Shafaq Khan is an Assistant Professor in the School of Computer Science at the University of Windsor. She holds a PhD in Computer Science from the University of Salford (2017). Her research spans machine learning, deep learning, data analytics, and database systems with applications in healthcare informatics, agricultural technology, educational systems, and blockchain. Recent work focuses on AI-driven healthcare transformation, privacy-preserving data methods, federated learning for disease prediction, and computer vision applications in agriculture. Additional interests include educational technology for addressing disparities, blockchain implementations in government services, and open-source search engine development. Her work demonstrates consistent integration of cutting-edge computing techniques with practical domain applications.
Dr. Yangjun Chen is a Full Professor in the Department of Applied Computer Science at the University of Winnipeg, Canada. He holds a Ph.D. from the University of Kaiserslautern, Germany (1995). His research focuses on database systems, graph algorithms, computational complexity, and theoretical computer science. Key areas include Federated Databases, Deductive Databases, DNA Databases, and the P vs NP problem. Education: Ph.D. in Computer Science, University of Kaiserslautern, Germany (1995). Research interests span graph query processing, algorithm design, big data optimization, and NP-completeness. Recent work includes polynomial-time solutions for 2-MAXSAT and advancements in string matching algorithms for DNA databases. Publications highlight contributions to graph indexing, efficient reachability queries, and algorithmic efficiency in databases. His work often bridges theoretical foundations with practical database applications. Awards: Excellent Merit Award (2022-2023, 2021-2022) - University of Winnipeg Best Article, ACTA Scientific Computer Sciences (2022) Multiple Best Paper Awards at conferences like DBKDA 2016 and CyberC Summit Teaching includes Advanced Databases, Distributed Database Systems, and Algorithms courses at both undergraduate and graduate levels. Active in supervising research in database systems and theoretical computer science. Laboratory and team focus on database innovation, including projects on graph databases and efficient query processing techniques.
Dr. Zhenghao Chen is a Lecturer in Data Science at the University of Newcastle, affiliated with the School of Information and Physical Sciences. He earned his Ph.D. from the University of Sydney in 2022, following a B.Eng. H1 degree from the same institution in 2017. Prior to his current position, Dr. Chen served as a Postdoctoral Research Fellow at the University of Sydney (2022-2024), a Research Engineer at TikTok (2024), and as a Visiting Research Scientist at Microsoft Research and Disney Research (2022-2023). Dr. Chen's educational background includes: Doctor of Philosophy, University of Sydney (2022) Bachelor of Information Technology (B.Eng. H1), University of Sydney (2017) Dr. Chen's research spans multiple domains within artificial intelligence, with particular expertise in Computer Vision, Natural Language Processing, and Machine Learning. His work in Generative AI has garnered significant recognition, with applications in both academic and industrial settings. His research interests are reflected in his Fields of Research percentages: Deep Learning (30%), Computer Vision (30%), Natural Language Processing (20%), and Multimodal Analysis and Synthesis (20%). His publications in top-tier venues like CVPR, ICCV, ECCV, and journals like IEEE TPAMI demonstrate the breadth and impact of his work. Analysis of Dr. Chen's recent publications (2022-2025) reveals a consistent focus on neural compression techniques, 3D perception, and multimodal AI systems. His work spans medical imaging (CXR bone suppression), video compression, point cloud processing, and neural surface reconstruction. A notable trend is his exploration of efficient AI systems that work well under resource constraints, as evidenced by his involvement in the EMCLR workshop. His research often bridges theoretical advances with practical applications across multiple domains. Dr. Chen has received several prestigious awards: Microsoft Research Asia StarTrack Fellowship (2025) ACM SIGMM Award for Outstanding PhD Thesis in Multimedia Computing (2024) Australia Government Research Training Program (RTP) Fellowship (2019) Google Australia Prize for Excellence in Computer Science (2017) Dr. Chen is actively involved in the academic community, serving on the Program Committee for major AI conferences including CVPR, ICCV, ECCV, SIGGRAPH, AAAI, and others. He also organizes workshops in Multimedia and ICCV conferences, and serves as a reviewer for prestigious journals. His teaching responsibilities include courses on Intelligent Visual Signal Understanding, Video Intelligence and Compression, Database and Information Management, and Computing Fundamentals at both the University of Sydney and University of Newcastle.
