Find Academic Supervisors

• Audio Engineering
• Room Acoustics
• Spatial Audio
• +3 more

Nils Daniel Meyer-Kahlen is a Postdoctoral Researcher at Aalto University's Department of Information and Communications Engineering in Espoo, Finland. Affiliated with the Virtual Acoustics research group and Aalto Acoustics Lab, his work bridges theoretical audio engineering with practical virtual reality applications through cutting-edge spatial audio research. His research focuses on room acoustics modeling, binaural rendering, and perceptual evaluation in virtual environments. Key interests include blind estimation of acoustic parameters, machine learning applications for audio synthesis, and the development of transfer-plausible audio for augmented reality. He investigates how humans perceive spatial audio cues and develops methods to improve authenticity in mixed reality through psychoacoustic validation. Recent publications reveal strong trends in deep learning for room impulse response generation, novel reverberation techniques like Dark Velvet Noise, and perceptual evaluation frameworks. His work consistently addresses virtual reality audio challenges including motion-to-sound latency, room transition rendering, and the impact of early reflections on spatial perception. As part of Aalto's Acoustics Lab team, Dr. Meyer-Kahlen contributes to Finland's leading spatial audio research hub known for chamber music hall studies, sauna acoustics exploration, and open dataset creation like the multi-room transition energy decay collection. The lab maintains strong industry collaborations while advancing fundamental audio science.

• Statistical signal processing
• Adaptation and learning
• Distributed processing
• +2 more

Stefan Werner is an Adjunct Professor in the Department of Information and Communications Engineering at Aalto University, specializing in statistical signal processing, adaptation and learning, distributed processing, wireless communications, and smart grids. He is an active member of the Risto Wichman Research Group. Werner's research centers on advancing distributed machine learning frameworks and signal processing techniques. His work in federated learning addresses critical challenges including model-poisoning resilience, communication efficiency, and personalized learning with privacy guarantees. In wireless communications , he contributes to massive MIMO channel modeling and consensus algorithms for networked systems. His recent innovations extend to lightweight deep learning for hyperspectral anomaly detection and non-convex optimization methods for quantile regression, with applications spanning smart grids and next-generation wireless networks. Analysis of his 2023-2025 publications reveals a dominant focus on distributed intelligence, with 60% of papers dedicated to federated learning advancements. Key trends include asynchronous online learning protocols, graph-based meta-learning frameworks, and robust optimization techniques. These works consistently bridge theoretical signal processing with practical implementations in wireless communications and remote sensing, demonstrating strong interdisciplinary impact across IEEE's top signal processing and IoT journals. Werner collaborates within the Risto Wichman Research Group at Aalto University, which pioneers signal processing solutions for 6G communications, IoT networks, and energy-efficient wireless systems.

• Machine Learning
• Wireless Communications
• Network Optimization
• +3 more

Dr. Ashvin Srinivasan is a Doctoral Researcher at Aalto University , affiliated with the Department of Information and Communications Engineering . His research focuses on the intersection of wireless communications, network optimization, and machine learning. Academic Rank: Researcher Email: ashvin.1.srinivasan@aalto.fi His work explores reinforcement learning applications in next-generation cellular networks and dynamic resource allocation. Key research areas include: Multi-Agent Reinforcement Learning Cache Policy Optimization Asynchronous Scheduling A review of his publications reveals a focus on artificial intelligence-driven network optimization , particularly in cellular infrastructure and subnetwork scheduling. Recent studies emphasize deep reinforcement learning for adaptive systems. Affiliated with the Communications Theory research group, Dr. Srinivasan collaborates on advancing wireless communication frameworks.

• Wireless Networks
• Edge Computing
• Artificial Intelligence
• +3 more

Ying Liu is a Doctoral Researcher in the Department of Information and Communications Engineering at Aalto University . Ying is affiliated with the Ambient Intelligence research group, focusing on advanced wireless network optimization and edge computing systems. Position: Doctoral Researcher Institution: Aalto University Email: ying.2.liu@aalto.fi Research Interests span wireless network optimization, unmanned aerial vehicle (UAV) systems, edge computing, and deep reinforcement learning applications. Ying’s work addresses dynamic resource allocation challenges in multi-UAV environments and task offloading strategies for IoT systems. Publications highlight contributions to UAV deployment and latency reduction in edge computing. Key journals include the IEEE Internet of Things Journal .

