Find Academic Supervisors

• Forensic Science
• Fingerprint Analysis
• Nanoparticle Applications in Forensics
• +3 more

Sebastien Moret is a Senior Lecturer in Forensic Science at the College of Science and Engineering, Charles Darwin University. His work focuses on advancing fingerprint detection methodologies and forensic document analysis, with a particular emphasis on nanoparticle applications and evidence reliability. Key Research Areas: Latent fingermark detection, nanoparticle functionalization, forensic profiling of fraudulent documents, and biodegradable material impacts on evidence recovery. Recent Contributions: Innovations in powder suspension formulations, synthetic secretions for controlled studies, and novel applications of silicon oxide and upconverting nanoparticles for fingerprint visualization. Collaborative Networks: Regularly collaborates with Scott Chadwick, Xanthe Spindler, Chris Lennard, and Claude Roux on forensic chemistry projects. Scientific Output Trends: Over the past decade, Moret has published extensively in Forensic Science International and related journals, addressing challenges in evidence standardization, non-destructive detection techniques, and forensic intelligence applications.

Daniel MoreiraView profile

Assistant Professor

• Computer Vision
• Machine Learning
• Media Forensics
• +3 more

Dr. Daniel Moreira is an Assistant Professor of Computer Science at Loyola University Chicago, affiliated with the College of Arts & Sciences. His research focuses on applying techniques from Computer Vision, Machine Learning, and Media Forensics to enhance scientific integrity and combat misinformation. He holds a PhD from the University of Campinas (Brazil), with postdoctoral experience at the University of Notre Dame's CVRL lab. Education: PhD in Computer Science (2016), University of Campinas MSc in Computer Science (2008), Federal University of Pernambuco BSc in Computer Science (2006), Federal University of Pará Research Interests: Media Forensics: Developing tools to detect synthetic content and paper mill fraud Biometrics: Post-mortem iris recognition and human-machine collaboration Scientific Integrity: Image provenance analysis for combating academic misconduct Computer Vision: Provenance tracking and manipulation localization Awards include the 2025 St. Ignatius Loyola Teaching Award and the 2017 Best Brazilian PhD Dissertation. He co-leads projects like Sci-Int (scientific integrity analysis) and SemaFor (semantic forensic analysis), with funding from NSF, DARPA, and HHS. Teaching includes courses on Machine Learning, Biometrics, and shell scripting at Loyola. He actively reviews for major journals/conferences like IEEE TIFS, CVPR, and ECCV, and serves as an Associate Editor for Pattern Recognition and IEEE Security & Privacy.

Nesma HOUMANIView profile

Lecturer

• Biomedical Engineering
• Signal Processing
• Neuroscience
• +5 more

Nesma HOUMANI is a Lecturer at Telecom SudParis, part of the SAMOVAR research team. Her work focuses on biomedical signal processing, neuroimaging, and biometric authentication systems. She has contributed to Alzheimer’s disease detection via EEG analysis, gait quality measurement for neurological disorders, and neurofeedback applications for cognitive enhancement. Education: PhD in Biomedical Signal Processing (2011) from National Institute of Telecommunications (Institut National de Télécommunications) Research Interests: EEG-based diagnostics for neurological disorders Biometric systems (online signatures, gait analysis) Neurofeedback and neuromodulation Machine learning for medical signal analysis Recent Article Trends: Her recent work (2020-2025) emphasizes clinical applications of signal processing in Alzheimer’s detection, gait deviation measurement for hemiparesis patients, and neurofeedback for cognitive reserve enhancement in the elderly. Collaborations span interdisciplinary teams in neuroscience, engineering, and medicine. Patents: "Method for generating information about the production of a handwritten, hand-affixed or printed trace" (US 11989979B2, 2024) "Identity verification method using handwritten signatures on digital sensors" (EP3942442, 2022) Grants & Labs: Active in SAMOVAR’s projects on biomedical signal analysis and neurotechnology. Involved in multidisciplinary initiatives like the BV2 wellbeing project for chronic disease patients.

Scott A. CraverView profile

Associate Professor

• Information Security
• Cryptology
• Steganography
• +3 more

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.

