جستجوی استاد راهنمای دانشگاهی

Mohsen Amidzadeمشاهده پروفایل

پژوهشگر

• Wireless Communications
• Caching
• Reinforcement Learning
• +۳ مورد دیگر

Mohsen Amidzade serves as a Postdoctoral Researcher in the Department of Computer Science at Aalto University, Finland, focusing on advanced optimization and machine learning techniques for next-generation wireless networks. His work bridges theoretical mathematics with practical network engineering to address critical challenges in cellular infrastructure. Amidzade's research centers on: Wireless network optimization through novel path-following methods Reinforcement learning applications for dynamic cache policy design Stochastic geometry analysis of cellular network performance Multicast transmission strategies for efficient content delivery Non-stationary environment adaptation in 5G/6G systems Bandwidth allocation for on-demand streaming services Analysis of his 15 most recent publications reveals a dominant research trajectory in cache-aided wireless communications, with 70% of works published between 2021-2024 focusing on reinforcement learning-driven cache optimization. His methodology consistently combines deep reinforcement learning with stochastic geometry to model dynamic network conditions, while recent 2024 publications demonstrate innovative applications of path-following techniques to time-varying optimization problems in heterogeneous networks. Scientific Recognition: Nokia Foundation Scholarship (2022) - Awarded for doctoral research in Information and Communications Technologies, specifically supporting work on cache-aided streaming optimization Amidzade's research is supported by competitive personal funding including the Nokia Foundation Scholarship, which targets high-impact ICT doctoral research. His extensive collaboration network includes leading figures such as Giuseppe Caire (Princeton), Olav Tirkkonen (Aalto), and Junshan Zhang (Purdue), with co-authorship on 80% of his publications. While no formal student advising is documented, his role as Postdoctoral Researcher positions him to mentor junior researchers within Aalto's wireless communications group.

Ahti Saloمشاهده پروفایل

استاد

• Operations Research
• Systems Analysis
• Portfolio Decision Analysis
• +۵ مورد دیگر

Professor Ahti Salo is a distinguished academic at Aalto University's School of Sciences, Department of Mathematics and Systems Analysis, specializing in operations research and systems analysis. Holding a Doctor of Science (Technology) degree, he has established himself as a leading expert in portfolio decision analysis, risk assessment, and scenario planning methodologies. His research interests span multiple domains including infrastructure management, energy systems optimization, healthcare decision support, and innovation ecosystem governance. Professor Salo's work bridges theoretical advances in decision analysis with practical applications in critical infrastructure, energy policy, and public health planning, creating robust frameworks for decision-making under uncertainty. His recent publication record demonstrates consistent scholarly productivity with 204 total publications spanning from 1989 to 2025. His research trajectory shows increasing focus on applying portfolio decision analysis to complex real-world problems including infrastructure resilience, energy transition planning, and healthcare optimization. His work often combines methodological innovation with practical case studies, particularly in Finnish contexts. Professor Salo has received seven scientific prizes for his contributions to operations research and decision analysis, though specific award names aren't detailed in the available materials. His research has generated significant impact with numerous highly-cited publications in top-tier journals. As an academic mentor, Professor Salo has supervised 24 theses, contributing to the development of the next generation of operations research specialists. His research is supported by multiple projects, including significant work on infrastructure networks, energy systems, and healthcare optimization. His work often involves interdisciplinary collaboration with experts in engineering, environmental science, and policy domains. Professor Salo leads research efforts in operations research and systems analysis at Aalto University, working within teams focused on portfolio decision analysis, risk management, and scenario planning. His research group contributes to both theoretical advances in decision support methodologies and their practical implementation in critical infrastructure, energy systems, and public health domains.

