Professor Yuefeng Li is a leading academic in Knowledge & Data Engineering, Artificial Intelligence, and Information Systems at Queensland University of Technology (QUT). He holds a PhD from Deakin University and has been affiliated with QUT's Faculty of Science and School of Computer Science since 2007. His research focuses on data mining, machine learning, natural language processing, and web intelligence, with 250+ refereed papers and 8,100+ citations (h-index=46). He has secured 5 ARC Discovery Grants, 1 ARC Linkage Grant, and led 6 CRC projects. His work bridges theoretical advancements and real-world applications in domains like healthcare, transportation, and geotechnical engineering. Research interests include: Data Mining & Knowledge Discovery, Machine Learning Algorithms, Natural Language Processing (NLP), Web Search & Information Retrieval, Deep Learning, and Knowledge Representation. He has pioneered methods like Semantic-LDA for topic modeling and adaptive multi-kernel learning for blast prediction. Key achievements include 10 Best Paper Awards, 14 articles with >100 citations, and editorial roles (e.g., Editor-in-Chief of Web Intelligence). He has supervised over 20 PhD/Masters students and currently oversees research training as Academic Lead HDR. His current projects include 5G/IoT ontologies, AI-driven medical diagnostics, and deep learning for imaging modalities. Teaching includes courses on search engine technology, machine learning for NLP, and data analytics. He actively participates in international conferences as a PC chair and conference organizer.
Youtao Zhang is a Professor at the Swanson School of Engineering, University of Pittsburgh. His research focuses on computer architecture, machine learning, quantum computing, and data storage systems. He has contributed to advancements in secure non-volatile memories, GPU optimization, and healthcare informatics through deep learning applications. His work addresses challenges in hardware security, memory management, and efficient quantum circuit simulation. Key research areas include: Hardware Security and Covert Channels Quantum Computing Algorithms and Hardware Storage Systems Optimization GPU Acceleration and Parallel Computing Medical Imaging and Healthcare AI Recent publications emphasize innovations in hybrid storage deduplication, quantum circuit evaluation, and adversarial defense in vision transformers. His work bridges theoretical computer science with practical applications in emerging technologies.
Frédéric Besson is a researcher affiliated with INRIA, a leading French national research institute in digital sciences. His work is centered on programming languages, formal verification, and software security, with a strong emphasis on verified compilation and program transformation for security enforcement. His research interests include formal verification , type systems , compiler correctness , software fault isolation , and side-channel security . He actively contributes to the development of formally verified systems using tools like Coq and CompCert. The recent publications highlight a consistent focus on verified compilers , security-by-design , and program transformation techniques, particularly leveraging formal methods and type systems to enforce correctness and security properties in low-level systems. Frédéric Besson has served on program committees for key conferences such as C&ESAR 2021 and PriSC 2020, demonstrating his active role in the research community. He has advised or collaborated on work in secure compilation and formal methods, though no formal students are listed. He is involved in projects that bridge theoretical type systems with practical system security, suggesting potential grant or collaborative research funding, though specific grants are not mentioned. His work is closely tied to formal methods labs and teams within INRIA, particularly those focused on verified systems, programming languages, and cybersecurity, such as the teams behind CompCert and Coq.
