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Gleb TikhonovView profile

Researcher

• Ecology
• Evolutionary Biology
• Statistical Modelling
• +3 more

Gleb Tikhonov is a postdoctoral researcher at the University of Helsinki , affiliated with the Faculty of Biological and Environmental Sciences and the Organismal and Evolutionary Biology Research Programme . He also holds a postdoctoral position at Aalto University in the Department of Computer Science . His work bridges statistical ecology, machine learning, and computational biology. Research Interests : Development of joint species distribution modelling (JSDM) for ecological communities Integration of statistical and machine learning methods High-performance computing implementations Phenological and dispersal pattern analysis Publications demonstrate expertise in applying advanced statistical frameworks to ecological questions, including fungal dispersal mechanisms, microbiome variation, and climate change impacts. His LIFEPLAN project (funded by the European Commission Joint Research Centre) focuses on planetary biodiversity synthesis using big data. Education : PhD in Biological and Environmental Sciences, University of Helsinki (2018) MS in Applied Mathematics, Lomonosov Moscow State University (2014)

Serio AgriestiView profile

Visiting Professor

• Transportation Engineering
• Urban Planning
• Traffic Simulation
• +3 more

Serio Agriesti is a Visiting Professor at the Spatial Planning and Transportation Engineering department within Aalto University's Department of Built Environment . His expertise spans transportation engineering, urban planning, and automated mobility systems. He holds a Doctor of Technology (2024) and a Master's degree in Engineering from Politecnico di Milano (2017). Research Focus: Dr. Agriesti investigates traffic simulation methodologies , automated mobility services , and sustainable urban transportation systems . Key areas include multimodal transportation integration, large-scale transport model calibration, and the application of AI techniques like graph neural networks for traffic flow analysis. Recent Work Trends: His 2025 articles emphasize seamless multimodal integration , automated mobility demand analysis , and city-scale simulation frameworks . Earlier work (2023–2024) focuses on Bayesian optimization for transport model calibration and sustainability assessment of automated vehicles . Collaborations: Active in international transport research networks, with presentations at conferences on topics like activity-based modelling integration and synthetic population assignment. Co-developed a Zenodo dataset supporting a traffic simulation review paper (2024). Teaching/Advising: No formal student advisees listed. Academic contributions include peer-reviewed articles (22+) and datasets shared via academic platforms.

Ville KolehmainenView profile

Professor

• Computational Inverse Problems
• Uncertainty Quantification
• Medical Imaging
• +6 more

Ville Kolehmainen is a Professor at the Department of Technical Physics within the Faculty of Science, Forestry and Technology at the University of Eastern Finland (UEF). His research focuses extensively on computational inverse problems and uncertainty quantification , with applications in advanced medical imaging techniques such as X-ray tomography , diffuse tomography , and MRI . He actively collaborates with clinical experts and engineering specialists to develop novel imaging modalities for 3D/4D imaging with sparse data Electrical impedance tomography (EIT) Multi-energy CT reconstruction Thermal tomography Ville Kolehmainen leads the Inverse Problems research group (active since 2010) and contributes to the Neuro-Innovation project (2021-2026). His work integrates Bayesian statistical inversion , parallel level sets , and nonlinear reconstruction methods to address challenges in Medical tomography Optical diffusion tomography Thermal conductivity estimation Atmospheric remote sensing Recent publications highlight his contributions to electrical impedance tomography for hemorrhagic stroke monitoring, dual-modality imaging of two-phase flows, and deep learning post-process corrections in remote sensing. His methodological innovations include model error compensation , sparsity-promoting inversion , and anatomical prior integration in image reconstruction. Collaborations span University of Eastern Finland , University College London , and Harvard Medical School researchers.

• Probabilistic Machine Learning
• AI in Drug Discovery
• Bayesian Methods
• +3 more

Arslan Masood is a Doctoral Researcher at the Department of Computer Science, School of Science, Aalto University. His research focuses on Probabilistic Machine Learning and its applications in Drug Discovery . University: Aalto University Department: Department of Computer Science Academic Rank: Researcher His work emphasizes data-efficient approaches using BERT-based models and Bayesian active learning , particularly for molecular property prediction and toxicity modeling. Recent publications explore imbalanced data handling in drug safety prediction and deep Bayesian optimization for experimental design.

