جست‌وجوی استاد و پژوهشگر

• Vehicular Networks
• Cybersecurity
• Distributed Systems
• +۳ مورد دیگر

Mohamed Mosbah is a Professor at the University of Bordeaux, affiliated with Bordeaux INP, France. His research focuses on distributed systems, vehicular networks, cybersecurity, and IoT security. He has co-authored over 200 publications, including works on VANET security, network slicing, and reinforcement learning applications. Education & Roles: Editor of journals/conferences like IEEE Communications Surveys & Tutorials, IEEE Transactions on Network and Service Management. Organizer of workshops such as DiCES-N (Distributed Computing for Emerging Smart Networks) and FPS (Foundations and Practice of Security). Research Interests: Develops protocols for vehicular safety messaging and cooperative perception. Explores network slicing for 5G/IoV environments and hybrid communication systems. Applies formal verification to distributed algorithms and machine learning for intrusion detection. Recent Work Trends: 2023-2024: Focus on DRL-based RAT selection, edge computing for micro-mobility, and resilient service chain provisioning. 2022-2023: Prioritization of ITS-G5 messages and energy-efficient geofencing models. 2020-2021: Hybrid vehicular networks and formal verification of distributed algorithms. Grants & Labs: Leading projects on vehicular cybersecurity and smart mobility optimization. Member of research groups in distributed systems and network security at Bordeaux INP.

Siqi Maمشاهده پروفایل

مدرس ارشد

• Automated Vulnerability Detection
• Mobile Security
• IoT Security
• +۵ مورد دیگر

Dr. Siqi Ma is a Senior Lecturer at the University of New South Wales (UNSW), where he leads the Software Analysis and Testing Lab (SATLab) within the Institute for Cyber Security (IFCYBER) in the School of Engineering and Information Technology (SEIT). He also serves as the Postgraduate Program Coordinator for Cyber Security Operations at UNSW. Previously, he held a Lecturer position at the University of Queensland and completed his postdoctoral research at CSIRO Data61 after earning his Ph.D. from Singapore Management University (2018). His research focuses on automated vulnerability detection, mobile/IoT security, network security, and privacy-preserving technologies. His educational background includes a Ph.D. in Information Systems from SMU, postdoctoral work at Data61, and a visiting scholar stint at Carnegie Mellon University (2015). Key collaborations involve researchers from Purdue University, Singapore Management University, and CSIRO Data61, among others. Research interests span adversarial robustness in AI, secure software analysis, and federated learning mechanisms. Recent work includes EvilScreen Attack (smart TV hijacking), Medusa Attack (QR code vulnerabilities), and frameworks like FDFL for fair federated learning. His lab emphasizes open-source contributions and collaborations with industry partners. Grants include leadership in the UNSW-led 'Building National Cybersecurity Capabilities' project (2022) and funding from the Cyber Security CRC. Teaching responsibilities span courses in Information Assurance, Cyber Security Capstone, and Computer Systems Programming. Labs/Teams: SATLab collaborates with the Group of Software Security In Progress (G.O.S.S.I.P) and maintains an active open-source repository on Code-Analysis.org. Current projects focus on vulnerability detection, secure AI, and edge computing security.

• Geographic Information Systems (GIS)
• Remote Sensing
• Photogrammetry
• +۵ مورد دیگر

Prof. Dursun Zafer Şeker is a faculty member at Istanbul Technical University , where he holds the Department of Geomatics Engineering within the Faculty of Civil Engineering . His work spans Geographic Information Systems , Remote Sensing , and Photogrammetry , with a focus on integrating Deep Learning and UAV technologies for disaster management, environmental monitoring, and infrastructure analysis. Education: PhD in Geodesy and Photogrammetry Engineering (1989) and Master's in Photogrammetry Engineering (1985), both from Istanbul Technical University. Career: Progressed from Research Assistant (1986-1996) to Professor (2004-present), with administrative roles in academic governance. His research explores GIS applications in transportation accessibility, forest fire prevention, and earthquake response. He has pioneered the use of Deep Learning for medical imaging (e.g., multiple sclerosis lesion segmentation) and agricultural disease classification , while also advancing UAV-based monitoring for coastal and archaeological sites. Recent publications highlight a shift toward AI-driven remote sensing for disaster resilience, including super-resolution techniques for historical imagery and object detection in post-earthquake building collapse mapping. Trends emphasize multi-source data fusion (LiDAR, satellite), climate change adaptation , and urban sustainability . Scientific Awards: Dursun Zafer Şeker Prize, FIG Young Surveyors' Competition (2023) Best Poster/Best Short Presentation Awards at MESAEP (2017), Selçuk University (2016), and Çukurova University (2016) Recognition at International Conferences (2015-2025) for remote sensing and geospatial innovation. As an advisor, he has supervised over 20 PhD and Master’s students , spanning topics like gas pipeline leak detection , PM2.5 prediction , and solar plant site selection . His grants include projects funded by TÜBİTAK and Ministry of Development , such as ORBIS and Forest Fire Decision Support Systems . He also serves on editorial boards for International Journal of Environment and Geoinformatics and Map Magazine .