Mihai Anitescu is a Senior Computational Mathematician in the Laboratory for Advanced Numerical Software (LANS) within the Mathematics and Computer Science Division at Argonne National Laboratory, a position he has held since 2002. He is also a part-time Professor in the Department of Statistics at the University of Chicago since 2009 and an adjunct Associate Professor in the Mathematics Department at the University of Pittsburgh. Additionally, he is a Senior Fellow of the Computation Institute, a joint Argonne-University of Chicago initiative. He leads the MACSER (MultiTimescale Control of Electric Power Systems) project and previously led the M2ACS project. Ph.D., Applied Mathematical and Computational Sciences, University of Iowa, 1997 Electrical Engineer, Polytechnic University of Bucharest, Romania, 1992 Dr. Anitescu’s research focuses on numerical optimization, uncertainty quantification, and numerical analysis, with applications spanning nuclear engineering, electric power grids, chemical engineering, materials science, biology, mechanical engineering, and robotics. His work develops scalable computational methods for complex systems, particularly leveraging high-performance computing. He has made significant contributions to optimization under uncertainty, stochastic programming, Gaussian process modeling, and simulation of multibody dynamics with contact and friction using differential variational inequalities. His recent publications (2020–2023) highlight a strong and consistent trend in applying advanced mathematical and computational techniques to critical energy infrastructure, particularly the electric power grid. Key themes include stochastic optimization for optimal power flow under uncertainty, risk assessment through extreme event simulation, frequency prediction and estimation using spatiotemporal and Bayesian methods, and the simulation of cascading failures. His work bridges core mathematical advances in optimization, sensitivity analysis, and scalable Gaussian process computation with high-impact applications in grid stability, reliability, and control. Dr. Anitescu is a senior editor of Optimization Methods and Software and a member of the editorial boards of Mathematical Programming and the SIAM Journal on Optimization . He has previously served on the editorial boards of the SIAM Journal on Scientific Computing and the SIAM/ASA Journal on Uncertainty Quantification . He is a dedicated mentor, having advised numerous postdoctoral fellows, Ph.D. students, and M.S. students at Argonne, the University of Chicago, and the University of Pittsburgh. His advisees have gone on to successful careers in national laboratories, academia (e.g., UC Santa Barbara, Purdue, University of Wisconsin), and industry (e.g., Amazon, Citibank, Morgan Stanley, IBM). He has secured and led significant research grants through projects like MACSER and M2ACS, which focus on the mathematical challenges of managing complex, uncertain energy systems. His work is highly collaborative, involving partnerships across institutions and disciplines. Dr. Anitescu leads the MACSER project, a major research initiative focused on developing mathematical and computational tools for the multi-timescale control of electric power systems. This work is central to ensuring the stability and reliability of modern power grids, especially as they integrate increasing amounts of renewable energy.
Professor Karl-Mikael Perfekt holds a Chair in mathematical analysis at the Department of Mathematical Sciences, NTNU. His research focuses on operator theory, complex analysis, harmonic analysis, potential theory, and spectral theory. He earned his Ph.D. from Lund University in 2013 under Alexander Aleman. Prior roles include Associate Professor at NTNU (2021–2023), Lecturer at the University of Reading (2017–2021), and postdoctoral positions at the University of Tennessee and NTNU. Perfekt has secured major grants including the Zemánek Prize (2018), EPSRC Research Grant (2019–2021), and a Norwegian Research Council project (2023–2027). He mentors postdocs like Carlos Mudarra and Marta de León-Contreras and supervises PhD students such as Athanasios Kouroupis. His editorial work includes roles at Analysis and Mathematical Physics . His recent publications explore Dirichlet series, Hardy kernel matrices, plasmonic eigenvalues, and spectral theory. Research highlights include contributions to functional analysis, PDEs, and applied mathematics, often addressing boundary value problems and operator properties in complex geometries.
Evangelos Magirou is a Professor of Operations Research at the Department of Informatics, Athens University of Economics and Business (AUEB) since 1986. He holds a B.S. in Electrical Engineering from Princeton University (1971), M.S. and Ph.D. in Decision and Control Sciences from Harvard University (1976). His research focuses on theoretical and applied operations research, optimization, game theory, and their applications in finance, shipping, and energy policy. Education: B.S. Electrical Engineering, Princeton University (1967-71) M.S. Decision and Control Sciences, Harvard University (1971-72) Ph.D. Decision and Control Sciences, Harvard University (1972-76) Research Interests: Theoretical and applied operations research, optimization techniques, game theory applications in transportation and energy, quantitative methods in shipping economics, and policy analysis. Notable projects include stochastic vessel positioning models, strategic petroleum reserve optimization, and spam email game theory. Professional Roles: Member of the Governing Board of Public Power Corporation S.A. (since 2004) Chairman of the Faculty Union of AUEB (1994-present) Deputy Chairman, Department of Informatics (2003-present) Key Contributions: Over 30 peer-reviewed publications spanning operations research, control systems, and maritime logistics. Active in academic leadership roles and policy advisory committees.
