جست‌وجوی استاد و پژوهشگر

JASON HANNAمشاهده پروفایل

استادیار

• Cancer Biology
• Molecular Biology
• Angiosarcoma Development
• +۴ مورد دیگر

Dr. JASON HANNA is an Assistant Professor in the Department of Biological Sciences at Purdue University, affiliated with the College of Science. His research focuses on cancer cell and molecular biology, particularly studying microRNAs in angiosarcoma development and metastasis. He leads the Hanna Lab, which investigates vascular sarcomas such as angiosarcoma and epithelioid hemangioendothelioma (EHE), aiming to uncover genetic drivers, tumor suppressors like DICER1, and develop precision therapies for these aggressive cancers. Education includes a Ph.D. from Yale University and postdoctoral research at St. Jude Children's Research Hospital. His work integrates genetic models, in vivo studies, and cell-line investigations to address tumor initiation, progression, and therapeutic design. Research interests span molecular mechanisms of sarcomagenesis, microRNA-mediated regulation, and translational approaches to combat rare vascular cancers. His lab’s goals include identifying metastasis mediators and advancing targeted treatments for these poorly understood malignancies. Publications span digital imaging forensics (e.g., JPEG compression detection, printer source identification) and biomedical engineering (e.g., force myography for locomotion analysis), reflecting interdisciplinary collaboration. Awards and grants are not explicitly listed, but his work demonstrates sustained innovation in both biological and computational domains.

• Forensic Science
• Fingerprint Analysis
• Nanoparticle Applications in Forensics
• +۳ مورد دیگر

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 Moreiraمشاهده پروفایل

استادیار

• Computer Vision
• Machine Learning
• Media Forensics
• +۳ مورد دیگر

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 HOUMANIمشاهده پروفایل

مدرس

• Biomedical Engineering
• Signal Processing
• Neuroscience
• +۵ مورد دیگر

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. 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.

• Biomedical Informatics
• Artificial Intelligence in Healthcare
• Machine Learning
• +۱۲ مورد دیگر

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 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.

B.S. Manjunathمشاهده پروفایل

استاد

• Image/Video Analysis
• Multimedia Databases
• Steganography
• +۳ مورد دیگر

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 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.

• 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.

Renjie Huمشاهده پروفایل

استادیار

• Machine Learning
• Data Science
• Computer Vision
• +۲ مورد دیگر

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 Bäckströmمشاهده پروفایل

استاد

• Speech Processing
• Audio Engineering
• Privacy and Security in Technology
• +۳ مورد دیگر

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 Wangمشاهده پروفایل

استاد

• Signal Processing
• Machine Learning
• Artificial Intelligence
• +۴ مورد دیگر

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 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.