Dr. Thomas Tan is an Associate Professor at the School of Computing Engineering and the Built Environment , Edinburgh Napier University . Specializing in Cybersecurity , Internet of Things , and Machine Learning , he leads cutting-edge research in secure distributed systems and edge computing. Focus on federated learning security, malware classification, and vehicular network trust models Recipient of National Research Award 2017 (Oman) and SICSA Supervisor of the Year Honourable Mention Supervises PhD/MSc students in topics ranging from cyberbullying detection to blockchain authentication His 117+ research outputs include IEEE journal articles on UAV task scheduling, graph neural network security, and privacy-preserving IoT frameworks. Active grants cover federated unlearning evaluation (Carnegie Trust £73,564) and AI-driven cybersecurity collaboration (SICSA £2,000).
Briana Wellman is a Professor and Associate Dean for Academics at the School of Engineering and Applied Sciences, University of the District of Columbia (UDC). She leads academic affairs while maintaining active roles in research and teaching, particularly in robotics, artificial intelligence (AI), and cybersecurity. Her academic career includes chairing the Department of Computer Science and Information Technology from 2017 to 2024. Education: Earned a Doctor of Philosophy in Computer Science from the University of Alabama. Research Interests: Focus on multi-robot systems, AI-driven cybersecurity solutions, computer science education innovation, and applying robotics in interdisciplinary contexts like fashion and disaster relief. Recent work emphasizes post-pandemic learning strategies for urban commuter campuses and hybrid anomaly detection models. Publications: Recent contributions include studies on malware detection using neural networks, post-pandemic education frameworks, and multi-robot coordination algorithms. Her work bridges theoretical advancements with practical applications in security and education. Awards: Recognized with the 2023 Women of Color Rising Star Award for her contributions to STEM education and research. Grants & Leadership: Secured multiple NSF grants, including funding for HBCU learning communities in computing, cybersecurity research centers, and AI-cybersecurity partnerships. Her leadership extends to academic program development and curriculum innovation. Labs/Teams: Involved in NSF-supported initiatives such as the Cyber Security Research and Development (CSRD) Center and the AI-CyS research partnership, fostering collaborative research ecosystems.
Dr. Maher Salem is a Senior Lecturer (Assistant Professor) in Cybersecurity at King's College London, affiliated with the Department of Informatics within the Faculty of Natural, Mathematical & Engineering Sciences. He holds a PhD in Security Engineering from Kassel University (2014) and is a HEA Fellow. His research focuses on intrusion detection systems, cyber threat intelligence, machine learning-based security solutions, and blockchain applications in cybersecurity and finance. He has 15+ years of industrial experience with firms like AUDI AG and E-Plus, and has secured funding from the German Federal Ministry of Education and Research (BMBF). Dr. Salem leads the Computing Education Research Centre (CERC) at King's, advancing cybersecurity education through pedagogical tools and PhD supervision. His work bridges academia and industry, addressing challenges in online learning equity, cloud security, and automotive/IoT security via blockchain. Notable contributions include frameworks for threat intelligence detection, privacy-preserving cloud search, and decentralized transportation authentication systems. Education: PhD in Security Engineering (Kassel University, 2014) Affiliations: Former roles at Higher Colleges of Technology (UAE) and Fulda University of Applied Sciences (Germany) Labs/Teams: Security Hub (King's College), CERC His awards include the Emirates Skills First Rank Award (2017) and HEA Fellowship (2019). Research outputs span 25+ peer-reviewed articles, focusing on AI-driven penetration testing, blockchain applications, and cybersecurity education innovations. He collaborates globally on projects like dynamic cloud congestion management and APT detection frameworks.
Stjepan Groš is an Associate Professor at the Faculty of Electrical Engineering and Computing (FER) , University of Zagreb. His research focuses on cybersecurity , industrial automation , and network traffic analysis . Department of Electronics, Microelectronics, Computer and Intelligent Systems Expertise in machine learning applications for security and formal methods in SCADA systems Extensive work on anomaly detection , firewall logs , and reputation systems His recent publications examine usability of cybersecurity solutions in industrial settings, JavaScript obfuscation analysis , and synthetic log generation for security testing. Themes include network anomaly detection , endpoint security , and attack modeling . No scientific awards were explicitly mentioned in the provided text. His research also addresses real-time processor modeling and distributed intrusion detection , with practical implementations in Linux environments.
