Antonia Teresa Spano is a Full Professor at the Department of Architecture and Design (DAD) of Politecnico di Torino, Italy. She serves as Vice Coordinator of the PhD Program in Architectural Heritage and is a member of the Future Urban Legacy Lab (FULL). Her research focuses on geomatics, 3D modeling, and digital documentation for cultural heritage preservation. University: Politecnico di Torino Department: Department of Architecture and Design (DAD) Spano's work integrates advanced geomatic techniques like LiDAR, UAV photogrammetry, and SLAM-based modeling to address challenges in architectural and archaeological heritage documentation. Her expertise spans GIS, digital photogrammetry, and machine learning applications in conservation processes. Her recent publications emphasize multi-sensor 3D surveys for Pier Luigi Nervi's structures, AI-driven decay detection, and semantic classification of LiDAR data. These works align with SDG goals for sustainable cities and quality education. Scientific Awards: Premio Giovani CNR (2005) Miglior Poster at GISTAM (2016) and GEORES (2019) Fellow of CIPA Heritage Documentation (2023-2033), CIRAAS ETS (2020-2030), and ISPRS (2017-2024) Spano supervises PhD students in architectural heritage programs and leads numerous research projects, including collaborations with institutions like the Pompeii Archaeological Park, CRAST, and international universities. She has contributed to editorial boards and program committees of journals and conferences such as Virtual Archaeology Review and ISPRS symposiums.
Dr Nour Ali is a Reader in the Department of Computer Science at Brunel University London , where she co-heads the Brunel Software Engineering Lab and serves as Vice-Dean of Education for the College of Engineering, Design and Physical Sciences. She holds a PhD in Software Engineering from Universidad Politecnica de Valencia, Spain, and a Major in Computer Science from Bir-Zeit University, Palestine. Research Interests: Software architecture for distributed and adaptive systems, integrating techniques like Model-Driven Engineering, Reverse Engineering, and Machine Learning. Teaching: Module leader for Software Project Management and supervisor of undergraduate group projects and final-year projects. Scientific Contributions: Over 70 publications in journals, conferences, and books. Key research areas include microservice architecture recovery, autonomic healthcare systems, and mobile self-adaptive architecture. Scientific Awards: Fellow of the Higher Education Academy (HEA). Membership: Deputy Editor-in-Chief for IET Software, member of multiple conference program committees, and reviewer for EPSRC, NWO, and other funding bodies.
Dr Ian Gray serves as a Senior Lecturer in the Department of Computer Science at the University of York, where he also holds the position of Deputy Head of Department (Teaching). His academic career at York began as a Research Associate in 2010, progressing to Research Fellow in 2012, Lecturer in 2017, and ultimately Senior Lecturer. His research focuses on real-time systems and their programming models , with significant contributions to embedded systems, FPGA and reconfigurable computing architectures, and many-core/multicore system design. His work extends to application-specific high-performance computing solutions and cloud computing infrastructure within distributed systems frameworks. Gray maintains active involvement in the Real-Time and Distributed Systems research group, where his expertise bridges theoretical computer science with practical hardware implementation challenges. Gray's professional trajectory demonstrates steady progression from industry (as Lead Software Developer at Stockholm Environment Institute in 2005) into academia, where he has developed substantial expertise across multiple computing domains requiring precise timing constraints and efficient resource utilization. His leadership role as Deputy Head of Department (Teaching) reflects his significant contribution to curriculum development and academic administration within the department.
