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Kok Sheik WongView profile

Professor

• Multimedia Signal Processing
• Cybersecurity
• Data Hiding
• +9 more

Kok Sheik Wong is a Professor and Deputy Head (Research) at the School of Information Technology, Monash University Malaysia. He holds a Doctor of Engineering from Shinshu University, Japan, and Master’s and Bachelor’s degrees in Computer Science and Mathematics from Utah State University, USA. His academic leadership and research excellence are central to his role at Monash. B.S. Computational Mathematics, Utah State University (2002) M.S. Computer Science, Utah State University (2006) M.S. Mathematics, Utah State University (2004) Doctor of Engineering, Shinshu University, Japan (2009) His research focuses on multimedia signal processing and cybersecurity , particularly in data hiding , reversible data hiding , coverless steganography , and multimedia encryption . He is also expanding into digital health , applying AI to mental health in workplace environments. His work aligns with UN SDGs, particularly in health and education. The recent publication trends show a strong emphasis on reversible data hiding , image watermarking , and AI-driven health applications . His interdisciplinary work spans computer science, engineering, and public health, with increasing focus on real-world impact through EU and national grants. He has received several honors, including: Academic of Science Malaysia - Young Scientist Network (2020) Best Paper Award, IWDW 2019 ITEX 2021 Gold Medal for BAITRADAR School of IT Excellence in Research Award (2022) Dr. Wong actively supervises PhD students and leads major research projects, including the EU-funded WAge project. He has served as an associate editor for IEEE Signal Processing Letters and the Journal of Information Security and Applications, and is a member of IEEE IFS and APSIPA technical committees. His grants reflect strong external collaboration and funding in cybersecurity and digital health. He is involved in key research labs and teams through Monash University and international consortia, particularly in the areas of multimedia security and digital health innovation. His leadership in the WAge project connects him with European and Asia-Pacific research networks, enhancing global impact.

• Audiovisual Media
• Image Processing
• Immersive Multimedia Communication
• +13 more

Christian Timmerer is a Professor at the Institute of Information Technology, Alpen-Adria-Universität Klagenfurt. His research focuses on adaptive video streaming , energy efficiency , MPEG standardization , and quality of experience (QoE) , with significant contributions to HTTP Adaptive Streaming (HAS), multi-codec optimization, and immersive media systems. Email: christian.timmerer@aau.at Office Hours: Monday 3:00-4:00 PM (by appointment) Projects: CD-Labor ATHENA, GAIA, SPIRIT His research integrates machine learning and generative AI to enhance video encoding, super-resolution, and voice dubbing, while prioritizing sustainability through energy-aware algorithms and open-source tools like GREEM and VEED. Current work emphasizes latency reduction and dynamic bitrate adaptation in live streaming environments. Recent publications address VVC optimization , multi-resolution encoding , and perceptual quality modeling , reflecting interdisciplinary efforts in networking , computer vision , and human-computer interaction . Awards include leading funded projects on adaptive streaming and green video systems.

Yvo G. DesmedtView profile

Professor

• Cryptography
• Quantum Computing
• Network Security
• +2 more

Yvo Desmedt is the Jonsson Distinguished Professor in Computer Science at the University of Texas at Dallas and Director of the Cyber Security Research and Education Institute. An IACR Fellow and member of the Belgium Academy of Science, he invented e-Passports and e-Visas in 1988. His research spans cryptography, quantum computing, network security, and critical infrastructure protection. Desmedt pioneered techniques in binary software hardening including control-flow integrity and object flow integrity protections. Recent innovations include crook-sourcing for intrusion detection improvement and confidential computing for deep learning inference. His work bridges theoretical cryptography with practical security applications, earning recognition including the NSF IUCRC Technology Breakthrough Award. With over 200 publications, he has chaired major conferences including Crypto and Public Key Cryptography. Current projects examine vulnerability detection using graph learning and renewable control-flow integrity mechanisms for software security.