• Artificial Intelligence
• Knowledge Representation and Reasoning
• Logic Programming
• +۵ مورد دیگر

Tomi Janhunen is a Professor in Computing Sciences at Tampere University, part of the Faculty of Information Technology and Communication Sciences. His expertise spans Artificial Intelligence, Logic Programming, and Theoretical Computer Science. He actively contributes to academic leadership roles, serving on boards of the Finnish Association of Computer Science and Finnish Artificial Intelligence Society. His research focuses on Answer Set Programming (ASP), probabilistic reasoning systems, explainable AI, and optimization techniques applied to cybersecurity and maintenance scheduling. Key grants include the XAILOG and AI-ROT projects funded by the Research Council of Finland. Notable achievements include three prestigious awards: Best Paper Awards at the European Conference on Logics in AI (2021) and International Conference on Logic Programming (2023), and the Harold Boley award for system description (2022). Dr. Janhunen’s work emphasizes practical applications of declarative programming paradigms. Recent projects include Plingo—a probabilistic ASP system—and cybersecurity compliance frameworks in DevSecOps environments. His contributions bridge theoretical advancements with real-world challenges in AI security and industrial optimization.

• Reinforcement Learning
• Robotics
• Imitation Learning
• +۶ مورد دیگر

Wenyan Yang is a Researcher affiliated with Aalto University's School of Electrical Engineering, specializing in Computing and Electrical Engineering. They hold a Bachelor of Science (Technology) degree from Sichuan University, earned on 23 Jun 2016. Research Focus: Their work primarily addresses Reinforcement Learning and Imitation Learning , with notable contributions in Hierarchical Reinforcement Learning and Tactile Feedback-based Manipulation . They apply computational methods to robotics, control systems, and machine learning. Publication Trends: From 2018 to 2024, Wenyan Yang produced 11 research outputs spanning Computer Science , Robotics , and Engineering . Key topics include Hierarchical Reinforcement Learning , Tactile Feedback Systems , and Control Algorithm Optimization . Grants: Held a research grant during 07.11.2016 - 31.12.2016.

Amir-Hossein Karimiمشاهده پروفایل

استادیار

• Causal Inference
• Explainable AI
• Neuro-Symbolic AI
• +۵ مورد دیگر

Dr. Amir-Hossein Karimi is an Assistant Professor in the Department of Electrical and Computer Engineering and Cheriton School of Computer Science at the University of Waterloo, with a cross-appointment and Vector Institute affiliation. He leads the CHARM Lab, focusing on safe human-AI collaboration through causal inference, explainable AI, and neuro-symbolic systems. Prior to academia, he held research roles at DeepMind, Google Brain, and Meta, and industry positions at BlackBerry and Meta (Facebook). Education: Ph.D. in Computer Science, Max Planck Institute & ETH Zürich (2018-2023) M.Math in Computer Science, University of Waterloo (2016-2018) B.A.Sc. in Engineering Science, University of Toronto (2010-2015) Research Interests: The CHARM Lab develops AI systems that integrate metacognitive strategies and causal reasoning to enhance safety, reliability, and human alignment. Key areas include algorithmic recourse, causal explainability, and applications in healthcare, finance, and transportation. Collaborations span social sciences, cognitive science, and reinforcement learning. Recent Trends in Publications: His work emphasizes causal foundations of AI explanations, fairness in recourse mechanisms, and robust system design. Notable contributions include defining algorithmic recourse frameworks and influencing Canada’s automated decision-making policies. Awards: 2024 Igor Ivkovic Teaching Excellence Award 2021 Google PhD Fellowship 2024 ETH Zurich Medal Advising & Grants: Supervises PhD/Master’s/postdoctoral researchers across disciplines. Secured funding from NSERC, CIFAR, Google, and Waterloo.AI. Alumni placements include OpenAI, Microsoft, and Google. CHARM Lab Activities: Hosts interdisciplinary collaborations with experts in human-computer interaction, game theory, and behavioral economics. Develops open-source tools and benchmarks for reproducible research.

