جست‌وجوی استاد و پژوهشگر

• Algorithms
• Distributed Systems
• Internet of Things
• +۴ مورد دیگر

Valderi Reis Quietinho Leithardt is a Full-time Assistant Professor at ISCTE - University Institute of Lisbon, Portugal, where he also serves as an Integrated Researcher at ISTAR/ISCTE. An IEEE Senior Member since 2019, he specializes in Computer Science with emphases on algorithms, distributed systems, and data privacy across Internet of Things, Cloud Computing, and Intelligent Systems domains. Education: Postdoctoral in Computer Engineering, University of Salamanca, Spain (2019-2021) Postdoctoral in Distributed Systems and Data Privacy, University of Coimbra, Portugal (2017-2019) Ph.D. in Computer Science, Federal University of Rio Grande do Sul, Brazil (2011-2015) Master's in Computer Science, Pontifical Catholic University of Rio Grande do Sul, Brazil (2006-2008) Graduate Certificate in Computer Networks, Federal University of Rio Grande do Sul, Brazil (2003-2004) Bachelor's in Data Processing Technology, Centro de Ensino Superior de Foz do Iguaçu, Brazil (1999-2002) His research centers on developing middleware solutions for privacy control in ubiquitous environments (UbiPri), cryptographic protocols for IoT security (PRISEC series), and machine learning applications for infrastructure monitoring. Recent work demonstrates strong interdisciplinary connections between data privacy frameworks, edge computing optimization, and real-world implementations in smart agriculture, healthcare, and power grid systems. His publications reveal consistent focus on solving resource-constrained environment challenges through algorithmic innovation. Research Impact: 2025 publications show expansion into transformer models for time-series forecasting and JVM optimization techniques 2024 work emphasizes healthcare data privacy, forged image detection, and elderly activity monitoring Core contributions to IoT security protocols and distributed system architectures since 2019 Leadership and Service: Principal investigator on 12+ funded projects including Physically Unclonable Functions (FCT, 2025) and Smart Textile for Dogs (Salamanca University, 2025) Organizer of WTTFC workshops (2024-2025) and IEEE CiSTI conference sessions Active thesis committee member for 30+ graduate students across Portugal, Brazil, and Spain Professor Leithardt maintains strong industry connections through IEEE activities and collaborates with research groups including UNINOVA, ISTAR, and University of Salamanca's computer engineering department on next-generation secure system architectures.

Sabah Jassimمشاهده پروفایل

استاد

• Biometrics
• Medical Imaging
• Image Processing
• +۷ مورد دیگر

Sabah Jassim is a Professor of Mathematics at the University of Buckingham, specifically within the School of Computing. He holds a BSc and MSc from Baghdad University and a PhD in Mathematics from Swansea University (formerly University of Wales). His roles also include visiting lecturer positions at City University, London, and Fachhochschule Wedel, Germany. Education: BSc in Mathematics, Baghdad University MSc in Mathematics, Baghdad University PhD in Mathematics, University of Wales (Swansea) Research Interests: Dr. Jassim focuses on interdisciplinary research at the intersection of mathematics, computer science, and biomedical engineering. His work spans biometric authentication systems , medical image analysis , cryptography , and machine learning . Specific areas include biometrics security, dynamic encryption protocols, and automated classification of medical imaging data (e.g., ultrasound, histology). Research Trends (2010–2024): Recent work emphasizes applications of topological data analysis in forgery detection and medical imaging. His publications address challenges in ultrasound-based tumor classification , crack detection in building facades , and automated diagnosis support systems . He actively explores steganography techniques for secure data embedding and develops algorithms for clustering evolving data streams. Advising & Grants: Supervises research students in biometric authentication, biomedical image analysis, and dynamic encryption. His projects often involve collaborations with clinical partners for medical imaging applications. Active in securing funding for interdisciplinary research initiatives. Labs/Teams: Leads efforts in the School of Computing’s research groups focused on applied mathematics and computational security . Collaborates with medical institutions on diagnostic imaging projects.

