جست‌وجوی استاد و پژوهشگر

Sven Lončarićمشاهده پروفایل

استاد

• Computer Vision
• Image Processing
• Medical Imaging
• +۵ مورد دیگر

Academician Professor Sven Lončarić serves as a Full Professor in the Department of Electronic Systems and Information Processing at the Faculty of Electrical Engineering and Computing, University of Zagreb. With his office located in room D-119 and contactable via phone at 6129-891, he maintains an active presence in both teaching and research activities within the institution. His work bridges theoretical computer science with practical applications across multiple domains. Professor Lončarić's research spans computer vision, image processing, and deep learning with significant contributions to medical imaging, particularly in retinal analysis using optical coherence tomography. His work in color constancy and illumination estimation has advanced techniques for handling multi-illuminant scenes, while his research in ultrasound image analysis has practical applications in non-destructive testing. He also explores computer security aspects of machine learning models, particularly in backdoor detection and removal. His recent publications demonstrate a strong trend toward applying deep learning techniques to solve complex problems in medical imaging, retail automation, and industrial applications. The concentration of work in retinal imaging and color constancy shows his commitment to advancing computer vision fundamentals while simultaneously addressing practical challenges in healthcare and industry. His research increasingly incorporates anomaly detection methods and robust security practices for machine learning systems. Professor Lončarić actively contributes to the academic community through organizing events like the Croatian Computer Vision Workshop and the First Croatian Symposium on Artificial Intelligence, providing platforms for knowledge exchange in his fields of expertise. His work on astronomical data processing demonstrates the versatility of his methodological approaches across diverse scientific domains. Within the Department of Electronic Systems and Information Processing, he participates in the Center of Excellence for Computer Vision, contributing to the institution's research profile in artificial intelligence and computer vision. His work on medical imaging applications connects the faculty with healthcare institutions, facilitating interdisciplinary collaborations that translate technical advances into medical practice.

Katarina Grolingerمشاهده پروفایل

دانشیار

• Sensor Data Analytics
• Machine Learning
• Deep Learning
• +۵ مورد دیگر

Katarina Grolinger is an Associate Professor at the Department of Electrical and Computer Engineering at Western University and holds the Canada Research Chair (Tier 2) in Engineering Applications of Machine Learning . She is also a Vector Institute Faculty Affiliate . Ph.D. and M.Eng. in Software Engineering from Western University M.Sc. and B.Sc. in Mechanical Engineering from University of Zagreb, Croatia Her research focuses on machine learning applications in energy systems , deep learning for structural and medical imaging , federated learning , and big data management . Recent publications emphasize energy forecasting , IoT data analytics , and reinforcement learning for autonomous systems . 2022 Engineering Junior Faculty Award for Excellence in Research She teaches courses in data analytics , operating systems , and database management . Her work integrates industry collaboration and has contributed to IEEE and Energy Reports publications.

Abdelkader Oudaمشاهده پروفایل

دانشیار

• Information Security
• Cryptography
• Network Security
• +۹ مورد دیگر

Dr. Abdelkader Ouda is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Western Ontario, with over three decades of experience in academia and the software industry. He holds a Ph.D. in Computer Science from Western University (2005), an M.Sc. in Computer Science and Pure Mathematics from Ain Shams University (1993), and a B.Sc. (Hons) in the same disciplines from Ain Shams University (1986), graduating with the highest honor (96.80%). His research spans information security, cryptography, network security, big data security and privacy, and data analytics . He leads the Information and Computer Networks Security (iCNS) Research Laboratory, focusing on innovative solutions for IoT, fog–cloud computing, and behavioral biometrics. His recent work includes developing the ADG Engine for synthetic data generation and HSM4SSL for intra-domain security. Dr. Ouda’s publications highlight advancements in authentication protocols, lightweight IoT security frameworks, database benchmarking, and machine translation . His industry experience includes 14 years in software development, project management, and system analysis, which he integrates into his teaching of courses like Software Design, Algorithms & Data Structures, and Information Security. He also contributes to engineering accreditation through the CEAMS application. Key trends in his articles include secure communication in hybrid architectures, energy-efficient IoT protocols, and reinforcement learning for continuous authentication . His work bridges academic theory with practical applications, emphasizing efficiency and scalability in modern computing environments.

