جست‌وجوی استاد و پژوهشگر

Heikki Kälviäinenمشاهده پروفایل

استاد

• Computer Vision
• Machine Vision
• Pattern Recognition
• +۴ مورد دیگر

Professor Heikki Kälviäinen is a distinguished academic at Lappeenranta-Lahti University of Technology LUT, where he serves as Professor of Computer Science and Engineering in the School of Engineering Sciences. He leads the Computer Vision and Pattern Recognition Laboratory (CVPRL) within the Department of Computational Engineering. Additionally, he has an upcoming appointment as Professor of Computer Science and Engineering at Brno University of Technology's Faculty of Information Technology from January 2025 to January 2028. Professor Kälviäinen received his Doctor of Science (Technology) degree in 1994 from Lappeenranta University of Technology. His academic journey includes significant positions beyond LUT, such as Professor of Computing at Monash University Malaysia's School of Information Technology, and Visiting Professor roles at Brno University of Technology, Czech Technical University's Center for Machine Perception, and University of Surrey's Center for Vision, Speech, and Signal Processing. Professor Kälviäinen's research focuses on computer vision, machine vision, pattern recognition, machine learning, and digital image processing and analysis. His work has significant applications in diverse fields including wildlife conservation (particularly animal re-identification), plankton recognition, and industrial applications like timber and wood species identification. His research demonstrates a strong bridge between theoretical computer vision techniques and practical real-world applications. His publication record is extensive, with over 230 peer-reviewed scientific articles, 49 other research publications, and leadership in 51 research projects totaling 6.3 million EUR. His work has achieved an h-index of 38 with over 6500 citations, reflecting substantial impact in his fields of expertise. Professor Kälviäinen has served as supervisor for 28 doctoral and 173 Master's theses, demonstrating his commitment to academic mentorship. He has also served as a Ph.D. thesis reviewer 35 times across 8 different countries, contributing to international academic quality assurance. His leadership extends to various administrative roles including Vice Dean, Head of Department/Degree Program/Laboratory at LUT, Director of East Finland Graduate School in Computer Science and Engineering (ECSE), and Deputy Head of School at Monash University Malaysia.

• High Energy Physics
• Experimental Particle Physics
• Medical Imaging
• +۳ مورد دیگر

Erik Brücken is a University Lecturer and Docent at the Department of Physics, University of Helsinki. He actively participates in the Doctoral Programme in Materials Research and Nanosciences, as well as the Doctoral Programme in Particle Physics and Universe Sciences. His research spans high-energy physics, experimental particle physics, and detector development. Primary affiliation: Department of Physics, University of Helsinki Supervisory roles in two doctoral programs Active in CMS and ALICE collaborations Research Interests: Brücken's work focuses on experimental particle physics at colliders like the LHC, particularly in detector optimization and data analysis. His projects include the ALICE Time Projection Chamber Upgrade and multispectral photon-counting for medical imaging. Current research examines B meson decays, Higgs boson properties, and jet substructure in heavy-ion collisions. Recent Publications: His 2025 contributions include analyses of B0 meson decays, Higgs self-coupling constraints, top quark production cross-sections, and jet substructure studies in nuclear collisions. Projects: He leads a 2024-2027 European particle physics infrastructure project (TOTEM) and participates in collaborations related to detector development and beam characterization. Past projects include CMS Tracker upgrades and CDF experiments spanning 2002-2017. Academic Activities: Brücken organizes and presents at international conferences like the European Researcher's Night and Euroschool on Exotic Beams. He serves as a doctoral thesis committee member and contributes to peer review processes.

