Ivan Tot is an Associate Professor at the University of Defense's Military Academy in Belgrade, with a career spanning roles as Teacher (1999-2010) and Assistant Professor (2015). His academic foundation includes a Doctorate from the Military Academy (2010), a Master's from the University of Kragujevac (2008), and an undergraduate degree from the Military Technical Academy (1999). His research bridges Information Systems , Biometric Verification , and IoT Security , with applications in defense logistics, mobile communications, and healthcare technology. Recent publications explore biometric maternity verification, encrypted SMS applications, and military data-gathering techniques. He contributed to key projects: New information technologies for analytical decision-making (Ministry of Science, 2010-2016) Performance Analysis of Tactical Telecommunications Systems (Military Academy, 2010-2015) Database models for logistics decision support (Military Academy, 2013-2015) He authored the textbook Information Systems for Decision Support (2013) and maintains a focus on practical, security-oriented solutions.
Marta Varela Anjari is an Honorary Senior Lecturer at the National Heart & Lung Institute, part of the Faculty of Medicine at Imperial College London. Her research integrates Physics-Informed Machine Learning, Magnetic Resonance Imaging (MRI), and digital twin technologies to advance understanding of human physiology and improve clinical treatments. Specializes in Medical Physics and Artificial Intelligence Focuses on cardiac mechanics and disease via computational modeling Develops machine learning applications for MRI-based cardiac diagnostics Her work explores: 1) Physics-Informed Neural Networks for simulating organ function and characterizing cardiac properties; 2) Atrial deformation analysis to study cardiac diseases; and 3) Machine learning-enhanced MRI for quantifying pathological features like aortic valve disease and pericardial fat. She has developed a publicly accessible Android app for cardiac arrhythmia treatment demonstration. Scientific Awards Honorary Senior Lecturer at Imperial College London
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.
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.
Dr. Jakub Kuzilek is a researcher at the Institute of Computer Science, Faculty of Mathematics and Natural Sciences, Humboldt University of Berlin. He is a member of the "Didaktik der Informatik / Informatik und Gesellschaft" (Computer Science Education | Computer Science and Society) research group, also affiliated with the Educational Technology Lab at the German Research Center for Artificial Intelligence. He joined the CSES research group in January 2020 after previously working at the Faculty of Mechanical Engineering, CTU in Prague, and completing a 4-year research stay at the Open University. Dr. Kuzilek completed his Ph.D. in Biomedical Signal Processing using Blind Source Separation methods. His academic journey transitioned from biomedical signal processing to educational data mining when Professor Zdenek Zdrahal from the Knowledge Media Institute (Open University) invited him to work on data mining of student data in the OU Analyze project. Dr. Kuzilek's primary research interests focus on the intersection of machine learning and education. His work centers on Educational Data Mining (EDM) and Learning Analytics (LA), with particular emphasis on explainable AI applications in educational settings. He investigates student behavior in online learning systems, develops predictive models for student success, and explores methods for providing actionable feedback to learners. His research bridges the gap between signal processing techniques from his earlier work and modern educational technology applications, with a strong focus on practical implementations that can directly benefit students and educators. Analysis of Dr. Kuzilek's recent publications (2021-2024) reveals a clear focus on advancing Learning Analytics and Educational Data Mining. His work consistently explores how machine learning can be applied to understand and improve educational outcomes, with particular attention to explainability of predictive models. Key research threads include student success prediction using behavioral data, algorithmic approaches to student grouping, automated feedback generation, and the use of self-assessments in higher education. His methodological approach combines rigorous statistical analysis with practical educational applications, often using real-world educational datasets to validate his findings. Best paper award for "An Automatic Method for Holter ECG Denoising Using ICA" (2011) Second place in CINC Challenge 2011 for "Simple Scoring System for ECG Signal Quality Assessment on Android Platform" (2011) Dr. Kuzilek actively mentors students at various levels, supervising numerous bachelor's and master's theses on topics related to Learning Analytics, Educational Data Mining, and machine learning applications in education. His grant portfolio demonstrates sustained research activity, including leadership roles on projects funded by the Czech Science Foundation, University Development Foundation, and BMBF. His current research focuses on AI-supported personalization in vocational training and implementing AI-based feedback systems in higher education institutions. As a core member of the Computer Science Education | Computer Science and Society research group at Humboldt University, Dr. Kuzilek collaborates with colleagues including Prof. Dr. Niels Pinkwart and other researchers to advance the field of educational technology. His work bridges theoretical research in machine learning with practical applications in real educational settings, contributing to both academic knowledge and tangible educational improvements.