Ahmet TekalpView profile

Professor

• Digital Image Processing
• Video Compression
• Motion-Compensated Video Filtering
• +5 more

Ahmet Tekalp is a Professor in the Department of Electrical and Computer Engineering at Koc University's College of Engineering since 2001. He holds dual citizenship in Turkey and the USA, with prior academic roles at the University of Rochester (1986-2005) and research positions at Eastman Kodak (1984-1987) and Rensselaer Polytechnic Institute (1981-1984). He chairs the Electronics and Informatics Group at TUBITAK since 2004 as a part-time position. B.S. (1980) in Electrical Engineering & Mathematics, Bogaziçi University M.S. (1982) and Ph.D. (1984) in Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute His research focuses on digital image and video processing, including video compression, motion-compensated filtering for high-resolution applications, video segmentation, object tracking, content-based video analysis, multi-camera surveillance processing, and digital content protection. He has led numerous European and U.S. grants, including FP7 STREP projects and NSF awards, emphasizing applications in sensor networks, visual databases, and medical imaging. His scholarly work spans diverse areas such as superresolution reconstruction, head gesture animation, 3DTV streaming, and reversible data hiding. He has played pivotal roles in editorial boards, including serving as Editor-in-Chief of Signal Processing: Image Communication, and has contributed to major standards bodies like ISO MPEG and ANSI NCITS. Member, Turkish Academy of Sciences (TUBA) Fellow, IEEE Fulbright Senior Scholarship (1999) TUBITAK Science Award (2004) IEEE Signal Processing Society Distinguished Lecturer (1998) He has led multiple international research collaborations and projects, including European FP6/FP7 networks and NATO programs, with substantial grant funding from NSF, NYSTAR, and industry partners like Eastman Kodak, Xerox, and Siemens.

Wong Kok SheikView profile

Associate Professor

• Multimedia Signal Processing
• Cybersecurity
• Digital Health
• +3 more

Associate Professor Wong Kok Sheik is the Deputy Head (Research) at the School of Information Technology, Monash University Malaysia. He holds a Doctor of Engineering from Shinshu University (Japan) and advanced degrees in Computer Science and Mathematics from Utah State University (USA). His research focuses on multimedia signal processing, cybersecurity, and digital health, with contributions to data hiding, encryption, and smart grid security. He leads a EU-funded WAge project on post-pandemic workplace health interventions. Education: PhD in Engineering, Shinshu University, Japan (2009) Masters in Computer Science & Mathematics, Utah State University, USA (2005-2004) Bachelor of Science, Utah State University, USA (2002) Professional Roles: Associate Editor, IEEE Signal Processing Letters Member, IEEE Signal Processing Society’s IFS Technical Committee Board Member, APSIPA Multimedia Security and Forensics (MSF) Committee His research interests span multimedia forensics, encrypted domain processing, and cybersecurity frameworks. Notable projects include recovery of missing coefficients in compression standards and HDR imaging enhancement. Recent work integrates digital health, addressing workplace mental/physical health in post-pandemic environments. Publications reflect expertise in watermarking, encryption, and biometric security. Key contributions include encryption-resistant data embedding and secure smart grid analytics. Awards include IEEE CES Service Awards (2015, 2016) and Best Paper recognitions. He supervises over 20 PhD/MSc students, focusing on topics like biometric recognition, data hiding, and anomaly detection. Grants include a €2M EU Horizon 2020 project (IDENTITY) and a RM146k smart grid initiative. His teaching emphasizes foundational IT research methods and theoretical computer science.

• Machine Learning
• Medical Image Analysis
• Computer Vision
• +5 more

Junxi Chen is an academic affiliated with Dalian University of Technology's School of Software. His research focuses on advanced machine learning techniques, medical image analysis, and computer vision applications in healthcare. He has contributed significantly to adversarial machine learning, robust deep learning models, and medical image segmentation. His work bridges theoretical advancements with practical applications in medical diagnostics and data security. Key research areas include adversarial attacks/defense mechanisms, medical imaging segmentation (e.g., skin lesions, tumors), and innovative neural network architectures like UNet variants. He also explores secure data-hiding techniques in encrypted images and hybrid memory systems optimization. Publications span top venues such as CVPR, IEEE Transactions, and Medical Image Analysis journals. Collaborations with institutions like Dalian University of Technology and international co-authors highlight his global research impact.

