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Sicong ShaoView profile

Assistant Professor

• Cybersecurity
• Machine Learning
• Artificial Intelligence
• +7 more

Dr. Sicong Shao is an Assistant Professor in the School of Electrical Engineering and Computer Science (SEECS) at the University of North Dakota (UND) , where he joined as a Tenure-Track Assistant Professor in August 2023. He previously held a Research Assistant Professor position at the University of Arizona, where he earned his PhD. His research focuses on cybersecurity , machine learning , AI , and software engineering , particularly in intrusion detection, system security, and AI-assisted cybersecurity techniques. Lead of the Intelligent Software and System Security Lab (iSSSL) Active in conference organization (Co-Chair, IEEE CARS 2024; Committee Member, IEEE HAST 2024, IEEE CEC 2025, etc.) Editorial roles: Editorial Board Member (Cluster Computing), Guest Editor (Electronics special issues) Dr. Shao’s recent publications highlight trends in cyber-physical systems security , transformer-based anomaly detection , edge computing resilience , and AI applications for intrusion detection . His work spans academic and industrial domains, including biomedical signal processing and IoT security. Early Career Scholars Program award (UND, 2024) SARJ award through NSF RII Track 2 FEC (2024) He mentors PhD students in cybersecurity and software engineering and teaches courses like Secure Software Engineering (CSCI 387/567) and Cyber Physical Systems Security (CSCI 552) . His lab welcomes motivated graduate students seeking interdisciplinary research opportunities.

Sasu TarkomaView profile

Professor

• Computer Science
• Artificial Intelligence
• Machine Learning
• +5 more

Sasu Tarkoma is a Professor and Acting Dean at the Faculty of Science , University of Helsinki, with affiliations to the Helsinki Institute of Sustainability Science and Helsinki Institute of Urban and Regional Studies . His research focuses on Computer Science , particularly in Artificial Intelligence , Internet of Things , and 5G/6G networks . Education : PhD in Computer Science (University of Helsinki) Academic Roles : Supervisor for Doctoral Programme in Computer Science, Docent in Department of Computer Science Research Interests span AI-driven network optimization, edge computing for 5G/6G, digital twinning, and IoT-based environmental monitoring. His work addresses challenges in federated learning , low-cost sensing , and autonomous drone deployments . Supervision and Projects : Supervised numerous Master's and PhD students, including Pengfeng Su and Peifeng Su. Leads projects like BF AgentFormers (AI for platform stability) and NordForsk - Tarkoma (2024-2028) focusing on 6G data-driven systems. Scientific Awards : Recipient of The best textbook of 2009 prize Collaborations include partnerships with institutions in Finland and international actors like ACM and IEEE, with activities in conferences and peer review (e.g., ACM DEBS 2025).

Roy Edgar HansenView profile

Professor

• Synthetic Aperture Sonar
• Signal Processing
• Underwater Acoustics
• +4 more

Roy Edgar Hansen is Professor II in acoustic imaging at the Department of Informatics, University of Oslo, with his primary position as Chief Scientist at the Norwegian Defence Research Institute (FFI). His work bridges academic research and defense applications in underwater acoustics and signal processing. Hansen's research focuses on synthetic aperture sonar (SAS), imaging techniques, sonar signal processing, array signal processing, and statistical signal processing. His work spans theoretical developments in signal processing algorithms to practical applications in underwater imaging, seabed mapping, and target detection. He has made significant contributions to interferometric SAS, artifact reduction techniques, and the application of machine learning to sonar image analysis. His recent publications demonstrate a strong trend toward integrating advanced signal processing with machine learning techniques for SAS applications. He has published extensively on interferometric SAS for bathymetric mapping, uncertainty quantification in deep learning applications for sonar, and the development of novel processing techniques for point scatterer detection and image enhancement. His work covers both fundamental signal processing theory and practical implementation challenges in underwater environments. Hansen teaches courses including IN5340 Statistical Signal Processing, IN3015 Ultrasound Imaging, and IN5450 Array Signal Processing at the University of Oslo. His research is associated with the Digital Signal Processing and Image Analysis (DSB) research group and the Centre for Innovative Ultrasound Solutions (CIUS) project.

