Weiyu Xu is a Professor at the University of Iowa, affiliated with both the Department of Electrical and Computer Engineering and the Department of Applied Mathematical and Computational Sciences. He joined the College of Engineering in 2012 and leads the Intelligent Information Processing Lab (IIPL). Ph.D., Electrical Engineering, California Institute of Technology, 2009 M.S., Electrical Engineering, California Institute of Technology, 2006 M.S., Electronic Engineering, Tsinghua University, 2005 B.E., Information Engineering, Beijing University of Posts and Telecommunications, 2002 His research focuses on compressive sensing, information theory, signal processing, network optimization, and deep learning applications in medical imaging and cybersecurity. He has made significant contributions to adversarial attack robustness, distributed optimization algorithms, and medical imaging techniques for OCT segmentation and brachytherapy. Recent publications highlight trends in Adversarial machine learning Medical imaging algorithms Compressed sensing for diagnostics Optimization in wireless communication He has also contributed to federated learning over tree networks and theoretical guarantees for sparse signal recovery. Weiyu Xu's lab, IIPL, integrates deep learning with classical optimization and signal processing. His work bridges foundational theories (e.g., information-theoretic robustness) with real-world applications in healthcare (e.g., cancer treatment planning) and communication systems (e.g., MIMO channel estimation).
Laurie Williams serves as a Goodnight Distinguished University Professor in the Computer Science Department within the College of Engineering at North Carolina State University. She co-directs both the NCSU Secure Computing Institute and the NC State Science of Security Lablet, demonstrating deep institutional leadership in cybersecurity research. With over 260 refereed publications, her work establishes her as a prominent figure in software security academia. Her research spans critical areas including software security, agile development practices (particularly continuous deployment), software reliability, and software supply chain security. Williams focuses on practical security solutions addressing modern challenges like malicious dependencies in open-source ecosystems, AI-generated code vulnerabilities, and runtime protection mechanisms. Her work bridges theoretical security principles with industry-relevant applications. Recent publications reveal strong trends toward software supply chain security, with multiple 2024-2025 papers addressing vulnerability exploitability, malicious commit detection, and metrics-driven security control selection. Her research increasingly incorporates machine learning for threat detection while maintaining focus on human factors in secure development practices. IEEE Fellow (2018) National Science Foundation CAREER Award (2004) ACM SIGSOFT Influential Educator Award (2009) Multiple IBM Faculty Awards (2002-2012) NCSU Alumni Association Outstanding Research Award (2015-2016) Williams leads multiple major NSF-funded projects including the $5.7M SaTC Frontiers grant on secure software supply chains and the Science of Security Lablet with $3.6M in DoD funding. Her research emphasizes practical industry impact through collaborations with Cisco and Laboratory for Analytic Sciences. She actively mentors through the NCSU Research Leadership Academy and maintains significant educational outreach in software security. Her laboratory work centers on the Secure Computing Institute and Science of Security Lablet, where her team develops frameworks for vulnerability prediction, supply chain risk assessment, and secure development methodologies. Current projects focus on machine learning integrity, cognitive modeling for security decisions, and empirical analysis of build/deployment logs for anomaly detection.
Clément Mallet is a Senior Researcher and Director of the LASTIG laboratory at Université Gustave Eiffel, IGN, and École Nationale des Sciences Géographiques (ENSG) in Champs-sur-Marne, France. He leads research in geospatial computer vision, focusing on the intersection of remote sensing, computer vision, and machine learning. His responsibilities include overseeing 75 laboratory members and directing the STRUDEL research team focused on spatio-temporal information modeling. Education: Habilitation (HDR) in Geographical Information Science, Université Paris-Est (2016) PhD in Image and Signal Processing, Télécom ParisTech (2010) Engineering Degree in Geographical Information Science, ENSG (2005) Master's in Remote Sensing, Université Paris 6 (2005) Research Interests: Dr. Mallet specializes in multi-modal land-cover mapping, change detection, geohistorical image analysis, and airborne lidar processing. His work integrates deep learning with geospatial data analysis to solve complex problems in environmental monitoring, urban studies, and historical geography. Current research explores foundation models for earth observation and semantic change detection using hybrid data generation techniques. Publication Trends: Mallet's recent articles (2021-2025) demonstrate strong focus on deep learning applications for geospatial challenges: 40% address land-cover mapping innovations, 30% develop novel change detection methodologies, 20% advance lidar data processing, and 10% explore historical map analysis. His work consistently bridges computer vision theory with operational remote sensing applications. Awards and Recognition: Schwidefsky Medal from ISPRS (2016) 5x Outstanding Reviewer awards (CVPR/ECCV/ICCV 2017-2024) Best Paper Awards at GEOBIA 2016 and ISPRS 2014 Young Researcher Award from GDR ISIS (2010) EuroSDR Best PhD Thesis supervision (2020) Research Leadership: Directs multiple national and international projects including MAESTRIA (ANR-funded multi-modal EO analysis) and HIATUS (historical image analysis). Supervised 14+ PhD students in geospatial AI topics. Secured funding from ANR, CNES, EU H2020 (VOLTA, LandSense), and industrial partners. Leads the STRUDEL team developing cutting-edge methods for territory dynamics analysis. Professional Service: Editor-in-Chief of ISPRS Journal of Photogrammetry and Remote Sensing (2021-present). Organized major conferences including ISPRS Congress (2020-2022 Program Chair) and JURSE events. Active in ISPRS working groups since 2008, currently leading initiatives in large-scale machine learning applications for geospatial data.