Ana Drobمشاهده پروفایل

پژوهشگر

• Prehistoric Archaeology
• Archaeometry
• Ceramic Analysis
• +۳ مورد دیگر

Ana Drob is a Researcher at the ARHEOINVEST Center within the Department of Exact and Natural Sciences at the Alexandru Ioan Cuza University of Iași. Her work focuses on prehistoric archaeology and archaeometry, with expertise in ceramic analysis, interdisciplinary techniques, and artifact authentication. She has conducted fieldwork at numerous archaeological sites across Romania, specializing in Bronze Age pottery technologies, raw material sourcing, and functional use studies. Her doctoral research centered on the interdisciplinary investigation of prehistoric pottery using advanced methods such as Optical Microscopy (OM), Scanning Electron Microscopy with Energy-Dispersive X-ray Spectroscopy (SEM-EDX), µFT-IR spectroscopy, and ThermoGravimetric Analysis/Differential Thermal Analysis (TGA/DTA). This work emphasized understanding ceramic production techniques, firing processes, and container functionality in the East-Carpathian region. Recent articles highlight her contributions to AI-driven artifact authentication, Bronze Age ceramic technologies, and non-invasive analysis of metal objects. She has explored topics such as pigment analysis in miniature vessels, interdisciplinary approaches to fragmented archaeological finds, and experimental kiln reconstruction to replicate Chalcolithic pottery production. While no scientific awards are explicitly noted, her research reflects a commitment to advancing archaeological methodologies. Her work has implications for cultural heritage preservation, historical authenticity verification, and understanding prehistoric technological innovations. She collaborates with the ARHEOINVEST Center on projects involving Cucuteni culture settlements and medieval numismatic studies.

• Biometrics
• Image Processing
• Signal Processing
• +۴ مورد دیگر

Patrizio Campisi is an academic affiliated with Roma Tre University in Rome, Italy. His primary research focuses on biometric systems, information forensics, and signal processing. He has published extensively in top-tier journals and conferences, including IEEE Transactions on Information Forensics and Security, IEEE Signal Processing Magazine, and EUSIPCO. His work spans areas such as vein recognition, EEG-based biometrics, and cryptographic methods for privacy preservation. He has collaborated with numerous researchers globally and contributed to standards for biometric authentication in the EU. Research Interests: Biometric systems (finger/hand vein, EEG, keystroke dynamics) Secure template protection mechanisms Forensic analysis of digital media Machine learning applications in pattern recognition Medical imaging and wearable devices Recent work emphasizes developing explainable AI for biometric systems and creating secure, unlinkable authentication methods. He has also contributed to policy initiatives like the European Association for Biometrics' position paper on Schengen travel security.

Miguel Colomمشاهده پروفایل

مدرس ارشد

• Digital Image Processing
• Signal Processing
• Satellite Design
• +۳ مورد دیگر

Miguel Colom is a Senior Lecturer and Researcher at the Centre Borelli of École Normale Supérieure Paris-Saclay. His primary research focuses on digital image processing, including blind noise estimation, satellite design (SMOS-HR mission), and image forgery detection, with an emphasis on reproducible research practices. PhD in Image Processing, Universitat de les Illes Balears, 2014 Habilitation à Diriger des Recherches (HDR), 2019 His research interests span digital image processing, satellite technology, and ensuring reproducibility in scientific research. He has contributed to the development of tools like the Image Processing On Line (IPOL) journal, promoting open and reproducible methodologies. Colom has led efforts in the European Open Science Cloud (EOSC) to improve research software quality and recognition. His work includes detecting image forgeries via noise analysis and advancing satellite imaging technologies. Recent research highlights include a Best Paper Award for 'NoiseSniffer' (2021) and supervising PhD student Tina Nikoukhah, recipient of the 2023 Thesis Prize GDR Sécurité Informatique. His articles address topics such as reproducible research policies, synthetic image detection, and noise estimation in video signals. Scientific Awards: Best Paper Award at the 9th IEEE International Workshop on Biometrics and Forensics (2021) Advising: Supervised Tina Nikoukhah’s thesis on JPEG forgery detection Colom is a board member of the IAPR-TC18 and TC22 (Reproducibility), and chairs the Reproducible Research track at the 2026 ICPR conference. He leads IPOL’s demo system technical direction and participates in EOSC initiatives to standardize research software infrastructure.

