جست‌وجوی استاد و پژوهشگر

Kai Virtanenمشاهده پروفایل

استاد مدعو

• Operations Research
• Decision Analysis
• Discrete-Event Simulation
• +۵ مورد دیگر

Kai Virtanen is an Adjunct Professor in the Department of Mathematics and Systems Analysis at Aalto University's School of Science. He also serves as a Senior Scientist in Operations Research and Systems Analysis. His research focuses on dynamic multi-objective decision making under uncertainty, human performance in complex systems, and applications in military and aviation contexts. He holds a Doctoral degree in Engineering from Helsinki University of Technology. Research interests include optimization, decision analysis, simulation, Bayesian networks, and game theory applications. He has led projects involving air combat simulation, radar defense systems, and maintenance scheduling. Virtanen has received awards such as the Operations Researcher of the Year (2015) and Teacher of the Year honors (2007-2008, 2011). Key contributions span over 90 publications in journals like Journal of Defense Modeling and Simulation and European Journal of Operational Research . His work addresses challenges in air combat tactics, pilot decision-making, and system reliability. Current projects involve federated learning applications in military coalition operations and unmanned systems optimization. Academic service includes chairing the Systems Analysis Section of Finland's Scientific Advisory Board for Defense. Teaching focuses on systems analysis, simulation, and optimization methods.

• Machine Learning
• Health Informatics
• Transportation Modeling
• +۴ مورد دیگر

Jaakko Hollmen serves as a Senior University Lecturer in the Department of Computer Science at Aalto University, affiliated with the Helsinki Institute for Information Technology (HIIT) and the Computer Science Lecturers research group. His interdisciplinary work bridges machine learning with critical applications in healthcare, transportation systems, and environmental science. His research focuses on advanced machine learning methodologies including Bayesian optimization, random forests, and principal component analysis. Key application areas span neonatal healthcare (mortality prediction, brain injury analysis), transportation modeling (activity-based model calibration), and environmental data science (weather-crop relationships, drug-environment interactions). His approach emphasizes practical implementations of complex algorithms for real-world problems. Hollmen's recent publications (2018-2023) reveal a consistent trajectory in developing machine learning solutions for high-dimensional data challenges, with increasing emphasis on medical applications. His work demonstrates strong cross-domain collaboration, particularly with medical researchers at Helsinki University Hospital. His notable recognition includes: Best paper finalist and runner-up award (Computer Track) at the first IEEE Life Sciences Conference (LSC) for research on predicting complications in very low birth weight infants (2017) As a core member of HIIT, Hollmen contributes to Finland's national information technology research infrastructure while maintaining active collaborations with medical and agricultural research groups. His current projects focus on optimizing transportation models and advancing clinical prediction systems through novel machine learning techniques.

• Probabilistic Machine Learning
• Statistical Inference
• Computational Neuroscience
• +۴ مورد دیگر

Luigi Acerbi is an Associate Professor at the Department of Computer Science, University of Helsinki, leading the Machine and Human Intelligence research group. He is affiliated with the Finnish Center for Artificial Intelligence (FCAI) and ELLIS (European Laboratory for Learning and Intelligent Systems). His research focuses on probabilistic machine learning, statistical inference methods (e.g., amortized and surrogate-based approaches), and computational and cognitive neuroscience, including Bayesian models of perception and resource-constrained rationality. Previously, he held postdoctoral positions at the University of Geneva and New York University. He earned his PhD from the Doctoral Training Centre in Computational Neuroscience at the University of Edinburgh, working with Sethu Vijayakumar and Daniel Wolpert. His work includes developing open-source tools like BADS (Bayesian Adaptive Direct Search) and VBMC (Variational Bayesian Monte Carlo), widely used for optimization and Bayesian inference in MATLAB/Python. He actively contributes to the academic community through teaching (e.g., BAMB! 2022 summer school tutorials on model fitting) and software development (GitHub repositories for optimization, inference, and AI tools like Athanor). His research bridges machine learning, neuroscience, and cognitive science, emphasizing robust and efficient statistical methods.

• Machine Learning
• Reinforcement Learning
• Online Learning
• +۳ مورد دیگر

Mahsa Asadi is a Postdoctoral Researcher in the Department of Computer Science, focusing on theoretical and applied aspects of machine learning. Her research emphasizes online learning, collaborative algorithms, and reinforcement learning, with contributions to regret minimization, state-action equivalence in RL, and distributed multitask learning. She explores intersections between statistical theory and algorithmic design, with applications in autonomous systems and multi-agent coordination. Her work spans foundational topics like concentration inequalities and upper confidence bounds, alongside practical implementations such as the CYRUS 2D simulation team and Miss PacMan AI controller. Notable contributions include model-based reinforcement learning frameworks that exploit structural equivalence properties, and collaborative mean estimation algorithms. Research Themes: Online Learning, Reinforcement Learning, Distributed Systems, Statistical Learning Theory Key Contributions: Human-in-the-loop systems, state-action abstraction techniques, regret-optimal algorithms No formal academic awards or grants are listed in the provided information. Her advising and collaborative work focus on advancing algorithmic foundations with practical implementations in robotics and game AI contexts.