Prof. Bernd Domer is an Associate Professor at the Geneva School of Landscape, Engineering and Architecture (HES-SO) specializing in Building Information Modeling (BIM) , Geographic Information Systems (GIS) , and digital transformation of civil engineering . He leads multiple ongoing research projects including CU_OFROU_PAB (CHF278,844) focused on BIM-GIS workflows for noise barriers, and SousEtoile (CHF50,000) developing subsurface prediction models for urban planning. His work addresses critical challenges in software interoperability and point cloud processing for infrastructure digital twins. BA HES-SO in Architecture (HEPIA) BSc Civil Engineering (EPFL) BSc HES-SO in Civil Engineering (HEPIA) MSc HES-SO in Engineering (HES-SO Master) His research explores digital workflows for infrastructure projects, with over 15 recent publications examining topics like: Semantic segmentation of point clouds (2024) IFC standard optimization (2023) Underground confidence level modeling (2021) Swiss BIM implementation frameworks (2020) Construction waste management platforms (2018) He serves as Head of the MIC Group and co-directs the CAS in BIM Coordination . Active in international committees like EG-ICE and Bauen digital Schweiz , his work bridges academic research with practical implementation through collaborations with HEPIA , HEIG-VD , and institutions like the Swiss Federal Roads Office (OFROU) .
Dr. YANG Guomin is an Associate Professor of Computer Science at Singapore Management University's School of Computing and Information Systems, where he coordinates the BSc Cybersecurity Track. His research focuses on privacy-preserving cryptography, authentication systems, and secure IoT frameworks. Research spans cryptographic protocols for cloud security, blockchain applications, and federated learning with emphases on efficiency and practical implementation. Recent publications demonstrate innovations in threshold authentication, redactable blockchains, and privacy-aware communication protocols. Advisees include LI Huilin and WANG Jiaheng, with research projects examining hardware-enhanced encryption, biometric authentication policies, and space network security. Work consistently addresses tension between security guarantees and computational efficiency in distributed systems.
Griffin Weber, M.D., Ph.D., is an Associate Professor of Medicine and Biomedical Informatics at Harvard Medical School (HMS) and Beth Israel Deaconess Medical Center (BIDMC). He directs the Biomedical Research Informatics Core (BRIC) at BIDMC. His research focuses on expertise mining, social network analysis, and biomedical informatics. Key contributions include developing Profiles RNS (an open-source research networking platform) and i2b2 / SHRINE federated query tools for clinical data. He holds MD and PhD degrees from Harvard (2007), and earlier degrees in bioengineering and computer science. Education: SB in Bioengineering (Harvard, 2000), SM/PhD in Computer Science (Harvard, 2004/2005), MD (Harvard, 2007). He served as Harvard Medical School's first Chief Technology Officer, building educational platforms for 500+ courses. His work spans DNA microarrays, breast cancer tumor modeling, and EHR bias analysis. Research Interests: Leveraging informatics to improve healthcare through federated data systems, team science dynamics, and EHR analysis. Projects include Profiles RNS for researcher networks and i2b2 for clinical data queries across institutions. He explores biases in EHR data filtering and visualizing healthcare system dynamics in biomedical data. Grant Leadership: Principal investigator on NIH grants addressing EHR biases (R01LM013345), healthcare system dynamics (U01CA198934), and scientific workforce networks (U01GM112623). Collaborator on PCORI and NIH-funded initiatives. Awards: 2020 Fellow of the American College of Medical Informatics; 2011 Top Podium Presentation (AMIA); 2007 Medical Technology Award (Massachusetts Medical Society). Labs/Teams: Leads BRIC at BIDMC, collaborates on i2b2/SHRINE, and contributes to the 4CE consortium for federated healthcare data analysis.
