جستجوی استاد راهنمای دانشگاهی

Kemal Akkayaمشاهده پروفایل

استاد

• Network Security
• IoT & Cyber-physical System Security
• Key Management
• +۵ مورد دیگر

Dr. Kemal Akkaya is a Professor at the Department of Electrical & Computer Engineering, Florida International University (FIU), where he leads the Advanced Wireless and Security Lab (ADWISE). He holds a Ph.D. in Computer Science from the University of Maryland Baltimore County and has expertise in Network Security, IoT/CPS Security, Blockchain Applications, and 5G Security. His professional roles include serving as Research Director for FIU’s Emerging Preeminent Program in Cybersecurity and as Program Director for the first BS degree in IoT in the U.S. Education: Ph.D. in Computer Science, University of Maryland Baltimore County M.S. in Computer Engineering, Middle-East Technical University, Turkey B.S. in Computer Science, Bilkent University, Turkey Research Interests: Dr. Akkaya focuses on securing IoT and cyber-physical systems, blockchain for micro-payments, and network defense mechanisms. His work emphasizes privacy-aware protocols, secure key management, and SDN/NFV-based solutions. Awards: FIU Faculty Senate Excellence in Research Award (2020) College of Engineering and Computing Faculty Research Award (2020) Top Cited Article Award from Elsevier (2010) Advisees and Grants: While no student names are listed, his lab (ADWISE) likely supports graduate research in IoT and cybersecurity. His grants include interdisciplinary initiatives at FIU, focusing on securing emerging technologies like 5G and blockchain. Labs/Teams: Leads the ADWISE Lab, collaborating on projects like secure IoT payment systems, drone communication security, and resilient smart grid networks.

John Mageeمشاهده پروفایل

دانشیار

• Computer Vision
• Human-Computer Interaction
• Assistive Technology
• +۲ مورد دیگر

John Magee is Associate Professor of Computer Science at Clark University and serves as Provost and Vice President of Academic Affairs. He holds a Ph.D. from Boston University with research focusing on computer vision, human-computer interaction, and assistive technologies that empower individuals with disabilities. His publications demonstrate strong focus on accessibility innovations, educational technologies, and computer vision applications. Recent work (2022-2024) includes affect-aware educational systems, accessibility tools like Camera Mouse, and security applications of computer vision. His research consistently addresses real-world challenges in disability access and learning technologies. Dr. Magee's work includes developing adaptive interfaces that respond to users' physical abilities and cognitive states, particularly for individuals with profound disabilities. His research extends to interdisciplinary applications combining computer vision with education, security, and healthcare domains.

Ganesh Krishnasamyمشاهده پروفایل

مدرس

• Computer Vision
• Machine Learning
• Pattern Recognition
• +۶ مورد دیگر

Ganesh Krishnasamy is a Lecturer at the School of Information Technology, Monash University Malaysia. He holds a PhD in Electrical Engineering from the University of Malaya, with earlier degrees from Universiti Kebangsaan Malaysia (B.Eng and M.Eng in Electrical and Electronic Engineering). His research focuses on computer vision, pattern recognition, manifold learning, and semi-supervised learning, with applications in multimedia processing and understanding. He has led three research projects including 'AEinstein: Adversarial AI amongst Materials Discovery Domains' and pioneered semi-supervised action recognition in videos. His work aligns with UN Sustainable Development Goals related to innovation and infrastructure. Research Interests: Computer Vision Pattern Recognition Manifold Learning Semi-Supervised Learning Optimization Algorithms HDR Image Reconstruction Recent Projects: AEinstein Project (2024-2026): Focuses on adversarial AI in materials discovery. Research Management System (2023-2024): Aims to optimize research workflows. Semi-Supervised Image-to-Video Adaptation (2020-2023): Developed ActNetFormer and other hybrid models. Publications: Over 16 outputs since 2014, with recent emphasis on HDR imaging, sports video analysis (badminton rally detection), and optimization techniques. His work bridges theoretical machine learning with commercial applications.

