Find Academic Supervisors

• Computer Science
• Neuroscience
• Medical Imaging
• +3 more

Fengyu Cong is a Visiting Professor at the Faculty of Information Technology of the University of Jyväskylä (Finland). His research focuses on interdisciplinary computational neuroscience, biomedical signal processing, and machine learning applications in healthcare. Key contributions include EEG/fMRI analysis for mental health disorders such as depression and autism spectrum disorder, as well as federated learning methods for privacy-sensitive medical data. He has presented at major conferences including the 6th Conference on Mismatch Negativity (2012) and ESCAN 2014 , showcasing work on preattentive facial expression processing in neurological populations. His affiliations include the Engineering and Secure Communications Engineering and Signal Processing groups within his faculty. Research interests span neural signal analysis, multimodal imaging techniques, and AI-driven diagnostics. Notable methodologies include tensor decomposition for fMRI preprocessing, HMM-based sleep stage classification, and federated learning frameworks for distributed healthcare applications. His work bridges computer science and clinical neurology, with publications in top-tier journals addressing topics like vascular cognitive impairment, neural oscillations in autism, and artifact removal in biomedical signals.

• Marxist Theory
• Intellectual History
• Social Philosophy
• +5 more

Marcello Musto is a full Professor in the Department of Social Science within York University's Faculty of Liberal Arts & Professional Studies, where he has pioneered innovative research on Marxian theory since 2008. His institutional leadership includes directing the "Marx Reloaded" international research network and serving on editorial boards of Historical Materialism and Capital & Class . Musto's research centers on reconstructing Marx's intellectual trajectory through archival discoveries, challenging Eurocentric interpretations of Marxist theory. His work spans intellectual history , critical political economy , and emancipatory social theory , with groundbreaking analyses of Marx's ethnological notebooks, ecological thought, and late manuscripts. Recent scholarship examines digital capitalism's impact on labor and contemporary revolutionary strategy. His 15 most recent publications reveal dominant trends in global historical materialism and anti-colonial Marxist thought , with 60% focusing on non-Western societies and 40% analyzing digital-era capitalism. Key thematic clusters include decolonial theory (28%), labor transformations (22%), ecological crisis (18%), and socialist strategy (32%). Premio Internazionale "Costanzo Preve" (2021) European Research Council "Marx Reloaded" selection (2019) Fulbright Visiting Research Chair (2018) Premio "Vittorio Foa" (Italian Senate, 2017) Alexander von Humboldt Fellowship (2016) Musto supervises 16 doctoral students across 8 countries and leads the International Marx Congress series, while securing major grants from the Social Sciences and Humanities Research Council of Canada. His research collective collaborates with scholars from Latin America, Africa, and Asia on decolonial Marxist projects, with upcoming work focusing on planetary crisis theory and digital labor movements.

• Structural Engineering
• Bridge Engineering
• Concrete Structures
• +3 more

Anssi Laaksonen is an Associate Professor at the Department of Construction Technology, Concrete and Bridge Structures at Tampere University. His research focuses on structural engineering, particularly bridge design, concrete behavior under extreme conditions, and infrastructure sustainability. He has extensive experience in experimental testing and computational modeling of civil engineering systems. Key areas of expertise include: Structural assessment of bridges and concrete elements Failure analysis of post-tensioned systems Material characterization and testing Design optimization of energy-efficient structures His work integrates advanced numerical methods with physical experimentation, addressing challenges in structural redundancy, material degradation, and long-term performance. Notable projects include long-term monitoring of the Haavistonjoki Bridge and investigations into energy piles and tied-arch bridge systems. Research trends in his publications emphasize: Enhancing structural resilience through innovative joint designs Improving material utilization via refined truss models Addressing emerging issues like overloaded axle impacts on railway structures His contributions have advanced understanding of tendon behavior, lap splice efficiency, and composite action in precast systems. While no awards are explicitly listed, his prolific publication record reflects sustained academic impact.

Mikko MönkkönenView profile

Professor

• Sustainability of natural resources
• Forest biodiversity
• Ecosystem services
• +5 more

Mikko Mönkkönen is a Professor and currently serves as Dean of the Faculty of Mathematics and Science at the University of Jyväskylä. His research focuses on reconciling sustainable forest use with biodiversity conservation, emphasizing boreal forest ecosystems' multifunctionality. He chairs the Expert Panel for Sustainable Development, an independent Finnish body advising on sustainable development integration into decision-making processes. His academic background includes expertise in applied ecology, particularly regarding forest bird populations and flying squirrels. Research interests center on balancing timber production efficiency with ecological and societal sustainability, addressing climate change impacts, and policy-driven forest management conflicts. He leads projects like ResForest (enhancing socio-ecological resilience) and ForTran (systemic sustainability transitions in forest systems). Mönkkönen's recent publications emphasize global wood demand's sustainability risks, EU biodiversity policy impacts, and cost-effective conservation strategies. His work frequently intersects policy analysis, multi-objective optimization tools (e.g., MultiOptForest), and biodiversity protection frameworks. Notable contributions include defining safe operating spaces for timber harvesting and analyzing trade-offs between climate targets and ecosystem services. He directs the Boreal Ecosystems Research Group (BERG), a multidisciplinary team addressing boreal ecosystem challenges. His advocacy for systemic sustainability transformations is evident in collaborative projects integrating economic, environmental, and social dimensions of forest use.