Daehyeok Kim is an Assistant Professor in the Department of Computer Science at The University of Texas at Austin, where he co-leads the UT Networked Systems Research Group and participates in the Wireless Networking and Communications Group and 6G@UT. He serves as co-PI for the LDOS NSF Expeditions in Computing project, a major initiative rethinking operating systems through AI. His educational background includes a Ph.D. in Computer Science from Carnegie Mellon University under advisors Vyas Sekar and Srinivasan Seshan, where his dissertation introduced abstractions for elastic in-network computing. He also earned B.S. and M.S. degrees in Computer Science and Engineering from POSTECH, South Korea, followed by research scientist work at KAIST prior to his Ph.D. Kim's research centers on hardware-software co-design for cloud and edge data centers, targeting speed, efficiency, and resilience. Key projects include resource management for programmable infrastructure, robust cellular network design, end-to-end network transport frameworks, and learning-directed operating systems. His work bridges computer networks, operating systems, distributed systems, and 5G/6G technologies, with emphasis on virtualized radio access networks (vRAN) and edge computing challenges. Analysis of his recent publications reveals a dominant focus on enhancing 5G/6G infrastructure reliability—particularly in virtualized RANs—through innovations in failover mechanisms, integrity protection, and latency-sensitive resource allocation. His research consistently addresses critical industry pain points like sub-second availability requirements, fronthaul security vulnerabilities, and end-to-end service-level objective (SLO) guarantees for mobile-edge applications. Notable scientific awards include: NSF CAREER Award (2025) for advancing cloud hardware efficiency Microsoft Research PhD Fellowship (2019) Bronze Award at Samsung HumanTech Paper Awards (2018) Qualcomm Innovation Awards (2016) His grant portfolio features leadership in the $10M+ LDOS NSF Expeditions project and the NSF CAREER award, both driving transformative work in AI-integrated operating systems and resilient network infrastructure. These projects demonstrate strong industry-academia collaboration with Microsoft Research, wireless vendors, and cloud providers. Kim co-leads the UT Networked Systems Research Group, which operates within the Wireless Networking and Communications Group and 6G@UT consortium. These labs maintain a 5G/6G testbed for Open RAN validation and focus on solving real-world problems in cellular infrastructure, edge computing, and network security through close partnerships with industry leaders.
Rasheed Hussain is an Associate Professor of Intelligent Network Security at the Smart Internet Lab and Bristol Digital Futures Institute (BDFI), School of Electrical, Electronic and Mechanical Engineering at the University of Bristol, UK. Previously, he served as a Senior Lecturer at the same institution from December 2021 to July 2025. He has held academic positions at Innopolis University, Russia, where he served as Associate Professor and Director of the Institute of Information Security and Cyber-Physical Systems, and as a guest researcher at the University of Amsterdam, Netherlands. His educational background includes a PhD in Computer Engineering from Hanyang University, South Korea (2011-2015), an MS in Computer Engineering from the same institution (2008-2010), and a B.Sc in Computer Software Engineering from the University of Engineering and Technology, Peshawar, Pakistan (2003-2007). Hussain's research focuses on network and cybersecurity, particularly future network security including 6G, the role of Digital Twins in future networks and systems security, and Responsible AI including fairness, trustworthiness, and explainability. His work spans information security, privacy, applied cryptography, vehicular networks, Internet of Things, Content-Centric Networking, cloud computing, API security, and blockchain applications. Senior member of IEEE Member of ACM ACM Distinguished Speaker Editorial board member for IEEE Communications Surveys & Tutorials, IEEE Access, and other journals His recent publications demonstrate a strong focus on the intersection of AI, networking, and security, with particular emphasis on Digital Twins, blockchain applications, federated learning, and 6G security. His research shows a clear trajectory toward addressing security challenges in emerging network architectures while incorporating responsible AI principles. Scientific Recognition: ACM Distinguished Speaker Netherlands University Teaching Qualification (Basis Kwalificatie Onderwijs, BKO) Hussain serves as a reviewer for major IEEE transactions, Springer and Elsevier journals, and participates in technical program committees for conferences including IEEE VTC, IEEE VNC, IEEE Globecom, and IEEE ICC. He is also certified as a trainer for the Instructional Skills Workshop (ISW) and contributes to the ESRC Centre for Sociodigital Futures (CenSoF) at the University of Bristol. His laboratory work centers around the Networks and Blockchain Lab, which focuses on security solutions for next-generation networks, with particular emphasis on Digital Twins security, blockchain applications, and AI-driven network security solutions. His current projects involve developing secure frameworks for future networks, trustworthy AI models, and privacy-preserving federated learning approaches.
