جست‌وجوی استاد و پژوهشگر

• 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.

Lu Chengمشاهده پروفایل

استاد مهمان

• Genomics
• Bioinformatics
• Microbial Genetics
• +۳ مورد دیگر

Lu Cheng is a Visiting Professor in the Department of Computer Science at the University of Helsinki, affiliated with the Vehtari Aki Professorship. He holds a Doctor of Philosophy in Natural Sciences from the University of Helsinki (2013). His research focuses on computational genomics, bioinformatics, and microbial genetics, with emphasis on DNA sequence analysis, nanopore sequencing technologies, and systems biology. He leads projects on alternative splicing in cancer and the impact of microbiota on human health. Notable contributions include the NanoBaseLib benchmark dataset and methods for RNA modification analysis. His work addresses UN Sustainable Development Goals related to good health and innovations in data science. Education: Doctor of Philosophy in Natural Sciences (2013), University of Helsinki; Doctoral degree in Natural Sciences (2013), University of Helsinki. Research Interests: Genomics, computational biology, microbial ecology, RNA sequencing technologies, and bioinformatics tool development. His projects explore bacterial population dynamics, host-pathogen interactions, and applications of machine learning in genomics. Advising: Supervises doctoral researchers including Guangzhao Cheng and Chengbo Fu. Active in grants such as the Academy of Finland Research Fellowship (2023-2025). Labs/Teams: Leads research groups focused on single-cell genomics and computational methods for biological systems.

• 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.

Timo Seppäläمشاهده پروفایل

مدرس

• Supply Chain Management
• Digital Platforms
• Artificial Intelligence
• +۴ مورد دیگر

Timo Seppälä is a University Lecturer in Digital Operations at Aalto University's School of Science, Department of Industrial Engineering and Management. His research focuses on global value chains, supply chain management, and digital technology implementations in technology- and service-based businesses. He explores platformization of supply chains, collaborative innovation management for complex digital ecosystems, and the implications of artificial intelligence platforms across industries. Global Value Chains Digital Platforms & Ecosystems Artificial Intelligence Governance Blockchain Applications in Industry Recent publications highlight trends in AI diffusion monitoring, blockchain-based systems for industrial applications, and sustainability challenges in digital economies. His work spans empirical analysis of corporate digitalization, platform governance, and multi-actor value creation. No scientific awards are currently documented.

• Bayesian Statistics
• Reliability Engineering
• Risk Analysis
• +۴ مورد دیگر

Ahmad BahooToroody is an Academy Research Fellow at Aalto University’s Department of Energy and Mechanical Engineering, specializing in Bayesian statistics, reliability engineering, and risk analysis for autonomous maritime systems. He is actively involved with the Marine and Arctic Technology research group and leads projects that integrate machine learning with safety-critical applications in the maritime and offshore sectors. Research Interests Bayesian statistical methods for reliability and risk modeling Machine learning and deep learning for anomaly detection in autonomous systems Safety assessment of offshore installations and maritime operations Prognostic health management of marine renewable energy systems Human factors and expert judgment in sociotechnical maritime systems His work often incorporates advanced techniques such as Gaussian processes, LSTM-based neural networks, and dynamic Bayesian networks to address uncertainties in complex engineering systems operating in harsh marine environments. Publication Trends Across 2022–2025, BahooToroody’s publications reveal a strong trajectory toward integrating data-driven models with physics-based simulations. Dominant themes include real-time risk monitoring of autonomous ships, failure prognosis for unattended machinery, and safety assessment frameworks for offshore structures. His collaborative outputs span high-impact journals such as Reliability Engineering & System Safety and Safety Science , underscoring his leadership in maritime safety analytics. Research Groups & Labs Marine and Arctic Technology Research Group, Aalto University Academy Research Fellow network within the Department of Energy and Mechanical Engineering Doctoral Supervision & Grants While specific grant details are not listed, his role as Doctoral Candidate Supervisor since 2020 indicates active mentorship of PhD researchers. He is also the Principal Investigator on projects funded under the Academy of Finland Fellowship scheme, supporting next-generation maritime risk analytics.