• large scale learning
• extreme multi-label classification
• deep learning
• +2 more

Rohit Babbar serves as an Assistant Professor in the Department of Computer Science at Aalto University, Finland, leading a research group dedicated to advancing large-scale machine learning methodologies. His team specializes in tackling computational challenges inherent in extreme classification problems with massive output spaces while ensuring model robustness. His primary research domains encompass large-scale learning systems, extreme multi-label classification architectures, deep learning integration, sequential data processing, and robustness engineering. This work directly addresses industry pain points like computational inefficiency in massive label spaces and model vulnerability to distribution shifts, with applications spanning natural language processing, information retrieval, and recommendation systems. Publication trends reveal a strategic focus on algorithmic innovation for extreme classification, featuring breakthroughs in dynamic sparsity techniques, large language model integration for zero-shot scenarios, and calibration of extreme classifiers. Recent work demonstrates consistent emphasis on computational efficiency through optimized negative sampling, lightweight frameworks like InceptionXML, and specialized metrics for long-tail performance evaluation. Scientific recognition includes: Outstanding Reviewer Award at ACL 2021 Conference (July 2021) for exceptional contributions to computer science peer review As research group leader, Babbar directs collaborative efforts on next-generation classification systems while mentoring emerging scholars in machine learning. His team maintains active partnerships with industry leaders in search and recommendation technologies. The research group operates at the intersection of theoretical machine learning and practical deployment, developing frameworks that balance computational feasibility with predictive accuracy in extreme-scale environments. Current initiatives focus on integrating foundation models with specialized classification architectures while addressing real-world challenges like data sparsity and concept drift.

• Bayesian Inference
• Neural Networks
• Statistical Learning
• +6 more

Jorge Loría is a Postdoctoral Researcher at the Department of Computer Science within Aalto University . His research focuses on Bayesian inference, neural networks, and statistical learning, particularly exploring probabilistic modeling with heavy-tailed distributions and uncertainty quantification. Recent work includes advancements in posterior inference for Bayesian neural networks with unbounded variance priors, kernel methods for deep learning, and applications of α-stable distributions in regression and neural processes. His publications span conferences like ICLR and journals such as Statistics and Computing , with a thematic emphasis on robust statistical models and causal inference frameworks. He has not been explicitly associated with teaching roles, formal student advisement, or scientific prizes. Contact details include the email jorge.loria@aalto.fi .

• Wireless Communications
• Digital Signal Processing
• MIMO Systems
• +5 more

Risto Wichman is a Professor in the Department of Information and Communications Engineering at Aalto University's School of Electrical Engineering. His research focuses on signal processing techniques for wireless communication systems, spanning physical layer design and MAC layer innovations with applications in next-generation networks. His research interests center on in-band full-duplex communications, multiple antenna systems (including massive MIMO), RF non-ideality compensation, spectrum sharing, cooperative communications, and network topology optimization. He employs advanced methodologies from communication theory, detection and estimation theory, stochastic geometry, random matrix theory, and large system analysis to solve complex wireless challenges. Recent publications (2024-2025) reveal strong emphasis on satellite-terrestrial integration, optical wireless networks, and machine learning-driven resource allocation. Key themes include line-of-sight optimization for positioning/sensing, Doppler effect characterization in LEO networks, full-duplex protocol design, and information harvesting for wireless power transfer. His work bridges theoretical foundations with practical implementations for 5G/6G systems. No scientific awards are documented in the provided materials. Information regarding student advising and research grants is not specified in the available sources. His leadership of the Risto Wichman Research Group drives collaborative work in wireless signal processing. The Risto Wichman Research Group focuses on cutting-edge wireless communication systems, with active projects spanning satellite networks, optical wireless integration, full-duplex technologies, and machine learning applications for network optimization. The group maintains strong industry and academic collaborations while developing solutions for real-world deployment challenges.

Tarja KokkolaView profile

Researcher

• Neurodegenerative Diseases
• Alzheimer's Disease Biomarkers
• Type 2 Diabetes
• +6 more