• Machine Learning
• Bayesian Deep Learning
• Gaussian Process Models
• +7 more

Rui Li is a Doctoral Researcher in the Department of Computer Science at Aalto University, Finland. Their research focuses on Bayesian deep learning, Gaussian processes, and robust machine learning methods. They collaborate closely with Professor Arno Solin and colleagues on problems involving uncertainty quantification, model generalization, and sensor fusion applications in robotics. Recent work highlights advancements in few-shot classification generalization, scalable Bayesian deep learning predictions, and hyperparameter optimization for Gaussian process models. Publications span conferences like WACV, ICLR, ICML, and ICCVW, addressing theoretical and applied challenges in machine learning and computer vision. Rui Li contributes to interdisciplinary research groups tackling problems in semantic segmentation robustness and visual-inertial SLAM optimization. Their collaborative efforts include participation in the Robust Semantic Segmentation UNCV2023 Challenge with a multi-institutional team.

Anisia KatinskaiaView profile

Researcher

• Natural Language Processing
• Language Learning
• Grammatical Error Correction
• +4 more

Anisia Katinskaia is a doctoral researcher at the University of Helsinki, holding dual affiliations with the Department of Digital Humanities and Department of Computer Science. Her research bridges computational linguistics and language education, focusing on practical applications of natural language processing to enhance language learning experiences. She actively contributes to the academic community through publications in major computational linguistics conferences and participation in significant research projects. Dr. Katinskaia's research interests center on natural language processing applications for language learning, with particular expertise in grammatical error correction, text difficulty assessment, and intelligent tutoring systems. Her work demonstrates a strong focus on applying transformer models and large language models to solve practical challenges in language education, with notable contributions in Russian and Tatar language processing. She has developed innovative approaches for assessing grammatical correctness, estimating text difficulty, and creating adaptive learning tools that respond to individual learner needs. Analysis of her publication record shows a clear trajectory from foundational work in grammatical error detection (particularly for Russian language) toward more sophisticated applications of modern language models like GPT-3.5 and transformer architectures. Her recent work demonstrates increasing sophistication in probing linguistic capabilities of AI models and developing practical educational applications, with a growing emphasis on minority languages like Tatar. The interdisciplinary nature of her research connects computational linguistics with educational theory and cognitive science. Dr. Katinskaia has received significant recognition for her work, including: Best Paper Award (2018) Outstanding paper award (2023) The Open Science Award (2018) She is actively involved in multiple research projects including Know-AI (funded by the Academy of Finland), Revita: Language learning and AI, LLL: Language Learning Lab, and Easy Language for accessible workplace (funded by Business Finland). Her collaborative approach is evident through her work with researchers like Yangarber, Hou, Vu Anh, and others across multiple projects and publications. She regularly presents her research at international conferences and has participated in prestigious events like the Lisbon Machine Learning School. Dr. Katinskaia is a key member of the Language Learning Lab research group at the University of Helsinki, where she contributes to developing cutting-edge tools that apply AI to language education challenges. Her work on the Easy Language project specifically targets making workplace communication more accessible through language technology solutions.

• Machine Learning
• Dynamical Systems
• Partial Differential Equations
• +5 more

Sahel Iqbal is a Doctoral Researcher at the Department of Electrical Engineering and Automation , affiliated with the School of Electrical Engineering at Aalto University . His work bridges Machine Learning with Signal Processing and Partial Differential Equations , focusing on applications in froth flotation and dynamical systems . His research spans: Physics-informed machine learning for mineral processing optimization. Probabilistic modeling using particle filters in experimental design. Parallel-in-time solutions for nonlinear PDEs leveraging Gaussian processes. Publications highlight interdisciplinary trends in Machine Learning (2025), Computer Science (2024), and Mathematics (2024), with subtopics including neural networks , dynamical systems , and probabilistic modeling . He is part of the Sensor Informatics and Medical Technology group, contributing to cutting-edge methodologies in signal processing and automation.

• Machine Learning
• Graph Neural Networks
• Bayesian Inference
• +3 more

Diego Parente Paiva Mesquita serves as a Visitor (Faculty) in the Department of Computer Science under Professor Samuel Kaski's research group. His work bridges theoretical and applied machine learning with emphasis on scalable probabilistic systems. His academic credentials include: Doctor of Technology in Computer Science (awarded December 16, 2021) Bachelor of Computer Science from the Brazilian Ministry of Education (awarded August 23, 2016) Mesquita's research focuses on advancing graph neural networks, Bayesian inference, and incomplete data handling. His fingerprint reveals strong contributions to probabilistic modeling (60% Approximates), random variable analysis (53%), and machine learning systems (50%), with particular innovation in neural network architectures (44%) for complex data structures. Recent publications (2022-2024) demonstrate a cohesive trajectory toward parallelizable machine learning frameworks. Key themes include temporal graph representation learning, embarrassingly parallel Monte Carlo methods, and thin/deep Gaussian process hybrids—addressing critical scalability bottlenecks in probabilistic AI while maintaining theoretical rigor.

