Find Academic Supervisors

Patrick RinkeView profile

Adjunct Professor

• Theoretical Physics
• Material Science
• Computational Science
• +1 more

Patrick Rinke serves as an Adjunct Professor in the Department of Applied Physics at Aalto University, Finland. His research bridges theoretical physics, materials science, and computational methodologies with a strong focus on machine learning applications. His computational work spans electronic structure theory, materials design, and atmospheric chemistry. Rinke's research integrates Bayesian optimization, active learning, and high-throughput computational screening to accelerate materials discovery, particularly in hybrid perovskites, catalysts, and biomaterials. Recent work demonstrates machine learning's transformative potential in predicting molecular properties, optimizing materials functionality, and solving complex physical chemistry problems. His scientific contributions have been recognized with multiple awards: Thesis Prize from the Institute of Physics (2003) DFG Research Scholarship (2007-2009) Outstanding Postdoctoral Achievement Award (2009) Outstanding Referee of Physical Review Letters (2014) August-Wilhelm Scheer Visiting Professorship (2017)

Simo SärkkäView profile

Professor

• Machine Learning
• Signal Processing
• Bayesian Filtering
• +7 more

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.

Antti HonkelaView profile

Professor

• Privacy-preserving machine learning
• Differential Privacy
• Bayesian Machine Learning
• +3 more

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.

• Machine Learning
• Computational Neuroscience
• Artificial Intelligence
• +7 more

Aapo Hyvärinen is a Professor of Computer Science at the University of Helsinki , affiliated with the Helsinki Institute for Information Technology and the Helsinki Probabilistic Machine Learning Lab . He previously held the position of Professor of Machine Learning at the Gatsby Computational Neuroscience Unit, University College London (2016-2019). Education : Undergraduate Mathematics at University of Helsinki, Vienna, and Paris; Ph.D. in Information Science from Helsinki University of Technology (1997) His research focuses on machine learning and computational neuroscience , particularly: Independent Component Analysis (ICA) Natural Image Statistics Causal Representation Learning Neural Signal Processing Applications to brain imaging (MEG, CryoEM) Recent publications emphasize causal discovery , identifiable machine learning , and nonlinear ICA . Key projects include: VETURI (AI for health) DIGIMIND (AI in mental health) CIFAR grants (2022-2025) Scientific awards : Highly Cited Researcher (2010) He serves as Action Editor for the Journal of Machine Learning Research and Neural Computation , and has held Area Chair roles at NeurIPS, ICML, ICLR, AISTATS, and UAI conferences. His work bridges theoretical machine learning with neuroscience and philosophical implications of artificial intelligence .

• Information Discovery
• Bayesian Networks
• Information Retrieval
• +5 more

Petri Myllymäki serves as Director of multiple prominent research entities at Aalto University's School of Science, including the Helsinki Institute for Information Technology (HIIT), the Department of Computer Science, the Finnish Center for Artificial Intelligence (FCAI), and his own research group. His leadership positions demonstrate significant influence in the Finnish and international AI research community. Dr. Myllymäki's research focuses on information discovery, Bayesian networks, information retrieval, and machine learning. His work bridges theoretical computer science with practical applications, particularly in developing systems that enhance how humans interact with information spaces. His research fingerprint prominently features Information Discovery (100%), Bayesian Networks (50%), Hashing (50%), Approximation Algorithms (50%), Information Retrieval (50%), structure learning (50%), Search Engine technology (37%), and Task Performance (33%). His recent publications (2015-2025) reveal a consistent trajectory in interactive information discovery systems, with increasing applications in diverse domains including decision science, public health, and social media analysis. Notably, his 2024-2025 work shows expansion into cognitive burden in decision-making, health outcomes research using machine learning, and political social media analysis – demonstrating how his core AI methodologies are being applied to address complex societal challenges. Recognized at AI Finland Gala 2024 for contribution reaching over 1.4 million users Featured in multiple media outlets including X (formerly Twitter) and Bluesky Work referenced in patents and Wikipedia pages Dr. Myllymäki actively hosts academic visitors and participates in collaborative research activities. His work contributes to UN Sustainable Development Goals through applications in information access and AI development. The Myllymäki Petri group (HIIT) serves as a hub for interdisciplinary research at the intersection of artificial intelligence, human-computer interaction, and information systems.

