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Gouenou COATRIEUX is a full Professor at IMT Atlantique Bretagne-Pays de la Loire, affiliated with the Department of Image and Information Processing (ITI) and the LaTIM Inserm U1101 laboratory . His research focuses on medical imaging security , cybersecurity in healthcare systems , and federated learning . Key interests include DNA-based data storage, privacy-preserving AI, and intrusion detection in cyber-physical systems. Research activities span medical image steganography , secure federated learning frameworks , and biomedical data anonymization . Notable projects address GDPR compliance in clinical data warehouses and watermarking techniques for AI model integrity. He has collaborated on digital twin technologies for anomaly detection in critical infrastructure. Recent work emphasizes non-IID federated learning defense mechanisms , CT imaging enhancement , and homomorphic encryption applications. Over 150 peer-reviewed publications demonstrate expertise in interdisciplinary topics like medical IoT security and deep learning for radiology. No scientific awards are listed, though his contributions to healthcare cybersecurity are widely recognized in academic circles. Advising and grant details are not explicitly stated in available materials. Primary affiliation is with IMT Atlantique's Brest campus.

• Medical Imaging
• Cybersecurity
• Federated Learning
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Denis Migdal is an Associate Professor (Maître de Conférence) at the University of Clermont-Ferrand and a member of the LIMOS laboratory . His research focuses on privacy-compliant biometric authentication systems, particularly leveraging keystroke dynamics for secure online transactions. He holds a PhD from 2019 titled "Contributions to keystroke dynamics for privacy and security on the Internet." Education: PhD in Computer Science, University of Clermont-Ferrand (2019) Internships at the University of Oslo (2016-2017) on the OffPAD project , resulting in 6 demonstrators presented at ACM CCS 2016. Research Interests: Privacy-preserving authentication protocols, keystroke dynamics anonymization, synthetic dataset generation for behavioral analysis, and trusted device architectures (e.g., OTDP, TLS Switching). He collaborates with institutions like the University of Reggio Calabria on social network identity verification. Key Contributions: Proposed Privacy Identity Code frameworks respecting user privacy. Developed methods to anonymize keystroke data and generate synthetic datasets (published in FGCS Q1). Co-designed the OffPAD trusted device architecture and TLS Switching protocols to mitigate malware threats in client devices. Collaborations: OffPAD consortium, University of Oslo, University of Reggio Calabria. His work addresses vulnerabilities in client-side authentication and enhances cybersecurity for critical sectors like healthcare and banking.

• Biometrics
• Privacy
• Authentication
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Davide Balzarotti is a Professor and Head of the Digital Security Department at EURECOM. His expertise spans reverse engineering, forensic memory analysis, advanced malware analysis, and web security. He holds a PhD in Computer Engineering from the Polytechnic Institute of Milan (2006), followed by a postdoctoral fellowship at the University of California, Santa Barbara (2006–2008), and an HDR (Habilitation à Diriger des Recherches) from EURECOM in 2015. His research focuses on understanding malware behavior, improving forensic techniques, and enhancing web security through machine learning and static analysis. Notable contributions include frameworks like ModSec-Learn for boosting intrusion detection and PhishEye for monitoring phishing kits. He has received multiple awards, including the Best Paper Award at SAC 2017 and the ACM Europe Student Best Paper Award at CCS 2016. His work bridges academic research and practical cybersecurity challenges, addressing topics such as adversarial attacks, embedded system security, and evasion techniques in malware. His projects include evaluations of voting machine security (Project Everest) and collaborations to improve vulnerability detection in software. Education: PhD in Computer Engineering, Polytechnic Institute of Milan (2006) Postdoctoral Research at UCSB (2006–2008) HDR, EURECOM (2015) Awards: Best Paper Award (2017) ACM Europe Student Best Paper Award (2016) Best Student Paper Award (2013) Distinguished Paper Award (2011) Key Research Themes: Memory forensics Malware analysis Fuzzing and vulnerability discovery Web application security

• Reverse Engineering
• Forensic Memory Analysis
• Advanced Malware Analysis
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Sukyoung Ryu is a Professor at KAIST, focusing on programming languages and static program analysis. Her work spans JavaScript analysis, WebAssembly, Rust translation frameworks, and language specification verification. Education : Not explicitly mentioned Research Focus : Programming languages, static analysis, JavaScript, WebAssembly, Rust, Android hybrid apps, type systems Conference Roles : Co-chair (OOPSLA Review Committee, New Faculty Symposium), Committee Member (ICFP, ECOOP, ASE, PLDI, ICSE), Session Chair (SCAM, Type Systems, Keynotes) Keynote Talks : PLDI 2021, ICFP 2020, Scala 2019 Tool Development : SAFE framework, SpecTec, Wasm-R3, HybriDroid, WALA extensions Her recent publications focus on WebAssembly benchmarks, language specification verification, and memory/concurrency safety in cross-language translation. She contributes to open-source analysis tools and standards development. As a committee member and session chair for conferences like PLDI, ICFP, and SPLASH, she actively shapes research directions in programming language design and analysis. Her lab at KAIST develops tools like SpecTec for mechanized language specifications.

• Programming Languages
• Static Program Analysis
• WebAssembly
• +3 more