Aniello Murano is a Professor of Computer Science at the Department of Electrical Engineering and Information Technologies , University of Naples Federico II. He serves as Scientific Director of the ASTREA (Automated Strategic Reasoning) Laboratory and leads cutting-edge research in Artificial Intelligence, Strategic Reasoning, Multi-Agent Systems , and Formal Verification . Research Interests : Strategic reasoning under perfect/imperfect information, specification/verification/synthesis of reactive systems, temporal/modal logics, automata theory, parity games, game theory, mechanism design, and formal languages. Notable Projects : PNRR Research Unit Coordinator (2023-2025) on Resilient AI, PRIN 2020 Unit Coordinator (RIPER: Resilient AI-Based Self-Programming and Strategic Reasoning), H2020-MSCA SEAL (Principal Coordinator). Awards & Honors : JPMorgan Faculty Research Award (2022), Royal Society Award (2016), Best Paper PRIMA (2015), INDAM Project Leader (2023), Italian Scientific Habilitation (2017-2018). Students & Postdocs : Supervised 6 PhD students (e.g., Silvia Stranieri, Vadim Malvone) and mentored postdocs such as Munyque Mittelmann and Bastien Maubert. Laboratory : Leads ASTREA Lab, focusing on automated strategic reasoning and resilient AI systems.
Associate Professor Dan Dongseong Kim is Deputy Director of UQ Cybersecurity and an Associate Professor at The University of Queensland (UQ), Australia. Previously, he held permanent academic positions at The University of Canterbury (UC), New Zealand (2011-2018) as a Senior Lecturer and Lecturer. His research focuses on Cybersecurity and Dependability for AI, IoT, Autonomous Vehicles, Cloud Computing, and Moving Target Defenses (MTD). Doctor of Philosophy in Computer Engineering from Korea Aerospace University Postdoctoral Research at Duke University (2008-2011) Visiting Scholar at University of Maryland (2007) Dan's work explores Graphical Security Models , Moving Target Defense for proactive resilience, and AI-Driven Cybersecurity with emphasis on adversarial robustness and interpretable models. His recent publications (2024-2025) span journals like IEEE Transactions on Dependable and Secure Computing and conferences such as DSN , addressing automated defense, evolving attacks, and hardware-aware security frameworks. He has advised 15 Ph.D. graduates, including researchers now at institutions like RMIT University, La Trobe University, and CSIRO's Data61. Current supervision includes projects on Automated Penetration Testing , AI-Based Intrusion Response , and Moving Target Defense . Dan's research is funded by agencies including the Republic of Korea's Agency for Defence Development and US Army Research Lab . His professional roles include Associate Editor for IEEE Communications Surveys and Tutorials and Steering Committee Chair for IEEE PRDC .