Quentin Stiévenart is a post-doctoral researcher at the Software Languages Lab of the Vrije Universiteit Brussel (VUB) in Belgium, specializing in programming languages and program analysis. His research spans multiple areas within static and dynamic analysis, with a particular focus on WebAssembly security and analysis techniques. His primary research interests include: Static and dynamic program analysis Abstract interpretation Type systems and effect systems Concurrent and higher-order programming languages WebAssembly security and analysis Interpreter and framework design Stiévenart's recent publications demonstrate a strong focus on compositional and modular analysis techniques, particularly applied to WebAssembly. His work addresses critical security concerns in WebAssembly, including information flow analysis, binary analysis, and vulnerability detection. He has also contributed to frameworks for static analysis of higher-order languages and techniques for improving analysis performance through parallelism and incremental computation. His academic service includes extensive peer review activities and program committee membership for major conferences including SAS, SCAM, ICFP, ECOOP, and OOPSLA, with a particular emphasis on artifact evaluation committees. Stiévenart has developed several research tools including Wassail (a static analysis tool for WebAssembly security), MAF (a framework for modular static analyses), and Scala-AM (a framework for developing static analyses in Scala).
Dr Marco Palomino is a Senior Lecturer in the School of Natural and Computing Sciences at the University of Aberdeen and holds a Visiting Associate Professor position at the University of Plymouth in the School of Engineering, Computing and Mathematics. He is actively involved in research, teaching, and PhD supervision, with a focus on data science and computing. His roles include Admissions Tutor, Programme Manager for the Digital and Technology Solutions Professional Degree Apprenticeship, and Athena Swan Lead at Aberdeen. His research interests span Natural Language Processing , Sentiment Analysis , Social Media Analytics , Spatiotemporal Databases , and Human-AI Interaction . He employs interdisciplinary methods to analyze public opinion, improve cybersecurity training, support elderly care through robotics, and develop decision-support systems. His work contributes to Sustainable Development Goals related to technology and societal well-being. His recent publications (2022–2025) reflect a strong trend in applying machine learning and data science to real-world problems, including sentiment analysis during the pandemic, adaptive cybersecurity training, human-AI collaborative decision-making, and assistive social robots for older adults. These works emphasize practical applications in healthcare, security, urban mobility, and policy. Scientific recognition includes: Highly Commended Paper award, Emerald Literati Network (2014) He has supervised numerous students on topics including sentiment analysis, malware detection, and cybersecurity education. His research is supported by affiliations with both the University of Aberdeen and the University of Plymouth, and he has contributed as a Guest Editor and reviewer for journals such as Applied Sciences , Big Data and Cognitive Computing , and Mathematics . He is a Fellow of the Higher Education Academy.
Dr. Jamie Twycross is an Associate Professor in the School of Computer Science at the University of Nottingham. He leads the Intelligent Modelling and Analysis Group and serves as the Modelling Group lead in the Synthetic Biology Research Centre. His work bridges computer science with biological sciences, focusing on interdisciplinary applications of AI to solve complex biological problems. His research spans computational and mathematical modeling, machine learning, data analytics, and software engineering. Key areas include computational biology, synthetic biology, systems biology, systems medicine, sustainable chemistry, artificial life, biologically-inspired computing, artificial immune systems, computer security, and robotics. He has developed computational approaches to address real-world biological challenges, with particular emphasis on modeling complex biological systems. Dr. Twycross's publications reveal a strong trend toward interdisciplinary research that integrates computational methods with biological applications. His recent work demonstrates expertise in metabolic modeling, synthetic biology tools development, machine learning applications in bioinformatics, and sustainable chemistry software solutions. His research consistently addresses complex problems at the intersection of computer science and life sciences. Discipline External Examiner, Computer Science, Northeastern University London (2023-2027) Panel Member, UKRI Interdisciplinary Assessment College (2023-2025) Quality Assessor, Office for Students (OfS) (2023-2027) Member of Pool of Experts, Biotechnology and Biological Sciences Research Council (BBSRC) (2023-2026) Dr. Twycross has supervised an extensive number of students across multiple levels, including 4 postdocs, 14 PhD students, 1 M.Phil student, 21 M.Sc. dissertation students, and 21 B.Sc. dissertation students. His grant portfolio includes funding from national and international agencies, reflecting the significance and impact of his interdisciplinary research. He has also served on numerous grant review panels for UKRI, BBSRC, EPSRC, and other major funding organizations. As group lead of the Intelligent Modelling and Analysis Group and the Modelling Group in the Synthetic Biology Research Centre, Dr. Twycross directs research teams focused on developing computational approaches to address hard, real-world biological problems. His team's work includes developing software tools like libtissue for implementing artificial immune systems, as well as contributing to significant advancements in metabolic modeling and synthetic biology applications.