Sergiy Vorobyovمشاهده پروفایل

استاد

• Optimization
• Signal Processing
• Multi-Linear Algebra
• +۹ مورد دیگر

Sergiy Vorobyov is a Professor at the Department of Signal Processing and Acoustics , Aalto University , Finland. He has held academic and research positions at multiple institutions, including the University of Alberta (Canada), Kharkiv National University of Radio Electronics (Ukraine), RIKEN (Japan), McMaster University (Canada), Duisburg-Essen University and Darmstadt University of Technology (Germany), and Heriot-Watt University (UK). His expertise spans optimization, signal processing, and multi-antenna systems. Dr. Vorobyov holds a Doctoral degree in Natural Sciences from the National Technical University Kharkiv Polytechnical Institute, awarded on January 15, 2002. His research interests focus on optimization and multi-linear algebra applied to signal processing challenges, including statistical and array signal processing, sparse signal processing, estimation and detection theory, and sampling theory. He explores multi-antenna, large-scale, cooperative, and cognitive systems, contributing to advancements in wireless communications and radar engineering. His work aligns with UN Sustainable Development Goals, emphasizing education and innovation. In recent years (2025), his publications emphasize cutting-edge advancements in wireless communications and signal processing. Topics include millimeter-wave MIMO channel estimation, optimization algorithms with momentum-based techniques, vehicular network communications, and robust covariance matrix estimation in challenging noise environments. These contributions highlight his expertise in developing efficient and adaptive methods for modern communication systems. He has received prestigious awards, including: 2004 IEEE Signal Processing Society Best Paper Award 2007 Alberta Ingenuity New Faculty Award 2011 Carl Zeiss Award for teaching and innovative methods 2012 NSERC Discovery Accelerator Award 1st Price Best Paper Award (2015) 1st Price Best Student Paper Award at CAMSAP 2015 As a researcher, Vorobyov has supervised seven theses and led multiple funded projects, such as: AI Based RAN (2023–2025): Scalable AI solutions for 5G/6G networks. MASSIVE AND SPARSE ANTENNA ARRAY PROCESSING FOR MILLIMETERWAVE COMMUNICATIONS (2019–2021): Advanced antenna design and processing techniques. M-CUBE SPA (2017–2021): EU-funded sparse antenna array research. Transmit beamspace for active compressive sensing and communication with multiple waveforms (2016–2020): Radar and MIMO system optimization. He leads the Sergiy Vorobyov Group , focusing on real-time signal processing algorithms and their applications in next-generation wireless systems. His research addresses practical challenges such as efficient channel estimation, robust detection in massive access scenarios, and improving network performance in urban environments.

Jouni Järvinenمشاهده پروفایل

مدرس

• Rough Set Theory
• Lattice Theory
• Non-Classical Logics
• +۴ مورد دیگر

Jouni Järvinen is a University Lecturer in Software Engineering at LUT University in Lahti, Finland, and a Docent in the Department of Mathematics and Statistics at the University of Turku. He holds a PhD in Mathematics and serves as a Mathematics Consultant at TXODDS. His work bridges theoretical and applied research across multiple domains. Affiliations : LUT School of Engineering Sciences, University of Turku Education : PhD in Mathematics Research Interests lie at the intersection of Rough Set Theory , Lattice Theory , and Non-Classical Logics . He explores algebraic structures like Nelson algebras, Kleene algebras, and Galois connections, applying them to problems in Artificial Intelligence and Bioinformatics . His work often connects abstract mathematical frameworks to practical applications in Information Systems and Machine Learning . Publication Trends reveal a focus on Rough Sets and Algebraic Structures across 51 works (2025–1998). Recent studies (2025–2020) examine multigranular rough sets, pseudocomplemented Kleene algebras, and relational correspondences for fuzzy rough approximations. Earlier works (2015–2007) address topics like Tolerance-Based Rough Sets , Galois Connections , and Natural Language Disambiguation . Peer Review Activities include contributions to journals such as Fuzzy Sets and Systems , International Journal of Approximate Reasoning , and Studia Logica . His research spans Theoretical Computer Science , Mathematical Logic , and Biomedical Data Analysis .

• Bioinformatics
• Computational Genomics
• Protein Structure Analysis
• +۳ مورد دیگر

Liisa Holm is a Professor at the Institute of Biotechnology, University of Helsinki, and a supervisor in the Doctoral Programme in Integrative Life Science. Her research focuses on computational genomics, structural bioinformatics, and protein function prediction. Research Interests : Computational biology, protein structure analysis, machine learning applications in genomics Key Projects : HiLIFE Grand Challenge (antimicrobial resistance), burn wound infection metagenomics, protein structural aging studies Recent Trends : Holm's work spans protein structure comparison (DALI algorithm), pan-genome analysis of protein crops (faba bean), and aging-related structural changes in proteins. She also contributes to AI-driven advancements in structural biology. Scientific Awards : 2024 Nobel Prize in Chemistry (for AI-driven protein research) Leadership Roles : Project leader for Academy of Finland grants, member of international scientific committees

Visa Koivunenمشاهده پروفایل

استاد

• Statistical Signal Processing
• Wireless Communications
• Multisensor Systems
• +۴ مورد دیگر

