جستجوی استاد راهنمای دانشگاهی

Simo Särkkäمشاهده پروفایل

استاد

• Machine Learning
• Signal Processing
• Bayesian Filtering
• +۷ مورد دیگر

Professor Simo Särkkä holds a position in Sensor Informatics and Medical Technology at the Department of Electrical Engineering and Automation (EEA), Aalto University. His research focuses on multi-sensor data processing, Bayesian filtering, machine learning, and their applications in medical technology, brain imaging, and inverse problems. He leads research groups including the Helsinki Institute for Information Technology (HIIT) and Sensor Informatics and Medical Technology. His work bridges theoretical advancements in probabilistic methods with practical implementations in healthcare and engineering. Key research interests include Gaussian processes, stochastic differential equations, quantum machine learning, and signal processing. He has contributed to advancements in algorithms for nonlinear state-space models, parallel computing techniques, and medical imaging technologies such as scatter correction in CT scans. His methodologies are applied across domains like autonomous systems, robotics, and bioengineering. Notable publications span topics like quantum-assisted Gaussian regression, physics-informed machine learning for industrial processes, and parallel-in-time numerical methods. His work emphasizes computational efficiency and robustness in high-dimensional and real-time systems.

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.

Antti Honkelaمشاهده پروفایل

استاد

• Privacy-preserving machine learning
• Differential Privacy
• Bayesian Machine Learning
• +۳ مورد دیگر

Antti Honkela is a Professor of Data Science at the University of Helsinki's Department of Computer Science, within the Faculty of Science. He also serves as the Coordinating Professor for the Privacy-preserving and Secure AI Research Programme at the Finnish Center for Artificial Intelligence (FCAI), and as Deputy Director of the Master's Programme in Data Science. His roles include membership in the Health and Social Data Permit Authority (Findata) and as an Action Editor for Transactions on Machine Learning Research. Honkela's research focuses on privacy-preserving machine learning, differential privacy, Bayesian methods, and their applications in computational biology and healthcare. He leads projects such as the European Lighthouse in Secure and Safe AI (ELSA) and the Data Literacy for Responsible Decision-making initiative. His work emphasizes developing robust frameworks for privacy-aware AI, including differentially private synthetic data and federated learning. Honkela has advised numerous PhD and Master's students, and his contributions span theoretical advancements and practical implementations, such as the D3p Python package for differentially private probabilistic programming. Key contributions include advancements in Bayesian inference from synthetic data, privacy accounting mechanisms, and computational methods for genomic epidemiology. His interdisciplinary approach bridges machine learning, statistics, and healthcare, addressing challenges in data privacy and secure AI deployment.

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

Garg Vikasمشاهده پروفایل

استادیار

• Quantum Computing
• Artificial Intelligence
• Machine Learning
• +۸ 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.

• Machine Learning
• Artificial Intelligence
• Neural Networks
• +۳ مورد دیگر

Daolang Huang is a Doctoral Researcher and Student in the Department of Computer Science at the School of Science, affiliated with Professor Samuel Kaski's group. He holds a Bachelor's degree in Engineering and Technology from Jinan University (2020). His research focuses on advanced machine learning techniques, including Bayesian inference, robust statistical modeling, and simulation-based methods. Key areas include experimental design optimization, neural processes, and equivariance in deep learning. Recent work emphasizes decision-aware algorithms and cost-effective simulation frameworks. Huang has collaborated internationally, with publications in top venues like NeurIPS. Despite no listed awards, his work demonstrates significant contributions to probabilistic modeling and optimization. Education : Bachelor's degree in Engineering and Technology, Jinan University (2020) Research Interests : Bayesian methods and amortized inference Robust statistics under model misspecification Continuous control and neural process architectures Optimization algorithms with decision-theoretic foundations Recent Research Trends : His articles (2020–2025) emphasize Bayesian experimental design, preference-based optimization, and equivariant neural networks. Themes include balancing statistical rigor with computational efficiency, particularly in high-dimensional decision-making contexts. Labs/Teams : Active member of Samuel Kaski’s research group, focusing on interdisciplinary applications of machine learning.