Ahmad Y. Javaid is an Assistant Professor in the Electrical Engineering and Computer Science Department within the College of Engineering at The University of Toledo. He serves as the founding director of the Paul A. Hotmer Cybersecurity and Teaming Research (CSTAR) lab and holds the position of Cyber Education and Cyber Threat Mitigation Faculty Fellow. Dr. Javaid received his B.Tech. (Hons.) Degree in Computer Engineering from Aligarh Muslim University, India in 2008 and his Ph.D. from The University of Toledo in 2015, where he was awarded the prestigious University Fellowship Award. Prior to joining the University of Toledo faculty, he worked for two years as a Scientist Fellow in the Ministry of Science & Technology, Government of India. Dr. Javaid's research spans multiple domains within cybersecurity and human-machine interaction. His primary expertise lies in cyber security of drone networks, smartphones, and wireless sensor networks. He has conducted extensive research on human-machine teams and applications of AI and machine learning to attack detection and mitigation. His work bridges theoretical security frameworks with practical implementations across various domains including UAV security, physical layer communications security, and multimodal human-computer interaction systems. His recent publications demonstrate a continued expansion into nuclear-renewable energy systems security and advanced cybersecurity education frameworks for K-12 students. Dr. Javaid has secured significant research funding, with collaborative proposals totaling $9.7M (including all partners along with UToledo), of which $1.4M has been allocated specifically to him. Approximately $4.1M has been allocated to the University of Toledo from these projects. His research has been funded by prestigious agencies including the NSF, AFRL, NASA-JPL, Department of Energy, and the State of Ohio. He has also played a critical role in cultivating private gifts to support the CSTAR lab. IEEE Graduate Student Member (2012) IEEE Member (2015) IEEE Senior Member (2019) University Fellowship Award As an educator, Dr. Javaid has developed innovative cybersecurity curriculum modules and educational tools. He has served as a reviewer for high-impact journals and as a member of technical program committees for reputed conferences. His work extends to cybersecurity education initiatives targeting K-12 students, with projects focused on developing interactive learning modules to improve cybersecurity awareness at the high school level. He has published more than 85 peer-reviewed publications across journals, conferences, and posters, demonstrating consistent scholarly productivity with significant output in recent years. Dr. Javaid leads the Paul A. Hotmer Cybersecurity and Teaming Research (CSTAR) lab, which focuses on cutting-edge research in cybersecurity, particularly in the areas of UAV security, physical layer security, and human-machine teaming. The lab has received support from both government agencies and private sources, enabling the development of novel security frameworks and educational tools. His interdisciplinary approach bridges computer science, electrical engineering, cybersecurity, and human factors research, making significant impacts in both academic and practical cybersecurity domains.
Dr. Fang Yu is an Associate Professor at the Department of Management Information Systems, National Chengchi University, specializing in software security, formal verification, and string analysis. They hold a Ph.D. in Computer Science from the University of California, Santa Barbara. Research Expertise: Dr. Yu focuses on cybersecurity, formal methods for software verification, and machine learning applications in data clustering and adversarial example detection. Their work bridges theoretical computer science with practical security solutions. Publication Trends: Recent articles address biomedical data clustering ( scGHSOM ), explainable AI ( XFlag ), and adversarial defense mechanisms. Topics span bioinformatics, security verification, and fairness testing in neural networks. Awards: 資深優良教師(10年) (2020, National Chengchi University) 國科會研究獎勵 (2019, National Science Council, Taiwan) Projects: Principal Investigator for 15+ grants from Taiwan's National Science and Technology Council and Ministry of Education, focusing on AI security, IoT verification, and financial technology.