Eric Bodden is a Professor for Secure Software Engineering at Paderborn University and co-director of Fraunhofer IEM. He is also a member of the directorate of the Collaborative Research Center CROSSING at TU Darmstadt. As an ERC fellow, Bodden leads the Attract-Group on Secure Software Engineering at Fraunhofer IEM, where he develops code analysis technology for security in collaboration with leading national and international software development companies. Bodden is one of the leading experts in secure software engineering with a specialty in building highly precise tools for automated program analysis. His research spans static program analysis, Android security, taint analysis, and cryptographic API security. He has made significant contributions to the field through frameworks like FlowDroid for Android analysis, DroidBench benchmark suite, and as one of the chief maintainers of the Soot program analysis framework. His work bridges theoretical advances with practical applications, focusing on creating tools that can be effectively used by developers to enhance software security. His recent publications demonstrate a continued focus on advancing static analysis techniques, with increasing attention to modern challenges like C/C++ analysis, large language models in program analysis, and addressing security vulnerabilities in dependency management. His work shows a consistent trajectory of improving analysis precision, scalability, and usability for developers. Heinz Maier-Leibnitz-Preis (2014) ERC fellow BITKOM Management Club membership (2013) Bodden has advised numerous doctoral students whose theses cover diverse aspects of secure software engineering, including static analysis for Android applications, secure integration of cryptographic software, and information flow security engineering. His research group has received recognition through multiple dissertation awards including Summa cum laude distinctions and the Ernst Denert Software Engineering Award. The group has developed influential tools like FlowDroid, CogniCrypt, and SootUp that have shaped the field of secure software development. At Fraunhofer IEM, Bodden heads the Attract-Group on Secure Software Engineering, which collaborates with industry partners to develop practical security analysis solutions. His team has created several influential frameworks including FlowDroid for Android security analysis and the DroidBench benchmark suite. The group's work bridges academic research with real-world applications, focusing on making security analysis tools more precise, scalable, and usable for developers.
Zhen Dong is an Associate Professor at Fudan University, China, specializing in software engineering with a focus on software reliability and security, particularly in mobile computing. Previously, he was a PostDoc and Senior Research Fellow at the National University of Singapore under the guidance of Abhik Roychoudhury. His educational background includes: PhD in Computer Science from Heidelberg University (2017), advised by Prof. Artur Andrzejak Dr. Dong's research centers on developing techniques and tools for improving software reliability and security. His work spans mobile application testing, Android security, flaky test detection, and vulnerability localization. He has made significant contributions to the field of software testing and analysis, with a particular emphasis on practical applications for mobile systems. His research bridges theoretical foundations with real-world software engineering challenges. Analysis of Dr. Dong's recent publications reveals a strong focus on leveraging AI/ML techniques for software engineering tasks, particularly using LLMs for test automation and program analysis. His work consistently addresses critical challenges in mobile computing, especially Android application reliability and security. There's a clear trajectory toward more sophisticated analysis techniques, from traditional testing methods to AI-driven approaches. His notable scientific achievements include: ACM Distinguished Paper Award at ICSE'20 Best Paper Award at AsiaCCS'21 (1/370 submissions) ASE'22 Distinguished Reviewer Award Dr. Dong serves on the Board of Distinguished Reviewers for ACM Transactions on Software Engineering and Methodology and has been an active member of numerous program committees for top software engineering conferences including ICSE, ASE, and ISSTA. His service to the academic community extends to reviewing for prestigious journals such as IEEE Transactions on Software Engineering and Methodology and ACM Transactions on Software Engineering and Methodology. His research has been supported through various academic channels, enabling him to maintain an active lab focused on software testing and analysis, particularly for mobile platforms. The lab has produced numerous tools and techniques that have influenced both academic research and industrial practice in software reliability.