Constantine Kotropoulosمشاهده پروفایل

استاد

• Digital Signal Processing
• Audio Processing
• Music Information Retrieval
• +۶ مورد دیگر

Constantine Kotropoulos is a Professor at the Department of Informatics, School of Informatics, Aristotle University of Thessaloniki. He holds a PhD (1993) and a Diploma in Electrical Engineering (1988), both from Aristotle University. His professional journey includes roles from Lecturer (1997) to current Professor (2015–present), with extensive postdoctoral and research experience in electrical and computer engineering fields. His research focuses on Digital Signal Processing , Audio/Music/Speech Processing , Pattern Recognition , and Human-Centered Multimodal Interaction . Recent work emphasizes forensic applications (e.g., source device identification), medical diagnostics (speech pathology classification), and explainable AI (face aging, camera model identification). Key research themes include electric network frequency analysis for audio authentication, multimodal fusion for media forensics, and optimization techniques for non-negative machine learning problems. His publications span signal processing, multimedia, and healthcare technology, with a focus on practical applications of advanced algorithms. He maintains active collaborations in interdisciplinary areas such as agricultural disease detection via hyperspectral imaging and route recommendation systems leveraging image-driven data. His work often bridges theoretical computer science with real-world challenges in security, healthcare, and environmental monitoring.

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.

Tina NIKOUKHAHمشاهده پروفایل

پژوهشگر

• Digital Forensics
• Image Processing
• Computer Vision
• +۳ مورد دیگر

Tina NIKOUKHAH is a researcher in applied mathematics at the Centre Borelli at École normale supérieure Paris-Saclay, appointed since 2022. She holds a Master's in industrial and applied mathematics from Grenoble-Alpes University (2014), an engineering degree from Ensimag (Grenoble-INP), and a PhD in applied mathematics from ENS Paris-Saclay. Her research focuses on detecting image falsifications through JPEG compression traces and deepfake identification, with applications in digital forensics and computer vision. Key Roles: Researcher at Centre Borelli, lecturer at ENS Paris-Saclay and ESJ Lille, and director of GetReal Labs. She develops tools like the ZERO detector for forgery localization, and contributes to open-source projects such as the Simplest_GOD library. Research Interests: Tina specializes in algorithms to uncover image forgeries, including JPEG grid detection, video noise analysis, and synthetic image identification. Her work bridges signal processing, forensic analysis, and machine learning to combat misinformation through digital forensics. Awards: Recipient of the 2022 L'Oréal-UNESCO Young Talent Award for Women in Science, recognizing her contributions to combating image falsification. Teaching & Outreach: Engages in science education through the Maison d'Initiation et de Sensibilisation aux Sciences (MISS) and public lectures on fact-checking, deepfakes, and algorithm ethics.

Hany Faridمشاهده پروفایل

استاد

• Digital Forensics
• Image Analysis
• Human Perception
• +۶ مورد دیگر

Hany Farid is a Professor at the University of California, Berkeley with a joint appointment in the Department of Electrical Engineering & Computer Sciences (EECS) and the School of Information. He specializes in digital forensics, forensic science, misinformation analysis, image analysis, and human perception. His research focuses on detecting manipulated media (photos, videos, audio) and understanding societal impacts of AI-generated content. Education: PhD in Computer Science (University of Pennsylvania, 1997), Postdoc at MIT (1999). Affiliations: Co-founder and Chief Science Officer at GetReal Security. Grants: Supported by NSF, DARPA, Adobe, Meta, Oak Foundation, and others. His work bridges computational techniques with human perception, addressing challenges like deepfake detection and forensic science reliability. He has authored over 200 papers and two book-length studies on photo forensics and synthetic media. Research Highlights: Developed algorithms to detect photo/video manipulation via lighting/shadow inconsistencies. Investigated AI-generated voice and facial synthesis detection. Explored societal impacts of misinformation and predictive algorithms in criminal justice. Awards: Alfred P. Sloan Fellowship (2002) John Simon Guggenheim Fellowship (2006) Fellow of the National Academy of Inventors (2020) IEEE Fellow (2018) Advising: Mentored over 40 students, including PhD candidates in computer vision and forensic science. Current focus includes M.S./PhD students in digital forensics and AI ethics.