Esfandiar Mohammadi is an Associate Professor at the Institute for IT Security, University of Lübeck, leading the Privacy & Security (PrivSec) group and directing the AnoMed competence cluster. He has held tenured faculty positions since 2019 after postdoctoral research at ETH Zürich (2016-2019) and a PhD at Saarland University (2015). University of Lübeck (2015-present) ETH Zürich (2016-2019) Saarland University (2015) His research focuses on privacy-preserving technologies in machine learning, anonymous communication protocols, and formal verification of security properties. Recent work includes advancements in Mixnet scalability and federated learning with differential privacy guarantees. Key publication trends reveal a strong emphasis on privacy-preserving algorithms for machine learning (2024), cryptographic protocols for anonymous communication (2025), and security analysis of decentralized systems (2023-2025). Collaborations span institutions like ETH Zürich, Saarland University, and industry partners EnergieDock/NAECO Blue for the VeDS project. His group includes 11 researchers (5 PhD students) and software engineers working on topics like Differential Privacy Secure Multi-Party Computation Trusted Execution Environments
Dr. Dima Alhadidi is an Associate Professor in the School of Computer Science at the University of Windsor. His research focuses on Cybersecurity, Data Privacy, Machine Learning, and their applications in Health Informatics, Cloud Computing, and Smart Grids. He holds a PhD in Computer Science and Software Engineering from Concordia University (2010). His research interests include secure federated learning frameworks, privacy-preserving techniques for genomic and health data, and adversarial machine learning defenses. Notable contributions include Trustformer (2025), secure aggregation methods in federated learning, and hybrid malware classification using deep learning. Recent work emphasizes mitigating membership inference attacks and developing privacy-preserving analytics for distributed systems. Dr. Alhadidi actively advises graduate students on topics like social network clustering (NICASN 2022) and federated learning security. No scientific awards are explicitly listed. His research spans theoretical frameworks (e.g., λ_AOP calculus) to applied systems in smart grids and healthcare informatics.
Stefan Decker is a full University Professor (Universitätsprofessor) at RWTH Aachen University, Germany, where he heads the Chair of Information Systems and Databases (Informatik 5) within the Faculty of Mathematics, Computer Science and Natural Sciences. He is actively involved in teaching, research, and the supervision of numerous ongoing and completed doctoral, master’s, and bachelor theses. Education & Academic Background Doctorate (Dr. rer. pol.) – field of Information Systems or related (exact institution/year not stated in text). Appointed as University Professor and Chair of Information Systems and Databases at RWTH Aachen University. Research Interests Prof. Decker’s work lies at the intersection of databases, knowledge graphs, semantic web technologies, data science, and cybersecurity . He investigates architectures and algorithms for large-scale, privacy-preserving, decentralized data analytics , develops ontology-driven information systems , and explores the use of large language models (LLMs) for educational technology, anomaly detection, and incident-response playbooks. Additional focal areas include smart energy systems, mixed-reality learning environments, FAIR data principles, and federated machine learning . Scientific Contributions & Trends His recent publications (2022-2025) demonstrate a clear trend toward explainable AI, LLM-enhanced systems, secure data spaces, and semantic interoperability . Key contributions include novel anomaly-detection frameworks for encrypted power-grid communications, knowledge-graph-driven chatbots for higher-education support, and methodological advances in decentralized analytics and FAIR data sharing. These works are disseminated in top-tier venues such as AAAI, IEEE ISGT Europe, ESWC, IDEAL, and various Springer LNCS and IEEE Transactions. Supervision & Grants Doctoral Theses Advised: A. T. Neumann – “Chatbots as professional companions in large-scale community information systems” (2024) S. M. Welten – “Methods for practical data sharing and decentralized analytics” (2025) Master’s Theses Co-Advised: A. R. Küsters – “Object-centric process constraints using variable bindings” (2025) Additionally supervising more than 30 ongoing bachelor, master, and doctoral projects covering topics such as LLM-driven cybersecurity playbooks, knowledge-graph construction for German law, privacy-preserving analytics in smart grids, and mixed-reality learning agents. Principal investigator or senior researcher in large collaborative projects including NFDI4DS, WestAI, champI4.0ns and several EU/national initiatives on sovereign data spaces and AI services. Labs & Teams Prof. Decker leads the Information Systems & Databases (DBIS) Research Group . The group operates well-equipped laboratories for knowledge-graph engineering, mixed-reality applications, privacy-enhancing technologies, and secure distributed analytics . Current team size exceeds 30 researchers including PhD candidates, postdocs, and scientific programmers, supported by national and EU funding streams.