• Interpretability
• Machine Learning
• Data Mining
• +۴ مورد دیگر

Martino Ciaperoni is a Postdoctoral Researcher and Adjunct Professor in the Department of Computer Science at Aalto University. He is affiliated with the research group of Adj. Prof. Gionis Aris. His primary focus is on advancing interpretable machine learning, algorithm optimization, and data mining techniques. Ciaperoni holds a doctoral degree (Tekn. toht.) in Computer Science from Aalto University (2024) and a Doctoral degree in Engineering and Technology from Università degli Studi di Roma 'La Sapienza' (2019). His research emphasizes interpretability in AI, multi-label classification, and efficient algorithms for speech recognition and Bayesian networks. Recent work includes exploring the Rashomon set of rule-based models, developing low-memory Viterbi decoding algorithms (SIEVE), and solving the Hadamard decomposition problem. His research spans topics like core decomposition in temporal networks and low-rank matrix approximation. Collaborations include international projects in computational linguistics, knowledge discovery, and algorithm design. He has contributed to open-source software, such as the 'Efficient Exploration of the Rashomon Set' codebase (Zenodo). His work balances theoretical rigor with practical applications, aiming to bridge gaps between complex algorithms and real-world problem-solving.

Jaakko Kuorikoskiمشاهده پروفایل

استاد

• Philosophy of Science
• Social Sciences
• Causal Reasoning
• +۵ مورد دیگر

Jaakko Kuorikoski is a Professor of Practical Philosophy at the University of Helsinki's Faculty of Social Sciences, specializing in the philosophy of science. His research focuses on scientific explanation and understanding, causal reasoning, modeling/simulation in social sciences, and the societal use of scientific knowledge. He leads a research project on data-driven social science funded by the Academy of Finland, affiliated with the TINT – Centre for Philosophy of Social Science. His work bridges interdisciplinary challenges in science and policy, emphasizing epistemic diversity and evidential methods. Research Interests: Scientific Explanation & Understanding Causal Mechanisms Data-Driven Methodologies Science-Policy Interfaces Ethics of AI & Scientific Trustworthiness Key Projects: "Making data speak: Towards a philosophy of data-driven social science" (2023–2027) "Social and Cognitive Diversity in Science" (2018–2022) Advising & Grants: Supervises doctoral students in Social Sciences and Philosophy programs, with extensive grant funding from national and international bodies. Active in peer review and academic leadership roles. Labs/Teams: Core member of TINT Centre, collaborating internationally on projects like modeling cognitive diversity and interdisciplinary problem-solving frameworks.

• Robotics
• Machine Learning
• Artificial Intelligence
• +۷ مورد دیگر

Nguyen Tran is a Doctoral Researcher affiliated with the School of Business and the Department of Information and Service Management . Their work focuses on advancing robotic grasping and manipulation techniques through data-driven and physics-based approaches. Research Interests: Robotics, Soft Robotics, Machine Learning, Human-Robot Interaction, Haptics, and physical property modeling for robotic systems. Recent Work: Developments in deformable object manipulation, 6-DOF grasp sampling, and multi-fingered hand designs inspired by human anatomy. Technical Contributions: Novel algorithms like SPONGE for contact prediction, force control systems for safe grasping, and simulation-based quality metrics for deformable object interactions.

Jing Tangمشاهده پروفایل

استادیار

• Network Pharmacology
• Drug Combination Discovery
• Systems Biology
• +۵ مورد دیگر

Jing Tang is an Assistant Professor at the Department of Biochemistry and Developmental Biology within the Faculty of Medicine at the University of Helsinki . She also holds a Docentship in Biometry and serves as a Supervisor in Doctoral Programmes for Computer Science, Biomedicine, and Integrative Life Science. Education: PhD in Statistics (University of Helsinki, 2016), Docentship in Biometry (2016) Key Research Areas: Network pharmacology for precision medicine, mathematical modeling of drug synergy, systems-level cancer pathway inhibition, computational drug discovery, and biomedical informatics Article Trends : Recent publications focus on network-driven drug response prediction in acute myeloid leukemia, causal inference for essential gene analysis, graph neural networks in drug-target interaction modeling, and seasonal brain-adipose tissue interactions via μ-opioid receptor imaging. Tools developed include SynergyFinder+ and DrugComb platforms. Awards: Academy of Finland Research Fellow (2018), Helsinki Challenge Award (2017), HiLIFE Research Fellow (2017) Supervision: Leads the Tang Lab team, mentoring PhD researchers in method development and software engineering for cancer drug discovery