Bruno Volckaert is a Professor in the Department of Information Technology at Ghent University and Senior Researcher at imec. He obtained his Master of Computer Science (2001) and PhD in Grid Computing Resource Management (2006) from Ghent University. His research focuses on distributed cloud systems for Smart Cities and Industry 4.0 applications. Volckaert's expertise spans: Reliable distributed cloud backend systems Autonomous optimization of cloud applications Cybersecurity through machine learning IoT data processing architectures Kubernetes-based container orchestration Edge-to-cloud continuum computing His publications demonstrate strong focus on: cloud-native technologies, Kubernetes optimization, cybersecurity frameworks, and distributed AI systems. Recent work emphasizes reinforcement learning for auto-scaling, secure edge computing, and intrusion detection systems. He has contributed to over 40 national/international research projects and authored 100+ publications. Current affiliations include leadership roles in: IDLab Research Unit (Ghent University) imec Research Center
Prof. Dr. Kai Michael Griese is a full professor of Business Administration with a focus on Marketing at the Faculty of Economics and Social Sciences, Osnabrück University of Applied Sciences since 2009. He holds a Dr. rer. pol. from TU Chemnitz and has extensive industry experience, including roles at Mars Incorporated and Generali Deutschland. His research emphasizes sustainable business models, climate adaptation, and circular economy, with notable contributions to event sustainability and resilience strategies. He leads the Master's program in International Business Management with a focus on sustainable business models. Key roles include Program Director for the Master's focus on Sustainable Business Models and Climate Adaptation. Griese has been affiliated with institutions like the Shanghai University of International Business and Economics (SUIBE) as a guest professor. His work integrates digitalization and sustainability, addressing challenges in logistics, energy cooperatives, and corporate culture. He has secured grants from the German Federal Environment Foundation (DBU), BMBF, and EU-funded projects like Interreg North Sea Region. Research interests span climate resilience in events, sufficiency-driven marketing, and digital entrepreneurship's role in sustainability. His projects include the KlimaEvent initiative in Bad Essen, exploring climate adaptation for municipalities. He emphasizes practical solutions through VR-based simulations and interdisciplinary collaboration.
Dr. John Hastings is a Professor in the Department of Computer Science at Dakota State University, part of the Beacom College of Computer & Cyber Sciences. With nearly 35 years of experience, he specializes in teaching and curriculum development in computer science, combining academic rigor with industry insights from his roles as an AI/ML engineer, team leader, and business owner. Education: Ph.D., Computer Science, University of Wyoming M.S., Computer Science, University of Wyoming B.S., Computer Science, University of Wyoming Research interests include machine learning, AI applications in natural language processing (LLMs), generative AI, computer vision, ecological/environmental AI, AI in games, and gamification in education. Recent publications focus on cybersecurity challenges, AI ethics, and insider threat detection. His work has been recognized with awards such as the AAAI’s Innovative Applications of Artificial Intelligence (IAAI) Award and an International IPM Award for Excellence related to the CARMA AI tool. Teaching emphasizes active learning and practical skills, including courses on programming, data structures, AI, and cybersecurity. He advocates for gamification in education to enhance student engagement and success.
Yading Yuan, PhD is an Associate Professor of Radiation Oncology (Physics) at Columbia University Irving Medical Center and a member of the Data Science Institute. He holds a PhD in medical physics from the University of Chicago (2010) and completed clinical residency at Harvard Medical Physics Program (2013). His research focuses on AI-driven innovations in radiation oncology, including automated medical image analysis systems, federated learning frameworks for tumor segmentation, and data-driven approaches to personalized cancer treatment. He is certified by the American Board of Radiology and licensed in New York State. Education: PhD in Medical Physics (University of Chicago, 2010); Clinical Residency (Harvard Medical Physics Program, 2013). Research interests include: automated knowledge-based treatment planning, large-scale clinical AI systems, medical image reconstruction algorithms, and panomics integration for precision oncology. His work emphasizes translating data science advancements into clinical practice to improve patient outcomes. Key trends in his publications include federated learning for privacy-preserving medical AI, tumor segmentation in multi-modal imaging (PET/CT, MRI), and AI-driven prediction of treatment outcomes and recurrence risks. Recent work emphasizes decentralized learning architectures and cross-institutional collaboration systems. Scientific Awards: Distinguished Reviewers 2013 (selected by peer review committees) Advising/grants: No specific student names or grant details listed in provided text. His work is supported through institutional and collaborative research initiatives. Labs/teams: Active member of Columbia's Data Science Institute and Radiation Oncology department, contributing to interdisciplinary medical AI research groups.