Prof. Dr. rer. nat. Reiner Creutzburg is a professor at the Brandenburg University of Technology Cottbus-Senftenberg in the Department of Computer Science and Media , specializing in Applied Computer Science with a focus on Algorithms and Data Structures . Research Focus: Cybersecurity, Machine Learning, Computer Vision, IoT Security, Open Source Intelligence (OSINT), and Critical Infrastructure Protection Recent Trends: Over 15 recent publications explore AI-driven cybersecurity solutions, image/video processing for event management, and secure voting systems using blockchain.
Professor Shujun Li is a distinguished academic at the University of Kent , where he has served as a Professor of Cyber Security since November 2017. He is also the Director of the Institute of Cyber Security for Society (iCSS) , a UK government-recognized Academic Centre of Excellence in Cyber Security Research (ACE-CSR), and leads the Cyber Security Research Group at Kent's School of Computing. His research spans interdisciplinary cyber security , focusing on human-centric approaches, privacy, digital forensics, multimedia computing, and AI applications. He actively collaborates across disciplines such as Electronic Engineering, Psychology, Sociology, Law, and Business. Previously held roles include Deputy Director of Surrey Centre for Cyber Security (2014–2017) at the University of Surrey. Key projects include EPSRC-funded initiatives on human-centric cyber security and privacy. His recent publications highlight expertise in areas like data privacy , deepfake analysis , password security , and MaaS (Mobility-as-a-Service) vulnerabilities , with contributions to journals such as IEEE Transactions on Dependable and Secure Computing and Frontiers in Big Data . Scientific Honors: Two Best Paper Awards ISO/IEC Certificate of Appreciation (2012) Fellow of BCS Senior Member of IEEE Member of ACM As a principal/co-supervisor, he has guided students including Mohamad Imad Mahaini , Nandita Pattnaik , and Ali Raza . He also serves on editorial boards and advisory groups like the Scientific Board of RISCS and the Steering Committee of ARES .
Britton D. Wolfe is a Professor of Computer Science at Grove City College's College of Engineering and Business. His career spans academic research, industry collaboration, and teaching across multiple CS domains. Ph.D. in Computer Science and Engineering, University of Michigan (2009) M.S. in Computer Science and Engineering, University of Michigan (2005) B.S. in Computer Science, Carnegie Mellon University (2003) Research focuses on applying machine learning to diverse challenges: Android malware detection using Google Play data, 3D vision for robotics, and collaborative tracking systems for biological research. He develops deep learning techniques for ant-tracking robots in natural habitats, working with students to refine computer vision algorithms. His publications (2005-2017) demonstrate sustained expertise in malware analysis , tracking algorithms , and predictive state modeling . Current work combines AI with biology studies through interdisciplinary robotics projects. Contact: bdwolfe@gcc.edu
Mattia Fazzini is an Assistant Professor in the Department of Computer Science & Engineering at the University of Minnesota's College of Science and Engineering. His primary academic appointment focuses on software engineering research and teaching, with active involvement in major conferences including ASE, ISSTA, ICSE, and MOBILESoft where he has served in leadership roles such as General Co-chair (MOBILESoft 2023) and Program Committee Co-chair. His research centers on software testing, maintenance, and security , with particular emphasis on mobile applications. Key research themes include: Developing techniques for automated Android testing and maintenance Addressing API compatibility issues across Android versions Creating tools for test oracle generation and bug reproduction Investigating security vulnerabilities in mobile ecosystems Optimizing test suites through test double analysis His recent publications (2021-2025) reveal strong focus on Android-specific challenges, with recurring themes in compatibility testing, automated test generation, and security analysis. Over 60% of his work involves tool development for practical testing scenarios, particularly targeting mobile platforms. Notable recognitions include: IEEE TCSE Distinguished Paper Award (2024) for work on test suite optimization ACM Distinguished Paper Award (2022) for COVID-19 app analysis As an educator, he advises multiple PhD and Master's students while teaching undergraduate and graduate courses including CSCI 3081W (Program Design) and CSCI 5802 (Software Engineering II). His service contributions span conference organization (MOBILESoft, ISSTA, ICSE) and extensive program committee work across top software engineering venues. He leads research projects focused on practical testing solutions with real-world applicability in mobile software development.