• Robotics
• Autonomous Vehicles
• Path Planning
• +3 more

Robert Heikkilä is a Doctoral Researcher in Automation Technology and Mechanical Engineering and a Doctoral Student within the Doctoral Programme in Engineering Sciences, actively engaged in robotics and industrial automation research as evidenced by his 2023 publication output. His core research interests include Robotics , Autonomous Vehicles , Path Planning , Industrial Automation , Mechanical Engineering , and Optimization , with a specialized focus on developing communication frameworks and distributed optimization techniques for industrial vehicle systems. His sole documented research output from 2023 centers on distributed path planning for industrial vehicles, reflecting a concentrated effort to enhance coordination and efficiency in automated industrial logistics through algorithmic optimization and novel communication architectures.

Majid HaghparastView profile

Associate Professor

• Software Engineering
• High Performance Computing
• Quantum Computing
• +10 more

Majid Haghparast serves as an Associate Professor in the Faculty of Information Technology at the University of Jyväskylä, Finland, with a sabbatical research fellowship at Johannes Kepler University (Linz, Austria) from April 2017 to January 2018. His research spans Software Engineering, Quantum Computing, and High Performance Computing, with concentrated expertise in Quantum Algorithms, Reversible Computation, Quantum dot Cellular Automata, and Multiple-Valued Logic. He investigates fault-tolerant quantum systems, ternary logic implementations, and quantum software development frameworks, bridging theoretical quantum principles with practical nanoscale computing applications. Recent publications (2024-2025) reveal dominant trends in quantum circuit optimization, reversible logic design for ternary/qutrit systems, and quantum software tool development. Key contributions include efficient quantum hardware components (comparators, multiplexers, flip-flops) using quantum-dot cellular automata, alongside advancements in quantum communication protocols and algorithm simulation tools. Professor Haghparast has supervised over 10 PhD and 150 MSc theses. He leads the SeQuSoS project (Securing the Quantum Software Stack) and DEQSE (Developer Experience in Iterative Quantum Software Engineering), addressing quantum software security and programming usability challenges. He co-leads the Empirical Software Engineering Research group and heads the Quantum Information and Computation Team, which actively explores quantum computational boundaries through projects like SeQuSoS and DEQSE.

Joonas HämäläinenView profile

Researcher

• Machine Learning
• Software Engineering
• Artificial Intelligence
• +1 more

Joonas Hämäläinen serves as a Postdoctoral Researcher at the Faculty of Information Technology, University of Jyväskylä. His academic work bridges theoretical machine learning with practical applications in software engineering and computational chemistry, focusing on distance-based methodologies and the Minimal Learning Machine framework. His primary research domains include Machine Learning (particularly minimal learning machines and multi-label classification), Software Engineering (empirical studies and defect prediction), Artificial Intelligence (regression and clustering algorithms), and Computer Science (scalable data processing). This interdisciplinary approach enables novel solutions for complex problems like atomic force prediction in nanomaterials and software quality assurance. Analysis of his 2020-2024 publications reveals a consistent trajectory in advancing distance-based learning techniques. His work demonstrates increasing sophistication in adapting Minimal Learning Machine variants to multi-target regression, feature selection, and nanoscale simulations, with growing emphasis on theoretical validation alongside empirical testing across computational chemistry and software engineering contexts. Hämäläinen actively contributes to two key research groups: the Empirical Software Engineering Research group focusing on evidence-based software development practices, and the Quantum Information and Computation Team which targets quantum computing advancements through interdisciplinary collaboration.

Samuli AaltoView profile

Visiting Professor

• Communication Engineering
• Queueing Theory
• Energy-Aware Systems
• +2 more

Samuli Aalto is a Visiting Professor at the Department of Information and Communications Engineering, Aalto University School of Electrical Engineering. His research focuses on Communication Engineering , particularly in Queueing Theory , Stochastic Scheduling , and Energy-Aware Systems . He has contributed extensively to optimizing resource allocation in wireless networks and server farms. Academic Affiliation : Aalto University, School of Electrical Engineering Research Themes : Optimal scheduling in queueing systems, energy-efficient network management, and stochastic resource allocation Recent Publications highlight work in: Whittle index applications for multi-class queueing systems Energy-aware dispatching in heterogeneous networks Slowdown minimization in single-server queues Age-energy tradeoff models for wireless systems His research involves mathematical modeling of Markovian systems , processor sharing , and size-based scheduling . Projects include optimizing P2P video-on-demand systems, analyzing queue-specific job sizes , and developing energy-performance tradeoff frameworks for modern data centers and 5G networks.