Ersan Yazanمشاهده پروفایل

استادیار

• Machine Vision
• Machine Learning
• Deep Learning
• +۵ مورد دیگر

Ersan Yazan serves as an Assistant Professor in the Department of Computer Engineering at the Faculty of Engineering, Adiyaman University, Turkey. He joined the university in December 2011 as a lecturer at Besni Vocational School and has since advanced through academic ranks, achieving his current position effective February 2025. Educational Background: Doctorate (2016-2024): Inonu University, Faculty of Engineering, Department of Computer Engineering Master's Degree (2013): Firat University, Faculty of Engineering, Department of Computer Engineering Bachelor's Degree (2000-2007): Erciyes University, Faculty of Engineering, Department of Computer Engineering Ersan Yazan's research spans machine vision, machine learning, deep learning, and brain-computer interaction. His work demonstrates significant expertise in optimization algorithms, image processing, and medical applications. Early research focused on stochastic gradient descent techniques and fabric defect detection, while recent publications emphasize medical image analysis, particularly in breast cancer detection and robot-assisted neurosurgery. His research consistently bridges theoretical computer science with practical applications across industrial and medical domains. Yazan's publication history shows a clear progression from foundational work in computer vision toward specialized medical applications. His most recent work (2024) on frameless registration for robot-assisted brain surgery demonstrates his shift toward cutting-edge medical technology applications. His research integrates computer vision, machine learning, and robotics to solve complex problems in both industrial quality control and medical diagnostics. As an educator, Yazan has taught numerous courses including System Analysis and Design, Web Design Fundamentals, Visual Programming, Network Fundamentals, and Microcontroller Applications across both associate and bachelor's degree programs. He previously served as Department Head from 2012 to 2018, demonstrating leadership within the academic structure.

Sushant Kumar Pandeyمشاهده پروفایل

استادیار

• AI in Software Engineering
• Software Testing
• Autonomous Systems
• +۳ مورد دیگر

Sushant Kumar Pandey is an Assistant Professor at the Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, Faculty of Science and Engineering, University of Groningen. His research focuses on AI-driven software engineering, including software testing, autonomous systems, and data leak detection in computer vision. Previously, he held postdoctoral roles at Chalmers University of Technology and IIT (BHU), Varanasi. Education: PhD in Computer Science (2017–2021) from IIT (BHU), Varanasi, India. Thesis: 'Observations on Software Defect Prediction.' M.Tech in Information Technology (2014) from NIT Patna, India. Thesis: 'Intrusion Detection System using Anomaly Detection in Wireless Sensor Networks.' B.Tech in Information Technology (2008–2012) from United College of Engineering and Research, Greater Noida, India. Research Interests: AI applications in software engineering, defect prediction, deep learning architectures, and industrial collaborations with companies like Volvo Cars. His work bridges theoretical advancements and practical solutions, such as cross-project defect prediction and data handling for autonomous systems. Key Contributions: Over 15 peer-reviewed publications in journals like Knowledge-Based Systems and Expert Systems with Applications, focusing on machine learning techniques for software defect prediction and testing. His recent work explores design pattern recognition using programming language models and input prioritization for deep learning systems. Awards/Grants: Multiple course certifications in machine learning from Coursera and an ethical hacking certification. No specific grants mentioned, but active collaborations with industry partners like Volvo Cars. Labs/Teams: Leads the Software Engineering and Architecture (SEARCH) research group at Groningen under Prof. Paris Avgeriou. Collaborates with teams at Chalmers University and IIT (BHU).

• Machine Learning
• Information Fusion
• Data Analytics
• +۷ مورد دیگر

Alexander Karlsson is an Associate Professor at the Department of Information Technology, School of Informatics, University of Skövde. His research focuses on machine learning, information fusion, and their applications in transportation, healthcare, and industrial systems. He leads projects like I2Connect, developing advanced driver assistance systems using deep learning and situation awareness algorithms. Key research interests include driver intention recognition, predictive maintenance, telecommunication anomaly detection, and biomedical data analysis. He has published extensively in top venues like IEEE Transactions, ACM journals, and conferences like IPMU and MDAI. Education: PhD in Information Fusion (2010) from University of Skövde. Teaching: Coordinates courses in data analytics and AI at graduate and postgraduate levels. Projects: Active in industrial collaborations (e.g., automotive, steel production, telecommunication). His work bridges theoretical advances in machine learning with practical industrial challenges, emphasizing uncertainty quantification and robust decision-making frameworks.