Luo Mai is an Assistant Professor at the University of Edinburgh's School of Informatics , with an upcoming promotion to Associate Professor (UK Reader) in August 2025. He leads the Large-Scale Machine Learning Systems Group and co-leads the UK EPSRC Centre for Doctoral Training in Machine Learning Systems and an ARIA Project on Scaling AI Compute by 1000X . PhD in Computer Science (Imperial College London, 2018) MRes in Advanced Computing (Imperial College London, 2012) His research focuses on the intersection of computer systems , machine learning , and data management . Key contributions include award-winning systems like WaferLLM (wafer-scale LLM inference), Tenplex (elastic ML), and ServerlessLLM (serverless LLM serving), published at top venues (OSDI, SOSP, ICML, NeurIPS, JMLR). Recent publications demonstrate trends in GPU-based distributed systems , LLM optimization , and adaptive machine learning . His team has developed groundbreaking open-source projects including TensorLayer , TorchOpt , and ServerlessLLM . Awarded Microsoft Research StarTrack Scholar (2024) , secured ARIA grant (2024) with Imperial College & Cambridge University, and received Google Fellowship during PhD (2012-2016). As an educator, he designed Edinburgh's popular Machine Learning Systems course (150+ students). His group supervises multiple PhD students including Yao Fu (recognized as 2024 Rising Star in ML & Systems) and Leyang Xue .
Professor Rebecca Hodge is a Professor in the Department of Geography at Durham University, where she has served since 2011 after progressing from Lecturer (2011-2016) to Associate Professor (2016-2022). Her academic foundation includes a PhD in Geography from the University of Cambridge (2007) and prior postdoctoral work at the University of Glasgow (2007-2011). Her educational background: PhD, Geography, University of Cambridge (2007) Professor Hodge's research centers on fluvial geomorphology , specializing in sediment transport mechanics within gravel-bed and bedrock-alluvial river systems. She pioneers the integration of high-resolution measurement techniques —including Terrestrial Laser Scanning, CT scanning, and 3D printing—with advanced numerical modeling (e.g., Cellular Automaton frameworks) to investigate how grain-scale interactions govern reach-scale sediment flux. Her work critically examines sediment cover dynamics, critical shear stress thresholds, and the upscaling of small-scale processes in river morphology. Analysis of her 15 most recent publications (2019-2025) reveals a dominant focus on quantifying river bed roughness , sediment cover dynamics , and innovative measurement methodologies . Key trends include the application of 3D point cloud analysis for critical shear stress estimation, acoustic monitoring of river systems, and network-scale modeling of alluvial processes across diverse environments from Antarctic ice shelves to U.S. bedrock rivers. Her significant recognition includes: Gordon Warwick Medal from the British Society for Geomorphology (2022) for excellence in geomorphological research within 15 years of her doctorate Professor Hodge actively mentors PhD candidates including Holly Wytiahlowsky, Jiao Chen, and Mel Oliveira Guirro, with research spanning Antarctic glacial channels, bedrock-alluvial transitions, and gravel-bed river mechanics. Her collaborative projects involve institutions across the UK, USA, and Brazil, addressing fundamental questions in sediment transport and river response to environmental change. Her laboratory work emphasizes experimental flume studies and field deployments of cutting-edge sensors, directly informing theoretical models of river evolution and sediment dynamics.
Valérie Fernandez is a Professor of Digital Economics at Télécom Paris – Institut Mines-Télécom, where she holds the Responsibility for Digital Identity Chair . She is affiliated with the Interdisciplinary Institute of Innovation (i3) and leads the Master's in Digital Innovation Management in partnership with Sciences Po Paris and Télécom Paris. Her research focuses on the socio-economic analysis of digital technologies , including governance of digital innovation, responsible innovation, market analysis, and social acceptability. She has contributed to European Commission projects and collaborations with international (e.g., Cai Yuanpei China) and national (e.g., ANR) research organizations, aiming to inform public policies and business strategies for teleservice platforms, cloud computing, and facial recognition technologies. Scientific Awards Prize for the Best Scientific Program in Telemedicine (Malakoff-Médéric laboratory) AEI Prize 2023 FNEGE Prize 2023 Best Educational Device in the Digital Age 2023 Educational Innovation Prize 2023 Valérie has supervised approximately fifteen doctoral theses, including Flavien Bazenet (awarded the 2023 Sphinx Thesis Prize). Her work with Thomas Houy on the Decision Model Canvas (DMC) has been widely adopted by industries and public sectors, emphasizing decision-making in unpredictable environments.