• Biomedical Informatics
• Artificial Intelligence in Healthcare
• Machine Learning
• +12 more

Dr. Shahzad Mumtaz is a Lecturer at the School of Natural and Computing Sciences, University of Aberdeen, where he contributes to research and education in computational health and data science. His work bridges artificial intelligence and healthcare, focusing on real-world applications in medical data modeling and digital health tools. Research Interests: His research spans biomedical informatics, machine learning, natural language processing, electronic health records (EHR), phenotype libraries, and medical image analysis. He is particularly active in developing tools for clinical data standardization, such as the Carrot tool for OMOP CDM, and in AI-driven solutions for fraud detection, deepfake identification, and clinical decision support. His interdisciplinary work integrates computer science with public health and clinical medicine. Publication Trends: His recent publications (2023–2025) emphasize AI applications in healthcare, including deep learning for fake medical image detection, NLP for financial text analysis, and vision transformers for weather and skin cancer classification. He frequently collaborates on large-scale health data initiatives like CO-CONNECT and the UK Phenotype Library, reflecting a strong focus on scalable, interoperable digital health infrastructure. Scientific Contributions: Core contributor to the CO-CONNECT project for national health data access during the pandemic. Developer of the Carrot tool for improving OMOP data curation. Active in advancing phenotype library standards and EHR-based research. Advising and Grants: While no formal students or grants are listed in the provided text, his extensive collaborative work suggests active involvement in research teams and potential supervision of graduate researchers. He is likely engaged in funded projects related to health data science and AI, given the scale and scope of his publications. Labs and Teams: Dr. Mumtaz is affiliated with research initiatives at the University of Aberdeen focused on biomedical informatics and trusted research environments. He collaborates with multidisciplinary teams across the UK, particularly in projects involving NHS data, digital phenotyping, and AI safety in healthcare.

Matthew StammView profile

Associate Professor

• Information Security
• Multimedia Forensics
• Machine Learning
• +5 more

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 VerdolivaView profile

Associate Professor

• Image and Video Forgery Detection
• Deepfake Video Detection
• GAN Fingerprints Attribution
• +5 more

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.

B.S. ManjunathView profile

Professor

• Image/Video Analysis
• Multimedia Databases
• Steganography
• +3 more

B.S. Manjunath is a Professor in the Department of Electrical and Computer Engineering at the University of California, Santa Barbara (UCSB). He serves as Director of the Center for Multimodal Big Data Science and Healthcare and the Center for Bio-image Informatics. His research focuses on image/video analysis, multimedia databases, steganography, and bio-informatics. Manjunath holds a Ph.D. from the University of Southern California (1991), M.E. from Indian Institute of Science (1987), and B.E. from Bangalore University (1985). He is an IEEE Fellow and ACM Fellow. Education: Ph.D. (1991), Signal & Image Processing Institute, EE-Systems, USC M.E. (1987), Systems Science and Automation, Indian Institute of Science B.E. (1985), Electronics Engineering, Bangalore University Research Interests: Image/video analysis (segmentation, registration, texture analysis) Multimedia databases and data mining Steganography and digital forensics Signal/image processing for bio-informatics His work includes developing the BisQue open-source platform for image informatics and leading NSF-funded initiatives in bio-image informatics and interactive multimedia. Key Contributions: Over 300 peer-reviewed publications 24 patented technologies Co-editor of the ISO/MPEG-7 multimedia standard Founder faculty of the Media Arts and Technology Program Awards and Affiliations: IEEE Fellow (2025) and ACM Fellow (2025) Affiliated with UCSB’s Computer Science Department and Dynamical Neuroscience Program Former Director of the NSF-sponsored Interactive Digital Multimedia IGERT program Labs and Platforms: Director of the Vision Research Lab Lead developer of the BisQue image informatics system Pioneering work in multimodal camera networks and healthcare analytics

Djamila AouadaView profile

Assistant Professor

• Computer Vision
• Signal Processing
• Image Processing
• +3 more

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.