• Machine Learning
• Bioinformatics
• Drug Discovery
• +۵ مورد دیگر

Sandor Szedmak serves as a Research Fellow in the Department of Computer Science at Aalto University, Finland, and is affiliated with the Helsinki Institute for Information Technology (HIIT). He operates within Professor Juho Rousu's research group, focusing on interdisciplinary machine learning applications with strong ties to computational biology and bioinformatics. His institutional presence is active, as evidenced by current contact details and ongoing publication output. Dr. Szedmak's research specializes in developing advanced machine learning frameworks for complex biological and educational challenges. His primary interests include drug combination effect prediction, protein function annotation through multi-view learning, strain design optimization using reinforcement learning, and personalized educational systems for computer science students. He pioneers methods in latent tensor reconstruction, scalable variable selection, and kernel generalization, targeting high-dimensional data problems in pharmacology and computational biology. His publication trajectory from 2020-2025 reveals a concentrated effort in bioinformatics applications, with landmark papers in Nature Communications and Bioinformatics on drug combination prediction. The work consistently leverages tensor factorization and multi-view learning to model biological systems, while recent expansions include educational data mining for programming behavior analysis. This demonstrates both methodological consistency in machine learning innovation and strategic domain diversification. No scientific awards or fellowships are documented in the provided materials. While no student advisees or grant details are explicitly listed, Dr. Szedmak's collaborative publication pattern—particularly within the Rousu research group and HIIT—suggests active participation in team-based research initiatives. His work appears funded through institutional channels given the consistent output in computational biology. He operates within the Helsinki Institute for Information Technology (HIIT), a joint research institute of Aalto University and the University of Helsinki, and is embedded in Professor Juho Rousu's research group. This environment facilitates cross-disciplinary collaboration between computer science and life sciences, with infrastructure supporting high-performance computing for large-scale biological data analysis.

• Machine Learning
• Probabilistic Modeling
• User Modeling

Alex Henry Benjamin Hämäläinen is a Doctoral Researcher at Aalto University, affiliated with the Probabilistic Machine Learning research group under Professor Samuel Kaski. His work focuses on developing differentiable user models through advanced probabilistic machine learning techniques. His research spans Machine Learning, Probabilistic Modeling, and User Modeling, with emphasis on creating adaptive user simulations using differentiable programming. He investigates uncertainty quantification in behavioral prediction systems and applies reinforcement learning to personalize user interactions in AI-driven environments. His publication at UAI 2023 demonstrates expertise in bridging differentiable methods with user modeling, contributing to subfields like probabilistic inference and behavior prediction. This work reflects broader trends in interpretable AI and human-centered machine learning. As an active member of the Probabilistic Machine Learning group, he collaborates on cutting-edge research in Bayesian inference and scalable user modeling frameworks.

• Bayesian Statistics
• Probabilistic Machine Learning
• Computational Modeling
• +۳ مورد دیگر

Osvaldo Antonio Martin is a Research Fellow at Aalto University's Department of Computer Science , affiliated with the Probabilistic Machine Learning research group under Professor Aki Vehtari . His work bridges Bayesian statistical methods, computational modeling, and Python-based probabilistic programming frameworks. Research Interests Bayesian inference and probabilistic programming Statistical software development Computational ecology and biophysics Data-driven modeling for environmental and biological systems Recent Contributions include the development of tools like PyMC and Bambi , which democratize Bayesian modeling in Python. His publications span disciplines from machine learning to molecular evolution, reflecting interdisciplinary applications of Bayesian methods. Publications Focus on probabilistic programming frameworks Applications in environmental systems and protein biophysics Theoretical work on Bayesian inference and prior elicitation Statistical software development for scientific domains

Alexandru Palerمشاهده پروفایل

دانشیار

• Quantum Computing
• Quantum Software
• Quantum Circuit Compilation
• +۲ مورد دیگر

Alexandru Paler serves as an Associate Professor in the Department of Computer Science at Aalto University, Finland, where he leads research in quantum software development. His work focuses on designing compilers and optimization frameworks for quantum circuits, with emphasis on quantum error correction implementation and fault-tolerant quantum computing systems. Based in Espoo at Konemiehentie 2, he maintains active research collaborations through the university's quantum computing initiatives. Dr. Paler's research spans quantum circuit compilation, quantum error correction (particularly surface codes and QLDPC codes), and quantum software optimization. His team develops high-performance quantum compilers for neutral atom architectures and modular superconducting systems, addressing critical challenges in resource estimation and fault tolerance. The Quantum Operating Systems (QUANTUM) research group he contributes to explores scalable quantum software frameworks that bridge theoretical algorithms with practical hardware constraints, with significant work on graph-state compilation and reinforcement learning for circuit optimization. Analysis of his 15 most recent publications (2023-2025) reveals concentrated efforts in quantum compiler design, error correction scalability, and hardware-aware quantum software. Key trends include machine learning applications for decoder optimization, novel approaches to measurement-free error correction, and queuing theory models for fault-tolerant circuit analysis. His work consistently addresses the practical barriers to large-scale quantum computing through compiler innovations and resource-efficient circuit design. Dr. Paler actively participates in the Quantum Operating Systems (QUANTUM) research group within Aalto's Department of Computer Science, focusing on Algorithms and Theoretical Computer Science. This team develops quantum software infrastructure for next-generation quantum hardware, with current projects including Pandora (ultra-large-scale circuit compilation), quantum circuit caching mechanisms, and standardized cell approaches for neutral atom systems. Their research directly supports the transition from theoretical quantum algorithms to executable, error-resilient quantum programs.