• Image Compression
• Cryptography
• Privacy-Preserving Systems
• +5 more

Ja-Ling Wu is a distinguished Professor in the Department of Electrical Engineering at National Taiwan University's College of Electrical Engineering and Computer Science. With an extensive publication record spanning over four decades (1984-2025), Professor Wu has established himself as a leading researcher in multimedia systems, image processing, and security technologies. His academic career demonstrates consistent productivity with 255 indexed publications showing no signs of slowing down, with numerous papers published in 2024 and 2025. Professor Wu's research interests center on image compression, cryptography, privacy-preserving systems, and machine learning applications. His work bridges theoretical foundations with practical implementations, particularly in multimedia security and efficient data representation. Over his career, he has developed innovative approaches to image and video coding, data hiding techniques, and secure computation methods that have influenced both academic research and industry applications. Analysis of his recent publications (2023-2025) reveals a strategic evolution of his research toward contemporary challenges in AI security, with increasing focus on learned image compression, privacy-preserving machine learning, and the integration of cryptographic techniques with deep learning systems. His work demonstrates a consistent ability to adapt to emerging technological landscapes while maintaining core expertise in multimedia processing. Professor Wu has mentored numerous students who have become active researchers in their own right, with many continuing to collaborate with him on cutting-edge projects. His leadership in the field is evident through his sustained publication output across top venues including IEEE Transactions, ACM Multimedia, and specialized cryptography conferences.

• Biomedical Engineering
• Biosensors
• Surface Plasmon Resonance
• +7 more

Brent D Cameron is a Professor in the Department of Bioengineering at the University of Toledo's College of Engineering. His research focuses on biomedical engineering, with emphasis on biosensors, surface plasmon resonance, non-invasive glucose monitoring, and optical polarimetry. Over a career spanning decades, he has pioneered advancements in real-time physiological glucose sensing, DNA aptamer-based diagnostics, and wearable biosensing technologies. Research Trends: His publications reflect expertise in Biosensors for glycated hemoglobin and cortisol Aptamer microarrays and DNA origami Surface plasmon resonance and optical polarimetry Machine learning for glycemic prediction Transdermal biomarker detection Collaborations: He has published extensively with co-authors including Scott M. Pappada and Dong Shik Kim, covering areas from cancer therapy to diabetes management. His work has been referenced in patents and widely shared in academic and clinical settings.

Heng-Ming TaiView profile

Professor

• Signal Processing
• Image Processing
• Power System Reliability
• +1 more

Heng-Ming Tai is a Professor of Electrical and Computer Engineering at The University of Tulsa, where he serves as the graduate advisor in the ECE department. His academic journey began with a B.S. in Electrical Engineering from National Tsing-Hua University in 1980, followed by an M.S. in 1983 and a Ph.D. in 1987, both from Texas Tech University. Dr. Tai's research spans multiple critical areas in electrical engineering, with primary focus on signal/image processing , power system reliability , and power electronics . His work bridges theoretical foundations with practical applications, particularly in thermal management of IGBT module, monitoring technique for power converter, image encryption, biomedical image segmentation using neural network technique, and load control using age-of-information scheme. His research addresses contemporary challenges in power systems, including the integration of renewable energy sources and the development of more reliable and efficient power electronics systems. Analysis of Dr. Tai's recent publications reveals a strong trend toward interdisciplinary research that combines power systems engineering with advanced computational techniques. His work increasingly focuses on the intersection of power electronics, control systems, and data science, particularly in areas like age-of-information based control systems, reliability evaluation of composite power systems with renewable integration, and advanced thermal management strategies for power semiconductors. His research demonstrates a consistent evolution from fundamental power electronics toward more complex system-level challenges in modern power grids. As the graduate advisor for the Electrical and Computer Engineering department, Dr. Tai plays a crucial role in mentoring the next generation of engineers. While specific details about his grant history are not provided in the available information, his extensive publication record spanning multiple decades suggests a sustained research program with likely significant external funding support. Dr. Tai's work is centered in the Electrical and Computer Engineering department at The University of Tulsa, where he contributes to both the academic and research missions of the college. His research on power electronics and thermal management likely involves laboratory facilities for power converter testing and thermal analysis, though specific lab names are not mentioned in the available information.