Anders la Cour-HarboView profile

Associate Professor

• Unmanned Aircraft Systems
• Drone Technology
• Control Systems
• +5 more

Anders la Cour-Harbo serves as an Associate Professor in the Department of Electronic Systems at Aalborg University's Technical Faculty of IT and Design. His academic career spans over two decades with consistent publication output since 2000, demonstrating sustained research activity in unmanned aircraft systems and related technologies. His institutional affiliation places him within Denmark's prominent engineering research environment focused on practical technological applications. Professor la Cour-Harbo's research interests center on unmanned aircraft systems engineering, with particular expertise in drone applications for industrial settings. His work spans drone load systems, emergency landing technologies, predictive maintenance, and offshore operations. The research fingerprint shows strong emphasis on Unmanned Aircraft Engineering (100%), Load System Engineering (87%), and Unmanned Aircraft System Engineering (46%), reflecting his specialized focus areas. His projects consistently address real-world applications of drone technology, particularly in challenging environments like offshore wind farms. Analysis of his publication trends reveals a strategic focus on practical drone applications with increasing emphasis on safety systems, regulatory compliance, and industrial implementation. Recent publications (2023-2024) show strong industry relevance with applications in offshore wind turbine maintenance, predictive maintenance systems, and vision-based control technologies. His research bridges theoretical control systems with practical implementation challenges in drone operations. Teacher of the Year 2015 Teacher of the Year 2006 Professor la Cour-Harbo leads multiple significant research projects including SafeEye (Automated emergency landing for small unmanned aircraft), UAS-ability (research infrastructure for drone development), and OPAL (Offshore Delivery of Packages). He serves as chair for JARUS (Joint Authority for Rulemaking of Unmanned Systems), significantly influencing European drone legislation. His spin-off company Vixos demonstrates successful technology transfer from academic research to commercial application. The Harm threshold for unmanned aircraft in European legislation impact shows his direct contribution to shaping regulatory frameworks. His laboratory and research team focus on practical drone applications with infrastructure supporting airborne data collection and drone development. The UAS-ability project specifically created research infrastructure for drone development and airborne data collection. His collaboration network spans multiple countries, with significant European partnerships focused on advancing drone technology standards and applications. His work with JARUS places him at the forefront of international drone regulation development.

Yu Guang WangView profile

Associate Professor

• Artificial Intelligence
• Computational Mathematics
• Statistics
• +10 more

Yu Guang Wang is an Associate Professor at Shanghai Jiao Tong University, holding positions across multiple institutes including the Institute of Natural Sciences, School of Mathematical Sciences, Department of Computer Science and Engineering, and AI Biomedicine Center of Zhangjiang Institute for Advanced Study. He also serves as Adjunct Associate Professor at both UNSW Sydney and Shanghai AI Laboratory, demonstrating his significant interdisciplinary reach. His research spans artificial intelligence, computational mathematics, statistics, and data science, with particular focus on geometric deep learning, graph neural networks, applied harmonic analysis, and Bayesian inference. His work bridges theoretical foundations with practical applications in biomedicine and protein design, creating impactful connections between mathematics and real-world problems. Wang's publication record shows a clear progression toward increasingly sophisticated applications of geometric deep learning, particularly in graph neural networks for protein engineering and biomedical applications. His recent work demonstrates strong interdisciplinary collaboration, combining topological data analysis with machine learning for complex biological systems. ICERM Semester Postdoctoral Fellowship of Brown University (2018) IPAM visitor of UCLA (2019) Long-term visitor of AI Group of Prof Pietro Lio at University of Cambridge (2022) ICML Top Reviewer (2020) University International Postgraduate Award, UNSW (2011-2015) As an advisor, Wang has successfully mentored multiple PhD and Master's students working on graph neural networks, geometric deep learning, and applications to biomedicine. His research has been supported by prestigious funding from Shanghai Jiao Tong University, Huawei Central Research Institute, Ministry of Education Key Lab in Scientific and Engineering Computing, Shanghai National Center for Applied Mathematics, National Natural Science Foundation of China, and the European Research Council. He leads an active research group focused on geometric deep learning and its applications to science and medicine.