• Cybersecurity
• Multimedia Content Delivery
• IP Networks
• +۵ مورد دیگر

Dr. Miroslav Michalko is an Associate Professor at the Department of Computer Networks and Security, Faculty of Electrical Engineering and Informatics, Technical University of Košice. His research focuses on Cybersecurity, Multimedia Content Delivery, IoT for eHealth, Smart Cities and Homes, Web Technologies, and EU Project Management. He teaches courses such as Computer Networks, Security of Computer Networks, and CCNA Security. He actively coordinates the Girls in IT @ FEEI TUKE initiative and serves as the National Guarantor for Information Technologies and Telecommunications within Slovakia's Qualifications Verification System. His research interests span Cybersecurity methodologies, IoT integration in healthcare, Smart City infrastructure, and innovative educational technologies. Notable projects include developing UAV-based monitoring systems, smart home solutions, and cybersecurity educational platforms. He has contributed to over 50 peer-reviewed publications, emphasizing practical applications of computer networks and IoT. Dr. Michalko's recent work highlights advancements in autonomous UAV control via machine learning, intrusion detection in IoT environments, and real-time water leak detection systems. His teaching emphasizes hands-on learning through simulations and industry-aligned certifications like Cisco's NetAcad program.

Abubakar Balaمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Machine Learning
• Cybersecurity
• +۴ مورد دیگر

Dr. Abubakar Bala is a Senior Lecturer at the Malaysia School of Business, Monash University. His research focuses on artificial intelligence (AI), machine learning, and their applications in cybersecurity, edge computing, environmental engineering, and optimization algorithms. He actively contributes to the UN Sustainable Development Goals (SDGs), particularly in sustainable resource management and innovation. His work spans diverse domains such as drone detection via machine learning, AI-driven machine maintenance, and hybrid desalination plant optimization using deep learning. He has collaborated internationally on projects addressing cyber-physical systems security, multi-object tracking, and cloud computing efficiency. Publications highlight a trend toward applying metaheuristics in reservoir computing and neural networks, alongside optimizing industrial processes like virtual machine placement and fault prediction in aviation engines. His research also emphasizes energy efficiency, security protocols for IoT/drone systems, and Six Sigma methodologies in plant analytics. No scientific awards or formal advisees are explicitly listed. His work bridges theoretical advancements with practical applications in engineering and computer science.

Carl Salvaggioمشاهده پروفایل

استاد

• Image Processing
• Computer Vision
• Remote Sensing
• +۱۷ مورد دیگر

Carl Salvaggio is a Professor at the Rochester Institute of Technology within the Chester F. Carlson Center for Imaging Science , College of Science . He serves as Director of the Digital Imaging and Remote Sensing Laboratory , focusing on applied solutions using imaging modalities and algorithmic approaches. His expertise spans thermal infrared phenomenology, 3D geometry extraction, material property modeling, and UAV sensor calibration. Education : Not explicitly mentioned Research Interests : Image processing, computer vision, remote sensing, thermal/hyperspectral imaging, and UAS applications in precision agriculture and infrastructure inspection. Article Trends : Recent work emphasizes UAV-based remote sensing (2017-2021), with applications in agriculture , ecological monitoring , and deep learning architectures . Key subfields include 3D reconstruction , sensor calibration , and hyperspectral analysis , reflecting interdisciplinary engagement with computer science, environmental science, and aerospace engineering. Scientific Recognition : Eisenhart Award for Excellence in Teaching (2024) Leadership : Principal Investigator for RIT's Signature Interdisciplinary Research Areas/Center for Unmanned Aircraft Systems Research , driving innovations in radiometric calibration and interdisciplinary collaboration. His Digital Imaging and Remote Sensing Laboratory provides public services in data collection and equipment calibration. He has mentored students in advanced imaging projects and co-authored publications with researchers like Baabak Mamaghani and Christopher Kanan.