Björn B. Brandenburg is a tenured faculty member at the Max Planck Institute for Software Systems (MPI-SWS), where he leads the Real-Time Systems Group. His role is equivalent to an associate professorship in the US system, and he is deeply engaged in both theoretical and practical aspects of real-time computing. Max Planck Institute for Software Systems (MPI-SWS), Kaiserslautern, Germany PhD, University of North Carolina at Chapel Hill (2006–2011) MSc, Technische Universität Berlin (TU Berlin, 2003–2006) His research centers on real-time systems , operating systems , and embedded systems , with a focus on combining formal analysis methods and systems building to create robust, analyzable, and efficient systems. He is particularly interested in work that bridges theory and practice, such as formally verified schedulability analysis and dynamic model extraction from real systems. The 15 most recent publications reflect a strong trend toward mechanized verification (especially using Coq/Rocq in the PROSA project) and real-world applicability (e.g., Linux, ROS 2). Key themes include response-time analysis, scheduling theory, model extraction, and formal foundations for real-time principles. The work spans from abstract theoretical frameworks to concrete tools like LiME and LITMUS-RT. His scientific recognition includes: ERC Starting Grant (TOROS, 2018) ACM SIGBED Early Career Award (2018, inaugural) Multiple Best/Outstanding Paper Awards at RTSS, RTAS, ECRTS, EMSOFT Fulbright and Klaus Murmann Fellowships ACM Future of Computing Academy (2017, inaugural class) Distinguished Dissertation Awards (EDAA, CGS/ProQuest, UNC) He has advised numerous PhD and master’s students, many of whom have secured academic positions or industry research roles. He has received significant research funding, including the ERC Starting Grant and bilateral ANR-DFG grants. His leadership extends to organizing major conferences (e.g., PC Chair of RTSS 2025, ECRTS 2021) and editorial roles (LITES, former associate editor for ACM TECS). He actively contributes to the open-source research ecosystem through tools like PROSA, LiME, LITMUS-RT, and SchedCAT. He leads the Real-Time Systems Group at MPI-SWS, which focuses on the PROSA and LiME projects. The group brings together systems hackers and formal provers to advance the state of the art in analyzable real-time systems. He collaborates with institutions such as INRIA, ONERA, and TU Braunschweig through funded projects.
Dr. Khong Wei Leong is a Lecturer in the Department of Common Engineering at the Malaysia School of Engineering, Monash University Malaysia. He is a registered graduate engineer with the Board of Engineers Malaysia (BEM) and actively involved in research on biomedical imaging, image/video processing, smart healthcare, and non-contact vital signs measurement. His current work focuses on developing non-contact methods for measuring human vital signs, such as heart rate and blood pressure using video imaging and LabVIEW-based tools. Dr. Khong holds a PhD in Electrical & Electronics Engineering (2018), MEng in Modelling and Estimation of Vehicle Tracking (2013), and BEng (Hons) in Computer Engineering (2009), all from Universiti Malaysia Sabah. He coordinates the unit ENG1013: Engineering Smart Systems and serves as a reviewer for the Elsevier journal Biomedical Signal Processing & Control . His research interests span biomedical engineering, artificial intelligence, and telehealth solutions. Notable projects include the 'Catch me before I fail' initiative to improve student academic performance and a LabVIEW-based biomedical toolkit for ECG analysis. His work aligns with UN Sustainable Development Goals, particularly in advancing education and health technologies. Key collaborations include work on robot vision systems, vehicle tracking algorithms, and mobile health applications. Dr. Khong has contributed to over 26 peer-reviewed publications and actively participates in international conferences.