Priyanka Singhمشاهده پروفایل

مدرس

• Cyber Security
• Digital Forensics
• Privacy and Security Aspects
• +۵ مورد دیگر

Dr. Priyanka Singh is a Lecturer in Cyber Security at the School of Electrical Engineering and Computer Science, University of Queensland. She holds a PhD in Image Forensics and has conducted postdoctoral research at SUNY Albany and Dartmouth College, collaborating with Prof. Hany Farid. Her research focuses on Cyber Security, Digital Forensics, Privacy, Homomorphic Encryption, and Cloud Computing applications. Key contributions include real-time child pornography detection and privacy-preserving frameworks for digital authentication. Education: PhD in Image Forensics (university unspecified), postdoctoral work at SUNY Albany and Dartmouth College. Notable collaborations include a talk invitation at MIT by Prof. Ramesh Raskar. Research Interests: Cyber Security mechanisms, Digital Forensics techniques, Privacy-Preserving algorithms, Homomorphic Encryption protocols, and Cloud Security solutions. Recent Articles: Focus on AI ethics (e.g., FakeFaceDiscriminator), privacy-preserving biometrics (e.g., IRIS-SAFE), and secure anomaly detection (e.g., SADHE). Her work spans 42 conference papers and 22 journal articles, addressing challenges like encrypted video tampering detection and federated learning resilience. She advocates for technology's societal impact through explainable AI and ethical AI use cases.

• Machine Learning
• Artificial Intelligence
• Deep Learning
• +۳ مورد دیگر

Amal Al-Shargabi is a Senior Lecturer in Data Analytics and Artificial Intelligence at the School of Computer Science and Informatics, part of the Faculty of Computing, Engineering, and Media at De Montfort University (UK). Previously, she served as an Associate Professor at Qassim University (Saudi Arabia). She holds a PhD in Information Technology and Quantitative Sciences from Universiti Teknologi MARA (UiTM), Malaysia, along with a Master's in Computer Science (awarded excellence) and a Bachelor's in Computer Science. Education: PhD in Information Technology and Quantitative Sciences, UiTM, Malaysia Master's Degree in Computer Science, UiTM, Malaysia Bachelor Degree in Computer Science Research Interests: Machine Learning, Artificial Intelligence (including Generative AI), Deep Learning, and their applications in real-world problems such as healthcare, education, and environmental management. She also explores empirical software engineering and data analytics. Research Trends in Publications: Her work focuses on AI-driven solutions for healthcare monitoring (e.g., elderly health), energy efficiency in arid climates, GAN-based generative models, and ethical AI in academia. Recent studies also address assistive technologies like Saudi Sign Language translation and Quranic speech recognition. Awards: Invention, Innovation and Design Exhibition (IIDX’16) Award, Malaysia (2016) Three Minute Thesis (3MT’16) Award, Malaysia (2016) Student-led projects under her supervision have won awards in Saudi Arabia, including Takaful and AI Challenge prizes. Advising & Grants: Research grants include projects on health monitoring via IoT, ethical considerations of ChatGPT/BARD, and solar panel energy optimization. Internally funded projects at Qassim University addressed noise pollution, COVID-19 diagnosis via AI, and job recommendation systems. Labs/Teams: Affiliated with the Institute of Artificial Intelligence (IAI) at DMU. Collaborates with institutions like Qassim University on interdisciplinary projects.

Scott A. Craverمشاهده پروفایل

دانشیار

• Information Security
• Cryptology
• Steganography
• +۳ مورد دیگر

Scott A. Craver is an Associate Professor and Undergraduate Director in the Electrical and Computer Engineering Department at Binghamton University. His research focuses on information security, cryptology, steganography, watermarking, and security engineering. He holds a BS and MS from Northern Illinois University and a PhD from Princeton University. Craver is known for his work on ambiguity attacks in watermarking systems and contributed to the SDMI Challenge, leading to legal disputes with the RIAA. He also patented multilinearization data structures for image browsing with Intel Corporation. Education: PhD in Electrical Engineering, Princeton University BS and MS in Electrical Engineering, Northern Illinois University Research highlights include cryptographic protocols, steganographic channel design, and security analysis of public-key systems. His Erdős number is 3, connected through co-authorships in mathematical publications. Awards include the Chancellor's Award for Excellence in Faculty Service (2013-2014). Key contributions include patents on data structures for efficient image storage, and pioneering work in steganography, including the supraliminal channel concept for secure communication. His teaching focuses on cryptography and information security (e.g., EE 405 course), emphasizing hands-on assignments and programming projects.