Janne Heikkilä is a Professor at the Faculty of Information Technology and Electrical Engineering, University of Oulu, Finland. With over 30 years of experience in computer vision and machine learning, he leads the Center for Machine Vision and Signal Analysis (CMVS) and has contributed extensively to both theoretical and applied research. Research Interests: 3D computer vision, biomedical image analysis, computational photography, and deep learning. Scientific Leadership: IAPR Fellow, Senior IEEE Member, and former President of the Pattern Recognition Society of Finland. His work spans computer vision, radiotherapy planning, and biomedical imaging, with over 200 publications and 14,000 citations. He has secured funding from prestigious organizations like the Academy of Finland and Business Finland. His recent research focuses on debiasing AI models, 6D object pose estimation, and radiotherapy dose prediction. Scientific Awards: IAPR Fellow Senior Member of IEEE
Prof. Dr. Gerald Urban is a distinguished Professor at the Institute for Microsystems Technology (IMTEK) within the Faculty of Engineering at the University of Freiburg, Germany. With over three decades of academic and research experience, he has established himself as a leading expert in biomedical microtechnology and sensor systems. His career spans prestigious institutions including the Vienna University of Technology and collaborations with major research centers worldwide. His educational journey includes: 1973: Graduated from Sigmund Freud Gymnasium in Vienna 1979: Completed studies in Technical Physics at Vienna University of Technology 1985: Earned Doctorate (Dr.-Ing.) with distinction (Summa cum Laude) from Vienna University of Technology 1994: Completed habilitation in Sensorics Prof. Urban's research focuses on the development and application of miniaturized integrated sensors for clinical and industrial applications. His work bridges the gap between fundamental materials science and practical medical devices, with particular emphasis on biomedical microtechnology , electrochemical biosensors , and organ-on-chip systems . His team has pioneered innovations in point-of-care diagnostics, therapeutic drug monitoring, and micro energy harvesting technologies. The research group maintains strong collaborations with clinical partners to ensure translational impact of their technological developments. Analysis of Prof. Urban's recent publications reveals a clear trajectory toward increasingly sophisticated multiplexed sensing platforms that integrate CRISPR-based diagnostics with electrochemical detection systems. His work demonstrates growing emphasis on point-of-care applications, with particular focus on making complex diagnostic capabilities accessible outside traditional laboratory settings. The integration of additive manufacturing techniques with sensor technology represents another significant trend in his recent work, enabling customized microreactor and organ-on-chip platforms. Among his notable scientific achievements: Stefan Schuy Prize for Biomedical Engineering (1990) AVL-List Prize (1993) Best Poster at Eurosensors (1993) Hoechst-Price (1994) Corresponding member of the Austrian Academy of Sciences (2010) EAMBES-Fellow (2018) Prof. Urban has successfully secured substantial research funding throughout his career, with accumulated third-party funding reaching approximately 5 million euros between 1986-1995. He has established multiple spin-off companies including Otto Sensorenfabrikationsgesellschaft (1985), Biosensor GnbR (1994), and Jobst Technologies GmbH (2002), demonstrating his commitment to translating research into practical applications. His leadership extends to major research initiatives including the excellence initiative "µMAT" and the graduate school "PolyMIC". At the University of Freiburg, Prof. Urban leads a vibrant research group within the Institute for Microsystems Technology, which forms part of the larger BrainLinks-BrainTools and BIOSS research clusters. His laboratory maintains state-of-the-art facilities for microsensor fabrication, including cleanroom access through the WebFab service center. The research environment benefits from strong connections with the Freiburg Material Research Center (FMF) and the Freiburg Institute for Advanced Studies (FRIAS), where he served as an Internal Fellow (2008-2010).