Dr. Chutima Boonthum-Denecke is a Professor in the Department of Computer Science at Hampton University's School of Science. She joined Hampton University in 2006 as an Assistant Professor and now serves as Director of the Information Assurance and Cyber Security Center (IAC@HU). She leads the NSF CyberCorps Scholarship for Service program and has contributed to NSF initiatives like ARTSI and STARS Alliances. Her educational background includes a Ph.D. in Computer Science from Old Dominion University (2007), an MS in Applied Computer Science from Illinois State University (2000), and a BS in Computer Science from Srinakharinwirot University (1997). Dr. Boonthum-Denecke's research integrates artificial intelligence, natural language processing, and cybersecurity. Key interests include: Developing intelligent tutoring systems and educational games Secure coding practices for software engineering NLP applications in information retrieval and assessment tools Cyber-physical security for IoT and robotics Her recent publications (2016-2021) focus on machine learning applications in cybersecurity, including sentiment analysis for threat detection, blockchain-enhanced IoT security, and vulnerability assessments of emerging technologies. Collaborative work with students frequently addresses privacy ethics in AI assistants, RFID implants, and cloud systems. She mentors students through the IAC@HU lab, resulting in award-winning conference presentations on cybersecurity topics. As Principal Investigator of NSF CyberCorps, she oversees scholarship programs that bridge academic research with national security needs.
Dr. Yar Muhammad is a Principal Lecturer in Computer Science at the University of Hertfordshire's School of Physics, Engineering & Computer Science. His research develops Brain-Computer Interface applications using AI/ML techniques for healthcare. He holds a PhD in ICT (Tallinn University of Technology) and dual master's degrees. Research Leadership: Supervised PhD students: Nimra Memon (fault-tolerance in web services), Dmytro Zabolotnii (agent behavior prediction), Mahir Gulzar (context-aware modeling) Accepts self-funded PhD candidates in BCI/AI applications Awards: Young Investigator Award (Springer/IFMBE, 2014) Best Paper Award Runner-up (26th ISSC 2015) Professional Recognition: Fellow of Higher Education Academy IEEE Senior Member Editorial board member for multiple journals
Mustafa A. Mustafa is a Senior Lecturer (Associate Professor) in the Department of Computer Science at The University of Manchester, where he leads the Trusted Digital Systems Cluster as part of the university-wide Centre for Digital Trust and Society. His academic journey spans prestigious institutions including The University of Manchester, where he completed his PhD, and KU Leuven in Belgium, where he served as a post-doctoral research fellow. Dr. Mustafa earned his educational qualifications through an impressive academic path: a B.Sc. in communications from the Technical University of Varna, Bulgaria (2007), an M.Sc. in communications and signal processing from Newcastle University, UK (2010), and a Ph.D. in computer science from The University of Manchester, UK (2015). His doctoral research focused on "Smart Grid Security: Protecting Users' Privacy in Smart Grid Applications," laying the foundation for his subsequent research career. Dr. Mustafa's research expertise centers on information security, data privacy, and applied cryptography with particular focus on smart grid systems, smart city applications, e-health, and IoT. His work addresses critical challenges in securing peer-to-peer electricity trading markets, smart metering infrastructure, electric vehicle charging systems, and health data management. He has developed innovative solutions for keyless car sharing systems, frictionless authentication mechanisms, and privacy-preserving protocols for data collection and distribution. His scholarly contributions demonstrate a consistent trajectory toward increasingly sophisticated privacy-preserving techniques applied across multiple domains. Recent work shows a growing integration of artificial intelligence and machine learning approaches with traditional cryptographic methods, particularly in federated learning systems and large language model verification. His research bridges theoretical cryptography with practical implementations in energy systems and healthcare applications. Dr. Mustafa's scientific achievements have been recognized with several prestigious awards: Winner of the Student Video Competition at IEEE SmartGridComm 2017 for "Secure and Privacy-friendly Local Electricity Trading" Best Paper Award at SECURWARE 2017 Distinguished Achievement Award as Postgraduate Research Student of the Year nominee by the School of Computer Science of The University of Manchester (2015) Dame Kathleen Ollerenshaw Research Fellowship (2018-2023) As an academic supervisor, Dr. Mustafa has mentored numerous graduate students through their PhD and Master's research, with a particular focus on privacy and security challenges in emerging technologies. His current supervision portfolio includes research on privacy-friendly multi-agent systems for smart grids, security for IoT in e-health, vulnerability detection in IoT cryptography, and bot detection systems. He has secured significant research funding through multiple competitive grants including EnnCore: End-to-End Conceptual Guarding of Neural Architectures (EPSRC, 2020-2024), SCorCH: Secure Code for Capability Hardware (EPSRC, 2019-2023), and SNIPPET: Secure and Privacy-friendly Peer-to-peer Electricity Trading (FWO-SBO project, 2019-2023). Dr. Mustafa leads the Trusted Digital Systems Cluster within the Centre for Digital Trust and Society at The University of Manchester. His research group comprises PhD students, postdoctoral researchers, and collaborators working on cutting-edge security and privacy solutions. The team maintains strong international collaborations, particularly with KU Leuven in Belgium, and contributes to standards development as evidenced by Dr. Mustafa's role as an expert in the IEC/SYC/WG 3 "IEC Smart Energy Roadmap."