Ding Li is an Assistant Professor in the School of Computer Science at Peking University. He holds a Ph.D. in Computer Science from the University of Southern California (USC) and a B.S. from Peking University. His research focuses on program analysis, energy optimization for mobile applications, and security, with publications in top conferences including ICSE, FSE, and ASE. His research interests span: Program Analysis : Techniques to optimize mobile application energy consumption. System Security : Identifying vulnerabilities in Android apps and WebAssembly binaries. Cloud/Edge Computing : Enhancing serverless computing efficiency and federated learning security. Dr. Li's recent work explores the integration of large language models into pointer analysis and automated optimization of resource inefficiencies. His publications demonstrate a consistent focus on practical system optimizations and security enhancements across mobile, cloud, and machine learning domains. Awards: Viterbi Undergraduate Research Mentoring Award (2014)
David Lie is a Professor in the Department of Electrical and Computer Engineering at the University of Toronto. He holds additional appointments in the Department of Computer Science and the Faculty of Law. He is a Tier 1 Canada Research Chair in Secure and Reliable Systems, a research lead at the Schwartz Reisman Institute for Technology and Society, an Associate Director at the Data Sciences Institute, a Vector Faculty Affiliate, and a Senior Massey College Fellow. His educational background includes: BASc from the University of Toronto (1998) MS from Stanford University (2001) PhD from Stanford University (2004) David Lie's research spans computer security, privacy, and cybersecurity. He is renowned for pioneering the XOM architecture—a foundational model for modern trusted execution environments like ARM TrustZone and Intel SGX—and developing the widely adopted PScout Android permission mapping tool. His current work emphasizes program analysis, fuzzing, and symbolic execution to enhance software security and reliability, addressing critical vulnerabilities in mobile and system software. His recent publications reveal a strong trajectory in integrating machine learning with program analysis techniques. Key themes include optimizing symbolic execution for Android apps, leveraging LLVM IR for bug detection, and using predictive models to guide test generation. These contributions underscore a practical focus on scalable, automated security tools that bridge theoretical advances with real-world software vulnerabilities. His notable awards include: Best Paper Award at SOSP Tier 1 Canada Research Chair in Secure and Reliable Systems Senior Massey College Fellow No specific information on student advising or research grants was provided in the available text, though his extensive program committee service for top security conferences (OSDI, IEEE Security & Privacy, CCS, etc.) indicates significant academic leadership. David Lie actively contributes to interdisciplinary research ecosystems through his roles at the Schwartz Reisman Institute for Technology and Society (as research lead), the Data Sciences Institute (as Associate Director), and the Vector Institute for Artificial Intelligence (as Faculty Affiliate), fostering collaborations between security research, law, and societal impact studies.
Dr. Ran Liu serves as an Associate Professor of Marketing in the School of Business at Central Connecticut State University. He holds active roles as a Faculty Senator and member of the Assessment of Learning and Strategic Planning Committees, demonstrating deep institutional engagement. His academic profile centers on bridging corporate strategy with global humanitarian objectives through marketing frameworks. Dr. Liu earned his Ph.D. in Marketing with a minor in International Business from Old Dominion University, establishing foundational expertise in cross-cultural business dynamics. His research program critically examines how multinational enterprises adapt marketing strategies across geopolitical and cultural boundaries to enhance consumer welfare. Key investigations include metaphysical approaches to conflict resolution through marketing systems, e-guanxi development in digital commerce, and the humanitarian dimensions of international business practices. This work consistently integrates ethical considerations with practical applications in volatile global contexts. Analysis of Dr. Liu's 15 most recent publications reveals dominant trends in digital consumer behavior (online reviews, mobile commerce), cross-cultural adaptations in emerging markets, and innovative applications of marketing theory to geopolitical challenges. His scholarship increasingly connects traditional marketing domains with macro-level humanitarian outcomes. Dr. Liu teaches core MBA and undergraduate marketing courses while maintaining significant editorial responsibilities as Associate Editor (Asia) for the Journal of Eastern Europe and Central Asian Research. He provides extensive peer review services for leading journals including Journal of Business Research and Journal of Marketing Theory and Practice, reinforcing his standing in the academic community.