Anastasios Zafeiropoulos serves as Assistant Professor at Harokopio University of Athens, specializing in Spatial Data Management and Analysis within the Postgraduate Studies Program for “Applied Geography and Spatial Management” (Direction C: Geoinformatics). His academic role encompasses teaching “Spatial Databases” and advancing research at the intersection of geospatial technologies and distributed computing systems. His research program focuses on Spatial Databases, Internet of Things (IoT), Cloud/Edge Computing, and 6G Network Orchestration, with significant extensions into Knowledge Graph applications for Sustainable Development Goals (SDGs) and socio-emotional learning in education. Key innovations include the EduCardia methodology for student competency assessment and frameworks for climate vulnerability analysis using knowledge graphs. Analysis of his 2024-2025 publications reveals three dominant thrusts: (1) AI-driven orchestration of 6G services across the computing continuum using reinforcement learning; (2) Knowledge Graph applications for SDG interlinkage analysis and materials science; (3) EU-funded IoT/Edge Computing project ecosystems. His work consistently bridges theoretical networking concepts with practical sustainability and educational applications. Dr. Zafeiropoulos actively contributes to EU-funded initiatives in IoT and Edge Computing standardization, particularly through AIOTI WG Standardisation. His project portfolio includes NEPHELE multi-cloud ecosystem development and O-RAN slice admission control research, demonstrating strong industry-academia collaboration in next-generation networking. He leads the development of innovative tools including Palindrome.js for distributed system visualization and the EmoSocio open-access emotional intelligence model, reflecting his commitment to translating research into practical educational and environmental solutions.
Amin Mesmoudi serves as Associate Professor in Data Engineering at the University of Poitiers' IUT (Institut Universitaire de Technologie), with dual laboratory affiliations at LIAS-ENSIP (Poitiers campus) and LIAS-ISAE-ENSMA (Chasseneuil campus). His research bridges theoretical database systems with practical large-scale data engineering challenges, particularly in semantic web technologies and machine learning applications. The laboratory maintains physical presences at both ENSIP's Bâtiment B25 in Poitiers and ISAE-ENSMA's Téléport 2 facility in Chasseneuil, facilitating cross-institutional collaboration. Mesmoudi's research program centers on scalable data management systems, with three interconnected pillars: (1) RDF and graph-based query optimization techniques for billion-triple datasets, (2) machine learning integration for spatial query performance and anomaly detection, and (3) explainability frameworks for complex black-box models. His work demonstrates consistent evolution from foundational database systems (2011-2016) toward contemporary AI-driven data engineering, particularly evident in his 2023-2025 publications on temporal dependency preservation and co-selection explainability. The Data Engineering team within LIAS laboratory provides the primary research context for these investigations. Publication analysis reveals strong methodological continuity in addressing scalability bottlenecks across database paradigms. Early work focused on SQL-on-MapReduce benchmarking for astronomy databases (2015-2016), transitioning to specialized RDF processing frameworks (2019-2021), and culminating in current hybrid approaches combining temporal modeling with machine learning (2023-2025). Key technical themes include fragmentation strategies for distributed data, optimizer feedback mechanisms, and graph-based query acceleration - all targeting real-world performance constraints in big data environments. As a core member of LIAS laboratory's Data Engineering team, Mesmoudi contributes to France's national research infrastructure in computer science and automation systems. The laboratory's dual-university structure enables unique cross-pollination between University of Poitiers' academic programs and ISAE-ENSMA's engineering specialization, with Mesmoudi's work exemplifying this synergy through applications spanning astronomy databases to wireless sensor networks.
Dr. Latifur Khan is a Professor in the Department of Computer Science at the University of Texas at Dallas' Erik Jonsson School of Engineering and Computer Science. He directs the Database and Data Mining Laboratory and conducts research in data mining, cybersecurity, and semantic web technologies. Research domains include: Large language models for threat detection Fairness in machine learning Vulnerability analysis in software systems Graph-based information retrieval Recent publications focus on AI security applications in transportation systems, political conflict analysis using NLP, and federated learning for IoT security. His work consistently bridges theoretical algorithms with practical cybersecurity implementations. Research grants include funding from NSF, NASA, Raytheon, Nokia, and SUN Microsystems. Teaching includes courses in Plant Breeding (PBG 450/550) and Breeding Clonal Crops (PBG 551).