Krishna D. Kumarمشاهده پروفایل

استاد

• Space Systems
• Spacecraft Dynamics and Control
• Internet-of-Things
• +۴ مورد دیگر

Dr. Krishna D. Kumar is a Professor in the Department of Aerospace Engineering at Toronto Metropolitan University. His expertise spans spacecraft dynamics, control systems, and innovative applications of artificial intelligence in aerospace systems. Education: PhD (1998) from Indian Institute of Technology MS (1993) from Birla Institute of Technology and Science BSc (1990) from University College of Engineering His research focuses on spacecraft dynamics and control , including attitude coordination, formation flying, and stabilization mechanisms. He applies Internet-of-Things and Big Data Analytics to develop predictive maintenance algorithms for terrestrial systems like train engines, enabling failure prediction up to six months in advance. Recent publications highlight advancements in spacecraft formation control , adaptive filtering , and actuator design , with applications in both space and industrial systems. His work emphasizes collaboration across disciplines to solve complex engineering challenges. Honors & Awards: Canada Research Chair (2005-2015) Ontario Early Researcher Award (2006-2011) Corresponding Member, International Academy of Astronautics (2015) JSPS Fellow (2001-2003) He supervises graduate students in projects related to space systems , predictive analytics , and miniature wireless technologies , with applications at organizations like Bombardier and MHICA.

• Transport Engineering
• Public Transit Analysis
• Smart Card Data Applications
• +۳ مورد دیگر

Dr. Mahmoud Mesbah is a Senior Research Fellow at the School of Civil Engineering , University of Queensland , specializing in transport engineering and urban mobility . His work leverages smart card data , GIS , and machine learning to analyze passenger behavior , trip purpose inference , and transit network optimization . Research Themes : Public transit modeling, traffic violation analysis, travel time reliability, and data-driven urban mobility solutions. Email : mahmoud.mesbah@uq.edu.au

Jouko Lampinenمشاهده پروفایل

استاد

• Machine Learning
• Bayesian Statistics
• Neural Networks
• +۴ مورد دیگر

Jouko Lampinen serves as the Dean of the School of Science (SCI) at Aalto University, Finland, overseeing academic and research operations across the institution. His professional contact includes the dean-sci@aalto.fi email address and phone number +358505604827. Lampinen maintains an active research profile in computational information technology while fulfilling his administrative leadership role, with expertise grounded in advanced algorithmic and statistical methodologies. His research spans machine learning, Bayesian statistics, neural networks, and their applications in brain imaging (fMRI/MEG) and computer vision. Key interests include probabilistic modeling for emotion recognition, object detection in autonomous systems, and medical diagnostics. His work addresses critical challenges in reproducibility, scalability, and interpretation of complex models, bridging theoretical machine learning with practical neuroscience and robotics applications. This interdisciplinary focus demonstrates consistent innovation from the late 1990s through 2018. Analysis of his recent publications reveals a dominant trend in applying Bayesian methods and neural networks to neuroimaging data, with significant contributions to emotion processing algorithms, brain-computer interfaces, and point cloud analysis for autonomous vehicles. His scholarly output shows increasing emphasis on real-world validation of computational models, particularly in medical diagnostics and human-computer interaction contexts, while maintaining foundational work in statistical learning theory. No scientific awards or honors were documented in the provided information. Details regarding student mentorship, grant funding, or specific research teams/labs are absent from the source material, though his deanship implies strategic oversight of research infrastructure within Aalto University's School of Science.

Christos Chousidisمشاهده پروفایل

مدرس ارشد

• Biomedical Acoustics
• Wireless Audio Networks
• Machine Learning
• +۶ مورد دیگر

Dr. Christos Chousidis is a Senior Lecturer in Audio Electronics at the University of Surrey 's Institute of Sound Recording (IoSR) within the School of Arts and Social Sciences. His research focuses on Biomedical Acoustics and Wireless Audio Networks , with additional expertise in Machine Learning, Audio Signal Processing, and Human-Computer Interaction.

