Tapio Westerlund is a Professor in Mathematics at Åbo Akademi University's Faculty of Science and Engineering. His research focuses on mixed-integer nonlinear programming (MINLP), convex optimization, and nonsmooth optimization techniques, with applications in chemical process engineering and environmental science. Key Research Areas: MINLP, global optimization, supporting hyperplane methods, and limestone dissolution modeling. Notable Contributions: Development of the Supporting Hyperplane Optimization Toolkit and reformulation frameworks for nonconvex MINLP problems. Collaborations: Active in interdisciplinary research networks with external collaborations across countries. His recent publications emphasize convex MINLP algorithms, hyperplane techniques, and applications to wet flue gas desulfurization. While no specific awards or students are listed in the provided data, his citations and Mendeley readership indicate significant academic impact.
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.
Andreas Lundell is an Associate Professor at the Åbo Akademi University within the Faculty of Science and Engineering, Department of Information Technology. His work focuses on mathematical optimization, particularly in convex MINLP, signomial programming, and sustainable AI. He leads projects like Data Analytics for Zero Emission Marine and contributes to the Wasa Zero Emission Data Centre initiative. Research spans global optimization algorithms and energy-efficient computing Active in EU-funded and national projects addressing climate action Recipient of the COIN-OR Cup 2018 His research drives sustainable industrial transitions through data analytics and optimization models, aligning with UN SDGs for climate action and sustainable cities.
Ali Zarei is a Research Fellow at Tampere University's Faculty of Medicine and Health Technology within the Department of Biomedical Technology. Based at the Hervanta campus Festia building, he serves as Grant Holder for a European Union Horizon 2020 project under Marie Skłodowska-Curie grant agreement No. 764713, focusing on measurement instrumentation and micro-robotics automation for biomedical applications. He holds a Bachelor of Science in Electronics Engineering (2014) and Master of Science in Communications Systems Engineering (2018). His technical foundation spans embedded systems, communications, and signal processing, directly enabling his current interdisciplinary work in biomedical instrumentation. Dr. Zarei's research integrates Micro-robotics and Measurement Instrumentation to automate mechanical testing of bio-based fibers like flax. He employs advanced Image Processing and Machine Vision for object detection, combined with Machine Learning and Deep Learning algorithms (DNN, CNN) for sensor data analysis. His technical implementation leverages Python, MATLAB, and C/C++ for both data analytics and embedded device programming, significantly enhancing micro-manipulation throughput. His scientific recognition includes the prestigious Marie Skłodowska-Curie Fellowship, which funds his mission to increase micro-robotic platform efficiency for biomaterial testing. This award supports his development of high-precision instrumentation within the EU's Horizon 2020 framework. As part of the BioMediTech research unit, he collaborates on interdisciplinary projects merging engineering and medicine. His current work involves optimizing micro-robotic systems at the Festia facility, with future implications for sustainable biomaterials research and tissue engineering applications.
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 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)
Prof. Dragos Truscan is a Professor of Computer Science at the Faculty of Natural Sciences and Engineering within Åbo Akademi University , focusing on Information Technology and Mission Critical Software Systems . Research interests include validation and verification , automated test generation using model-based technology and AI , test left-shifting , and runtime monitoring of industrial software systems Active in Security Requirements Engineering , DevOps , Deep Learning , and Software Component analysis Recent work explores: 2025 - Reinforcement learning for security testing and visual fault localization 2024 - Explainability in network attack detection and hyperparameter optimization 2023-2024 - Model-based requirements engineering in large collaborative projects Scientific Recognition Best Paper Award (2024) Best Paper Award (2013) Academic Activities Organizing member of IEEE International Conference on Software Services Engineering (2025) Hosted scholars like Victoria Zsók and Dr. Dumitru Iulian Nastac
Professor Peter Österholm at Åbo Akademi University's Faculty of Natural Sciences and Engineering specializes in Environmental Geology with a focus on Acid Sulfate Soils . His work addresses critical environmental challenges through interdisciplinary approaches. 2023-2025 : Active principal researcher in two major projects, including EU BIONEER for post-mining waste management Research Themes : Geochemical remediation, microbial interactions, water quality impacts Recent publications highlight: 2025: Microbial responses to limestone/peat treatments in hypermonosulfidic sediments 2024: Machine learning applications for acid sulfate soil mapping 2023: Innovative macropore targeting to reduce acid-metal release Collaborations include European Regional Development Fund , Kiertokaari , and Finnish Transport Agency . He organized the 2024 GeoDays conference and serves as co-investigator in multiple international projects.