Henri Hansenمشاهده پروفایل

مدرس

• Partial Order Reduction
• Stubborn Sets
• Petri Nets
• +۴ مورد دیگر

Henri Hansen is a University Lecturer at Tampere University's Computing Sciences Department within the Faculty of Information Technology and Communication Sciences. His research focuses on concurrency theory, partial order reduction, stubborn sets, Petri nets, and formal methods. He holds a Doctor of Science (Technology) and Master of Science in Technology from Tampere University. Key research interests include optimizing state space exploration through techniques like stubborn sets, analyzing financial networks (e.g., stock market dynamics), and applying formal methods to industrial systems. His work bridges theoretical foundations with practical applications in software verification and blockchain systems. Recent publications emphasize network analysis in financial markets and Bitcoin systems, alongside advancements in partial order reduction algorithms. Hansen has received three notable awards for his contributions to stubborn set theory and coverability algorithms.

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.

• Probability Theory
• Stochastic Processes
• Brownian Motion
• +۵ مورد دیگر

Paavo Salminen is a faculty member in the Mathematics department at the Faculty of Natural Sciences and Engineering. With a prolific publication record spanning from 1990 to 2025, he has established himself as a prominent researcher in probability theory and stochastic processes. His research interests primarily focus on: Brownian Motion and its variants Optimal Stopping problems Diffusion processes and spider processes Excessive functions and Green kernels Theta functions and their probabilistic aspects Markov processes and their applications Salminen's recent work demonstrates a continued focus on theoretical aspects of stochastic processes with applications in mathematical finance and other fields. His publications show consistent contributions to understanding diffusion processes, optimal stopping problems, and the mathematical properties of Brownian motion variants. The research spans both theoretical developments and potential applications in financial mathematics and other domains requiring sophisticated stochastic modeling techniques. His scientific contributions include: 34 publications documented between 2009-2024 Multiple collaborations with international researchers including Christophe Vignat, Ernesto Mordecki, and Pierre Vallois Hosting academic visitors at his institution in 2022 and 2024 Salminen maintains an active research program, with several publications appearing in 2024 and even some listed for 2025, indicating ongoing productive research activities. His work bridges pure probability theory with potential applications in mathematical finance and other fields requiring advanced stochastic modeling techniques.

• 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 .

• Probabilistic Machine Learning
• Probabilistic Circuits
• Probabilistic Programming
• +۲ مورد دیگر

Martin Trapp is an Academy Postdoctoral Researcher in the Department of Computer Science at Aalto University, specializing in probabilistic machine learning. He is affiliated with Professor Arno Solin's research group, focusing on advancing tractable probabilistic models for real-world applications. His research centers on Probabilistic Circuits , Probabilistic Programming , and Bayesian Nonparametrics , with emphasis on hardware-efficient implementations for edge devices and multimodal systems. Key interests include uncertainty quantification in deep learning, neurosymbolic AI integration, and medical imaging applications. His work bridges theoretical foundations with practical deployment constraints, particularly in resource-limited environments. Analysis of his 15 most recent publications (2022-2025) reveals three dominant trends: (1) hardware-aware probabilistic inference for TinyML applications, (2) scalable Bayesian methods using bitstring representations and probabilistic programming, and (3) multimodal robustness in vision-language systems and medical imaging. His contributions span from theoretical circuit representations to real-world implementations in mammography analysis and vision-language models. Trapp secured a HIIT short-term project grant (November 2022) for "Positive Semi-Definite Circuits" under the Department of Computer Science. No formal advising relationships are documented in available sources. He actively collaborates with researchers including Arno Solin, Rui Li, and Marcus Klasson across institutions like Aalto University and the Helsinki Institute for Information Technology. As a core member of Aalto's Probabilistic Machine Learning group, he contributes to advancing probabilistic AI methodologies with applications in healthcare, edge computing, and multimodal reasoning. His current work emphasizes deployable probabilistic systems that maintain rigorous uncertainty quantification while meeting hardware constraints.