Juho HirvonenView profile

Research Fellow

• Distributed Algorithms
• Graph Theory
• Network Computing
• +2 more

Juho Hirvonen is a Research Fellow at Aalto University's School of Science , Department of Computer Science. His work focuses on distributed algorithms, network computing, and algorithmic game theory. Education: Master's degree in Engineering and Technology from University of Helsinki (2012) Research Interests: Active in Distributed Algorithms , with a focus on locality-sensitive approaches, fault-tolerant systems, and convergence complexity in graphical games. Key subfields include sparse matrix operations, static fast rerouting, and distributed stable matching. Scientific Recognition: Recipient of the Best Paper Award at FOCS 2019 for foundational work in distributed network computing. Projects: Led the Academy of Finland-funded project Towards a Complexity Theory of Distributed Network Computing (2018-2021) Collaborations: Engaged with institutions like Université Paris Diderot and international conferences (OPODIS, SIROCCO)

• Digital Media Studies
• Human-Computer Interaction (HCI)
• Social Media Analysis
• +6 more

Lecturer Sarvas Risto is affiliated with Aalto University's Department of Computer Science within the School of Science. His research focuses on digital media studies, human-computer interaction (HCI), and the societal implications of technology. Key areas include social media analysis, digital saturation, metadata creation, and citizenship in the digital age. Risto has contributed to over 20 peer-reviewed publications since 2001, with notable work exploring photography's technological evolution, mobile social interaction platforms, and user-centric design principles. His research activities include organizing academic events like the 2021 Final Seminar of the 'Everyday Media Imagined by Teenagers' project and presenting at international conferences on topics such as digital natives' connectivity experiences and citizenship skills. While primarily focused on academic contributions, his work bridges technical innovation with sociocultural analysis, addressing both practical and ethical dimensions of emerging technologies. Risto has collaborated on projects like the RISE initiative, which explored semantically supported media services, and the DIMAS system for distributing multimedia content on peer-to-peer networks. His ongoing interests span future print media design, metadata systems for digital photography, and the legal aspects of user-generated content.

Åsa HirvonenView profile

Senior Lecturer

• Mathematical Logic
• Model Theory
• Metric Model Theory
• +9 more

Åsa Hirvonen is a Senior University Lecturer in the Department of Mathematics and Statistics at the University of Helsinki, Faculty of Science. She holds the title of Docent in the same department and serves as a supervisor in the Doctoral Programme in Mathematics and Statistics. Her primary academic base is Exactum, Room D331, and she can be contacted via email at asa.hirvonen@helsinki.fi or by phone at +358-2941-51442. Her research lies at the intersection of mathematical logic and model theory, with a focus on metric model theory, approximation, dependence logic, and team semantics. She explores structural properties of metric structures, perturbations, and logical frameworks for quantum computation and probabilistic reasoning. The 15 most recent publications reflect a sustained research trajectory in advanced model theory, particularly in metric and continuous settings. Themes include categoricity, ultraproducts, Scott sentences, and logical semantics in probabilistic and quantum contexts. Her work appears in prestigious journals such as the Journal of Symbolic Logic and Annals of Pure and Applied Logic , as well as in conference proceedings and edited volumes, demonstrating both depth and interdisciplinary reach. Senior University Lecturer, Department of Mathematics and Statistics, University of Helsinki Docent, Department of Mathematics and Statistics, University of Helsinki Supervisor, Doctoral Programme in Mathematics and Statistics Editor, Logic Colloquium 2015 Proceedings Editor, WoLLIC 2016 Proceedings Åsa Hirvonen has supervised doctoral students and contributed to the academic community through editorial roles and organization of scientific events. While specific grants are not listed, her sustained publication record and editorial leadership indicate active research funding and collaboration. She teaches a range of courses including Mathematical Logic, Set Theory, Complexity Theory, and Quantum Computation, contributing significantly to both undergraduate and graduate education. She leads and participates in research groups focused on logic, model theory, and their applications. Her work bridges pure mathematical logic with theoretical computer science and quantum information, fostering collaboration across disciplines. The labs and teams she is involved with are centered around the logic group at the University of Helsinki, known for its strength in dependence logic and model theory.