Dr. David Corsar is a Principal Lecturer in Informatics at Robert Gordon University's School of Computing, Engineering and Technology. He is an active researcher in Artificial Intelligence with a focus on knowledge representation, reasoning methods, and explainable AI systems. His academic journey began with a Bachelor's Degree in Computing Science (Artificial Intelligence) from the University of Aberdeen (2000-2004), followed by a PhD in Computing Science (2004-2009) where he researched knowledge reuse through ontology mapping and ontology based knowledge acquisition. After his doctoral studies, he worked as a Research Fellow at the University of Aberdeen in interdisciplinary teams before joining RGU. Dr. Corsar's research interests span across multiple AI domains: Knowledge Representation and Reasoning Semantic Web and Knowledge Graphs Provenance in AI systems Explainable Artificial Intelligence (XAI) Case-Based Reasoning Integration of machine learning with symbolic AI Transparency and trustworthiness of AI systems His recent publication record (2023-2025) shows a strong emphasis on developing practical XAI solutions, particularly through the iSee platform which addresses the challenge of personalized explanations for diverse AI users. His work bridges theoretical AI research with practical applications in healthcare, regulatory compliance, and industrial domains like Oil and Gas decommissioning. Dr. Corsar has received research funding for multiple projects: iSee: Intelligent Sharing of Explanation Experience (2021-2024) Prophecy Project: Intelligent Decommissioning Materials Management (2019-2020) Intelligent System for assisted medical diagnosis for CARDIOvascular diseases (2020-2021) Building resilience in Health and Social Care students during Covid-19 (2020) As an educator, Dr. Corsar coordinates several technical modules including Native Android App Development, Internet of Things, Data Structures and Algorithms, and Knowledge Modelling and Reasoning. He is actively involved in supervising PhD students working on cutting-edge AI research topics and welcomes Honours and Masters projects aligned with his research areas. Dr. Corsar is a member of the Artificial Intelligence & Reasoning Research Group at RGU, contributing to the university's research theme 'Living in a Digital World.' His work on the W3C Provenance Working Group has helped develop standards for provenance information interchange on the Web, demonstrating his commitment to advancing foundational aspects of AI systems.
Dr. Janaka Senanayake is a Lecturer in Cybersecurity at Robert Gordon University (RGU), attached to the School of Computing, Engineering and Technology. He is affiliated with the Cybersecurity Research Group and contributes to the university's 'Living in a Digital World' research theme. Dr. Senanayake completed his PhD in Cybersecurity at RGU, specializing in software security. He earned a Bachelor of Science (Special) in MIT with First Class Honors from the University of Kelaniya, Sri Lanka in 2017. Prior to joining RGU, he worked as a Software Engineer at DirectFN Pvt. Ltd. and NDB Bank Information Technology and R&D Department for approximately three years, and also served as a lecturer and researcher at several international universities. His research focuses on cybersecurity, software security, mobile computing, artificial intelligence, and software engineering. Dr. Senanayake has published extensively on Android security, AI-powered vulnerability detection, and network security solutions. His recent work demonstrates a strong emphasis on practical security applications using advanced AI techniques while addressing privacy concerns through approaches like federated learning. Dr. Senanayake has produced 23 publications from 2021-2025, with a significant concentration of high-impact work in 2024-2025. His research output shows a clear trajectory toward integrating cutting-edge AI technologies with practical cybersecurity applications, particularly in mobile and network security domains. His notable contributions include: MADONNA: A browser-based malicious domain detection system using optimized neural networks FedREVAN: Real-time detection of vulnerable Android source code through federated neural networks with XAI DroidKey: A framework for API key security in Android applications Research on integrating large language models for automated vulnerability scanning Dr. Senanayake is actively involved in PhD supervision with research topics including Software Security, Cybersecurity, Security and Artificial Intelligence, Mobile Security, and Law and AI. His industry background combined with academic expertise provides a valuable practical perspective to his research and teaching. He maintains active research collaborations, as evidenced by his numerous co-authored publications with researchers from different institutions, particularly focusing on cybersecurity challenges in both Western and Sri Lankan contexts.