• Geropsychology
• Wellbeing and health
• Innovation expertise
• +۹ مورد دیگر

Tuomas Leisti is a Lecturer and Doctor of Psychology (PsT) at Metropolia University of Applied Sciences, affiliated with the Wellbeing Team 3. His professional roles include teaching psychology and research methods, supervising Master's theses, and contributing to RDI (Research, Development, Innovation) projects. He holds certifications as a licensed psychologist and professional teacher (AmO), with proficiency in Finnish (native), English (excellent), Swedish (average), and German (basic). Leisti's research focuses on geropsychology, wellbeing and health, innovation expertise, cognitive psychology, and the philosophy of mind. His doctoral work explored the role of consciousness in decision-making, with prior experience in human-technology interaction. He is particularly interested in decision-making processes, problem-solving, and the metacognitive skills underlying these activities. Key projects include the Wellbeing Hybridist initiative (2021–2023) and the pan-European COMHOM project (2025–2028), which addresses homelessness through data-driven solutions. His research outputs span over two decades, emphasizing subjective image quality evaluation, cognitive modeling, and quantitative methodologies. Leisti's work integrates empirical and philosophical approaches, addressing topics such as qualia, decision heuristics, and the interplay between conscious thought and judgment. Despite no explicitly listed awards, his extensive publication record reflects sustained contributions to psychological and applied research fields.

Jukka Pekolaمشاهده پروفایل

استاد

• Quantum Technology
• Mesoscopic Physics
• Superconductivity
• +۴ مورد دیگر

Jukka Pekola is a Professor at Aalto University's School of Science, holding positions in the Department of Applied Physics, the Centre of Excellence in Quantum Technology (QTF), and the Quantum Phenomena and Devices group. He earned a Doctoral degree in Engineering and Technology from Helsinki University of Technology (1984) and a Master's degree (1982) from the same institution. His research focuses on quantum thermodynamics, nanoscale superconductivity, and mesoscopic physics, with contributions to quantum refrigeration, superconducting circuits, and cryogenic measurement techniques. He leads multiple EU and Academy of Finland-funded projects, including TcQTD (2024–2027) and QNet (2024–2027). Pekola has received prestigious awards such as the Academy Professor title (2014–2018) and the Simon Memorial Prize (2020). His research explores thermalization in quantum systems, Josephson junction dynamics, and quantum heat transport. Over 470 publications and 31 active projects highlight his impact. Notable contributions include bolometric detection of Josephson radiation and ultra-sensitive calorimetry of quantum phase slips. He actively collaborates globally, presenting invited talks on topics like quantum thermodynamics and nano-thermometry. Pekola's work contributes to UN Sustainable Development Goals through advancements in low-temperature physics and quantum technology. His lab focuses on developing quantum sensors and understanding nanoscale energy transfer mechanisms. He supervises research leading to 26 theses and chairs the Finnish National Quantum Institute (InstituteQ).

Dunwei Wenمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۳ مورد دیگر

Dr. Dunwei Wen is Associate Professor and Chair at the School of Computing and Information Systems within Athabasca University's Faculty of Science and Technology. With academic credentials from Ph.D. in Pattern Recognition and Intelligent Systems (Central South University) M.Sc. in Computer Science (Tianjin University) B.Eng. in Electrical Engineering (Hunan University) , he bridges theoretical AI research with practical implementations in information systems. His research program focuses on statistical learning and deep learning for Natural language processing Sequential data analysis Multimodal content understanding with applications spanning education, healthcare, and industrial domains. Publication trends show increasing emphasis on deep learning architectures for medical image analysis (SRTNet 2024), contextual topic modeling in education (2013-2015), and multimodal systems combining text/image analysis (2015-2018). Recent projects (2021-2022) center on self-supervised learning for natural language understanding. Professional engagements include Member, AAAI (Association for the Advancement of Artificial Intelligence) Member, ACM and ACM SIGAI Senior Member, IEEE Former CAAI Board Member (2001-2010) As academic advisor, he has supervised over 25 graduate students and interns, including Co-supervised 4 PhD candidates (Jilin University) Mentored 12+ Master's students (AU, Jilin University) Hosted 6 MITACS Globalink Research Interns with projects spanning from cardiac detection systems to educational data mining.