Maikel Lázaro PÉREZ GORTمشاهده پروفایل

پژوهشگر

• Computer Science
• Data Security
• Database Systems
• +۳ مورد دیگر

Maikel Lázaro PÉREZ GORT is a Researcher at the Department of Environmental Sciences, Computer Science and Statistics (DAIS) at Ca' Foscari University of Venice. He is affiliated with the Research Institute for Complexity Communications and teaches advanced courses on relational data watermarking in doctoral programs. His primary research focuses on database security, specifically developing robust watermarking techniques for relational data integrity protection against attacks in untrusted environments. Teaching Activities: Doctoral Course (DM226/2021): 'Relational Data Watermarking: Fundamentals' and 'Robust Schemes' Past courses include 'Programming and Laboratory-2' for undergraduate Computer Science students and 'Lab of Human-Centered Design' for Digital Management programs. Research Interests: His work addresses challenges in data watermarking resilience, including topology optimization for watermark sources, query impact analysis, and prevention of additive attacks. Recent efforts emphasize earth observation data integrity and quantile-based distortion minimization techniques. Publications: Over 13 peer-reviewed articles from 2017-2025, focusing on watermarking methodologies for relational databases, earth observation systems, and forensic image analysis. Key themes include brute-force attack resilience, semantic-driven marking, and high-quality key generation schemes. Awards/Grants: No specific awards or grants mentioned in the provided materials. Labs/Teams: Affiliated with the DAIS department's research groups working on information systems security and complexity science.

• Multimedia Forensics
• Image and Video Watermarking
• Deepfake Detection
• +۶ مورد دیگر

Hannes Mareen is a Postdoctoral researcher at Ghent University's Faculty of Engineering and Architecture, specifically within the Department of Electronics and information systems (EA06), and is also affiliated with IMEC. His research spans multimedia security, image and video processing, and digital forensics, with a focus on watermarking techniques, deepfake detection, and media authentication. Dr. Mareen's research interests primarily focus on multimedia forensics and digital content security . His work addresses critical challenges in media authentication, including developing robust watermarking techniques that withstand geometric transformations, creating methods for deepfake video detection using advanced vision transformer architectures, and building tools for identifying AI-generated image manipulations. He has made significant contributions to perceptual hashing techniques and the analysis of compression fingerprints for forgery detection. His research has practical applications in copyright protection, content authentication, and combating misinformation through advanced media analysis tools. The recent publication trends reveal a strong focus on leveraging deep learning and vision transformers for multimedia security applications. Dr. Mareen's work demonstrates increasing sophistication in addressing complex challenges like geometrically robust watermarking, deepfake detection in video content, and the development of comprehensive datasets for training forgery detection systems. His research bridges theoretical advances with practical implementations, particularly evident in his work on real-time video delivery systems and applications in sports rehabilitation through virtual reality. Dr. Mareen has established a productive research trajectory with consistent high-impact publications in top conferences and journals. His collaborative network is extensive, with frequent co-authorship with Glenn Van Wallendael and Peter Lambert, indicating strong research group dynamics within Ghent University's multimedia security research team. His work spans both theoretical contributions and practical implementations, with several publications demonstrating real-time systems and applications.

• Forensic Science
• Fingermark Detection
• Nanoparticles in Forensics
• +۴ مورد دیگر

Dr. Sebastien Moret is a Senior Lecturer in Forensic Science at the University of Technology Sydney (UTS), where he has been since 2017. He serves as Course Director for the Bachelor of Forensic Science program and coordinates subjects like Forensic Imaging and Document Examination. His academic journey began at the University of Lausanne, where he earned a PhD in Forensic Science (2013) after completing undergraduate studies in the same field. PhD, Forensic Science - University of Lausanne (2008–2013) MSc, Forensic Science - University of Lausanne (2006–2008) BSc, Forensic Science - University of Lausanne (2003–2006) Dr. Moret's research focuses on advanced fingermark detection methodologies, particularly leveraging nanoparticle technology and chemical optimization. His work addresses critical challenges in forensic science including solvent alternatives (e.g., Solstice® PF replacement for HFE7100), standardization of quality assessment protocols, and environmental impacts on detection techniques through studies of biodegradable plastics. He has pioneered synthetic secretion formulations for artificial fingermark production, enabling controlled research through modified inkjet printing systems. His recent publications examine diverse forensic challenges across 15 articles including: carrier solvent performance in amino acid techniques, dark web document fraud analysis, surfactant optimization in powder suspensions, and biodegradable plastic substrate interactions. These works demonstrate his expertise in detection chemistry, forensic material science, and digital criminal marketplace analysis. Swiss National Science Foundation Postdoctoral Fellowship (2014–2015) Swiss National Science Foundation Grant (2016) US National Institute of Justice Funding (2018–2019) As a leading forensic scientist at UTS's Centre for Forensic Science, Dr. Moret bridges academic research with operational forensic practice through his method development and validation studies. His work emphasizes practical implementation challenges while maintaining rigorous scientific standards.