Taesoo Kim is a Professor at Georgia Tech's College of Computing, jointly affiliated with the School of Cybersecurity and Privacy and the School of Computer Science. He serves as Director of the GTS3 lab and leads research in systems security, operating systems, programming languages, and distributed systems. His work emphasizes foundational security principles and practical tools for system resilience. Education: PhD in EECS from MIT (2014), SM from MIT (2011), BS in Computer Science/Electrical Engineering from KAIST (2009) Affiliations: Georgia Tech's School of Computer Science, School of Cybersecurity and Privacy, ML@GT, and Online Master of Science in Computer Science program Kim's research focuses on building secure computing systems through formal system design, implementation analysis, and trusted component isolation. Notable contributions include tools for automatic vulnerability detection, intrusion recovery frameworks, and secure enclave technologies like SGX-Tor. His work has secured grants from ONR, NSF, DARPA, and industry partners. Awards include the 2015 Internet Defense Prize and a finalist placement in the DARPA Cyber Grand Challenge. Key projects include QSYM (concolic execution engine), RAIN (attack tracing), and FREEDOM (DOM fuzzer). Kim teaches courses on information security and blockchain technologies. His lab (SSLab) collaborates with industry on critical security challenges, including hardware-software co-design for trusted execution environments.
Liesbeth Janssen is an Associate Professor and Chair of the Soft Matter and Biological Physics group at the Department of Applied Physics, Eindhoven University of Technology (TU/e). Her research focuses on non-equilibrium soft matter, including glass formation, active materials, and bio-inspired systems. She holds a PhD in Theoretical Chemistry from Radboud University (cum laude) and has held postdoctoral positions at Columbia University and Heinrich-Heine University. Notable awards include the NWO Vidi Award, Mildred Dresselhaus Guest Professorship, and membership in the KNAW Young Academy. Her work explores the physics of materials far from equilibrium, such as glasses and active matter, using theory, simulations, and machine learning. Current research includes understanding glass dynamics, applications in recyclable materials, and cancer metastasis modeling. She leads a multidisciplinary team with students and postdocs investigating topics like cell migration, polymer fragility, and smart materials. Janssen has secured grants including the NWO Vidi, ENW-GROOT, and European ITN. Her lab collaborates internationally, emphasizing diversity and inclusion. Recent achievements include the NWO Athena Award (2024) for promoting neurodiversity in science and groundbreaking work on glass theory published in *Phys. Rev. Lett.* and *PNAS Nexus*.
Gábor Lencse is a Senior Research Fellow at the Budapest University of Technology and Economics, working in the Department of Network Systems and Services. His research focuses on IPv6 transition technologies, network performance analysis, and benchmarking methodologies. Dr. Lencse's research interests include: IPv6 Transition Technologies and Security Analysis Network Performance Benchmarking Stateless and Stateful Network Address Translation Multipath Networking (MPT-GRE) DNS64 and NAT64 Implementations Statistical Synchronization Methods His recent publications show a strong focus on IPv6 transition technologies, particularly analyzing the performance and security aspects of various transition mechanisms like MAP-T, 464XLAT, and DS-Lite. He has developed benchmarking methodologies and tools such as siitperf, an RFC-compliant tester for evaluating IPv6 transition technologies. His work often involves detailed performance analysis of network protocols and implementations, with applications in real-time video streaming and multipath networking. Dr. Lencse has received recognition for his contributions to networking standards, having co-authored IETF RFCs including RFC 9693 (Benchmarking Methodology for Stateful NATxy Gateways) and RFC 9313 (Pros and Cons of IPv6 Transition Technologies for IPv4-as-a-Service). He has supervised numerous research projects and collaborated with international researchers, particularly in the areas of IPv6 transition technology performance and security analysis.
Christian Böhm is an Associate Professor at the Faculty of Computer Science, leading the Research Group Data Mining and Machine Learning. His work focuses on clustering algorithms, density-based analysis, and graph construction. He has been active in interdisciplinary projects, including computational modeling for biomedical applications and algorithm benchmarking. Notable contributions include ADOD (Adaptive Density Outlier Detection) and DynoGraph (Dynamic Graph Construction for Nonlinear Dimensionality Reduction). His research bridges theoretical data science with practical applications in healthcare and engineering. Research Group: Data Mining and Machine Learning Key Areas: Clustering, Density Analysis, Graph Algorithms, Medical Data Analytics Collaborations: Biohybrid heart valves, computational biomechanics, and algorithmic benchmarking Recent work emphasizes deep learning integration in clustering, medical outcome prediction, and scalable graph classification. His publications span conferences like IEEE ICDM and interdisciplinary journals.