Matthew Stammمشاهده پروفایل

دانشیار

• Information Security
• Multimedia Forensics
• Machine Learning
• +۵ مورد دیگر

Matthew Stamm is an Associate Professor at Drexel University's College of Engineering, specializing in Electrical and Computer Engineering through the Multimedia and Information Security Lab (MISL). His work bridges signal processing, machine learning, and information security to detect multimedia forgeries and develop anti-forensic countermeasures. Education : PhD, MS, BS from University of Maryland, College Park Research Interests focus on Information Forensics , including synthetic media detection, deepfake analysis, adversarial dynamics, and forensic algorithm optimization. His lab explores techniques to identify AI-generated content using statistical fingerprints and scene context. Recent Publications (2025-2023) emphasize synthetic audio/video detection, forensic self-description methods, and adversarial attack mitigation. These works often leverage deep learning architectures and ensemble approaches for robust identification. Scientific Awards 2016 NSF CAREER Award 2017 Drexel College of Engineering Outstanding Early-Career Research Achievement Dean's Doctoral Research Award (University of Maryland) Ann G. Wylie Dissertation Fellowship Clark School of Engineering Future Faculty Fellowship Distinguished Teaching Assistant Award Service Roles include General Chair of ACM Workshop on Information Hiding and Multimedia Security (2017), lead organizer for IEEE Signal Processing Society's Student Competition (2018), and member of the Information Forensics and Security Technical Committee. He has received funding from NSF, DARPA, ARO, and DFBA.

Luisa Verdolivaمشاهده پروفایل

دانشیار

• Image and Video Forgery Detection
• Deepfake Video Detection
• GAN Fingerprints Attribution
• +۵ مورد دیگر

Luisa Verdoliva is an Associate Professor at the Department of Industrial Engineering, University Federico II of Naples, Italy. She holds a Hans Fischer Senior Fellowship at the Technical University of Munich (TUM) Institute for Advanced Study (2020–present), hosted by Prof. Matthias Nießner. Her research focuses on multimedia forensics, deepfake detection, and generative AI analysis. She has been a visiting professor at Friedrich-Alexander-University (FAU) and a visiting scientist at Google AI's Perception team. Her research interests include image/video forgery detection, GAN fingerprints, biometrics liveness detection, and remote sensing applications. Verdoliva’s work has been funded by DARPA, Google, and the Italian Ministry of Research. She serves as Chair of the IEEE Signal Processing Society’s Information Forensics and Security Technical Committee and is an IEEE Fellow (2021). Key achievements include pioneering FaceForensics++ (a benchmark dataset for manipulated facial images), winning the IEEE Signal Processing Cup (2018), and receiving the Google Faculty Research Award (2018). Her awards span best paper recognitions at CVPR, ICIP, and ICIAP conferences. As part of TUM’s Visual Computing Focus Group, her fellowship research advances digital twin technology for built environments and AI-driven media forensics. Collaborations span academia and industry, with impactful contributions to combating synthetic media misuse and improving multimedia trustworthiness.

Djamila Aouadaمشاهده پروفایل

استادیار

• Computer Vision
• Signal Processing
• Image Processing
• +۳ مورد دیگر

Djamila Aouada is an Assistant Professor and Senior Research Scientist at the University of Luxembourg's Interdisciplinary Centre for Security, Reliability and Trust (SnT), where she heads the Computer Vision, Imaging, and Machine Intelligence (CVI2) research group. She earned her State Engineering degree from École Nationale Polytechnique, Algeria, and PhD from North Carolina State University. Her research spans computer vision, signal processing, pattern recognition, and data modeling. Dr. Aouada leads several national and European projects including FNR FAVE, 3D-Act, and H2020 STARR, focusing on AI-driven solutions for industrial and security applications. She has received four IEEE Best Paper Awards for her contributions. Dr. Aouada has supervised 5 completed PhD theses and currently mentors 5 PhD candidates, while maintaining collaborations with Los Alamos National Laboratory and Mitsubishi Electric Research Labs. She serves as Senior IEEE Member and previously chaired IEEE Benelux Women in Engineering.

