision Group and has a tenure-track career at Tampere University since 2012, achieving full professorship in 2020. Prior, he conducted postdoctoral research at the University of Surrey’s Center of Vision, Speech and Signal Processing and held a faculty position at LUT University. His research spans robot vision and robot learning , focusing on computer vision and machine learning applications. Key applied projects address industrial collaboration with Huawei, Nokia Technologies, and others, emphasizing robust visual perception, autonomous systems, and depth sensor integration. Notable work includes long-term visual place recognition, RGB-D tracking, and color constancy solutions. Recent publications (2023–2025) reflect expertise in digital twins for industrial manipulators, probabilistic subgoal modeling, depth-aware video deblurring, and LiDAR-place recognition datasets. His team’s outputs bridge robotics, AI, and signal processing, with interdisciplinary impacts in healthcare and autonomous navigation. Teaching responsibilities include graduate and doctoral colloquia, and the textbook Koneoppimisen perusteet (2023) . Funding sources include the Academy of Finland, EU Horizon 2020, Business Finland, Huawei, and Nokia Technologies.

• Sociolinguistics
• Sociophonetics
• Finnish Dialects
• +۵ مورد دیگر

Liisa Mustanoja is a Senior University Lecturer at Tampere University's Faculty of Information Technology and Communication Sciences, within the Department of Language Studies. She leads the Languages Unit and directs the Doctoral Programme in Language Studies (DPLA). Her research focuses on sociolinguistics, sociophonetics, Finnish dialects, and historical language change, particularly analyzing wartime letters and affective speech. She heads the Plural research center and collaborates on projects like Tunteiden Tampere (Kone Foundation grant 2024–2027) and the Sulka corpus initiative. Her academic career includes a PhD in Language Studies and a Docent title. She has held leadership roles in ethics committees and networks like Langnet. She has received teaching awards (2021) and contributed over 38 publications since 2003, focusing on Finnish language variation, speech corpus development, and sociolinguistic methodology. Key grants: Kone Foundation grants for Sulka (2023–2026), Kippo (2017–2022), and Tunteiden Tampere (2024–2027) Research groups: Sumu (Sulka), Plural (Languages and Cultures) Her work bridges human-machine interaction in language, emphasizing ethical communication and inclusivity. Recent projects explore affective speech processing and language policy innovations like the 'Data Protection Passport' concept.

• 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 .