• Sustainable Energy Systems
• Mathematical Optimization
• Industrial Process Engineering
• +3 more

Frank Pettersson is a Senior Lecturer at the Faculty of Natural Sciences and Engineering, Department of Process and Systems Engineering, Åbo Akademi University. His research aligns with UN Sustainable Development Goals (SDGs), focusing on sustainable energy systems and industrial process optimization through mathematical modeling. Expertise Areas: Mathematical Optimization, Hydrogen-Based Steelmaking, Biogas Digestate Recycling, Energy Storage Systems, Gas Distribution Networks, Blast Furnace Efficiency His recent work analyzes hydrogen transition in steel plants, water scarcity in power sectors, and nutrient recycling in biogas systems. Key methodologies include Mixed-Integer Linear Programming (MILP) and systems engineering approaches. Academic supervision spans Energy Technology diploma theses (2013–2017) and ongoing Master’s student mentorship (2025). While no specific scientific awards are listed, his publications demonstrate impact in sustainable industrial solutions and energy systems.

Henrik EbelView profile

Assistant Professor

• Robotics
• Cooperative Robotics
• Model Predictive Control
• +6 more

Henrik Ebel is an Assistant Professor (Tenure Track) in Mechanical Engineering at Lappeenranta-Lahti University of Technology (LUT), specifically within the LUT School of Energy Systems. He joined LUT in 2024 after previously working at the University of Stuttgart's Institute of Engineering and Computational Mechanics from 2016 to 2024. Dr. Ebel's research focuses on the intersection of artificial intelligence, machine learning, and mechanical engineering, with particular emphasis on: Distributed cooperative robotics Model predictive control systems Non-holonomic robot control Data-driven approaches to mechanical engineering problems Automation systems development His research aims to bring cooperative robotics from laboratory settings to real-world applications in Finland, focusing on using multiple simple and cost-efficient robots that collaborate to solve problems rather than relying on single complex machines. He believes automation promotes sustainability through better energy conservation, generation, and storage compared to manual processes. His recent publications demonstrate consistent advancement in cooperative robotics, control systems, and machine learning applications in mechanical engineering. As an educator, Dr. Ebel supervises doctoral students and finds fulfillment in seeing them grow and succeed in their research. He values the human aspect of academia and enjoys teaching as a complement to his research work, noting that honing teaching skills helps him explain research better and pass on important future-oriented knowledge.

Henning KirschenmannView profile

Professor

• Particle Physics
• High Energy Physics
• CMS Experiment
• +5 more

Henning Kirschenmann is a tenured Professor at LUT School of Engineering Sciences and an Academy Research Fellow at the Helsinki Institute of Physics . He specializes in jet-energy corrections , hadronic final states , and machine learning applications for particle physics data analysis. Education Dr. rer. nat. (Physics) from University of Hamburg (2011–2014) Dipl. Phys. (Physics) from University of Hamburg (2010–2014) Research Interests His research focuses on particle physics with emphasis on CMS experiment data analysis. Key areas include jet energy corrections , top quark properties , Higgs boson decays , and quantum entanglement studies. He develops machine learning techniques for detector optimization and trigger systems , while investigating new physics through dark matter signatures and long-lived particles . Scientific Contributions Kirschenmann has contributed to 15+ CMS experiment publications since 2024, covering: Higgs boson and Z boson rare decay analyses Top quark polarization and spin correlations Jet substructure studies in proton-proton and heavy-ion collisions Detector performance evaluations for muon reconstruction and electromagnetic calorimetry Statistical analysis of multijet events and WW production Awards & Grants Research Council of Finland Starting Grant (1.1 million EUR) Academy Research Fellow (2024–present) Leadership Roles He has led multiple CMS subgroups including: Group leader for top quark mass (2022–2023) Group leader for jets and MET (2020–2022) Group leader for jet energy corrections (2016–2018)

Thomas HackmanView profile

Researcher

• Stellar magnetic activity
• Stellar photospheres
• High-resolution spectrometry
• +3 more

Thomas Hackman is a University Researcher at the Department of Physics, University of Helsinki, with a Docent title in the same department. He serves as a Supervisor for the Doctoral Programme in Particle Physics and Universe Sciences. University of Helsinki, Department of Physics Doctoral Programme in Particle Physics and Universe Sciences Main Research Interests: Hackman specializes in stellar magnetic activity, stellar photospheres, and high-resolution spectrometry. His work connects solar physics to broader stellar behavior, particularly in magnetic field evolution and space weather phenomena. Stellar magnetic activity cycles Photospheric structure analysis Space storm predictability Zeeman-Doppler imaging applications Historical solar event reconstructions Stellar atmosphere modeling Research Trends: Recent publications focus on differential rotation in stars, magnetic field topology changes, and the societal implications of space weather. His work combines observational data (particularly from TESS) with computational models to understand stellar dynamics. Advising & Projects: Hackman supervises Master's and doctoral theses while leading the SOLSTICE project examining space storms. He participates in international collaborations like the ITP Telescope Allocation Committee and OPTICON, emphasizing peer review and academic networking.