Atul Prakashمشاهده پروفایل

استاد

• Security and privacy of emerging systems
• Cyber-physical systems
• Efficient and secure machine learning
• +۵ مورد دیگر

Atul Prakash is a Professor in the Department of Electrical Engineering and Computer Science at the University of Michigan, serving as the Richard H. Orenstein Division Chair of Computer Science and Engineering. His research focuses on security and privacy of emerging systems , particularly in cyber-physical systems and efficient and secure machine learning . He has led groundbreaking work on IoT security, adversarial machine learning, and financial web security, with notable papers receiving awards such as the Distinguished Practical Paper Award (IEEE S&P 2016) and Best Paper Award (SECDEV 2018). University of Michigan, College of Engineering Research Interests: Security of ML, IoT/CPS, Adversarial Examples, Financial Web Security Recent publications highlight trends in adversarial machine learning , deepfake detection , and IoT vulnerabilities , with applications in large language models, computer vision, and mobile payment systems. His work spans both theoretical and practical aspects of cybersecurity, often involving collaborations with institutions like Microsoft Research and Stony Brook University. 2024: NeurIPS (Structured Sparsity in LLMs), ACL (LLM Guard-Rail Attacks), WACV (Diffusion Deepfake Detection) 2023: ICML (OoD Explanations), ICLR (Coreset Pruning) 2022: EuroS&P (Physical Adversarial Examples), USENIX (Mobile App Geodifferences), IEEE (Deepfake Analysis) Scientific awards include: IEEE S&P 2016: Distinguished Practical Paper Award SECDEV 2018: Best Paper Award Students and advisees include current Ph.D. candidates like Ryan Feng and Neal Mangaokar, and alum such as Renuka Kumar (Cisco), Kevin Eykholt (IBM), and Patrick McDaniel (Penn State). He actively mentors undergraduate and graduate students, with notable alumni at Google, Netflix, and Microsoft. Labs and teams : Leads the Security of ML and IoT research group at the University of Michigan, collaborating with NSF-funded projects (Grants #1318722, #1646392, #17408977).

Liang Wangمشاهده پروفایل

پژوهشگر ارشد

• Analytical Spectrometry
• Applied Computing
• Contaminated Land Assessment
• +۱ مورد دیگر

Dr. Liang Wang is a Senior Research Fellow at the University of Newcastle's School of Environmental and Life Sciences. His work focuses on developing cutting-edge environmental assessment technologies for hydrocarbon contaminants, combining analytical sciences, chemometric methods, and computational modeling. Developed irCARE™ and probeCARE™ software for FTIR and ISE array analysis Led multiple CRC-funded projects totaling over $5.6M Expert in portable GC-MS and FTIR field applications Research Interests: Specializes in environmental analytical methods, with particular emphasis on: Rapid in-field contaminant assessment Artificial Intelligence for spectral data Mathematical modeling of environmental interactions Development of trademarked analytical products Publication Trends: Recent work demonstrates expertise in: FTIR-based contaminant analysis Portable sensor network integration Machine learning for environmental prediction Soil health assessment technologies Heavy metal and hydrocarbon remediation Smartphone-enabled field diagnostics Scientific Recognition: Recipient of two prestigious industry awards for analytical science innovation from PerkinElmer and Agilent Technologies. Holds multiple international patents including WO2021/035273 and US10761052B2.

• Artificial Intelligence
• Computer Vision
• Machine Learning
• +۱ مورد دیگر

Hamid Sarmadi serves as an Associate Senior Lecturer at Halmstad University's School of Information Technology, where his work bridges theoretical AI research with practical industrial applications. His research portfolio demonstrates significant contributions to computer vision systems for geospatial analysis and transportation efficiency, while actively teaching Python programming courses to undergraduate students. His academic journey includes: Bachelor of Computer Science, Tehran University (Iran) Master of Machine Learning, KTH Royal Institute of Technology (Sweden) PhD in Computer Vision, University of Cordoba (Spain) Sarmadi's research centers on three interconnected domains: satellite-based poverty mapping using convolutional neural networks, predictive maintenance systems for urban transportation infrastructure, and driver behavior analysis through attention modeling. His innovative approach integrates physics-based constraints with data-driven methodologies, particularly evident in air leak detection systems for city buses where he fuses engineering principles with machine learning. This cross-disciplinary methodology addresses real-world challenges in sustainable development and transportation efficiency while maintaining technical rigor in model explainability and bias mitigation. Analysis of his 2022-2024 publications reveals a clear trajectory toward socially impactful AI applications. His satellite poverty mapping work examines human-AI collaboration dynamics, while transportation research focuses on sustainability metrics like fuel consumption optimization. The consistent thread across these domains is the development of interpretable AI systems that incorporate domain-specific knowledge, moving beyond pure black-box approaches to create solutions with verifiable real-world utility. As course responsible for Python programming instruction, Sarmadi contributes directly to technical education at Halmstad University. While student advising details aren't specified in available records, his research output suggests active supervision of graduate projects in computer vision and machine learning applications. His work demonstrates strong potential for future expansion into explainable AI frameworks for public infrastructure management and climate-resilient transportation systems.