Leena JärviView profile

Professor

• Urban Climate
• Air Pollution
• Greenhouse Gases
• +7 more

Leena Järvi is a Professor at the Institute for Atmospheric and Earth System Research (INAR) and Helsinki Institute of Sustainability Science (HELSUS) , University of Helsinki. Her work bridges urban climate science , air pollution , and greenhouse gas dynamics through experimental and theoretical approaches. Research Interests : Urban micrometeorology, carbon sequestration in green spaces, climate mitigation strategies, and air quality modeling. Key Projects : CO-CARBON (Strategic Research Council), GHUGS (Research Council of Finland), and PAUL (EU Horizon 2020). Her recent publications focus on urban CO2 fluxes , carbonyl sulfide as a carbon proxy , and climate impacts of urban vegetation . She has supervised 12 PhD students, 7 postdocs, and 17 undergraduates, while serving on editorial boards and organizing international workshops. Scientific Awards : Timothy Oke Award 2021 (IAUC). Teaching : Courses on Urban Climate and Atmospheric Sciences .

• Wireless Communications
• Coding Theory
• Quantum Information Processing
• +5 more

Olav Tirkkonen serves as a Full Professor in the Department of Communications and Networking at Aalto University, Finland, a position he has held since August 2006. He leads the Communication Theory research group, driving innovation in wireless communication systems. His academic journey includes a distinguished career spanning industry and academia, with significant contributions to 3G, 4G, and 5G technologies. His educational qualifications are: Doctor of Science (Ph.D.) in Theoretical Physics, Helsinki University of Technology, 1994 Master of Science (M.Sc.) in Theoretical Physics, Helsinki University of Technology, 1990 Professor Tirkkonen's research interests are centered on wireless communications, with a focus on physical layer processing, coding theory, and quantum information processing. His group explores advanced topics including 5G and beyond wireless networks (spectrum management, large-scale MIMO, ultra-reliable low-latency communication), network-level interference coordination, collaborative caching, machine learning applications for wireless channel geography, coding on manifolds, and quantum communication systems. This research bridges fundamental theory with practical implementation in next-generation wireless networks. Analysis of his recent publications (2024-2025) indicates a predominant focus on machine learning techniques for wireless channel modeling (channel charting), pilot allocation in MIMO systems, and quantum error correction. His work is instrumental in addressing key challenges in 5G/6G networks, particularly in scenarios demanding ultra-reliability, low latency, and efficient resource utilization. His scientific contributions include: Co-inventor of approximately 80 families of patents and patent applications Co-author of the book "Multiantenna transceiver techniques for 3G and beyond" Throughout his career, Professor Tirkkonen has mentored numerous graduate students and secured substantial research funding from various sources. His industry experience at Nokia Research Center (1999-2010) and visiting position at Cornell University (2016-2017) have enriched his research perspective and fostered strong industry-academia collaborations. The Communication Theory group, under his leadership, maintains active collaborations with leading institutions and companies worldwide, positioning Aalto University at the forefront of wireless communications research.

• Strategic Marketing
• Marketing Management
• Value Propositions
• +7 more

Mika Yrjölä serves as a University Lecturer in Marketing at Tampere University's Faculty of Management and Business. He is an active member of the Customer-Oriented Research Group with extensive research experience in strategic marketing, value propositions, and business models. His academic work spans over a decade with 53 research outputs from 2013 to 2025. Yrjölä's research primarily focuses on strategic marketing, including value propositions, business models, capabilities, and customer orientation. He employs both quantitative and qualitative methods in his investigations, with particular interest in digital transformation, artificial intelligence in retail, and business-to-business platforms. His work contributes to understanding how organizations can create value in evolving market contexts, especially through digital channels and platform-based business models. His publication record shows consistent scholarly output with recent emphasis on AI-enhanced platforms, value creation in ecosystems, and retail transformation. The research demonstrates a clear trajectory toward understanding digital transformation in marketing contexts, with increasing focus on AI applications in services and retail environments. His work spans both theoretical contributions and practical applications for businesses navigating digital disruption. Marvin Jolson Award (2022) - for research in sales management Yrjölä has supervised master's students and doctoral candidates while actively participating in academic service as a journal reviewer and conference committee member. His activities include 41 scientific journal reviews, 19 media appearances, and 11 master's student supervisions. He has also served as an external reviewer for The French National Research Agency (ANR). As a member of the Customer-Oriented Research Group, Yrjölä collaborates with colleagues on projects examining consumer behavior, retail transformation, and digital marketing strategies. His research increasingly focuses on the intersection of AI and service delivery, particularly in platform-based business models and retail contexts.

