Yan Chen is a Professor of Computer Science at Northwestern University's Robert R. McCormick School of Engineering and Applied Science. He leads the Northwestern Lab for Internet and Security Technology (LIST) and the Center for Ultra-scale Computing and Information Security. His research focuses on cybersecurity, network measurement, and distributed systems security. Chen holds a Ph.D. from UC Berkeley (2003), M.S. from SUNY Stony Brook, and B.E. from Zhejiang University. Research interests include securing networking systems, intrusion detection, cloud-native platforms, and mobile security. Notable awards include the DOE Early CAREER Award (2005), Air Force Young Investigator Award (2007), and ACM ASPLOS'18 Most Influential Paper Award. His work has been cited over 17,000 times with an h-index of 62 (2024). Key contributions include the LIST lab's advancements in APT detection, provenance tracking in microservices, and security frameworks like FlowCog. He advises numerous Ph.D. students and has graduated over 20 researchers now in academia and industry.
Lorenzo Cavallaro is a Full Professor of Computer Science at University College London (UCL), specializing in Trustworthy AI for Systems Security. His research focuses on developing learning-based methods that are robust against adversaries by understanding the interplay between program analysis, representations, and machine learning models. His research interests span multiple critical areas in cybersecurity, including adversarial machine learning, malware detection, program analysis, and security evaluation. Cavallaro's work particularly emphasizes the challenges of concept drift in security systems and the development of robust defenses against evolving threats. His research has significant implications for Android security, binary analysis, and memory safety in embedded systems. Analysis of his recent publications (2024-2025) reveals a strong focus on addressing fundamental challenges in ML-based security systems. His work spans malware detection systems that maintain reliability under distribution shifts, adversarial attacks in the problem space, context-driven approaches using LLMs for security applications, and temporal invariance in malware detection. A recurring theme is the critical examination of whether ML-based security systems are truly robust and reliable in real-world scenarios. Cavallaro serves in significant editorial and advisory roles including the NDSS Steering Group (2023-2026), Associate Editor for Computer & Security and ACM TOPS, and Scientific Advisory Board for SERICS. He has been actively involved in program committees for top security conferences including IEEE S&P, USENIX Security, CCS, and NDSS from 2021-2025. He teaches Malware (COMP0060; 2022—ongoing), Research in Information Security (COMP0057; 2021—23), and Computer Security 2 (COMP0055; 2021—ongoing) at UCL, contributing to the next generation of security researchers and practitioners.
Hassan Khan is an Associate Professor at the University of Guelph's School of Computer Science, with research spanning security, systems, and human-computer interaction (HCI). He is a member of the Centre for Advancing Responsible and Ethical Artificial Intelligence (CARE-AI). Research Interests: His work focuses on improving AI-driven mobile security systems through human-in-the-loop evaluations, addressing vulnerabilities in continuous authentication, shoulder surfing, and privacy in enterprise/repair settings. He explores how users interact with security mechanisms and designs interfaces to enhance usability. Scientific Recognition: He has received the NSERC Early Career Researcher Award and a NSERC Discovery Grant, with media coverage in outlets like Time Magazine, The Globe and Mail, and New Scientist. Teaching: Khan teaches courses such as Computer Security Foundations and Advanced Penetration Testing, emphasizing practical cybersecurity and AI systems architecture.
Yepang Liu is a tenured Associate Professor in the Department of Computer Science and Engineering at Southern University of Science and Technology (SUSTech) in Shenzhen, China. He leads the Software Quality Lab and serves as director of the Trustworthy Software Research Center within the Research Institute of Trustworthy Autonomous Systems. His educational background includes a B.Sc. with honors from Nanjing University (2010) and a Ph.D. from the Hong Kong University of Science and Technology (2015), where he was supervised by Prof. Shing-Chi Cheung. Prior to joining SUSTech, he worked as a postdoc at HKUST's CASTLE Lab and Cybersecurity Lab. Liu's research primarily focuses on software testing and analysis, empirical software engineering, AI for SE, software security, and trustworthy AI. His work bridges traditional software engineering with cutting-edge AI technologies, particularly in automated testing, security analysis, and quality assurance for mobile, blockchain, and extended reality applications. Recent projects explore how large language models can enhance bug detection, improve testing automation, and address fairness issues in machine learning systems. His contributions have been recognized with three ACM SIGSOFT Distinguished Paper awards (ICSE 2021, ASE 2016, ICSE 2014) and one Distinguished Artifact award (ICSE 2019). He has also received the ACM SIGSOFT Service Award and Distinguished Reviewer Award for his extensive service to the software engineering community. Top-10 Most Active Early-Stage Software Engineering Researcher (2013-2020) Top-10 Most Popular Instructor Among 2024 Undergraduate Graduates at SUSTech Junior Faculty of the Year (2021) SUSTech Teaching Excellence Award (2021) Outstanding Mentor Award (2020, 2024) Liu actively serves on the editorial boards of Empirical Software Engineering (EMSE) and Journal of Computer Science and Technology (JCST). He has participated in over 80 conference committees including leadership roles in ICSE, FSE, ASE, and ISSTA. His research is supported by the National Natural Science Foundation of China, National Key Research and Development Program, and leading Chinese IT companies. He regularly mentors PhD and MSc students and has guided multiple national competition award-winning teams. The Software Quality Lab under Liu's direction focuses on innovative approaches to software testing, security analysis, and quality assurance across various platforms including mobile, blockchain, and extended reality applications. Current projects emphasize the integration of AI techniques with traditional software engineering practices to address emerging challenges in software quality.