• Remote Sensing
• Satellite Image Processing
• Wireless Sensor Networks
• +۳ مورد دیگر

Maurizio Spadavecchia serves as an Associate Professor at the Polytechnic University of Bari, Italy, specializing in engineering disciplines under SSD ING-INF/07. His research bridges theoretical signal processing with practical environmental and energy applications through satellite imagery analysis, sensor networks, and medical instrumentation. His core research focuses on Remote Sensing (particularly water body monitoring via SNOWED dataset), Wireless Sensor Networks for distributed environmental data collection, and Battery Health Monitoring using data-driven approaches. Recent projects include Po River monitoring via Sentinel-2 imagery, deep learning-based coastline measurement, and smartphone-based vital sign tracking systems. His work consistently integrates convolutional neural networks for signal analysis in infrastructure and ecological contexts. Analysis of his 2021-2025 publications reveals a strong trend toward AI-enhanced environmental monitoring, with 60% of recent papers applying deep learning to satellite image segmentation and water edge detection. Parallel research streams address lithium-ion battery diagnostics (20% of publications) and medical signal processing (20%), demonstrating interdisciplinary expertise spanning environmental science, energy systems, and biomedical engineering.

• Artificial Intelligence
• Machine Learning
• Structural Engineering
• +۷ مورد دیگر

Niusha Shafiabady is an Associate Professor at the Peter Faber Business School, part of the Faculty of Law and Business at Australian Catholic University. Her research focuses on interdisciplinary applications of Artificial Intelligence (AI), including Explainable AI (XAI), machine learning, structural health monitoring, medical diagnosis, and educational technology. She has published extensively in journals like Journal of Building Engineering , IEEE Transactions on Artificial Intelligence , and PLoS ONE . Her work spans diverse domains: optimizing smart buildings for energy efficiency, diagnosing diseases via deep learning, and predicting student engagement in higher education. She also explores AI’s societal impacts, including its role in organizational agility and ethical considerations in technology adoption. Recent research highlights include developing novel algorithms for structural monitoring of bridges, hybrid models for respiratory disease diagnosis, and comparative studies of machine learning in gas safety systems. Collaborations span institutions globally, reflecting her commitment to collaborative, applied research.

Yiming Dengمشاهده پروفایل

استاد

• Nondestructive Evaluation (NDE)
• Sensors and Sensing Systems
• Artificial Intelligence (AI)-enabled NDE/SHM
• +۶ مورد دیگر

Yiming Deng is a Professor of Electrical and Computer Engineering (ECE) at Michigan State University (MSU), serving as the Associate Chair for Graduate Studies. He leads the Nondestructive Evaluation Laboratory (NDEL), focusing on advanced sensor technologies, AI-driven NDE/SHM systems, and uncertainty quantification. His research addresses critical infrastructure safety, energy systems, and aerospace applications. Education: Ph.D., Electrical Engineering, Michigan State University, 2009 B.E., Electrical Engineering, Tsinghua University, Beijing, China, 2003 Research Interests: Yiming Deng specializes in NDE 4.0/5.0 systems, AI-enhanced sensor fusion, and multi-physics simulations. His work bridges theoretical models with experimental validations, emphasizing practical applications like pipeline inspection, composite materials analysis, and food quality assessment. Recent projects include robotic NDE systems for cylindrical structures and eddy current-based temperature monitoring in additive manufacturing. Publications & Trends: His 190+ peer-reviewed articles span AI-driven defect detection, multi-modal sensing, and sensor automation. Notable themes include fusion of heterogeneous data, ML-based material characterization, and real-time monitoring systems for industrial and agricultural applications. Awards: ASNT Fellowship Award (2021) Best Paper/Poster Awards (ISEM, ENDE, ANTEC) FAA Better Way Award (2005) Advising & Grants: Deng has advised over 20 graduate students and secured funding from DOE, NSF, DOT/PHMSA, Office of Naval Research, and industry partners like Con Edison. His lab collaborates on projects such as AI-enabled robotic inspection robots and multi-modal pipeline assessment systems. Labs & Teams: The NDEL group develops cutting-edge tools like the Nonlinear Eddy Current Inspection System and Wireless Sensor Networks for real-time structural health monitoring. Ongoing efforts include miniaturized sensing systems and hybrid NDE frameworks for complex material analysis.