Ella Peltonen is an Assistant Professor at the M3S research unit, University of Oulu, Finland. She joined the Ubicomp Oulu research centre and 6Genesis research programme in November 2018. Prior to this position, she was a postdoctoral researcher at the Insight Centre for Data Analytics in Cork, Ireland. She completed her PhD in the Nodes group at the University of Helsinki, Finland, working on the Carat project of collaborative energy diagnostics for mobile devices. Her educational background includes: PhD in Computer Science, University of Helsinki, Finland (Carat project on collaborative energy diagnostics) Ella Peltonen's research focuses on ubiquitous computing, large-scale data analysis, and applied machine learning. Her work particularly emphasizes everyday sensing and mobile and wearable devices. She aims to apply machine learning algorithms to large, complex data in real-time systems, with a focus on distributed machine learning and data analysis of smart devices. Her research spans various applications including energy consumption monitoring of mobile devices, wearable technology for measuring physiological signals, and exploring future sensing technologies. Peltonen has expressed interest in how future devices might sense human states, become smarter, and provide greater benefits, potentially through innovations like augmented reality glasses or subcutaneous chips. Analysis of her recent publications shows a strong focus on edge computing, vehicular networks, and sustainable computing systems. Her work bridges the gap between theoretical machine learning approaches and practical applications in transportation, healthcare, and environmental monitoring. Many of her papers address challenges in distributed systems, real-time data processing, and privacy-preserving techniques for edge intelligence. Her notable scientific awards include: Nominated to the list of 10 Rising Stars in Networking and Communications by N2 Women 2017 Selected as one of 50 Finnish Researchers by the Finnish Union of University Researchers and Teachers Nokia Scholarship 2015 and 2016 Jorma Ollila Grant 2018 Young Teacher of the Year 2012 Young Researcher of the Year 2015 Peltonen is actively involved in teaching and mentoring, with a teaching philosophy focused on supporting students' independent learning rather than lecturing from above. She enjoys guiding small groups where she can discuss topics together with students and get to know them personally. As a researcher, she describes herself as precise, detail-oriented, and committed to verifying the correctness of her work carefully. She values the combination of mathematical work with experimental work and creativity in technology, noting that research tasks are diverse and can apply different types of methodology. She is part of international research collaborations with several major universities worldwide, as required by Finnish Academy funding. Peltonen is also an advocate for diversity in technology fields, noting that technology is used by all kinds of people from various backgrounds, yet the producers of technology lack diversity. She has highlighted the importance of encouraging more women to pursue technology careers from an early age.
Muhammad Ardiyansyah is a Postdoctoral Researcher at the Department of Biological and Environmental Sciences , University of Jyväskylä. His work bridges robotics and mathematical frameworks, focusing on computational methods for motion control. Research Interests: His research applies Lie Algebra to robotics optimization, integrating principles from applied mathematics and bio-inspired engineering . This interdisciplinary approach aims to enhance robotic systems' efficiency and adaptability in dynamic environments. Recent Publication Trends: His 2025 work highlights the intersection of robotics , mathematical modeling , and environmental science , indicating a focus on real-world applications of theoretical advances.
Sandra Winters is a Postdoctoral Researcher at the University of Helsinki , affiliated with the Faculty of Biological and Environmental Sciences and the Organismal and Evolutionary Biology Research Programme . Her research is funded by the Academy of Finland project "The evolution of warning signal diversity" from 2022 to 2025. She also serves as a supervisor for the Doctoral Programme in Wildlife Biology. Academic rank: Researcher Email: sandra.winters@helsinki.fi Research Interests: Sandra specializes in evolutionary biology and ecology, with a focus on adaptive coloration, visual ecology, and complex signaling systems. Her work spans predator-prey dynamics, sexual selection, and evolutionary adaptations across diverse taxa including primates, moths, and fruit structures. Key Research Trends: Her recent publications highlight: Ecological drivers of warning signal diversity Mechanisms of protective coloration in mammals Signal evolution in primate societies Interdisciplinary approaches combining computer vision and evolutionary theory Academic Activities: She organized the Oikos Finland 2023 Conference and contributes to multidisciplinary collaborations in wildlife biology.