• Computer Science
• Neuroscience
• Medical Imaging
• +۳ مورد دیگر

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.

• Indoor Positioning
• Wireless Localization
• Machine Learning Applications
• +۳ مورد دیگر

Lucie Klus is a Postdoctoral Researcher in Electrical Engineering actively advancing indoor positioning systems through innovative wireless signal processing and machine learning techniques. Her work bridges theoretical algorithms with practical applications for indoor localization, focusing on fingerprinting methodologies and real-world dataset optimization. Core research spans Indoor Positioning (100% fingerprint relevance), Wearable Device integration (46%), Radio Map analysis (42%), and K-means clustering (35%) Recent breakthroughs include dynamic localization using Intersection over Union metrics and multidimensional compression of positioning datasets via EWOk framework Key publications demonstrate strong synergy between Wi-Fi fingerprinting, deep learning interpretation, and quality-of-service optimization in constrained environments Her 18 research outputs (2019-2024) reveal consistent focus on solving data sparsity challenges through autoencoders, extreme learning machines, and novel radio map compression techniques, with increasing emphasis on multi-device compatibility and measurement density.

Marijn van Vlietمشاهده پروفایل

پژوهشگر ارشد

• Neuroscience
• Biomedical Engineering
• Computational Neuroscience
• +۳ مورد دیگر

Marijn van Vliet is a Research Fellow at Aalto University's Department of Neuroscience and Biomedical Engineering, specializing in computational neuroscience and brain imaging. He focuses on decoding cognitive processes through advanced analysis of MEG/EEG data. Academy Research Fellow (2021-present) Principal investigator in projects like 'Unraveling language in the brain through biologically plausible modeling' His research combines machine learning with neuroimaging to explore language comprehension, semantic processing, and functional connectivity. Key methodologies include convolutional networks, representational similarity analysis, and beamforming techniques. Active in open science initiatives Developed tools like mne-rsa and mne-faster Recent work investigates cortical dynamics during semantic processing, feedforward/backward visual word recognition, and test-retest reliability of MEG connectivity metrics. His projects are supported by the Academy of Finland and RCF Academy funding.

Ella Binghamمشاهده پروفایل

دانشیار

• Science Policy
• Research Funding
• Data Science
• +۴ مورد دیگر

Ella Bingham serves as Head of Research Services at Aalto University, leading an 80+ member team that supports researchers in funding acquisition, doctoral education, research data management, and open science initiatives. She concurrently holds the position of Executive Director at the Foundation for Aalto University Science and Technology and previously served as Vice Director for research administration at the Helsinki Institute for Information Technology (HIIT). Education: Doctoral degree in Engineering and Technology, Helsinki University of Technology (2003) Master's degree in Engineering and Technology, Helsinki University of Technology (1998) Docent in Computer Science, University of Helsinki (2009) Her research spans science policy, research funding systems, and data science applications in academic administration. With expertise in statistical analysis and artificial intelligence, she investigates research evaluation methodologies and science-policy interfaces, emphasizing practical implementations in higher education contexts. Her work bridges technical data analysis with institutional research management frameworks. Recent publications (2015-2023) reveal a consistent focus on research administration infrastructure, particularly data management systems and impact assessment methodologies. The works demonstrate increasing emphasis on practical implementation frameworks within Finnish academia, with recurring themes of digital transformation in research support services and evidence-based science policy development. Through her leadership in Research Services, she oversees management of external research funding applications and doctoral education processes. Her unit facilitates multidisciplinary collaboration via the Aalto Networking Platform while advancing research ethics compliance and national research profiling initiatives. External engagements include advisory roles for Finland's Ministry of Education and Culture and the European Commission on research policy matters.

Henning Kirschenmannمشاهده پروفایل

استاد

• Particle Physics
• High Energy Physics
• CMS Experiment
• +۵ مورد دیگر

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)

• Sparse Arrays
• Beamforming
• Sensor Arrays
• +۴ مورد دیگر

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.