Visa Koivunen is a Distinguished Professor of Signal Processing at Aalto University (since 1999), with positions as Academy Professor (2010) and Aalto Distinguished Professor (2020). He holds an honorary D.Sc. (Tech.) from the University of Oulu and has held visiting roles at Princeton University, the University of Pennsylvania, and EPFL. His research focuses on statistical signal processing, wireless communications, radar systems, and integrated sensing and communications (ISAC). He has published over 490 papers, including award-winning works, and advised 31 doctoral theses. Key roles include leadership in conferences (e.g., Asilomar 2018 General Chair) and technical committees (IEEE SPS). Recognitions include the EURASIP Technical Achievement Award (2015), IEEE Signal Processing Society Best Paper Awards (2007, 2017), and EURASIP Fellow status (2020). He co-chairs NATO panels on cognitive radars and ISAC. His research interests span signal processing fundamentals and applications in radar, communications, and machine learning. Recent work emphasizes ISAC, reinforcement learning for resource allocation, and causal inference in federated systems. He has pioneered waveform design techniques using GANs and Bayesian methods for spatial signal analysis. Educations: D.Sc. (Tech.) with honors from the University of Oulu (1994), Primus Doctor Award (1989-1994). Awards: IEEE Fellow, EURASIP Fellow, Member of Academia Europaea. Service: Associate Editor for IEEE Transactions, Chair of IEEE SPAWC and Asilomar conferences. His work bridges theory and practice, addressing challenges in radar-communication coexistence, energy-efficient edge computing, and secure distributed inference. He has delivered over 50 invited talks globally and actively contributes to NATO initiatives on cognitive radar systems.

Jenni Nikuمشاهده پروفایل

مدرس ارشد

• Multivariate Statistical Modeling
• Latent Variable Models
• Statistical Method Development
• +۳ مورد دیگر

Jenni Niku is a Senior Lecturer at the Department of Mathematics and Statistics, University of Jyväskylä, specializing in multivariate statistical modeling and computational methods for ecological data. She is actively involved in the Predictive Community Ecology Group and leads research projects on latent variable models for complex ecological structures. Research Focus: Latent variable models for community ecology Spatial-temporal pattern analysis Statistical methodology for multivariate data Joint species distribution modeling Computational tools for ecological datasets Recent Publications: Jenni has contributed to advancements in analyzing compositional count data, fungal dispersal dynamics, chronic disease modeling, and peatland restoration. Her work bridges statistical innovation with ecological applications, particularly in handling high-dimensional data. Collaborations: She works extensively with researchers from Biological and Environmental Science departments, focusing on interdisciplinary applications of her statistical models.

• 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.

Sangita Kulathinalمشاهده پروفایل

استاد

• Statistics
• Public health, environmental and occupational health
• Sociology
• +۳ مورد دیگر

Sangita Kulathinal is a Professor in the Department of Mathematics and Statistics at the University of Helsinki and holds an Associate Professor position at Tampere University since 2008. Her research integrates statistics, public health, and sociology, focusing on survival analysis, multistate models, and health outcomes. Statistics Public health, environmental and occupational health Sociology Bayesian methods Health informatics Recent publications highlight her work on: Medical statistics for age-related macular degeneration Gestational weight trajectories in Indian populations Longitudinal studies of marital stability and well-being Colorectal cancer modeling with misclassified data Statistics communication in postgraduate education She has received the Excellence in Doctoral Supervision Award (2024) and leads two major projects: Mathematical and statistical modeling of communicable/noncommunicable diseases (2025-2027) Algebraic statistics and multistate models (2025)

Satu-Pia Reinikainenمشاهده پروفایل

استاد

• Multivariate Statistical Analysis
• Process Control
• Spectroscopy
• +۳ مورد دیگر

Satu-Pia Reinikainen is a tenured Professor in Computational Engineering at the Lappeenranta-Lahti University of Technology (LUT) School of Engineering Sciences . With expertise in chemometrics and multivariate analysis, her work bridges statistical modeling, spectroscopy, and environmental monitoring. Research Focus Development of advanced kernel-based methods for process control Application of hyperspectral imaging in material and environmental analysis Microplastic pollution dynamics in aquatic systems Integration of spectroscopic techniques for real-time monitoring Conservation of geological and heritage materials through data-driven approaches Her research combines chemometric algorithms, environmental data analysis, and industrial process monitoring to solve complex analytical challenges.

• Machine Learning
• Sports Science
• Health Informatics
• +۳ مورد دیگر

Susanne Jauhiainen is a Postdoctoral Researcher affiliated with the Faculty of Information Technology at the University of Jyväskylä . Her work primarily focuses on applying machine learning and data science techniques to interdisciplinary problems in sports science and health informatics. Research Themes : Machine Learning, Sports Injury Prediction, Student Well-Being Analytics Key Collaborations : Computational Data Science Research Group, with partners in health science and spectral imaging domains Her recent publications highlight predictive modeling applications in sports injury detection, cluster analysis techniques, and educational data mining. Jauhiainen's work emphasizes the practical implementation of advanced analytics to solve real-world problems across multiple domains. She contributes to open-access research dissemination and participates in multidisciplinary teaching initiatives that integrate digital well-being data analysis.