Arash Aminiمشاهده پروفایل

استاد

• Theoretical Signal Processing
• Statistical Signal Processing
• Graph Signal Processing
• +۵ مورد دیگر

Arash Amini is a Professor at the Electrical Engineering Department of Sharif University of Technology, Tehran, Iran. He received dual B.Sc. degrees in Electrical Engineering (Communications) and Petroleum Engineering (Reservoir) in 2005, followed by M.Sc. and Ph.D. degrees in Electrical Engineering (Signal Processing) in 2007 and 2011, respectively, all from Sharif University of Technology. During his doctoral studies, he spent a year (2009–2010) as a visiting scholar at the Biomedical Imaging Group (BIG), EPFL, Switzerland. Current Position: Professor (since May 2025) Previous Roles: Assistant Professor (2013–2018), Associate Professor (2018–2025), Researcher at BIG, EPFL (2011–2013) Editorial Role: Associate Editor for IEEE Signal Processing Letters (2014–2018) Research Interests : Theoretical and Statistical Signal Processing Graph Signal Processing Large Language Models Signal Processing for Communications Compressed Sensing Recent Publication Trends focus on graph signal processing, compressed sensing, biomedical imaging, and mathematical optimization. Key subfields include harmonic retrieval, subspace-informed matrix completion, sparsity-driven algorithms, and AI alignment benchmarks. Scientific Awards : Silver medal at the International Mathematical Olympiad (IMO2000) Advising includes 15+ Ph.D. and Master’s students, with collaborative projects involving institutions like EPFL and researchers such as Prof. F. Marvasti and Prof. S. Rini.

Erik Meijeringمشاهده پروفایل

استاد

• Biomedical Image Computing
• Bioimage Analysis
• Neuron Reconstruction
• +۳ مورد دیگر

Erik Meijering is a Professor of Biomedical Image Computing at the School of Computer Science and Engineering of the University of New South Wales in Sydney, Australia. With a PhD and status as an IEEE Fellow (FIEEE), he leads research in bioimage analysis, neuron reconstruction, and cell tracking. His work bridges computer science, biomedical engineering, and neuroscience, with applications across multiple medical disciplines. Meijering's research focuses on developing computational methods for analyzing biological images, particularly in the areas of neuron tracing, cell tracking, and medical image segmentation. His work employs advanced techniques including deep learning, algorithm benchmarking, and large-scale image analysis. He has pioneered community efforts like the Cell Tracking Challenge and BigNeuron initiative, which have established standardized benchmarks for evaluating bioimage analysis algorithms. His research has significant implications for neuroscience, cancer diagnostics, and personalized medicine. His publication record shows a clear evolution from foundational work in image processing to cutting-edge applications of deep learning in bioimage analysis. Recent work emphasizes cross-domain generalizability of medical AI models, high-plexity microscopy techniques, and large-scale neuron reconstruction. Meijering's research consistently addresses the challenge of translating computational methods into practical biomedical applications. Distinguished Lecturer, IEEE Signal Processing Society (2022-2023) Guest Editor, Special Issue on Deep Learning in Biological Image and Signal Processing (2022) Chair, IEEE Technical Committee on Bio Imaging and Signal Processing (2018-2019) Associate Editor, IEEE Transactions on Medical Imaging (2004-2024) Meijering has secured substantial research funding from multiple sources including NHMRC, EU, NWO, and UNSW. His current projects include Revolutionizing Immunotherapy Response Prediction in Non-Small Cell Lung Cancer (2022-2025) and the Academic Start-Up Fund for Biomedical Image Computing (2019-2024). He has successfully led international collaborations through initiatives like BigNeuron and the Cell Tracking Challenge, which have brought together researchers from Australia, Europe, Asia, and the USA. His research group maintains active collaborations with numerous institutions worldwide, spanning neuroscience, medical imaging, and computational biology. Through his leadership of the BigNeuron project and Cell Tracking Challenge, Meijering has established international communities focused on advancing bioimage analysis. These initiatives have created standardized benchmarks and resources that accelerate progress in neuron reconstruction and cell tracking. His work has helped establish best practices for algorithm evaluation in bioimage analysis, significantly impacting how researchers develop and validate computational methods for biological imaging.