• Data Mining
• Machine Learning
• Temporal Networks
• +۳ مورد دیگر

Nikolaj Tatti is a Visiting Professor at Aalto University's Department of Computer Science , affiliated with the Helsinki Institute for Information Technology (HIIT) and the Adj. Prof. Gionis Aris research group . His work focuses on algorithmic analysis of temporal networks and graph theory. Research Interests: Temporal network analysis Graph decomposition and density Segmentation algorithms Pattern discovery in time-series data Interactive data analysis frameworks Publication Trends (2018-2021): Research spans Temporal Networks (dynamic hierarchies, cascade reconstruction), Graph Theory (density, decomposition), and Data Mining (periodic patterns, event detection). Collaborative work with Aris Gionis and Polina Rozenshtein dominates his publications. Technical Collaborations : Works with multidisciplinary teams including researchers from ACM Transactions on Knowledge Discovery and IEEE conferences.

Leo Lahtiمشاهده پروفایل

استاد

• Data Science
• Artificial Intelligence
• Machine Learning
• +۱۱ مورد دیگر

Leo Lahti is a Professor in Data Science at the University of Turku, Finland. His research focuses on computational analysis and modeling of complex natural and social systems, with applications in microbiome science, ecological modeling, and computational humanities. He is actively involved in open science initiatives and leads a dynamic research team. University: University of Turku Role: Professor, Data Science Contact: leo.lahti@utu.fi Lahti holds a Doctor of Science (DSc) from Aalto University (2010), where he developed probabilistic machine learning methods for life science data. He conducted postdoctoral research at EBI/Hinxton (UK), Wageningen University (NL), and VIB/KU Leuven (BE), establishing a strong international research profile. His research interests span Data Science, Artificial Intelligence, Machine Learning, Applied Statistics, Probabilistic Models, Microbial Ecology, Computational Humanities, and Open Science . He applies computational and statistical methods to understand complex systems, particularly in microbiome dynamics, ecological resilience, and historical data analysis. His work integrates interdisciplinary approaches, combining data analytics with domain-specific knowledge in biology, medicine, and humanities. The recent publications (2021–2025) reflect a strong focus on gut microbiome analysis , including maternal-infant health, dietary impacts, disease risk (CKD), and methodological advances in differential abundance and phylogenetic diversity. Other themes include ecological modeling (resilience, early warning signals), infectious disease modeling (COVID-19), and open science education (Bioconductor). The keyword trends show deep engagement with microbiology, bioinformatics, and mathematical modeling, with subfields emphasizing reproducibility, host-microbe interactions, and computational methods. Lahti plays a significant role in open science and academic leadership: Vice Chair, Open Science Finland Member, International Science Council Committee on Data (2023–2025) Member, Bioconductor Community Advisory Board Founder, Open Science Working Group, Open Knowledge Finland He is also active in teaching computational and data science, statistical programming, AI, and open science. His research homepage is available at iki.fi/Leo.Lahti, and his full publication list is accessible via ORCID and bibtex.

• Artificial Intelligence
• Computational Argumentation
• Knowledge Representation
• +۳ مورد دیگر

Tuomo Lehtonen is a Postdoctoral Researcher at Aalto University's Department of Computer Science , focusing on computational argumentation and knowledge representation. His work bridges artificial intelligence and formal logic through algorithm design and complexity analysis. Research interests include: Computational argumentation frameworks (ASPIC+, ABA) Algorithmic reasoning under incomplete information SAT-based logic programming applications Complexity theory in structured argumentation Benchmarking methodologies for AI systems Recent publications highlight his expertise in translating theoretical argumentation models into practical computational tools, particularly for admissibility verification and preferential reasoning tasks. Contact: tuomo.lehtonen@aalto.fi