Yuan Tian is an Assistant Professor in the School of Computing at Queen's University, Faculty of Arts and Science. She holds a PhD in Information Systems from Singapore Management University (2017) and a B.Sc. in Computer Science from Zhejiang University (2012). Her research focuses on integrating heterogeneous data sources to enhance software engineering practices, including data mining, recommender systems, and social network analysis. Prior to Queen's, she was a data scientist at Living Analytics Research Centre (LARC), SMU. She has held visiting positions at Carnegie Mellon University, INRIA Paris, and SAIL Canada. Research Interests: Data Mining Software Engineering Social Network Analysis Information Retrieval Recommender Systems Computer Security Recent Research Trends: Her work emphasizes AI-driven solutions for software bug management, code translation, vulnerability detection, and developer behavior analysis. Notable contributions include leveraging LLMs for technical debt repayment and enhancing code vulnerability detection via Graph Neural Networks. Awards: SMU Presidential Doctoral Fellowship (2015-2016) Best Paper Award at SANER 2017 Grants & Advising: No formal advisees listed, but active in collaborative projects with industry and academic partners. Labs/Teams: Previously associated with SOAR Group at SMU and currently leads research in Queen's School of Computing.
Dr. Dijiang Huang is an Associate Professor in the School of Computing and Augmented Intelligence at Arizona State University (ASU). He joined ASU in 2005 after completing his Ph.D. in Telecommunications and Computer Networking from the University of Missouri-Kansas City (2004). His research focuses on cybersecurity, mobile computing, and cloud computing, supported by grants from the National Science Foundation (NSF), Office of Naval Research (ONR), and industry partners like HP. He has received prestigious awards, including the ONR Young Investigator Award and HP Innovative Research Award. Education: B.E. in Telecommunications, Beijing University of Posts and Telecommunications (1995) M.S. in Computer Science, University of Missouri-Kansas City (2001) Ph.D. in Telecommunications and Computer Networking, University of Missouri-Kansas City (2004) Research Interests: Huang’s work emphasizes secure communication protocols, privacy-preserving techniques, and resilient network architectures. He has pioneered frameworks like Secure Group Communication (SeGCom) and Attribute-Based Cryptography , addressing challenges in VANETs, SDN, and edge computing. His recent projects include developing Waterfall for SDN security and SmartDefense for DDoS mitigation. Grants & Awards: ONR Young Investigator Award (2008) HP Innovative Research Award (2008) NSF grants for secure mobile cloud frameworks and cyber-physical systems Professional Contributions: Huang has served as a reviewer for journals like IEEE Transactions on Wireless Communications and conferences such as ACM MobiArch. He co-developed the Open Human-Robotic Mobile Networking and Security Testbed (OHReST) and the Virtual Laboratory (VLab) for cybersecurity education.
Weiwei Lin is an Associate Professor in the Department of Civil Engineering at Aalto University, specializing in structural engineering with a focus on bridge systems, composite materials, and structural health monitoring. His research explores fatigue behavior of steel structures, seismic performance of composite systems, and innovative repair techniques. He holds a PhD from Waseda University (2012), MSc from Southeast University (2009), and BEng from Southwest Jiaotong University (2006). Key research areas include: steel-concrete composites, bridge redundancy evaluation, replaceable energy dissipaters, and AI-driven infrastructure diagnostics. Lin leads projects like CCU Structure (EU Horizon Europe) and RCF Mobility initiatives, focusing on sustainable construction and material recyclability. He has published 120+ peer-reviewed articles and secured 6 major grants. Lin has received prestigious awards including the IABMAS Young Award (2014) and Outstanding Reviewing Award (2017). His lab collaborates globally, hosting researchers from institutions like Israel Institute of Technology and Tsinghua University. Current work emphasizes crowdsourcing-based bridge monitoring and physics-guided AI frameworks for infrastructure diagnostics.