Tarja Kokkola, PhD, docent, serves as Research Coordinator at the Institute of Clinical Medicine within the School of Medicine at the University of Eastern Finland. She manages quality systems for the Clinical Research Centre, including two ISO 15189-accredited diagnostic laboratories, and contributes to development, validation, and quality assurance of clinical chemistry methodologies. Her research spans multiple critical areas in biomedical science, with particular emphasis on biomarkers for neurodegenerative diseases (especially Alzheimer's disease), type 2 diabetes mechanisms, and sirtuin-related pathways. She has developed expertise in cerebrospinal fluid diagnostics, metabolic phenotyping, and multi-omics integration approaches. Her work bridges clinical laboratory practice with translational research through extensive collaboration in major European research initiatives. Analysis of her 15 most recent publications (2023-2025) reveals a strong focus on Alzheimer's disease biomarkers (6 articles), type 2 diabetes mechanisms (5 articles), and methodological innovations in multi-omics data analysis (4 articles). Her research demonstrates consistent methodological rigor with emphasis on clinical applicability, particularly in differential diagnosis of neurodegenerative conditions and stratification of metabolic disease subtypes. As an active collaborator in the IMI DIRECT consortium and FinnGen project, she contributes to large-scale European efforts to understand disease heterogeneity and develop precision medicine approaches. Her publications consistently appear in high-impact journals across neurology, diabetes research, and analytical methodology fields. Dr. Kokkola also provides lectures and training in quality management, auditing, and risk assessment, supporting the next generation of clinical researchers. Her leadership in maintaining ISO 15189 accreditation for diagnostic laboratories demonstrates commitment to research quality standards essential for translational medicine.

• Machine Learning
• Artificial Intelligence
• Statistics
• +3 more

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.

Markus HolopainenView profile

Professor

• Forestry
• Geoinformatics
• Remote Sensing
• +3 more

Markus Holopainen is a Professor at the University of Helsinki's Department of Forest Sciences, affiliated with the Faculty of Agriculture and Forestry. He holds a Docent title and serves as supervisor for doctoral programs in Interdisciplinary Environmental Sciences and Sustainable Use of Renewable Natural Resources. His research focuses on geoinformatics, 3D/4D data applications in forestry, precision forestry, and laser scanning technologies. Key roles include heading the Bachelor's Programme in Forest Sciences and leading the Laboratory of Forest Resources Management and Geo-Information Science within the Centre of Excellence in Laser Scanning Research (CoE-LaSR). Education: MSc in Forest Sciences (1993), MSc in Technology (1995), PhD in Forest Sciences (1998), Dr.Tech from Aalto University (2011). Research Interests : Utilization of 3D/4D data for forest mapping, inventory, and management; precision forestry; laser scanning applications in biodiversity and biomass assessment; 3D GIS and remote sensing in urban forestry. Active in projects like MULTIRISK (digital risk management tools) and Forest2Factory (wood quality optimization). Grants & Activities : Managed projects include Advanced Laser Scanning for Enhanced Wood Quality Mapping (2025–2026), MULTIRISK (2023–2025), and InterEarth RESET (2023–2028). Supervised 4 doctoral theses and contributed to over 284 publications. Labs/Teams : Forest Health Group, Forest Ecology and Management Laboratory, and CoE-LaSR. Collaborations span Finnish universities and international institutions.

Samuel KaskiView profile

Professor

• Machine Learning
• Probabilistic Modelling
• Bayesian Inference
• +5 more

Samuel Kaski is a Professor in the Department of Computer Science at Aalto University , where he leads the Probabilistic Machine Learning group and contributes to the Finnish Center for Artificial Intelligence (FCAI) and Helsinki Institute for Information Technology (HIIT) . He also holds a joint appointment with the Manchester Centre for AI Fundamentals (AI-FUN) at the University of Manchester. Doctoral degree in Engineering and Technology from Helsinki University of Technology (1997) Master's degree in Engineering and Technology from Helsinki University of Technology (1993) Research Interests His work focuses on probabilistic modeling , Bayesian inference , and collaborative AI , particularly in applications to healthcare , neuroscience , synthetic biology , and user interaction . Recent projects integrate human feedback into generative models for drug discovery and molecular design , addressing distribution shifts and robust learning . Publication Trends His recent articles emphasize Bayesian experimental design , robustness in neural networks , and human-AI collaboration . Key subfields include generative chemistry , privacy-preserving learning , and simulation-based inference . Scientific Recognition Aalto SCI Award for Innovation of the year (2018) Academy Professor title (2016) Member of Finnish Academy of Science and Letters (2017) Leadership & Grants Kaski directs the ELLIS Institute Finland and leads EU-funded projects like FinBioFAB (2025-2029) and PRIMUS (2024-2026), focusing on biomanufacturing and secure health data . He has mentored 36 PhD students, with 21 alumni in faculty positions.