• Internet of Things
• Machine Learning
• Data Security
• +4 more

Farhan Amin is a prolific researcher in computer science with a focus on Internet of Things (IoT) technologies, data security, and smart systems. He has published extensively in high-impact journals like IEEE Access and PeerJ Comput. Sci., collaborating with international experts such as Rashid Abbasi and Gyu Sang Choi. His work spans domains including smart city analytics, medical data protection, and next-generation IoT frameworks. Key Contributions: Dynamic trust management in IoT, 3D arthritis detection models, and hybrid data watermarking techniques. Collaborators: Rashid Abbasi, Salabat Khan, Abdul Mateen, Isabel de la Torre Díez. His research bridges theoretical innovation and practical applications, particularly in cybersecurity for IoT, network navigation, and healthcare analytics. Recent publications highlight advancements in zero-knowledge proofs, deep learning for medical imaging, and 6G-enabled healthcare systems. Farhan’s work on anticancer peptide prediction and child custody decision modeling demonstrates interdisciplinary reach. While not explicitly stated, his output suggests a faculty or senior research role at an institution engaged in cutting-edge computational science.

Leonardo QuerzoniView profile

Professor

• Cybersecurity
• Stream Processing
• Dependable Distributed Systems
• +6 more

Leonardo Querzoni is a Professor at the Department of Computer, Automatic and Management Engineering Antonio Ruberti within Sapienza University of Rome . He currently serves as Deputy Rector for Digital Technologies and Cybersecurity (2024-2026) and President of the CYBER 4.0 Competence Center since 2022. His academic career spans over two decades with 120+ publications. PhD in Computer Engineering (2007) with thesis "Techniques for Efficient Event Routing" Coordinates Italy's first joint PhD program in Cybersecurity (Sapienza-Luiss) Co-author of Italy's National Cybersecurity Framework Research spans Cybersecurity , Stream Processing , Dependable Distributed Systems , and Big Data Scalability . Particular focus areas include: Malware analysis and incident forensics Secure distributed systems Binary code analysis using neural networks Security for cyber-physical systems IoT attack triage Stream processing optimization Key awards: 2023 DEBS 10 Years Time Award 2021 ASPLOS Distinguished Paper 2019 DIMVA Best Paper Runner-Up 2017 DEBS 10 Years Time Award Active in European projects EU-GUARDIAN, ATENA, and national initiatives like PRIN TENACE. Industry collaborations include: Presidenza del Consiglio dei Ministri Leonardo-Finmeccanica CISCO Research Banca MPS

• Nuclear Engineering
• Uncertainty Quantification
• Nuclear Safety
• +2 more

Hany Abdel-Khalik is an Associate Professor of Nuclear Engineering in the College of Engineering at Purdue University. His research focuses on nuclear engineering with specialization in uncertainty quantification for nuclear systems, anomaly detection methodologies, and nuclear criticality safety. He is affiliated with the Global Engineering Program at Purdue. Abdel-Khalik's work bridges nuclear engineering with advanced computational methods, including data-driven approaches for monitoring additive manufacturing processes and securing industrial control systems. His recent research examines in-situ monitoring of material properties, void fraction estimation in nuclear fuels, and advanced methodologies for nuclear criticality safety assessment. His technical contributions include developing deceptive infusion techniques for data security, feature extraction methods for anomaly detection, and uncertainty reduction frameworks for model validation. These innovations support safer and more reliable nuclear energy systems.