• Wireless Communications
• Signal Processing
• 5G/6G Networks
• +3 more

Mikko Valkama is a Professor at the Department of Communications Engineering , part of the Faculty of Information Technology and Communication Sciences at Tampere University . His research focuses on advanced wireless communication systems, positioning technologies, and integrated sensing and communication (ISAC). He holds an Orcid ID ( 0000-0003-0361-0800 ) and can be reached at mikko.valkama@tuni.fi . Research interests span 5G/6G networks , RF antenna design , deep learning for signal processing , and millimeter-wave systems . He leads projects on positioning algorithms (e.g., mmWave SLAM, NLOS mitigation), ISAC architectures, and hardware-efficient transmitter linearization. Notable contributions include works on DECT-2020 NR standards, phase-based localization, and RIS-assisted systems. In 2025 alone, his group published over 30 articles on topics such as: Antenna array design for Ka-band and wideband applications Machine learning for power amplifier predistortion Bistatic radio SLAM and mmWave mapping Covert transmission and physical-layer security His work bridges theoretical advancements with practical implementations, often validated through experimental setups (e.g., TUJI1 dataset for indoor localization). No scientific awards were explicitly listed in the provided texts.

• Networked Systems
• IoT Security
• Machine Learning
• +3 more

Petri Mähönen is a Full Professor at the Department of Information and Communications Engineering , Aalto University. His research focuses on networked systems, machine learning applications in telecommunications, and smart grid technologies. University: Aalto University Department: Information and Communications Engineering Research Interests span networked systems, IoT security, UAV communication, and AI-driven network optimization. His work addresses predictive QoS in cellular-connected drones and generative adversarial networks for cybersecurity. Recent Publications include studies on GAN-based traffic augmentation, anomaly detection in mobile networks, and regulatory frameworks for data platforms. His articles reflect expertise in both theoretical and applied network science.

Pekka MarttinenView profile

Associate Professor

• Machine Learning
• Large Language Models
• Reinforcement Learning
• +8 more

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.

Toni KotnikView profile

Associate Professor

• Computational Design
• Architectural Engineering
• Structural Design
• +4 more

Toni Kotnik is an Associate Professor in the Department of Architecture at Aalto University, Finland. He holds dual expertise in mathematics and architecture, with a Ph.D. from the University of Zurich and a second degree in architecture. His roles include principal of d’HKL , a Zurich-based experimental architecture firm, and adjunct lecturer at institutions like Harvard University and the Bartlett School of Architecture. His research focuses on integrating science, engineering, and computation into architectural design, emphasizing structural innovation and sustainability. Education: Ph.D. in Mathematics (Dr. sc. Nat.), University of Zurich, 1999 Second degree in Architecture Research Interests: Computational design and generative systems Structural aesthetics and neuroaesthetics Sustainable urban and architectural solutions Parametric modeling and material systems Key Contributions: Advances in graphic statics and form-finding Design strategies for high-density urban environments Integration of AI and generative tools in architectural practice Recent Work Trends: Recent articles emphasize computational methods for industrial layout optimization, AI’s role in architecture, and embodied perception in structural design. His work bridges theoretical research with practical applications, such as the Singapore Hawker Centre redesign and experimental structures like hypar-combined shells. Awards: Aalto University Doctoral Thesis Awards (2022) Best Presentation Award (2019) Oscari-Vilamo Award (2023) Advising & Grants: Supervised 4 theses, including recipients of awards like the Metex Award shortlist. Active in international academic networks, serving on conference committees (e.g., International Association for Shell and Spatial Structures) and editorial roles (e.g., Automation in Construction journal). Labs & Teams: Leads the d’HKL studio, collaborating globally on projects like the AA/ETH Pavilion and the Hypercube for Gardens by the Bay. Research focuses on experimental design and material systems.