Işıl Dillig is an Associate Professor of Computer Science at the University of Texas at Austin, where she leads the UToPiA research group. Her academic career spans over a decade of significant contributions to programming languages research, particularly in program analysis, verification, and synthesis. Dr. Dillig received all her academic degrees (BS, MS, and PhD) from Stanford University before joining the faculty at UT Austin. Her educational background established the foundation for her innovative research approach that bridges theoretical computer science with practical applications. Her research focuses on developing techniques to make software systems more reliable, secure, and easier to build through advanced program analysis, verification, and synthesis methods. She has pioneered approaches that combine symbolic reasoning with machine learning to tackle complex software engineering challenges across multiple domains including security, databases, and programming language theory. Her work demonstrates exceptional depth in creating practical tools that address real-world software development problems while maintaining strong theoretical foundations. Analysis of Dr. Dillig's publication record reveals a consistent trajectory of innovation in program synthesis, with recent work expanding into neurosymbolic approaches that bridge neural networks with formal methods. Her research shows strong connections between theoretical foundations and practical applications, particularly in security-critical systems, database technologies, and blockchain applications. The evolution of her work demonstrates increasing sophistication in handling complex program structures while maintaining practical usability. Dr. Dillig has received prestigious recognition for her research contributions: Sloan Fellowship NSF CAREER award As a dedicated educator and research leader, Dr. Dillig has served in significant roles including Program Chair for PLDI 2022 and Steering Committee member for PLDI. She has mentored numerous students through her UToPiA research group, guiding research in program synthesis, verification, and analysis. Her work has been supported by substantial research grants that have enabled innovative projects at the intersection of programming languages and security. Dr. Dillig leads the UToPiA (UT Austin Programming, Languages, and Analysis) research group, which focuses on developing novel techniques for program analysis, verification, and synthesis. The group maintains strong collaborations with industry partners and academic institutions worldwide, translating theoretical advances into practical tools that address real software engineering challenges.
Sakeena Muntaha serves as a Junior Researcher at the University of Applied Sciences St. Pölten, affiliated with the Institute of Creative\Media/Technologies and the Department of Media and Digital Technologies since 2017. Currently on leave, she contributes to the institution's research mission through interdisciplinary projects spanning computer vision and applied machine learning. Her academic foundation includes a Master's degree in Computer Engineering from the National University of Sciences and Technology (NUST), Pakistan (2016) and a Bachelor's degree in Computer System Engineering from the NFC Institute of Engineering and Technology (NFCIET), Pakistan (2012). These qualifications underpin her technical expertise in visual computing systems. Dr. Muntaha's research program centers on machine learning and computer vision with dual application tracks: medical diagnostics (skin lesion segmentation, dermoscopy analysis) and environmental/urban systems (building footprint extraction, flood monitoring, real estate analysis). Her methodological approach integrates deep learning architectures with classical image processing techniques like level sets and Gabor filters, demonstrating versatility across domains from cultural heritage preservation to cybersecurity. Recent work shows increasing focus on robustness evaluation and real-world deployment challenges in vision systems. Analysis of her 15 most recent publications reveals strong thematic continuity in computer vision applications, with growing sophistication in handling real-world data constraints. Early work focused on medical imaging and malware detection, while recent publications emphasize urban infrastructure analysis and environmental monitoring, reflecting strategic alignment with societal challenges. The consistent use of deep learning frameworks across diverse domains highlights her technical agility. As an active member of the Media Computing Research Group, she contributes to projects including IMREA (Intelligent Multimodal Real Estate Assessment), Scribe ID AI (cultural heritage analysis), and ImmBild (location assessment via computer vision). Her collaborative research involves partnerships with institutions across Austria and Pakistan, though specific grant details and advising activities are not documented in available sources.