• Econometrics
• International Finance
• Foreign Exchange Forecasting
• +۲ مورد دیگر

Biing-Shen Kuo is a Professor at the Department of International Business, College of Commerce, National Chengchi University (NCCU) in Taiwan. His research focuses on econometrics and international finance, with expertise in exchange rate forecasting, financial econometrics, and machine learning applications in economics. He has led numerous research projects funded by Taiwan's National Science and Technology Council, including studies on exchange rate predictability and econometric inference methods. Education: Ph.D. in Economics, University of Rochester (1989-1995) M.S. in Public Finance, National Chengchi University (1983-1985) B.S. in International Trade, National Chengchi University (1978-1983) Research Focus: Dr. Kuo's work bridges theoretical econometrics and empirical finance, with consistent themes in exchange rate dynamics, predictive modeling, and economic policy impacts. His recent publications integrate machine learning with traditional econometric approaches, particularly for financial forecasting and market analysis. Honors and Awards: Special Appointment Professor, NCCU (multiple years) Teaching Excellence Awards (2011, 2015, 2016, 2019, 2020, 2021) National Science Council Award for Outstanding Research Talent Academia Sinica Young Scholar Research Award (1999) 20-Year Senior Excellent Teacher Award Professional Service: Served as Department Chair (2008-2014) and Vice Dean of the College of Commerce (2005-2008). Editorial board member for multiple economics journals including Economic Papers and Management Review .

Branko Markovićمشاهده پروفایل

استادیار

• Speech Processing
• Whispered Speech Recognition
• Computer Engineering
• +۳ مورد دیگر

Dr. Branko Marković serves as an Assistant Professor at the Department of Computer and Software Engineering, Faculty of Technical Sciences in Čačak, University of Kragujevac, Serbia. His academic career spans over two decades, transitioning from industry experience in telecommunications to dedicated academic work following the merger of the College of Technical Vocational Studies with the Faculty in 2020. Dr. Marković earned his Bachelor's degree from the Faculty of Electrical Engineering in Belgrade in 1992, followed by a Master's degree in 2004 and a Doctorate in 2018, all focusing on telecommunications and speech processing. His doctoral research centered on speech processing and recognition, establishing the foundation for his subsequent research trajectory. His primary research focuses on speech processing , with groundbreaking work in whispered speech recognition - a challenging domain due to reduced acoustic energy in whispered speech. His research explores sophisticated methodologies including data augmentation techniques, wavelet transformations, inverse filtering, and various feature extraction approaches. He has made significant contributions to Serbian speech database development, notably the "Phonemes_1.0" database. His work bridges theoretical research with practical applications in assistive technologies, security systems, and human-computer interaction, demonstrating consistent innovation through 2024 publications. Analysis of his publication trends reveals an evolution from fundamental techniques like Dynamic Time Warping (DTW) and Mel-Frequency Cepstral Coefficients (MFCC) toward more advanced approaches involving deep learning and multimodal systems. His recent work emphasizes data augmentation for model robustness and wavelet-based feature extraction, reflecting the field's progression toward more sophisticated machine learning methodologies while maintaining focus on the unique challenges of whispered speech. Dr. Marković teaches across multiple academic levels, covering essential computer engineering subjects including Computer Assembly and Service, Internet Technologies, Computer Networks, Distributed Software Systems, Web Programming, and Computer Systems Design. His educational contributions extend to several textbooks for vocational studies in computer engineering fields. His professional journey includes valuable industry experience at the Mihailo Pupin Institute in Belgrade and Canadian telecommunications companies Bell Sygma, Architel, and Nortel Networks, followed by founding his own company Big engineering before transitioning to academia. This industry-academia trajectory enriches his practical teaching approach and research relevance.

Christian Beecksمشاهده پروفایل

استاد

• Machine Learning
• Data Mining
• Knowledge Discovery
• +۷ مورد دیگر

Univ.-Prof. Dr. Christian Beecks is a full Professor at the Faculty of Mathematics and Computer Science, FernUniversity in Hagen, and heads the Intelligent Data Analysis research group at the Fraunhofer Institute for Applied Information Technology FIT. His work bridges theoretical advancements in machine learning with practical applications across industry and biomedicine. Education: PhD in Computer Science (RWTH Aachen University, 2007-2013) Diploma in Computer Science (RWTH Aachen University, 2001-2007) As a leading figure in data science, Beecks specializes in machine learning and big data analytics , focusing on scalable algorithms for complex data spaces. His research has produced over 100 peer-reviewed publications and notable contributions in time series analysis , clustering methods , and IoT data processing . Recent work explores Gaussian process modeling for anomaly detection and spatiotemporal signal analysis. His publications demonstrate expertise in automated pattern discovery and interpretable AI systems . Key themes include: clustering validation , time series representation , industrial applications , and knowledge ontologies . Technically, his team leverages Ptolemaic geometry , skyline queries , and component mining for real-world data challenges. 2021 SIAM Best Research Paper Award 2018 Warwick Workshop Best Poster Award 2015 & 2011 Best Paper Awards Through leadership roles at FernUniversity and Fraunhofer FIT, Beecks drives initiatives in competency-based education and AI workforce empowerment . His research groups develop frameworks for edge-to-cloud AI orchestration and automated model inference , with applications in manufacturing, biomedicine, and digital humanities. Current projects focus on data pooling, zero-touch orchestration, and educational AI tools like virtual tutors.