Jung-Eun Kim is an Assistant Professor in the Department of Computer Science at North Carolina State University, where she conducts research at the intersection of artificial intelligence, machine learning, and cyber-physical systems. Her work focuses on creating trustworthy, interpretable, and efficient AI systems, particularly for safety-critical applications. Education: Ph.D. in Computer Science, University of Illinois at Urbana-Champaign (2017) M.S. in Computer Science and Engineering, Seoul National University (2009) B.S. in Computer Science and Engineering, Seoul National University (2007) Dr. Kim's research primarily investigates how to make AI systems more trustworthy, interpretable, and efficient, with particular emphasis on understanding failure modes, safety risks, vulnerabilities, and biases in deep learning models. Her work bridges theoretical understanding with practical applications in safety-critical systems. She explores how efficiency considerations interact with these issues, seeking to fundamentally anatomize neural networks to understand what causes failure modes and how to mitigate them. Her approach has been described as 'like a heart surgeon, we open the heart of a neural network architecture, look into it, interpret it, and cure it.' Her recent publications demonstrate a strong focus on safety alignment in large language models, mitigation of spurious correlations, privacy preservation against membership inference attacks, and sustainable AI development. Her work spans theoretical foundations of trustworthy AI while addressing practical challenges in model deployment, particularly for resource-constrained environments. She has made significant contributions to understanding how model compression techniques like pruning and quantization can inadvertently amplify biases and vulnerabilities. Scientific Awards: ICLR Spotlight, 2025 IBM Faculty award, 2023 CRA Early & Mid Career Mentoring Workshop, 2023 Cloud GPU provided by Lambda, worth $17,280, for course, Spring 2023 NeurIPS Spotlight and nomination for Best Paper Award, 2022 CRA Career Mentoring Workshop, 2022 GPU Grant by NVIDIA Corporation, 2018 The MIT EECS Rising Stars, 2015 The Richard T. Cheng Endowed Fellowship, 2015-2016 Dr. Kim actively mentors PhD students, currently advising Xingli Fang, Varun Mulchandani, Jianwei Li, Rishi Singhal, and Minseon Kim. She has secured significant research funding, including an NSF SaTC (Secure and Trustworthy Cyberspace) grant as Co-PI for 'Partition-Oblivious Real-Time Hierarchical Scheduling' ($281,629.00, 2022-2024). Her research has also been supported by an NVIDIA GPU Grant and cloud resources from Lambda. She serves on program committees for top AI conferences including ICLR, ICML, NeurIPS, AAAI, and IJCAI, and has held roles such as Publicity Chair for IJCAI 2024. Her research group focuses on developing methods to make AI systems more trustworthy, interpretable, and efficient, with particular attention to safety-critical applications. The group investigates how to identify and mitigate failure modes in neural networks while maintaining efficiency, exploring the fundamental relationship between model architecture, safety risks, and computational constraints.
Christophe Danjou is an Associate Professor in the Department of Mathematical and Industrial Engineering at Polytechnique Montréal . He joined as a professor in January 2018 and serves as Scientific Director of the Poly-Industries 4.0 Laboratory since June 2021. His expertise spans Industrial Engineering , Industry 4.0/5.0 , Manufacturing Systems , and Blockchain . His research focuses on solving interoperability challenges in digital transformation through ontological approaches (OntoSTEP-NC) and blockchain technology. Key themes include strategic positioning frameworks for Industry 4.0/5.0, knowledge management , and smart manufacturing . Recent work explores digital twins for system-of-systems resilience , data integrity in IoT , and AI-driven food processing optimization . He teaches courses like Industry 4.0 and Manufacturing Processes . Under his supervision, 7 PhD and 6 Master’s students are advancing research in areas such as blockchain-based smart maintenance , distributed manufacturing , and carbon emission traceability . He is affiliated with institutions including IVADO (Member), CIRRELT (Member), and Data Intelligence Lab (Member). His publications highlight contributions to digital transformation across construction, agri-food, and SMEs, with 83 total publications (15+ recent articles shown).
Alireza Poshtkohi is an interdisciplinary researcher at the University of Hertfordshire , affiliated with the School of Physics, Engineering & Computer Science and the Department of Computer Science . He applies computational and mathematical approaches to neuroscience, physics, and engineering challenges, focusing on modeling the human nervous system and brain diseases at the cellular level using supercomputing technologies. Education: PhD in Neuroscience (Ulster University, 2023), MSc in Parallel Simulation of Electronic Systems (Shahed University, 2011), BSc in Embedded Systems and Computer Networks (2006). His research spans computational neuroscience , mathematical modeling , and high-performance computing , with publications on microglia dynamics, P2X receptors, and parallel system modeling. Recent work includes a 2024 study on the PI3K/Akt pathway and a 2023 book on distributed systems. He collaborates with experimental neuroscientists from the University of Reading and Michigan State University , integrating molecular neurobiology with computational frameworks. His technical expertise includes grid computing, cybersecurity, and simulation environments.