• Electronic Design
• Lung Acoustics
• Biomedical Signal Processing
• +3 more

Yasemin P. Kahya is a Full-Time Professor in the Department of Electrical and Electronics Engineering at Boğaziçi University. She has held roles such as Department Chair (2012–2021), Deputy Dean (2003–2006), and has served on various administrative and scientific committees including TÜBİTAK and IEEE-EMBS. Her research focuses on electronic design, lung acoustics, and biomedical signal processing, with contributions to respiratory sound analysis and smart stethoscopes. Education: Ph.D. in Biomedical Engineering, Boğaziçi University (1987) M.S. in Electrical Engineering, Yale University (1981) B.S. in Electrical Engineering and Physics, Boğaziçi University (1980) Research Interests: Her work centers on respiratory sound analysis, lung acoustics, and the development of diagnostic tools like smart stethoscopes. Key areas include signal processing for disease detection (e.g., asthma, COPD), wavelet transform applications, and multi-channel biomedical instrumentation. Grants & Projects: "Smart Stethoscope System with Mobile Facilities" (TÜBİTAK TEYDEP, 2021–2023) "Wireless Respiratory Sound Acquisition System" (Boğaziçi University, 2006–2008) "Lung Acoustic Mapping" (TÜBİTAK, 1994–1998) Awards: Multiple TÜBİTAK fellowships and international scholarships Patents for smart stethoscope systems Editorial roles in Medical & Biological Engineering & Computing and TÜBİTAK Elektrik Dergisi Labs & Teams: Founder of the Lung Acoustics Laboratory (LAL) at Boğaziçi University, focusing on smart stethoscope development and interdisciplinary research with students and collaborators.

Ali DogruView profile

Professor

• Software Engineering
• Component-Based Systems
• Complex Event Processing
• +7 more

Ali Dogru is a Professor of Computer Science at the University of Texas at San Antonio (UTSA), affiliated with the College of Sciences. He holds a Ph.D. in Computer Science from Southern Methodist University, an M.S. in Electrical Engineering from the University of Texas at Arlington, and a B.S. in Electrical Engineering from the Technical University of Istanbul. His research focuses on software engineering methodologies, particularly component-based systems, compositional software development, complex event processing, and fault management. He explores interdisciplinary solutions through connector technologies to address IoT heterogeneity and variability modeling in microservices architecture. Dr. Dogru's work emphasizes axiomatic design theory applications in software systems, including service-oriented computing, model-driven engineering, and cloud-enabled development. His contributions span domain-specific languages for fault management, adaptive workflows in cloud environments, and synthetic medical data generation for healthcare analytics. He has published extensively on topics like UML connector enhancements, microservice interoperability, and EEG emotion recognition using wavelet transforms. His interdisciplinary research bridges theoretical concepts with practical industry applications, as evidenced by his work on legacy system migration and mashup strategies in service-oriented computing. While no advising or grant details are explicitly mentioned, his academic profile reflects a strong focus on innovation in software development practices. His involvement in conferences such as the Turkish National Software Architecture Conference highlights his role in advancing software engineering standards and methodologies. Dr. Dogru's research lab (if applicable) is not explicitly detailed in the provided texts, but his publications indicate sustained engagement with cutting-edge computational challenges in software systems.

• Astrophysics
• Cosmology
• Dark Energy
• +3 more

Marco Raveri is an Associate Professor in the Department of Astrophysics, Cosmology and Space Science at the University of Genoa. His research focuses on cosmology and astrophysics, particularly in weak lensing, dark energy studies, and multiwavelength cluster analysis. He is actively involved in the Dark Energy Survey (DES) and Euclid Ricerca projects. He teaches courses such as 'FISICA 1' for the Engineering Management program, and specialized graduate courses like 'ASTROFISICA E COSMOLOGIA COMPUTAZIONALE' and 'FISICA DELLE STRUTTURE COSMICHE' in the Physics Master's program at the University of Genoa. Raveri's recent work emphasizes cosmological constraints from galaxy clusters, supernova biases, and advanced analysis techniques like wavelet harmonics. His research addresses critical issues in modern cosmology, including the S8 tension and structure growth modifications. No scientific awards are explicitly mentioned in the provided materials. He advises no listed students and no grants are detailed here. His research is affiliated with Euclid Ricerca, contributing to space-based cosmological surveys.

Yanghao ZhangView profile

Researcher

• Adversarial Machine Learning
• Robust Machine Learning
• Safety Verification
• +3 more

Yanghao Zhang is a Researcher at Imperial College London's Department of Computing, part of the Faculty of Engineering, working under Prof. Alessio Lomuscio at the Safe AI Lab (SAIL). He also holds a Research Scientist position at Safe Intelligence. He completed his PhD at the University of Liverpool (2020–2024), supervised by Prof. Wenjie Ruan and Prof. Xiaowei Huang, with earlier PhD studies at the University of Exeter. His research focuses on adversarial machine learning, safety verification, robust deep learning applications, and graph neural networks. Education: MSc Artificial Intelligence (Distinction, University of Southampton); BEng Software Engineering (Huizhou University). His work emphasizes adversarial attacks/defenses, semantic robustness in perception systems, and safety of large language models. Key contributions include frameworks for evaluating robustness in autonomous systems and risk-aware learning algorithms. Recent achievements include AAAI and CVPR publications, invitations as a reviewer for top conferences (e.g., AISTATS, ICML, NeurIPS), and PhD defense in 2024. He collaborates with industry (Safe Intelligence) and academia to advance trustworthy AI systems. Labs/Teams: Primary affiliation with Safe AI Lab (SAIL) at Imperial, and research at Safe Intelligence. Active in reviewing for journals like TKDE and conferences like ICLR, CVPR, and NeurIPS.