Lauri Vuorenkoski is a Doctoral Researcher affiliated with the Department of Computer Science at the University of Helsinki. His current project, EM4QS (Enhanced Middleware for Quantum Software), focuses on advancing quantum computing infrastructure in collaboration with Business Finland and other researchers. Role: Doctoral Researcher Institution: University of Helsinki Department: Computer Science His research bridges computer science and health informatics, with a dual focus on quantum algorithm development and policy analysis in healthcare systems. Earlier work includes studies on pharmaceutical regulation, patient safety, and healthcare equity across Europe and Finland. Recent publications highlight his expertise in quantum computing trends (e.g., graph algorithms on quantum annealers) and interdisciplinary healthcare policy analysis. While specific awards or students are not documented, his collaborations with international networks suggest broad academic engagement.
Mika Teräs serves as Professor at the University of Turku's Faculty of Medicine (Institute of Biomedicine) and Chief Physicist at Turku University Hospital, while concurrently teaching medical physics at Turku University of Applied Sciences' Faculty of Health Care. His career spans nearly three decades with foundational experience as Physics Assistant in UTU's biomedical department. Professor Teräs's research centers on PET methodology development , specializing in quantitative accuracy for cardiac and neurological imaging. His work addresses critical challenges in attenuation correction , myocardial perfusion quantification using [15O]H2O, and respiratory motion correction in dual-gated cardiac PET. Operating within the internationally recognized Turku PET Centre , he maintains extensive collaborations with PET manufacturers and research institutes worldwide. Analysis of his 2020-2024 publications reveals consistent focus on translational medical physics - developing clinically viable protocols validated through flow phantom studies and experimental models. Key contributions include digital/analog PET system comparisons, Bayesian reconstruction optimization, and MRI-based attenuation correction techniques for brain imaging. His work bridges engineering principles with clinical implementation to enhance diagnostic accuracy. As an educator, Professor Teräs emphasizes interactive learning through real-world examples, progressing from fundamentals to advanced concepts. He actively contributes to scientific discourse through peer review and conference abstract evaluation for major nuclear medicine societies, maintaining strong connections between academic research, clinical practice, and industry innovation.
Jukka Kemppainen is a Professor in the Department of Imaging and Clinical Diagnostics at the University of Turku, Faculty of Medicine, Finland. His research bridges nuclear medicine, oncology, and artificial intelligence to develop advanced cancer diagnostics. His core research domains include: Medical Imaging & Nuclear Medicine (PSMA/FDG PET, MRI) AI-Driven Oncology (prostate, pancreatic, head-neck cancers) Deep Learning Applications (convolutional neural networks for segmentation/classification) Theranostics Development & Biobank Research Recent publications (2023-2025) demonstrate a clear trajectory toward AI-enhanced precision oncology, with increasing focus on real-world clinical implementation. His work frequently appears in high-impact surgical and nuclear medicine journals, emphasizing multi-institutional Finnish collaborations. Professor Kemppainen maintains active clinical-academic engagement through the University of Turku, with direct contact available at jukkem@utu.fi .
Robin Rajamäki is a Visiting Professor in the Department of Information and Communications Engineering at Aalto University, Finland. He is affiliated with the Visa Koivunen Group and holds an ORCID ID (0000-0002-5028-6022). His academic credentials include a Doctor of Technology (Tekn. toht.) in Electrical Engineering (2021), a Master's in Engineering and Technology (2016), and a Bachelor's in Telecommunications Engineering (2014), all from Aalto University. Research interests focus on Sparse Arrays , Beamforming , ISAC (Integrated Sensing and Communications) , and Array Configuration models. His work explores optimal array geometries, waveform design, and identifiability guarantees in active sensing systems, with applications to MIMO radar, millimeter-wave communications, and future 6G networks. Key methodologies include statistical signal processing, machine learning, and computational optimization. Recent publications highlight advancements in generative deep synthesis , array geometry optimization , and sensor array applications in ISAC. His research spans 2015–2025, including 5 projects (e.g., FUN-ISAC, INSTINCT) and collaborations with institutions like the University of California, San Diego (2019–2020), Technion (2017–2018), and University of Pennsylvania (2016). Scientific Awards: Best Student Paper Award (3rd place, 2019) Projects include fundamental limits in ISAC, joint sensing-communications systems for immersive connectivity, and sparse antenna array processing for 6G and millimeter-wave applications. His work has been cited in Scopus and supported by the Academy of Finland and EU Horizon grants.