Guillaume Pierre is a Professor and research leader at Univ Rennes, affiliated with Inria, CNRS, and IRISA, where he leads the Magellan research team. He is based at the Institute of Science and Technology of Information and Communication (ISTIC), Department of Computer Science and Electronics. His research focuses on fog computing, cloud computing, and large-scale distributed systems, with applications in scalable web hosting and edge intelligence. Research Interests: Fog and Edge Computing Cloud Computing and Resource Management Scalable Web Application Hosting Peer-to-Peer and Decentralized Systems Stream Processing and Kubernetes Orchestration Elasticity and Energy Efficiency in Distributed Environments His recent publications highlight a strong trend in geo-distributed systems, particularly focusing on Kubernetes cluster federation, fog-based environmental monitoring, and elasticity in stream processing. His work bridges theoretical advances with practical implementations in real-world fog and cloud infrastructures. Scientific Awards: Best Paper Award, IEEE International Symposium on Applications and the Internet (2005) Best Paper Award, IEEE International Conference on Cloud Engineering (IC2E 2014) Guillaume Pierre has advised numerous PhD students, many of whom now hold positions at Google, Amazon, Ericsson, and Ansys. He has coordinated major research projects such as the H2020 FogGuru initiative and the DiPET project on distributed data stream processing. His work is supported by EU funding and institutional collaborations. Labs and Teams: He leads the Magellan research team at the INRIA/IRISA lab, which is at the forefront of innovation in fog and cloud computing technologies.
Martin Carlsson-Wall is a Full Professor at Stockholm School of Economics (SSE) in the Department of Accounting within the House of Governance and Public Policy. He serves as Center Director for both the Center for Municipality Governance and the Center for Security and Resilience. In 2024, he was named "The Linder Chair in Sports and Business," the first Chair at SSE on Sports and Business topics. Since 2017, he has been Program Director for the MSc in Accounting, Valuation and Financial Management, and in 2015, he founded the Center for Sports and Business, which now includes 40 faculty members and has strategic partnerships with major Swedish sports organizations across football, hockey, golf, and skiing. Martin Carlsson-Wall's research focuses on accounting in sports organizations and accounting in inter-organisational relationships across both private and public sectors. His work extensively examines management control systems , particularly in contexts of innovation , growth companies , and risk management . His research often takes a qualitative approach, investigating how accounting practices function in extreme situations (such as the Swedish migrant crisis), high-intensity organizations (like football clubs), and during organizational change processes. Analysis of Professor Carlsson-Wall's recent publications reveals a strong thematic focus on the intersection of accounting practices with sports management and public sector administration. His work demonstrates how management control systems adapt to different organizational contexts, from football clubs to technology firms and public administration during crises. A recurring theme is how accounting practices mediate tensions between different stakeholder interests and how they evolve in response to organizational challenges. His research methodology typically employs in-depth case studies that examine accounting as a social practice rather than merely a technical function. Professor Carlsson-Wall has received significant recognition through his appointment as "The Linder Chair in Sports and Business" in 2024, establishing the first dedicated research position at SSE focusing on the business aspects of sports. As an educator, Professor Carlsson-Wall serves as Program Director for the MSc in Accounting, Valuation and Financial Management and teaches several key courses including BE901 Hybrid Organizations: Value Creation and Strategy at the bachelor's level, and course 3313 Investments and Value Creation in Global Sports (which he directs) and course 3302 Management Control at the master's level. He also supervises master's theses in Accounting and Financial Management. In Executive Education, he teaches on management control, costing, performance management, transfer pricing, investment management, and financial analysis through both open programs and customized corporate offerings. Professor Carlsson-Wall founded and directs the Center for Sports and Business, which has grown to include 40 faculty members and established partnerships with major Swedish sports organizations. He also leads the Center for Municipality Governance and the Center for Security and Resilience, demonstrating his commitment to applying accounting and management control research to practical governance challenges in both public and private sectors.