Xiaojun QiView profile

Professor

• Image Processing
• Machine Learning
• Computer Vision
• +5 more

Dr. Xiaojun Qi is a Professor and Department Head of Computer Science at Utah State University (USU), where he has been since 2002. He holds a Ph.D. in Computer Science from Louisiana State University. His research focuses on Image Processing, Machine Learning, Computer Vision, and Deep Learning, with notable contributions to medical imaging, object detection, and multimedia security. Qi leads the Computer Science department and oversees research projects funded by grants from organizations like UDOT and GRCO. He has advised numerous graduate and undergraduate students across projects such as fake face detection, facial expression recognition, and LiDAR-based traffic sign detection. His work integrates computational geometry and AI to address challenges in vision systems and biomedical applications. Education: Ph.D., Computer Science (Louisiana State University); M.S., Systems Science (Louisiana State University); M.S., Computer Science (Shenyang Institute of Automation, Chinese Academy of Sciences); B.S., Computer Science and Engineering (Donghua University). Research Interests: Image Processing, Machine Learning, Deep Learning, Computer Vision, Medical Imaging, Object Detection, Digital Watermarking, and Data Mining. He has organized numerous conferences and served on technical committees for VISAPP, CompIMAGE, and DEXA. Grants include UDOC (Traffic Sign Detection), GRCO (Medical Image Segmentation), and NSF REU (Computer Vision). His lab focuses on interdisciplinary projects such as vision-based navigation, steganalysis, and bio-inspired algorithms.

• Machine Learning
• Signal Processing
• Remote Sensing
• +4 more

Wei Xue is an active academic researcher affiliated with Tsinghua University's Department of Computer Science and Technology. Their work spans multiple disciplines, including Machine Learning , Signal Processing , Medical Imaging , and Natural Language Processing . Key research areas include adversarial robustness in DNNs for remote sensing, hierarchical speaker representation learning for speech extraction, and multi-view fuzzy classification for brain network analysis. Recent publications focus on imbalanced data classification, spectrum sensing in cognitive radio, and innovative applications in soft robotics and medical diagnostics. Collaborations include institutions like Tsinghua University, Jiangnan University, and Hong Kong Baptist University, with frequent contributions to journals such as IEEE Transactions on Geoscience and Remote Sensing and Neural Networks .

• Optimization
• Renewable Energy Systems
• Metaheuristics
• +3 more

Pandian Vasant is a prominent researcher in optimization, renewable energy systems, and metaheuristic algorithms. His work focuses on applying computational intelligence techniques to solve complex engineering problems, particularly in energy systems, manufacturing, and supply chain optimization. Collaborating with institutions globally, he has extensively published in reputable journals and conferences, including Computers & Industrial Engineering , Applied Soft Computing , and Wireless Networks . Key research areas include hybrid renewable energy optimization, nature-inspired algorithms (e.g., Grey Wolf Optimizer, Bat Algorithm), and fuzzy logic applications in decision-making under uncertainty. Major contributions include optimizing biogas plant processes, solar photovoltaic systems, and electric vehicle charging infrastructure. He has also explored industrial applications such as green sand mould systems and biofuel supply chains. His methodologies often integrate metaheuristics with fuzzy logic to handle real-world uncertainties. Vasant frequently collaborates with researchers like Timothy Ganesan, Igor Litvinchev, and José Antonio Marmolejo Saucedo, co-authoring over 100 peer-reviewed articles between 2002 and 2024. Notable trends in his publications include multi-objective optimization, energy efficiency, and smart grid technologies. Recent works emphasize Industry 4.0 integration, intelligent social systems for visually impaired individuals, and advanced machine learning models for stress detection in drivers.