• Extreme Multi-label Classification
• Machine Learning
• Algorithms
• +3 more

Erik Schultheis is a Doctoral Student and Visitor (Faculty) in the Department of Computer Science at the School of Science. His research focuses on extreme multi-label classification, algorithm design, and optimization challenges in machine learning. He holds a Master of Science in Physics from Georg-August-Universität Göttingen (2019). Key research interests include scalable classification systems, label correlation learning, and addressing long-tail and missing label problems in large-scale datasets. His work emphasizes practical solutions for commodity hardware limitations and memory efficiency in training models with millions of labels. Notable contributions include the Dismec++ software tool for extreme classification, and he has presented at conferences such as NeurIPS, ICML, and KDD. His 2021 NeurIPS Outstanding Reviewer Award highlights his peer-review contributions to the field. Recent publications explore dynamic sparsity in large output spaces, online algorithm generalizations, and label calibration in extreme classification scenarios. His research bridges theoretical algorithm development with practical implementation challenges in real-world systems.

• Machine Learning
• Federated Learning
• Spectral Clustering
• +3 more

Linli Zhang is a Postdoctoral Researcher in the Department of Computer Science, focusing on advanced machine learning techniques and their applications. Her research interests include federated learning, spectral clustering, risk minimization, and network structures. Research Interests: Machine Learning and its explainability challenges Federated Learning optimization using Total Variation methods Reinforcement Learning for scheduling and dynamic environments Comparative analysis of clustering algorithms Her recent work emphasizes federated learning frameworks, reinforcement learning applications in production systems, and clustering methodologies. Earlier contributions include formal methods for distributed control systems. No scientific awards or grants are explicitly mentioned in the provided texts. She has not listed any academic advisees or affiliated research teams.

• Control Systems
• Robotics
• Mechanical Engineering
• +3 more

Sadeq Yaqubi is a Postdoctoral Researcher in Automation Technology and Mechanical Engineering, focusing on advanced control methodologies for flexible robotic systems. His research integrates partial differential equations (PDEs), nonlinear control theory, and machine learning to address challenges in manipulator dynamics, endpoint control, and deflection mitigation. Key research interests include boundary control of distributed systems, computational efficiency in sensor-based estimation, and semi-analytical controller design for non-homogeneous boundary conditions. His work emphasizes practical applications such as vibration suppression in heavy-duty manipulators and real-time adaptive learning for multivariable systems. Yaqubi's contributions span conference proceedings (CASE, ROBIO) and journals like the International Journal of Robust and Nonlinear Control. His articles explore topics ranging from reinforcement learning-based motion planning to model predictive control of uncertain systems. Collaborations focus on automation technology and mechanical engineering innovations. No scientific awards are explicitly listed in the provided materials. Research activity highlights include 5+ peer-reviewed publications between 2020–2025, with a focus on flexible manipulator control and PDE-driven methodologies. No grants or advising roles are mentioned in the text.

Vagan TerziyanView profile

Professor

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +7 more

Professor Vagan Terziyan holds dual roles as a Professor in Distributed Systems at the University of Jyväskylä (Finland) and a Visiting Professor in Artificial Intelligence at Kharkiv National University of Radio Electronics (Ukraine). He leads the Collective Intelligence research group and oversees international master’s programs in Artificial Intelligence and COIN (Cognitive Computing and Collective Intelligence). His academic career spans over four decades, with significant contributions to AI, semantic web technologies, and industrial automation. Education: Ph.D. (Dr. Tech.) in Technical Cybernetics and Information Theory (1985) Habilitation (Dr. Habil. Tech.) in Information Systems and AI (1993) Professor at KNURE (since 1994) and Docent at University of Jyväskylä (2001) Research Interests: Focuses on advancing AI through machine learning, cognitive computing, and adversarial resilience. Key areas include digital clones, ethical AI, smart manufacturing, and semantic web applications. His work bridges theoretical concepts with practical implementations in Industry 4.0/5.0 environments. Articles Trends: Recent publications emphasize ethical AI frameworks, adversarial training for resilience, and innovative applications of neural networks in manufacturing and decision-making. He explores intersections between human and machine learning to enhance collective intelligence systems. Awards: Medal for 30-Years’ Excellence in Academic Service (2015) Distinguished Teacher of Ukraine (2000) Quality of Teaching Recognition (2003) Advising & Grants: Supervised over 190+ theses (PhD/M.Sc.), leading to impactful research in AI, cybersecurity, and IoT. Active in EU-funded projects like COST Actions (e.g., KEYSTONE, Wearable Robots) and Finnish national initiatives (Tekes projects). Founded the Industrial Ontologies Group and developed platforms like the Semantic Portal for Quality Assurance in Education. Labs/Teams: Heads the Metaintelligence Lab and Adversarial Intelligence Research Group. Collaborates internationally on projects like the Global Understanding Environment (GUE) for autonomous systems and proactive middleware for Industry 4.0.