Yonghwi Kwon is a Visiting Assistant Professor in the Department of Computer Science at the University of Virginia. His research focuses on software systems security, cyber forensics, and software engineering. He received the CAREER Award for developing dynamic defenses against cyber threats. His work emphasizes securing software from cyber attacks, recovering forensic evidence, and improving software testing and reverse engineering techniques. Key research areas include memory safety mechanisms, automated vulnerability detection in web applications and mobile systems, and forensic analysis of phishing campaigns. He has pioneered frameworks like CMASan for memory allocator-aware sanitization and Racedb for detecting race conditions in database-backed systems. His contributions span cloud security automation, kernel exploitation analysis, and embedded system fuzzing. Notable achievements include the 2025 CAREER Award supporting his dynamic defense research, and impactful publications in areas like Android information leakage detection (DryJIN), Bluetooth protocol fuzzing (BTFuzzer), and autonomous driving bug discovery (Drivefuzz). His work bridges theoretical computer science with practical cybersecurity solutions.
Dr. Siqi Ma is a Senior Lecturer at the UNSW Institute for Cyber Security (IFCYBER) within the School of Systems & Computing at the University of New South Wales (UNSW). He previously served as a Lecturer at the University of Queensland's School of Information Technology and Electrical Engineering (ITEE). He holds a Ph.D. in Information Systems from Singapore Management University (2018) and was a Postdoctoral Research Fellow at Data61, CSIRO. He also visited Carnegie Mellon University (CMU) in 2015. Current Role: Senior Lecturer, UNSW Institute for Cyber Security Former Role: Lecturer, University of Queensland Education: Ph.D. (Singapore Management University), Postdoc (Data61, CSIRO) His research spans automated vulnerability detection, mobile security, IoT security, network authentication, and graph-based adversarial robustness. Recent work focuses on drone configuration bugs, Android malware analysis via GNNs, federated learning privacy, and credential leakage in open-source projects. Key trends in his 2024-2025 publications include automated security analysis for embedded systems, deepfake detection in multimedia, and privacy-preserving mechanisms for distributed networks. He collaborates with institutions like Purdue University, Singapore Management University, and CSIRO Data61.
George Vasilakopoulos is a Professor in the Department of Digital Systems at the University of Piraeus , where he also serves as Vice-Chancellor for Academic Affairs and Personnel. By law, he is President of the Quality Assurance Unit (MODIP) and the Employment and Career Structure (DASTA) of the university, overseeing the development of modern information systems. He earned his PhD from the University of London and has held leadership roles including Department President, Director of Postgraduate Programs, and Scientific Director of the Digital Health Services Laboratory. PhD: University of London Current Roles: Vice-Chancellor, Department of Digital Systems Professor Labs: Digital Health Services Laboratory His research focuses on Health Informatics , Cloud Computing , and Medical Data Security , with key contributions to: Emergency healthcare process automation Privacy-preserving personal health record systems Context-aware authorization models Cloud-based medical service frameworks Machine learning in clinical data analysis Interoperable health information systems The trends in his 15 most recent articles (2010-2015) reveal a consistent emphasis on integrating cloud infrastructure , semantic technologies , and mobile platforms to enhance emergency care, chronic disease management, and patient data security. His work bridges biomedical engineering , software architecture , and public health policy . He has held advisory roles for the Minister of Health on IT issues, served on hospital boards, and contributed to national committees for healthcare technology standards. His professional activities include project evaluation for Greek and European research programs and authoring three books on health informatics.