Sang Won YoonView profile

Professor

• Smart Manufacturing
• Decentralized Decision Modeling
• Realtime Control Systems
• +5 more

Sang Won Yoon is a Professor at the School of Systems Science and Industrial Engineering, Binghamton University. He holds a PhD from Purdue University, an MS from Korea University, and a BS from Jeonbuk National University. His research focuses on smart manufacturing, decentralized decision modeling, and large-scale data mining optimization. He specializes in applying machine learning and neural networks to improve manufacturing processes, particularly in solder joint prediction, thermal profile optimization, and automation in pharmaceutical distribution. Research Interests include real-time control systems, quality enhancement in surface mount technology (SMT), and healthcare supply chain automation. His work bridges theoretical models with practical applications, such as optimizing reflow oven profiles and AGV systems. Recent publications highlight advancements in physics-informed neural networks, explainable AI for defect prediction, and simulation-driven process optimization. He collaborates with the PRISM Center, focusing on global research networks in systems integration. His contributions span smart manufacturing, healthcare automation, and environmental sustainability in energy systems. Professional Activities include advising on pharmacy robotic systems, discrete event simulation models for healthcare operations, and renewable energy feasibility studies. He actively publishes in top-tier journals and conferences, emphasizing interdisciplinary solutions for industrial challenges.

• Telecommunications
• Information Theory and Coding
• Metamaterials
• +7 more

Maria Kovaci is a Senior Lecturer at the Department of Communications, Faculty of Electronics and Telecommunications, Politehnica University of Timisoara. She has been affiliated with the university since 1999, holding roles as preparator (1999–2002), assistant (2002–2013), and currently serving as Șef de lucrări (Head of Works) since 2013. Her PhD in 2009, supervised by Prof. Miranda Naforniță, focused on improving turbo code performance in flat fading channels. Educations: PhD in Electronics and Telecommunications (2009), Politehnica University of Timisoara Key Research Areas: Turbo coding, metamaterials, supercapacitors, spintronics, wireless communications, and information theory Her research emphasizes optimizing communication systems through advanced coding techniques and material science innovations. Notable contributions include interleaver design for turbo codes, supercapacitor material optimization, and multiferroic nanocomposite studies. Over 50 peer-reviewed papers and four co-authored books reflect her expertise in telecommunications and materials engineering. Recent articles (2023–2024) explore supercapacitor energy density and conductive polymer coatings, while earlier work (2015–2020) addresses turbo coding performance in fading channels and metamaterial applications. She has received awards including the 2013 Excellence Award at IManE Conference and 2005 Dean’s Excellence Diploma. Grants: Postdoctoral funding via POSDRU/159/1.5/S/137070 (2014) Advising: Supervised diploma project on WIMAX routing protocols (student: Savastre Dorian, 2018) Administrative roles include organizing International Symposium of Electronics and Telecommunications (ISETC), serving as Chair (2014), and editorial work for the Scientific Bulletin (UPT). She is also an active reviewer for IEEE conferences and journals since 2002.

Konstantinos VoudourisView profile

Professor

• Wireless Communications
• 5G/6G Networks
• Millimeter Wave Technology
• +4 more

Konstantinos Voudouris is a full Professor in the Department of Electrical and Electronics Engineering at the University of West Attica, Athens, Greece. Previously, he served as a full Professor at the Higher Colleges of Technology in Abu Dhabi (2015-2018) and as a Professor at the Technological Institute of Athens (2004-2015). His career spans academia, industry, and government, including roles as telecoms expert at OTE, technical advisor to the Greek Ministry of Transports and Telecommunications, and Embassy Counsellor to the EU for Telecommunications. Education: PhD in Wireless Telecommunication Systems, University of Bradford, UK Professor Voudouris's research focuses on wireless communications with emphasis on 5G/6G networks, millimeter-wave technology, antenna design, and relay networks. His work includes developing relay stations for WiMAX networks, 60 GHz transceivers, and RF energy harvesting systems. He has led significant projects such as the FP7 ICT REWIND project (5.5M€) and the WiCEAR group in the NexGenMiliwave project, demonstrating expertise in both theoretical and applied telecommunications engineering. His publication record (2024-2014) reveals consistent innovation in wireless power transfer, antenna array optimization, substrate integrated waveguide technology, and green RF solutions for mobile networks. Key trends include reducing mutual coupling in antenna arrays, enhancing industrial wireless sensor networks through distributed fusion, and developing sustainable energy harvesting techniques for 5G infrastructure. No scientific awards are listed in the provided documentation. Professor Voudouris has supervised numerous diploma and PhD theses while coordinating major research initiatives including the FP7 ICT REWIND project. He serves on the University of West Attica Quality Assurance Committee and has held significant administrative roles including chairing the Promotions Committee at HCT, coordinating the ERASMUS program, and managing the internship office for the Electronic Engineering Department. He leads the WiCEAR research group and has developed electromagnetic wave transmission prediction software to enhance laboratory instruction for electronic engineering students, demonstrating commitment to both research innovation and educational advancement.