• Machine Learning Security
• Adversarial Machine Learning
• Model Ownership
• +3 more

Sebastian Szyller serves as an Assistant Professor in the Department of Computer Science at Aalto University, Finland, where he is a core member of Adjunct Professor N. Asokan's research group. His academic role centers on advancing security frameworks for machine learning systems through rigorous theoretical and empirical research. His research expertise spans machine learning security , adversarial machine learning , and model ownership verification , with particular emphasis on defense mechanisms against model extraction attacks, unintended interactions among security measures, and intellectual property protection for AI models. Key contributions address the tension between model confidentiality and practical deployment requirements in real-world systems. Analysis of his 2019-2024 publications reveals an evolving research trajectory from foundational model extraction defenses (e.g., PRADA, 2019) to sophisticated analyses of defense interactions (SoK, 2024) and ownership resolution frameworks (False Claims, 2024). His work consistently bridges theoretical security principles with practical ML deployment challenges, establishing critical benchmarks for model protection. Dr. Szyller actively contributes to premier security venues including IEEE Symposium on Security and Privacy, USENIX Security Symposium, and AAAI conferences. His collaborations within Asokan's research group demonstrate strong interdisciplinary engagement in securing machine learning ecosystems, with recent work expanding into multimedia security and eCommerce fraud detection applications.

Eetu MäkeläView profile

Professor

• Digital Humanities
• Human Sciences–Computing Interaction
• Computational Humanities
• +3 more

Eetu Mäkelä is a Professor of Digital Humanities at the University of Helsinki, leading the research group at the Helsinki Centre for Digital Humanities. He focuses on computational methods in humanities and social sciences, including datafication and interdisciplinary collaboration. He serves as Technical Director of DARIAH-FI and a Research Programme Director at the Helsinki Institute for Social Sciences and Humanities. Currently, he heads the preparatory group for the Helsinki Liberal Arts and Sciences Bachelor’s Programme. His research emphasizes technological and theoretical foundations of computational research, with notable contributions to linked open data, sociolinguistic analysis, and historical text mining. He has developed widely used tools like Recon, Palladio, and Octavo, which are employed in academic and public sectors. His work has garnered over 20 awards, including best paper and open science recognitions. Key areas of expertise include digital humanities methodologies, data integration, and open science practices. He actively contributes to teaching, designing courses such as 'Methods for Digital Humanities' and demonstrating innovative pedagogical approaches. His recent projects involve analyzing 18th-century philosophical texts and sociolinguistic change using computational methods. Awards: Multiple best paper awards, open data awards, and open science awards. Grants/Advising: Leads research initiatives funded by the Academy of Finland and other bodies. Mentors interdisciplinary research teams but no specific student names listed. He maintains active roles in academic infrastructure, including the DARIAH-FI initiative and the Helsinki Institute’s datafication program. His work bridges technical innovation with humanities scholarship, emphasizing practical, enduring systems for academic and public use.

• Organizational Change
• Strategic Change
• Discourse Analysis
• +7 more

Eero Vaara serves as a Visiting Distinguished Professor in the Department of Management Studies at Aalto University, where his research centers on organizational and strategic change through discursive and narrative lenses. His work critically examines strategy processes, radical transformations like mergers and acquisitions, institutional legitimation, multinational corporation dynamics, and historical perspectives on management. His research interests encompass organizational change, strategic discourse, narrative analysis, institutional theory, and multinational corporate identity. Vaara investigates how language constructs organizational realities during crises, mergers, and cross-cultural interactions, with particular attention to power dynamics in identity politics and the role of historical narratives in strategic renewal. His scholarship bridges theoretical rigor with practical implications for managing complex change in global contexts. Recent publications (2020-2022) reveal consistent thematic focus across top management journals including Academy of Management Journal and Strategic Management Journal. Key trends include the interplay of language and social inequality during global crises like COVID-19, entrepreneurial phenomena in isolated economies such as North Korea, strategic ambiguity in organizational protection, and national identity construction within multinational corporations. His methodological innovations integrate critical discourse analysis with computational approaches like structural topic modeling. Scientific awards highlight his scholarly impact: Best Article Finalist 2023 (Academy of Management Discoveries) 2014 OMT Best Published Paper Award for advancing theoretical understanding of organizations While specific advising relationships and grant funding details aren't documented in the source material, Vaara's collaborative publications demonstrate extensive international research partnerships. His work with scholars across Europe and North America indicates active supervision of doctoral research and leadership in multinational research consortia, particularly in discourse-focused organizational studies. Though laboratory affiliations aren't specified, his methodological work suggests engagement with digital humanities research groups and discourse analysis laboratories, especially evident in publications combining computational text analysis with critical theoretical frameworks.