V.S. Subrahmanian is the Walter P. Murphy Professor of Computer Science at Northwestern University and a Faculty Fellow at the Northwestern Buffett Institute for Global Affairs. His research focuses on AI-driven solutions for security challenges, including forecasting terror attacks, preventing poaching, and analyzing social media threats. Education includes a PhD and MS in Computer Science from Syracuse University (NY) and an MSc (Tech.) from Birla Institute of Technology and Science (India). Research interests span AI applications in cybersecurity, predictive modeling for security policy, and machine learning for geospatial/social network analysis. Recent work addresses deepfakes, banking crisis forecasts, and airline profit optimization. Notable publications include works on Android malware detection, adversarial attack defenses, and geospatial conflict analysis. His research has influenced international security policies and industry practices. No scientific awards are listed in the provided text. Advising/grants information appears incomplete in the source material. Active involvement in multidisciplinary teams tackling global security challenges is implied through his institutional affiliations.
Mitra Bokaei Hosseini is an Assistant Professor in the Department of Computer Science at the University of Texas at San Antonio (UTSA), part of the College of Sciences. She holds a Ph.D. in Computer Science from UTSA, an M.S. in Information Technology from K.N. Toosi University of Technology, and a B.S. in Information Technology from Qazvin Islamic Azad University. Her research focuses on legal compliance, natural language processing (NLP), privacy, and software engineering, with an emphasis on regulatory compliance frameworks, privacy policy analysis, and automated tools for policy adherence. Her work bridges NLP techniques with practical applications in software development and mobile security. Key research trends in her articles include privacy policy analysis, automated extraction of regulatory requirements, and the use of machine learning (e.g., few-shot learning, large language models) to align code with privacy policies. Her work addresses challenges in disambiguating policy ambiguities, identifying third-party entities, and ensuring compliance in mobile applications. No scientific awards are explicitly mentioned. Her advising record and grants are not detailed in the provided texts. She may be affiliated with research teams or labs focused on privacy and NLP, though specifics are not listed.
Nidhi Rastogi is an Assistant Professor at the Department of Software Engineering within the Golisano College of Computing and Information Sciences (GCCIS) at Rochester Institute of Technology (RIT). She leads the AI4Sec Research Lab, which focuses on data-driven AI solutions for cybersecurity, emphasizing interpretability and practical applications. Her research interests span Cyber Threat Intelligence, Artificial Intelligence, Graph Analytics, and Healthcare Analytics. Education: Ph.D. in Computer Science (2018), Rensselaer Polytechnic Institute M.S. in Computer Science (2008), University of Cincinnati Bachelor of Information Technology (2003), University of Delhi Research Interests: Transdisciplinary work in cybersecurity, AI, heterogeneous networks, and graph analytics. She develops systems for threat intelligence, explainable AI, and security monitoring. Notable projects include the CyNER library for cybersecurity NER, TINKER framework for open-source CTI, and personal health knowledge graphs. Awards & Mentoring: Students advised include Le Nguyen (1st place at UPSTAT23), Tanvirul Alam (IEEE SP Travel Grant), and Dipkamal Bhusal. Recent recognitions include program committee roles for ACM CCS'24 and ACSAC'24. Labs & Collaborations: The AI4Sec Lab collaborates with federal agencies, national labs, and enterprises. Current projects address autonomous vehicle security, healthcare analytics, and systemic cyberattack detection using graph-based methods.
Dr. Mohammad Iftekhar Husain is a Professor and Graduate Coordinator in the Department of Computer Science at California State Polytechnic University, Pomona (Cal Poly Pomona). He serves as the Inaugural Director of the PolySec Cyber Lab, a federally funded center for cyber security and forensics education, research, and outreach (~$2.5M in grants), and directs the university's Virtual Reality Lab. His career spans over a decade of leadership in cyber security program development, extramural funding, and academic governance. Education: B.S., Computer Science, Yamagata University (Japan) M.S. & Ph.D., Computer Science and Engineering, SUNY-Buffalo Dr. Husain's research focuses on data privacy in social networks, neurophysiological cyber security solutions, and blockchain applications. He has secured $18.4M in principal investigator grants, including NSF SFS, EAGER, and REU Site projects, and trained students placed in top institutions like UC campuses, MIT Lincoln Lab, and government agencies such as NSA and DHS. His work on brainwave authentication earned a US patent (USPTO 10,198,566) and media coverage in Time Magazine and PC Magazine . Scientific Awards: 2016 College of Science Distinguished Teaching Award Early Promotion and Tenure (2016) 2020 Faculty Learning Community for Leadership Pipeline Development Cohort As academic leader, he founded the Cal-Bridge CS Ph.D. pathway program for underrepresented students, chairs the CPP Academic Senate Academic Programs committee, and led university IT initiatives including Cyber Security Cluster Hiring and High-Performance Computing Lab development.