Luca Tiozzo Pezzoliمشاهده پروفایل

استاد مهمان

• Econometrics
• Machine Learning in Economics
• Time Series Analysis
• +۳ مورد دیگر

Dr. Luca Tiozzo Pezzoli serves as a Visiting Professor in the Department of Applied Economics at the University of the Balearic Islands. His teaching portfolio spans undergraduate and graduate programs including Business Administration, Economics, and the Master's Degree in Economics of Tourism, where he instructs courses such as Economic Data Analysis, Microeconometrics, and Causal Analysis and Prediction. His research focuses on the intersection of econometrics and machine learning, with particular expertise in big data applications for economic forecasting. Key interest areas include: Real-time nowcasting using mixed-frequency data News sentiment analysis for financial markets Climate economics and event-driven market shocks Alternative data sources in crisis monitoring Interpretable AI for economic policy analysis His publication record reveals a strong trend toward integrating unconventional data streams (news, social media, seismic economic indicators) with machine learning techniques to enhance economic forecasting precision, particularly during volatile periods like the COVID-19 pandemic. Recent work emphasizes Bayesian interpretability and climate-economic linkages. Dr. Pezzoli actively contributes to the Econometrics and Data Science (ECD) Consolidated R+D+I Group at UIB. His teaching responsibilities include supervising numerous Final Degree Projects across Business Administration programs, though specific student names aren't documented. He maintains technical engagement through ORCID and Dialnet profiles while utilizing advanced data infrastructure for his research initiatives.

Iman Mossavatمشاهده پروفایل

مدرس

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۴ مورد دیگر

Iman Mossavat serves as a Teacher-researcher at Fontys University of Applied Sciences within the School of ICT and AI & Big Data research group, joining in 2022 after 15 years at Eindhoven University of Technology and ASML Research. His academic credentials include a Bachelor's in Electrical Engineering from Ferdowsi University of Mashhad, Master's from Sharif University of Technology, and M.Eng from National University of Singapore. Research centers on applying AI to strengthen democracy through journalism-focused misinformation analysis using Natural Language Processing and Graph Algorithms. Teaching spans mathematics (linear algebra, graph algorithms), statistics, decision theory in Academic Preparatory Programs, and data analytics/machine learning for Smart Industry specializations. As part of Fontys' AI & Big Data research group, he trains students in creative problem-solving while developing pragmatic AI applications for societal challenges.

Martin Zachمشاهده پروفایل

پژوهشگر ارشد

• Biomedical Imaging
• Inverse Problems
• MRI Reconstruction
• +۲ مورد دیگر

Martin Zach is a postdoctoral researcher at the Center for Biomedical Imaging (CIBM), EPFL , specializing in inverse problems in biomedical imaging. He joined the Mathematical Imaging Section under the supervision of Prof. Michael Unser in September 2024. PhD : Graz University of Technology (2024), advised by Thomas Pock Martin’s research bridges model-based reconstructions and data-driven approaches in imaging, with a focus on MRI reconstruction and diffusion models . His work explores regularization techniques, energy-based priors, and probabilistic modeling. Recent publications highlight advancements in Gaussian mixture models , non-linear inversion , and Langevin sampling for biomedical imaging. His Google Scholar profile reveals a strong focus on inverse problems (2020–2025), with applications in MRI , CT , and quantitative phase imaging . Key methodologies include diffusion models , generative priors , and regularization algorithms , spanning disciplines from machine learning to computational biology . Martin’s current role at EPFL involves collaborative research in mathematical imaging , with affiliations to the BioMedical Imaging (BIG) Group in Lausanne, Switzerland.