• Distributed Computing
• Quantum Computing
• Cellular Automata
• +۳ مورد دیگر

Augusto Modanese is a Postdoctoral Researcher in the Department of Computer Science at Aalto University. His research focuses on distributed computing, quantum computing, and computational models like cellular automata and fungal automata. He investigates local problems in tree networks, quantum advantage limitations, and sublinear-time algorithms. Research Trends: Recent publications emphasize distributed quantum computing, algorithmic equivalence between quantum and classical models, and computational complexity in constrained topologies. Key themes include probabilistic cellular automata, graph coloring, and bio-inspired computing frameworks.

Jussi Rintanenمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Automated Planning
• Constraint-Based Search
• +۳ مورد دیگر

Jussi Rintanen is an Associate Professor at the Department of Computer Science , Aalto University . He is an internationally leading researcher in constraint-based planning and decision-making , with a focus on applying AI technologies to automating software production and synthesis of intelligent systems . Helsinki University of Technology (Doctoral Degree, 1997) Albert-Ludwigs-University Freiburg (Venia Legendi, 2005) National ICT Australia / Australian National University (Academic Positions) His research spans automated planning , SAT solving , temporal planning , and constraint-based search methods . Recent work highlights symmetry-breaking constraints , partial observability planning , and acyclicity encodings in SAT frameworks. The 15 most recent articles reflect trends in AI planning , graph algorithms , and formal verification , with applications in software synthesis , temporal reasoning , and uncertainty handling . His methodologies integrate integer programming , SMT encodings , and propositional logic .

• Machine Learning
• Artificial Intelligence
• Statistics
• +۳ مورد دیگر

Arto Klami serves as an Associate Professor in the Department of Computer Science at the University of Helsinki's Faculty of Science, where he also leads the Diversity in Society and Life (DIVSOL) doctoral program. His research spans machine learning, artificial intelligence, and statistics with applications across multiple domains including food science, astronomy, and industrial engineering. Klami maintains active collaborations with Helsinki Institute for Information Technology and leads multiple research projects funded by the Academy of Finland and Business Finland. Professor Klami's research focuses on probabilistic modeling, Bayesian methods, and geometric approaches to machine learning. His work bridges theoretical foundations with practical applications, particularly in developing methods for small data scenarios, prior knowledge integration, and geometric deep learning. His recent publications demonstrate expertise in normalizing flows, manifold learning, and Riemannian geometry applied to statistical problems, alongside domain-specific applications in food science, astronomy, and industrial monitoring systems. Klami's publication record shows a strong trajectory with 97 research outputs through 2025, including numerous high-impact articles in leading journals and conferences. His work spans both theoretical machine learning advancements and applied research in diverse fields such as food preservation, asteroid surface analysis, and industrial fouling detection. A notable trend is his increasing focus on cross-disciplinary applications of probabilistic AI methods, particularly in addressing real-world challenges with limited data. Scientific recognition includes: Best paper award at the 15th Koli Calling conference on computing education research (2015) Best paper award in Asian Conference on Machine Learning (2012) Senior good researcher award from the Department of Computer Science (2014) As an academic leader, Klami has supervised 9 doctoral theses and contributed to 33 academic activities including peer review for top journals like the Journal of the American Statistical Association. He leads five major research projects through 2030, with significant funding from the Academy of Finland, including FoodID (NSF Global Centers), DIVSOL profiling initiative, and projects on flexible priors and virtual laboratories. His work with the Nordic Probabilistic AI School demonstrates commitment to community building in machine learning. Klami directs research teams working on probabilistic AI applications, with particular emphasis on the Virtual Laboratories project and Sustainable Industrial Ultrasonic Cleaning initiative. His Helsinki-based research group maintains strong industry connections through partnerships with Orion Oyj and Silo AI Oy, translating academic research into practical industrial solutions.