Alan Katerinsky serves as a Clinical Assistant Professor in the Management Science and Systems department at the School of Management, University at Buffalo, teaching information security, networking, IT for business, applied programming, and database management. Education: MS, School of Management, University at Buffalo Certificate of Advanced Study in Information Assurance, University at Buffalo BA, University at Buffalo His research critically examines information security frameworks, regulatory compliance mechanisms, and international cybersecurity policy with specialized focus on Eastern European transitions and cloud infrastructure vulnerabilities. Work explores intersections between technical security systems and geopolitical telecommunications regulations. Publications demonstrate consistent focus on policy-education integration in cybersecurity, analyzing post-communist regulatory landscapes and advocating for legal curriculum components in information assurance training to address real-world compliance challenges. Professional Recognition: Security+ Certification (2016) Industry experience includes Federal Trade Commission security research analysis and private sector roles across finance, communications, and manufacturing sectors. Professional affiliations encompass CompTIA, ISSA, InfraGard, and InfoTech WNY. No public records indicate graduate student advising or research grant administration.
Dr. Chao Tian is an Associate Professor in the Department of Electrical and Computer Engineering at Texas A&M University, part of the College of Engineering. He holds a Ph.D. from Cornell University (2005) and a B.E. from Tsinghua University (2000). His research focuses on information theory, distributed storage systems, coding theory, and machine learning applications, including large language models (LLMs) and privacy-preserving techniques like steganography and private information retrieval (PIR). He has received notable awards, including the 2017 IEEE Jack Wolf ISIT Best Student Paper Award and the 2014 IEEE ComSoc DSTC Best Paper Award. Dr. Tian’s work bridges theoretical foundations and practical applications, such as optimizing storage codes for distributed systems, developing secure PIR protocols, and exploring LLMs in energy and steganographic contexts. His group has pioneered methods for variable-order Markov chain modeling with transformers and designed quantization techniques for perceptual quality. Recent activities include a faculty development leave at MIT/Harvard and delivering distinguished lectures globally on AI and information theory. He advises students on cutting-edge topics like LLM-based steganography and diffusion models. His publications span journals like IEEE Transactions on Information Theory and conferences such as NeurIPS and ISIT, with a focus on coding theory, privacy, and machine learning. He has also contributed to open-source tools like the CAI toolbox for investigating information-theoretic limits. Current projects include optimizing cryptocurrency mining in energy markets and advancing federated learning with adversarial robustness.
Assoc Prof Frederique Elise Oggier is an Associate Professor in the School of Physical and Mathematical Sciences, Division of Mathematical Sciences at Nanyang Technological University (NTU). She holds a PhD from the Swiss Federal Institute of Technology (EPFL) and has held visiting positions at Caltech and the Research Center for Information Security (Tokyo). Her research focuses on algebraic coding theory, lattice-based cryptography, and applications of number theory to secure and reliable communication systems. Education: Bachelor’s and Master’s in Mathematics from the University of Geneva PhD in Mathematics from EPFL Research Interests: Her work bridges abstract algebra with practical coding challenges, emphasizing lattice codes for wiretap channels, distributed storage systems, and security protocols. Specialized in algebraic structures like cyclic division algebras and modular lattices, her contributions advance both theoretical foundations and real-world implementations of secure communication systems. Publications: Her recent work addresses cutting-edge topics such as MDS codes, non-GRS code constructions, and lattice-based security in noisy channels. These contributions highlight her expertise in coding theory and its interdisciplinary applications. Grants/Advising: While specific grants are not detailed, her prolific publication record reflects sustained research activity. She advises on projects related to distributed storage and secure coding, though student names are not listed in the provided texts. Labs/Teams: Affiliated with NTU’s mathematical sciences division, she collaborates with global researchers on projects such as lattice coding for 5G/6G systems and cryptographic protocols leveraging algebraic number theory.