Mihiran Galagedarage DonView profile

Researcher

• Digital Twins
• Materials Science
• Mechanical Engineering
• +5 more

Mihiran Galagedarage Don is a Postdoctoral Researcher at Memorial University of Newfoundland, affiliated with the Department of Energy and Mechanical Engineering and the Materials to Products department. He holds a Ph.D. in Mechanical Engineering (2023) and a Master of Engineering (2019) from Memorial University, alongside multiple degrees from the University of Moratuwa, including a B.Sc. (Hons) in Materials Engineering (2009). His research focuses on digital twin applications in oil and mining industries, fatigue analysis of drilling systems, finite element modeling, and predictive maintenance. Key contributions include frameworks for ESP systems, vibration prediction in drill strings, and hybrid models for process fault prognosis. Publications emphasize digital twin development, fatigue life prognosis, and Industry 4.0 analytics. No scientific awards are explicitly mentioned. Advising students or grants are not listed. No specific labs or teams are highlighted in the provided texts.

Jorma LaaksonenView profile

Senior Lecturer

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +4 more

Jorma Laaksonen is a Senior Lecturer at the Department of Computer Science, Aalto University (School of Science). His research focuses on artificial intelligence, machine learning, computer vision, and multimedia systems, with notable contributions to cross-lingual evaluation, hyperspectral imaging, and audio-visual attention mechanisms. He holds a Doctoral degree in Engineering and Technology from Helsinki University of Technology (1997). Education: Doctoral degree, Engineering and Technology, Helsinki University of Technology (1997) Licentiate degree, Engineering and Technology, Helsinki University of Technology (1991) Master's degree, Engineering and Technology, Helsinki University of Technology (1990) His research interests span AI-driven visual computing, human-computer interaction, and multimedia systems. Key projects include USSEE (audio-visual scene understanding) and ARTISDIG (AI for forest spectral analysis). He has published over 280 papers and supervised multiple theses. Awards include the IEEE Best Paper Award (2017) and Microsoft's Best Practice Award (2016). Recent work includes cross-cultural language modeling, efficient text-to-video retrieval, and open-world video instance segmentation. He leads the Lecturer Laaksonen Jorma group and actively participates in editorial roles and doctoral thesis evaluations.

• Probabilistic Machine Learning
• Bayesian Inference
• Markov Chain Monte Carlo (MCMC)
• +3 more

Anirudh Jain holds the role of Visitor (Faculty) in the Department of Computer Science at Aalto University, while also serving as a Doctoral Student in the School of Science and a Doctoral Researcher in the Probabilistic Machine Learning group under Professor Samuel Kaski’s professorship. His research focuses on Bayesian methods, generative models, and optimization techniques. Education: Master’s degree in Engineering and Technology from the Indian Institute of Technology (Indian School of Mines), Dhanbad (2019). Research Interests: Probabilistic Machine Learning, Bayesian Inference, Markov Chain Monte Carlo (MCMC), Generative Models, and Parameter Estimation. His work includes developing latent space models for multi-target property prediction and exploring variational inference alternatives to MCMC for parameter estimation. Collaborations: Active in projects like Veturi VL4Pharma (2023–2025), focusing on virtual laboratories for pharmaceutical R&D. Labs/Teams: Affiliated with the Probabilistic Machine Learning group and Professorship Kaski Samuel.

• Statistics
• Population Genetics
• Computational Biology
• +3 more

Jukka Corander serves as Professor in the Department of Mathematics and Statistics at the University of Helsinki, holding a Docent title in the same department. He is affiliated with the Helsinki Institute for Information Technology and supervises three doctoral programs: Integrative Life Science, Mathematics and Statistics, and Population Health. His academic contact includes email jukka.corander@helsinki.fi and phone +358504155294. Corander's research centers on statistical genetics and computational biology , with expertise spanning population genetics, genomic epidemiology, and Bayesian inference methods. His work integrates advanced statistical modeling with microbial genomics to address evolutionary dynamics and pathogen spread. Recent projects examine bacterial evolution in Streptococcus pneumoniae , Enterococcus species, and Escherichia coli , emphasizing computational solutions for high-dimensional genomic data. His 2025 publications reveal a strong focus on genomic epidemiology and computational methodology , featuring studies on Streptococcus pyogenes spread in Australia, plasmid-driven E. coli evolution, and innovations in approximate Bayesian computation. These works demonstrate interdisciplinary collaboration across microbiology, statistics, and public health, often published in high-impact journals like Nature Communications and The Lancet Microbe . Notable recognition includes the Per Brahe Prize for Young Scientist (2008) . Additional professional activities encompass: 62 academic activities including conference organization (e.g., 4th Permafrost Workshop) International research visits to Wellcome Trust Sanger Institute Membership in Norwegian Research Council Editorial roles and doctoral thesis examination Corander actively mentors doctoral candidates through three university programs and participates in media engagement, including Finnish science programs and radio interviews discussing statistics' societal role. His current research includes the active project Comparative genome analysis, population genetics and functional genomics of animal gastric Helicobacter species (since 2013).