• Bayesian Statistics
• Probabilistic Programming
• Machine Learning
• +۴ مورد دیگر

Aki Vehtari is a Professor in the Department of Computer Science at Aalto University’s School of Science. His academic roles include leadership in probabilistic machine learning, Bayesian statistics, and computational inference. He is a Principal Investigator for projects like the Finnish Center for Artificial Intelligence and the Iterative Bayesian Model Building initiative. Vehtari holds a Doctoral degree (2001) and Master’s degree (1997) in Engineering and Technology from Helsinki University of Technology. Research Interests: Vehtari specializes in integrating Bayesian methods with computer science, focusing on workflow optimization, probabilistic programming, Gaussian processes, and model diagnostics. His work emphasizes practical inference techniques and their application to real-world problems like aging analysis and RNA sequencing. Recent Research Trends: Recent publications highlight advancements in Bayesian cross-validation, projection predictive inference, and active learning for molecular data. His work often bridges computational efficiency with statistical rigor, addressing challenges in model selection and uncertainty quantification. Grants & Projects: Leading the Finnish Center for Artificial Intelligence (2023–2026) and the Safe Iterative Bayesian Model Building project (2021–2025). Awards: 2016 De Groot Prize, Youden Award (2020), and recognition for contributions to time series prediction (2004). Advising: Supervises doctoral researchers including Guangzhao Cheng and Lu Cheng, advancing topics in RNA analysis and probabilistic modeling. Labs & Teams: Involved in probabilistic AI initiatives, contributing to tools like ArviZ and Stan, and collaborates globally on projects in computational statistics and machine learning.

• Machine Learning
• Computer Vision
• Generative Models
• +۳ مورد دیگر

Heikki Timonen is a Researcher and Doctoral Student at the Department of Computer Science within the School of Science. His research focuses on generative models, neural networks, and imaging inverse problems, with notable contributions in machine learning and computer vision. He collaborates with Professor Jaakko Lehtinen's research group and has published work on invertible hierarchical generative models and conditional normalizing flows. Doctoral Researcher under Professorship Lehtinen Jaakko His research interests emphasize advancing AI-driven solutions for imaging challenges and developing scalable generative architectures. Recent work includes peer-reviewed contributions to Transactions on Machine Learning Research. While no formal advising roles or grants are explicitly listed, his research activities involve collaborations across international teams, as indicated by cross-border research networks. He is associated with computational imaging and machine learning labs, contributing to interdisciplinary projects at the intersection of theory and application.

• Machine Learning
• Artificial Intelligence
• Reinforcement Learning
• +۳ مورد دیگر

Alexander Ilin is a Visiting Professor and part-time teacher in the Department of Computer Science at Aalto University, specializing in Artificial Intelligence and Machine Learning. He holds roles in both the Computer Science - Artificial Intelligence and Machine Learning (AIML) research area and the Professors of Practice group. His research focuses on Machine Learning, Reinforcement Learning, and Deep Learning applications, with contributions to areas like neural networks, generative models, and healthcare informatics. Dr. Ilin earned a Doctoral degree in Engineering and Technology from Helsinki University of Technology in 2006. His work aligns with UN Sustainable Development Goals, particularly in advancing education and innovation. Key projects include the Finnish Center for Artificial Intelligence (FCAI) and the APPIA project on privacy-aware AI applications. His research explores cutting-edge topics such as diffusion models for dynamical systems, reinforcement learning for robotics, and self-supervised forecasting in healthcare. He has led projects like APPIA (2020–2021) and FCAI (2020–2026), securing grants from the Academy of Finland and Business Finland. His work bridges theory and practice, with applications in autonomous systems, nanotechnology, and medical diagnostics. Notable activities include visiting research at the UK Met Office Hadley Centre and presentations at top conferences like NeurIPS and AAMAS. His lab focuses on scalable AI solutions for real-world challenges, emphasizing ethical AI and privacy-preserving techniques.