Manuel Sánchez Rubio is an Associate Professor at the Department of Computer Science, University of Alcalá (Spain). He holds a Ph.D. in Computer Science from the University of Alcalá (2013), focusing on data post-processing methodologies for unmanned aerial vehicles (UAVs) under critical conditions. His research interests span cybersecurity, malware analysis, UAV data processing, and educational technology. He is affiliated with the LITE (Laboratory of Information Technologies in Education) and has contributed to security training platforms and critical infrastructure protection projects. Ph.D. Thesis: "Aportaciones para el postproceso de datos en vehículos aéreos no tripulados" (2013) His work emphasizes practical applications in security, including hybrid assessment methodologies for web applications, malware evasion techniques, and unsupervised learning for network analysis (e.g., TOR clustering). He has developed automated virtual machine tools for security training and explored hyperspectral sensor integration in UAVs for environmental monitoring. Key contributions include datasets for attack pattern modeling, spam honeypot systems in cloud environments, and SCADA security studies. His research bridges theoretical computer science with real-world challenges in aerospace, education, and critical infrastructure.
Yi Huang is a Professor in the Department of Atmospheric and Oceanic Sciences at McGill University, Canada. His research focuses on atmospheric radiation and its role in climate and weather variability, with emphasis on remote sensing techniques and radiative feedback analysis. He leads a research group integrating satellite observations and numerical modeling to address climate and weather challenges. His group actively collaborates with international institutions and has supervised numerous graduate students and postdocs. Key research areas include radiative kernel analysis, stratospheric water vapor dynamics, and Arctic climate feedbacks. The group maintains an active website with publication lists and research data. Education: Ph.D. in Atmospheric and Oceanic Sciences (not explicitly detailed in text), but mentions visiting at McGill (2018-2020). Research interests span atmospheric radiation, radiative remote sensing, and climate feedback mechanisms. Recent work includes studies on Arctic radiative processes, spectral radiative kernels, and stratospheric water vapor impacts on global circulation patterns. The Huang Group collaborates with institutions like Environment and Climate Change Canada and has secured grants for climate monitoring projects. Graduate students and postdocs are involved in satellite data analysis, climate model evaluation, and field campaigns. Labs/Teams: The group operates within McGill's Atmospheric and Oceanic Sciences department, leveraging advanced computational facilities and satellite datasets. Collaborations include international initiatives like the HiSRAMS instrument development project.
Mahalakshmi Sabanayagam is a Researcher at the Technical University of Munich (TUM), affiliated with the School of Computation, Information and Technology and the Department of Theoretical Foundations of Artificial Intelligence. Her research focuses on the theory of deep learning and graph-based learning, with notable contributions to adversarial robustness, neural network certification, and graph representation learning. She currently serves as a Teaching Assistant for the course 'Gems of Informatics 3 (IN2176): Modelling and analysis of real-world graphs.' Her work bridges theoretical foundations and practical applications, including analysis of neural tangent kernels in graph networks, robustness certification against adversarial attacks, and cross-domain machine learning techniques. Key research directions include understanding decision boundaries via Hessian analysis, tensorized autoencoder architectures, and graphon-based network clustering. Publications span topics from adversarial machine learning to interdisciplinary applications in physics and finance. While currently no awards or grants are listed, her active publication record reflects ongoing contributions to artificial intelligence theory and graph-based methodologies. She is part of Prof. Debarghya Ghoshdastidar's research group at TUM's Faculty of Computer Science. Lab affiliations include the Theoretical Foundations of Artificial Intelligence (I7) group, focusing on rigorous mathematical analysis of AI systems and their real-world graph applications. No advising relationships or student collaborations are explicitly documented in available materials.