Gang Wangمشاهده پروفایل

دانشیار

• Security and Privacy
• Machine Learning
• Internet Measurement
• +۴ مورد دیگر

Gang Wang is an Associate Professor in the Department of Computer Science at the Siebel School of Computing and Data Science at the University of Illinois Urbana-Champaign (UIUC) . He serves as the Associate Director of the Capital One Illinois Center for Generative AI Safety and holds courtesy affiliations with the Department of Electrical and Computer Engineering , Security and Privacy Research at Illinois (SPRAI) , and the Coordinated Science Laboratory (CSL) . His research focuses on developing explainable and robust machine learning systems to enhance internet security and privacy. Key areas include adversarial machine learning , deepfake detection , phishing prevention , and security of social computing platforms . He actively contributes to major conferences like USENIX Security , CCS , NDSS , and ICML . Recent publications highlight his work on LLM benchmark contamination , VLM jailbreaks , and deepfake profile detection . His research team has produced award-winning papers at CHI and IEEE SP , with grants from NSF , Amazon , and Google . Notable students include Qingying Hao and Limin Yang , who co-authored multiple high-impact papers.

• Machine Learning
• Data Privacy
• Computer Vision
• +۴ مورد دیگر

Ji Zhang is a Professor in the Department of Mathematics, Physics and Computing at the University of Southern Queensland. His research focuses on machine learning, data privacy, computer vision, and blockchain technology. He holds an MSc from Singapore and a PhD from Dalhousie University. His work addresses challenges in secure multiparty computation, adversarial machine learning, and deepfake detection. Key research themes include privacy-preserving techniques for data publishing, generative models for image manipulation, and graph neural networks for network analysis. Recent studies explore efficient neural dynamic data valuation and fair algorithmic systems. He has contributed to scalable graph mining frameworks and secure blockchain applications in healthcare and smart contracts. His publications reflect interdisciplinary innovation across cybersecurity, computer vision, and distributed systems. Notable projects include EVRACE for e-bike charging analysis and Tara-Net for takeaway rider accident detection. Despite no listed awards, his work demonstrates significant impact in AI-driven solutions for real-world problems. Collaborative activities span academic-industry partnerships in fraud detection systems and edge computing frameworks for worker safety monitoring (DeepSafety). Current research emphasizes improving transparency in graph neural networks and developing robust anomaly detection methodologies.

• Computer Vision
• Biometrics
• Face Recognition
• +۳ مورد دیگر

Xiaoming Liu is a Professor at Michigan State University with extensive contributions to computer vision and biometrics. His research spans face recognition, 3D reconstruction, image forgery detection, and adversarial machine learning, with over 300 publications from 1999 to 2025. His primary research interests include Computer Vision , Biometrics , and Adversarial Machine Learning . Liu's work focuses on developing robust systems for face recognition at scale, detecting image manipulations, and creating 3D reconstruction techniques. Recent projects include SapiensID for human recognition, FRCSyn for synthetic face recognition, and proactive watermarking schemes. Liu's publication trends show increasing focus on multi-modal biometrics (combining face, body, and gait), image forgery detection using hierarchical approaches, and adversarial defense mechanisms . His 2023-2025 work emphasizes synthetic data applications and physics-driven recognition systems. Liu has mentored numerous students including Feng Liu, Minchul Kim, and Xiao Guo, who frequently co-author his papers. His research has been supported by grants enabling projects like FarSight (long-range biometrics) and ProMark (proactive watermarking). He leads research in biometrics security and computer vision, with recent work focusing on ethical AI applications and robust recognition systems. His team develops tools for detecting deepfakes and improving recognition in challenging conditions.