Renjie HuView profile

Assistant Professor

• Machine Learning
• Data Science
• Computer Vision
• +2 more

Renjie Hu is an Assistant Professor in Information Science Technology at the University of Houston's Cullen College of Engineering, focusing on machine learning applications in computer vision and healthcare analytics. His research develops computational solutions for real-world challenges including medical diagnostics and algorithmic bias detection. Research interests include developing adaptive machine learning algorithms for imbalanced healthcare datasets, detecting biases in generative AI systems, and creating efficient visualization techniques. Current projects explore multi-view learning for enhanced computer vision tasks and clinical applications of predictive modeling. Recent publications (2019-2025) demonstrate innovation in extreme learning machines, healthcare data analysis, and human-computer interaction. Works consistently bridge theoretical algorithms with practical applications in healthcare and media analytics. Teaching responsibilities include courses on data science fundamentals and machine learning applications. Research collaborations extend to healthcare institutions for clinical data analysis projects.

Tom BäckströmView profile

Professor

• Speech Processing
• Audio Engineering
• Privacy and Security in Technology
• +3 more

Professor Tom Bäckström is a faculty member at Aalto University, focusing on advanced speech and audio processing technologies with a strong emphasis on privacy and security in voice communication systems. His work addresses challenges posed by smart devices and speech interfaces, particularly in ensuring privacy in public spaces and open environments. Research interests include speech privacy preservation, distributed speech/audio coding, federated learning for on-device speaker recognition, and the ethical design of conversational AI systems. His contributions span technical innovations like anonymization techniques for speaker attributes, real-time noise suppression, and privacy-aware acoustic systems. Recent studies highlight user perceptions of anonymity in voice assistants across cultures, optimization of vector quantization methods, and the integration of machine learning into speech enhancement frameworks. While no specific awards or grants are explicitly listed in the provided texts, his prolific publication record reflects sustained impact in signal processing and privacy research. He leads a research group exploring the intersection of audio engineering and privacy, emphasizing practical solutions for emerging technologies like IoT audio networks and smart office acoustics. His work bridges theoretical advancements with real-world applications, addressing both technical and socio-ethical dimensions of speech technology.

Wenwu WangView profile

Professor

• Signal Processing
• Machine Learning
• Artificial Intelligence
• +4 more

Wenwu Wang is a Professor of Signal Processing and Machine Learning at the University of Surrey's School of Computer Science and Electronic Engineering, and serves as Associate Head (External Engagement). He is Co-Director of the Machine Audition Lab within CVSSP and an AI Fellow at Surrey's Institute for People Centred Artificial Intelligence. Education: Holds a BSc, MSc, and PhD (specific disciplines not specified). Research focuses on unsupervised learning techniques , computational auditory scene analysis , and multimodal machine learning . Notable projects include audio captioning systems, text-to-audio generation (e.g., AudioLDM), and anomalous sound detection frameworks. Grants: £30M+ total portfolio (as PI: £2.2M; as CI: £14M+). Funders include EPSRC, MoD, EU, and industry partners like BBC/Huawei. Key Projects: AudioTurbo (fast text-to-audio), SemantiCodec (ultra-low bitrate audio), DCASE Challenge contributions. Lab Leadership: Machine Audition Lab explores intersections of audio perception and AI. Recent work emphasizes large audio models , cross-modal generation, and real-world applications in aquaculture monitoring and urban soundscapes.

Andrew SungView profile

Professor

• Machine Learning
• Deep Learning
• Cybersecurity
• +7 more

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.

SoumyashreeView profile

Assistant Professor

• Machine Learning
• Artificial Intelligence
• High-Performance Computing
• +3 more

Soumyashree is an Assistant Professor at the School of Computer Engineering, Manipal University, with 8 years of experience in academic and research domains. Her work bridges theoretical and applied aspects of Machine Learning, Artificial Intelligence, and Wireless Networks, focusing on solving complex problems in industrial and computational systems. Education: B.E., M.Tech, Ph.D. Research Interests: Machine Learning, AI, 5G Wireless Networks, Energy Efficiency Optimization, and Industrial Monitoring Systems. She has published extensively on topics like graph convolutional networks for resource allocation in 5G, AI-driven energy management in HPC, neural network-based tool wear monitoring, and deep learning architectures for agricultural disease detection. Recent Research Trends: Her publications emphasize applying ML/AI to diverse domains: optimizing wireless networks, enhancing energy efficiency in computing systems, predictive maintenance in manufacturing, plant disease classification, and digital forensics. Keywords include graphs , resources , neural networks , and computer vision . Email: soumya.shree@manipal.edu ORCID: 0000-0001-8988-0021

• Hardware-Software Co-design
• Deep Learning Security
• Intellectual Property Protection
• +5 more

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.