Muhammad IkramView profile

Senior Lecturer

• Cybersecurity
• Privacy-Preserving Systems
• Machine Learning for Security
• +5 more

Dr. Muhammad Ikram is a Senior Lecturer in Cybersecurity at Macquarie University's School of Computing, affiliated with the Information Security and Privacy (ISP) group and the Macquarie Cybersecurity Hub (MCHUB). His research focuses on privacy/security in mobile/Web platforms, leveraging machine learning for fraud detection and vulnerability analysis. He holds a PhD in Electrical Engineering from UNSW, a Master's from Ajou University, and a Bachelor's from UET Peshawar. Ikram has published 64+ works in top conferences/journals like Usenix Security, NDSS, and ACM TOPS. His notable contributions include analyzing iOS app vulnerabilities, tracking prevention mechanisms, and mobile health app privacy risks. He received the Best Paper Award at AsiaCCS 2019 and Outstanding Paper at Mobiquitous 2021. Research Interests: Mobile/Web Security, Malware Detection, Privacy-Preserving Systems Grants: Led projects on network security, telecommunications obligations, and cybersecurity hubs Media Impact: Featured in The Guardian and over 50 outlets for work on web resource loading (~11M audience reach) Service Roles: Technical Program Committee member at WWW/PETS, reviewer for CCS/PETS He previously held roles as a Postdoc at University of Michigan and Research Scientist at Data61. His work bridges academic research with real-world impact in cybersecurity policy and industry standards.

Christophe BidanView profile

Professor

• Network Security
• Information-Flow Control
• Privacy in Distributed Systems
• +5 more

Christophe Bidan is an active French computer-security researcher whose work centers on network security, information-flow control, trust models, and privacy in distributed and mobile systems. He is a key member of the CIDre security research group based in Rennes, France, and has co-authored 44 scientific publications between 2023 and 2025. Education & Affiliations: Long-standing affiliation with Université de Rennes 1 / INSA Rennes and the CIDre security research group. Regular collaboration with French academic and defense institutions on tactical network security. Research Interests: His research spans several inter-locking domains: Secure Routing in MANETs: design of preventive and corrective mechanisms for protocols such as OLSR and AODV in tactical environments. Information-Flow Control: formal models and runtime enforcement techniques for browsers and distributed systems. Privacy Engineering: policy languages, conflict resolution, and encryption techniques for images and social networks. Trust & Reputation: implicit trust analysis, autonomic reasoning, and misbehavior detection in ad-hoc networks. Scientific Awards: No specific awards were mentioned in the provided text. Advising & Grants: No explicit list of PhD students or funded projects was supplied. Laboratories & Teams: Christophe Bidan is a core investigator within the CIDre security research group in Rennes, which brings together faculty and researchers from Université de Rennes 1, INSA Rennes, and Supélec to address challenges in network security, distributed systems, and critical infrastructure protection.

Filippo StancoView profile

Professor

• Image Processing
• Computer Vision
• Machine Learning
• +7 more

Filippo Stanco is a Full Professor at the Department of Mathematics and Computer Science at the University of Catania. He serves as President of the Bachelor's Degree in Computer Science (since 2017) and Deputy Rector for Technological Innovation (since 2019). His career includes positions as Associate Professor (2014-2023), Assistant Professor (2006-2014), and research fellowships at the Universities of Catania and Trieste. Education: PhD in Computer Science (2003) from University of Catania Bachelor's Degree in Computer Science (1999, summa cum laude) from University of Catania His research spans image processing, computer vision, and machine learning with applications in cultural heritage preservation, medical imaging, industrial automation, and multimedia systems. He leads the Archeomatica Project developing digital tools for archaeology and coordinates multiple national research projects including DREAMIN and CLEAR. Stanco has published extensively on food image analysis, document authentication, video processing, and heritage conservation. His work frequently employs deep learning, 3D modeling, and color science techniques across interdisciplinary domains. Honors: Best Session Paper Award at IPAS 2020 Cookpad Student Travel Grant at CEA 2018 Meritorious Papers recognition in Computers in Biology and Medicine (2016) He coordinates the Archeomatica research group and serves on editorial boards for multiple journals including ACM Journal of Computing and Cultural Heritage and IET Image Processing. Stanco has supervised over 200 theses and teaches courses on multimedia systems, game development, and image processing.