Dr. Przemysław Falkowski-Gilski serves as an Assistant Professor at the Department of Geoinformation Systems within the Faculty of Electronics, Telecommunications and Informatics at Gdańsk University of Technology. His research bridges geospatial engineering with computational intelligence, focusing on real-world applications in urban environments and sensor networks. His primary research domains include: Geoinformatics and GNSS positioning systems IoT infrastructure for smart cities Swarm intelligence optimization algorithms Edge computing resource allocation AI-driven data analysis for urban mobility Recent publications (2024-2025) reveal a cohesive research trajectory addressing critical challenges in smart city resource management through UAV-assisted edge computing, Android-based urban navigation, and sensor ontology integration. His work consistently applies advanced optimization techniques—such as Golden Jackal and Beetle Swarm algorithms—to solve practical problems in geospatial data processing and IoT systems, demonstrating strong interdisciplinary methodology.
Dr. Vicente Alarcon-Aquino is a Professor in the Department of Computing, Electronics, and Mechatronics at Universidad de las Americas Puebla (UDLAP), Mexico. He received his Ph.D. and D.I.C. degrees in Electrical and Electronic Engineering from Imperial College London in 2003. He previously served as department head from October 2012 to June 2018 and spent a research stay at King's College London in 2017. Dr. Alarcon-Aquino is a Senior Member of IEEE, a Level I member of the Mexican National System of Researchers (SNI), and a Fellow of the Mexican Academy of Sciences. His educational background includes: Ph.D. and D.I.C. in Electrical and Electronic Engineering, Imperial College London, UK (2003) Dr. Alarcon-Aquino's research focuses on cybersecurity, network monitoring, anomaly detection, wavelet analysis, and machine learning. His work spans theoretical foundations to practical applications in network security, with significant contributions to intrusion detection systems, cryptographic techniques, and machine learning approaches for security applications. He has developed innovative methods combining wavelet transforms with neural networks for various security and signal processing applications. His scholarly contributions include over 180 research articles in refereed journals and conference proceedings, a book on MPLS networks, and numerous citations. As an editor, he serves as Associate Editor for IEEE Access Journal and as Academic & Section Editor for PeerJ Computer Science, focusing on Security & Privacy. Notable professional recognitions include: Senior Member of IEEE Mexican National System of Researchers (SNI Level I) Member of the Mexican Academy of Sciences Dr. Alarcon-Aquino has supervised over 70 theses, including 8 Ph.D. dissertations, 15 Master's theses, and more than 37 Bachelor's theses. His supervision spans topics including network intrusion detection, information security, encryption algorithms, wavelet-based signal processing, neural networks, EEG signal processing, and biometric cryptosystems. He has hosted international research students from institutions including the Polytechnic University of Madrid and Kiel University of Applied Sciences. His research group at UDLAP focuses on developing advanced security solutions for modern network environments, with particular emphasis on applying machine learning techniques to cybersecurity challenges. Current projects include blockchain-based security solutions, federated learning approaches for intrusion detection, and advanced anomaly detection systems for IoT environments.
Dr. Ray R. Hashemi is a Professor in the Department of Computer Science within Georgia Southern University's College of Engineering and Computing. His academic career spans over 14 years of continuous research output from 2003-2017, with significant contributions as co-editor for four International Conferences on Information Technology and Knowledge Engineering (2005, 2010, 2014, 2017). His research focuses on innovative applications of data mining across diverse domains: Bioinformatics: DNA sequence analysis, organ toxicity prediction, and liver cancer predictive systems Medical Informatics: Bone mineral density analysis using DEXA data and dendrograms Financial Systems: Extraction of essential constituents from S&P500 index Environmental Science: Climate prediction using algae sedimentation patterns Computer Vision: Video mining for theatrical analysis and Android-based OCR for non-flat documents Methodologically, Dr. Hashemi specializes in neighborhood systems analysis, association rule mining, and grid-based approaches for sparse data. His work consistently bridges theoretical data mining concepts with practical applications, developing tools for signature-based prediction, record layout discovery, and intent analysis through web behavior. Recent publications (2015-2017) show increased focus on domain-specific applications in finance and toxicology while maintaining core data mining expertise. His collaborative work includes partnerships with international researchers across multiple continents, demonstrated through conference editorial roles and co-authored publications. Dr. Hashemi's research demonstrates sustained scholarly activity with practical implementations in medical diagnostics, financial analysis, and environmental prediction systems.