Aravind Machiry is an Assistant Professor at Purdue University's Electrical and Computer Engineering Department and a founding member of the Purdue Systems and Software Security (PurS3) Lab . His research focuses on system security, particularly vulnerability detection, prevention, and secure system development using static/dynamic program analysis, fuzzing, type systems, and machine learning. Designing practical solutions for software and embedded system security Recipient of NSF CAREER and Amazon Research awards Active participant in SPLASH 2025 as OOPSLA Review Committee member His recent work includes automated vulnerability detection in embedded software, spatial memory safety enhancements, and security analysis of GitHub workflows. He has received recognition for his research through multiple distinguished paper awards and industry funding. Selected scientific awards include NSF CAREER Award (2024) Amazon Research Award (2022) Test of Time Award at FSE 2023 for DynoDroid Distinguished Paper Award at OOPSLA 2022 for 3c Qualcomm Innovation Fellowship (2025) His research team has developed frameworks like ARGUS for taint analysis of CI/CD workflows and FuzzUEr for UEFI interface fuzzing, discovering hundreds of critical vulnerabilities in open-source projects and thousands of command injection flaws in GitHub repositories.
Dr. Lenka Mareková is a postdoctoral researcher in the Applied Cryptography Group at ETH Zürich. Previously, she completed her PhD in the Information Security Group at Royal Holloway, University of London, supervised by Martin R. Albrecht. Affiliation: ETH Zürich - Department of Computer Science Cryptographic analysis of secure messaging platforms Research focus on end-to-end encryption and real-world security implementations Her research interests span cryptographic protocol analysis, secure communication systems, and privacy-preserving technologies. The 10 most recent publications in her DBLP profile reflect expertise in: Secure messaging protocols (Telegram, Delta Chat) Cloud storage security (MEGA analysis) E-voting systems (Zerovote, E-cclesia) Mobile security frameworks (Android policy enforcement) Mesh network vulnerabilities Current work appears to focus on practical cryptographic implementations and their real-world security implications. Contact via hi@lenka.sh or lenka.marekova@inf.ethz.ch .
Prof. Dr. Johannes Kinder is a Professor and Chair of Programming Languages and Artificial Intelligence at the Institute of Informatics , Ludwig Maximilian University of Munich. His research focuses on software security through program analysis and machine learning, particularly targeting malware detection , vulnerability analysis , and reverse engineering . He has held faculty positions at Royal Holloway, University of London, and Bundeswehr University Munich. Research Interests include: Securing software systems via program and machine learning techniques Detection of software vulnerabilities and malware Preventing exploitation through binary analysis Applications of formal methods in systems security Recent Publications highlight advancements in binary function embedding , malware detection in npm , and speculative execution attack modeling . His work appears in top venues like USENIX Security and IEEE S&P . Education : Diplom from TU Munich (2005), Doctorate from TU Darmstadt (2010). Professional Roles : General Chair, ACM CCS 2019 Doctoral Symposium Chair, ESSoS 2016 Program Committee member for NDSS 2026, IEEE S&P 2022-2025
Andrea Continella is an Associate Professor at the Faculty of Electrical Engineering, Mathematics and Computer Science of the University of Twente, where he contributes to the International Secure Systems Lab (iSecLab). His research focuses on systems security, particularly embedded firmware security, Android app security, malware detection, and program analysis techniques for vulnerability discovery. Ph.D. in Computer Science and Engineering, Politecnico di Milano (cum laude) Postdoctoral Researcher, Computer Science Department, UC Santa Barbara Visiting Researcher, School of Computer Science, University of Sydney Key research contributions include: Developing automated analysis techniques for embedded firmware (KARONTE, ShieldFS) Creating privacy leak detection mechanisms for mobile applications Designing ransomware defense systems using self-healing filesystems Advancing IoT security through misconfiguration detection (S3 buckets) and protocol analysis Pioneering semi-supervised methods for network traffic fingerprinting (FlowPrint) Scientific awards: Dutch Cyber Security Best Research Paper Award 2024 Runner-up USENIX Security Distinguished Reviewer Award 2024 Professional activities: Keynote speaker on firmware security (2024) Member of IPN Cyber Security Special Interest Group Oral presentations on automated vulnerability research (2023)