Andrew Sungمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Cybersecurity
• +۷ مورد دیگر

Andrew Sung is a Professor at the University of Southern Mississippi (USM) in Hattiesburg, affiliated with the Chain Technology Center (TEC). His distinguished academic career spans over four decades, focusing on the critical intersection of machine learning and cybersecurity, with significant contributions to multimedia forensics and digital security. His educational background includes: PhD from Stony Brook University (1984) MS from University of Texas System Office (1980) BS from National Taiwan Normal University (1976) Professor Sung's research centers on applying machine learning to security challenges, with groundbreaking work in deepfake detection, malware analysis, and steganalysis. His publications demonstrate expertise in developing privacy-preserving techniques like machine unlearning, creating robust deepfake detection frameworks, and innovating Android security solutions. He has pioneered methods in moving target defense for software-defined networking and advanced forensic techniques for identifying digital forgeries in images and audio, addressing evolving threats in AI-generated content and network infrastructure. Analysis of his 15 most recent publications (2019-2024) reveals a dominant research trajectory toward combating AI-generated threats. His work increasingly focuses on deepfake detection through ensemble learning and dataset distillation, machine unlearning for privacy compliance, and sophisticated malware analysis techniques. This trend highlights his strategic pivot toward securing digital ecosystems against next-generation AI threats while maintaining strong foundations in traditional cybersecurity domains like network defense and steganalysis. No scientific awards were mentioned in available sources. Information regarding students advised or research grants was not provided in the available documentation. Professor Sung conducts his research at the Chain Technology Center (TEC) at USM, where he leads investigations into cutting-edge security challenges at the forefront of artificial intelligence and digital forensics.

• Hardware-Software Co-design
• Deep Learning Security
• Intellectual Property Protection
• +۵ مورد دیگر

Huili Chen is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of California San Diego's Jacobs School of Engineering. Her research spans the intersection of hardware security, deep learning, and human-robot interaction, with a particular focus on intellectual property protection for AI systems and socially interactive robotics for education and family settings. Her research interests focus on hardware-software co-design for secure and robust deep learning systems, with specific expertise in neural network watermarking, hardware Trojan detection, and privacy-preserving AI. She has made significant contributions to federated learning security, developing frameworks like GALU for logic unlocking and AdaTest for hardware Trojan detection. In human-robot interaction, she investigates long-term multi-person interactions, particularly in home environments with children and parents, exploring how robots can enhance engagement and learning through adaptive role-playing. Her publication trends reveal a dual research trajectory: one branch focused on deep learning security and hardware co-design (accounting for approximately 60% of her recent work), and another dedicated to socially assistive robotics and human-robot interaction (40%). The security research often involves innovative approaches combining reinforcement learning with hardware constraints, while her HRI work emphasizes longitudinal studies of robot-child-parent dynamics in naturalistic settings. Dr. Chen actively collaborates with leading researchers including Farinaz Koushanfar at UC San Diego and Cynthia Breazeal at MIT Media Lab. Her work has been published in top-tier venues including IEEE Transactions on Affective Computing, ACM Transactions on Embedded Computing Systems, International Conference on Computer Vision (ICCV), and the ACM/IEEE International Conference on Human-Robot Interaction (HRI). She is a key contributor to the DAMI-P2C project, which has developed datasets and models for analyzing parent-child multimodal interactions, and leads research on hardware security frameworks for deep neural networks. Her laboratory combines expertise in computer architecture, machine learning, and social robotics to develop systems that are both technically secure and socially effective.

• 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.

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.