Dominik BaumannView profile

Assistant Professor

• Systems and Control Theory
• Machine Learning
• Communication Networks
• +5 more

Dominik Baumann is an Assistant Professor at Aalto University's Department of Electrical Engineering and Automation since 2023. His research focuses on systems and control theory, machine learning, and cyber-physical systems, with an emphasis on safety, optimization, and robotics applications. His work addresses challenges in reinforcement learning, distributed control, and safe exploration strategies. Notable contributions include methods for non-ergodic reward systems, lightweight safe learning algorithms, and scalable optimization for dynamical systems. He is affiliated with the Cyber-physical Systems research group at Aalto. Awards include the 2019 Future Award from the Ewald Marquardt Foundation and multiple conference best paper/demonstration awards. His publications span journals like IEEE Transactions on Automatic Control and venues such as the International Conference on Cyber-Physical Systems. Research themes include: Safe learning and control for robotics Optimization under computational/resource constraints Ergodicity principles in reinforcement learning Event-triggered communication strategies

Aleksei TiulpinView profile

Assistant Professor

• Machine Learning
• Medical Imaging
• Bayesian Deep Learning
• +4 more

Aleksei Tiulpin is an Assistant Professor at the University of Oulu since January 2022 and a member of the European Lab for Learning and Intelligent Systems (ELLIS). His research focuses on Intelligent Medical Systems at the intersection of machine learning and healthcare. Current affiliation: Faculty of Medicine, University of Oulu Research group: Intelligent Medical Systems (IMEDS) Academic rank: Assistant Professor Aleksei's research interests span Machine Learning , Medical Imaging , Bayesian Deep Learning , and Human-in-the-loop Machine Learning . He develops methods that: Use multimodal data Minimize labeled data requirements Quantify prediction uncertainty Enable human-AI interaction Applications target musculoskeletal disorders, breast cancer, and dementia diagnostics. Scientific recognition : 2020: Best doctoral thesis at University of Oulu 2022: Docent title in Machine Learning for Medical Imaging Academic background : PhD in Medicine (2017-2020) from University of Oulu, with advisors Prof. Simo Saarakkala, Dr. Jérôme Thevenot, and Prof. Esa Rahtu.

Milica TodorovicView profile

Associate Professor

• Computational Materials Science
• Artificial Intelligence for Materials Design
• First-Principles Simulations
• +6 more

Milica Todorovic serves as an Associate Professor in the Department of Materials Science within the Faculty of Science and Engineering at the University of Turku (UTU). She leads the Materials Informatics Laboratory (MIL) and heads the Modern Industrial Materials MSc track. Her institutional roles include Vice-director of the Sustainable Materials and Manufacturing (SUSMAT) UTU profiling area, Vice-chair of COST Action CA22154 DAEMON, Vice-leader of the Human-Centric Artificial Intelligence for Sustainable Future (HAIF) Doctoral Training Network, and Co-lead of Finnish Centre for AI (FCAI) Highlight E. Her research integrates artificial intelligence algorithms with first-principles simulations to optimize functional materials and device performance. The MIL group develops data-driven solutions spanning aerosol research, chemical engineering of bio-based materials, and experimental-computational integration. Current projects focus on battery recycling optimization, magnetic materials design, perovskite engineering, and atmospheric aerosol analysis using Bayesian optimization and active learning techniques. Her work demonstrates significant interdisciplinary reach across computational chemistry, nanotechnology, and sustainable engineering. Scientific contributions include: Development of AI workflows for materials discovery Bayesian optimization frameworks for adsorption structure prediction Question answering systems for materials literature mining Data-efficient methods for molecular property prediction Her teaching portfolio includes advanced courses in Data Visualisation and Analysis, Simulations and New Materials, and Machine Learning for Materials Science. She actively contributes to European research networks through DAEMON and FCAI initiatives, driving collaborative efforts in data-driven materials engineering.