Dr. Lennart Johnsson is a Professor of Computer Science at the University of Houston, with affiliations at the Royal Institute of Technology (KTH) in Sweden. He has held faculty positions at Caltech, Yale University, Harvard University, and KTH, and industry roles at ABB Research and Thinking Machines Corp. His research focuses on High-Performance Computing (HPC), energy-efficient systems, parallel algorithms, and grid computing. He has collaborated with institutions like PRACE, and companies including AMD, Intel, and Texas Instruments, leading to innovations such as energy-efficient HPC servers and DSP-based architectures. Research interests include optimizing HPC for energy efficiency, embedded processors (e.g., DSPs), and novel interconnection networks. He has pioneered software libraries like CMSSL and contributed to standards such as MPI and High-Performance Fortran. Awards include the Machtey Best Student Paper Award and recognition in the Gordon Bell Prize competition. Dr. Johnsson has supervised numerous students, including those working on adaptive scheduling, grid computing, and bioinformatics. He founded the Texas Learning and Computation Center and led initiatives like the Texas GigaPoP and RENoH network. Current projects explore energy-efficient HPC using DSP architectures. Key contributions include the first No. 1 system on the Top500 list (1993), grid computing frameworks, and infrastructure for distributed applications. He has served on boards for PRACE, NSF, and Swedish research councils, and advises on national HPC strategies.
Милорад Б. Тошић is a Full Professor at the Faculty of Electronic Engineering, University of Niš, specializing in Computer Science. He earned all his academic degrees (BSc 1989, MSc 1992, PhD 1998) from the same institution in Electrical Engineering and Computer Science. His research spans Semantic Web Technologies, Distributed Systems, and E-Learning Systems, with notable contributions in federated testbeds, trust-based peer assessment, and collaborative wiki tagging. His work bridges theoretical computer science with practical applications in education and embedded systems. His publication trends show consistent contributions from 1991 to 2014, with recent focus shifting from hardware design (1990s) to semantic technologies and e-learning systems (2000s-2010s), demonstrating adaptability across evolving technological domains. US Patent Application No. 09/636,552 for Internet-Enabled Embedded Device Technology validated by Motorola, Microchip, Philips, and Delphi As former Science and Technology Advisor to the Serbian Minister (2002-2003), he contributed to national science policy. His current research involves 4 national and 3 international projects totaling 7 impact-factor journal publications. His patented embedded device technology has achieved commercial validation through major global electronics firms.
Josep Lluís Berral García is an Associate Professor in the Department of Computer Architecture at the Barcelona School of Informatics (FIB), Polytechnic University of Catalonia · BarcelonaTech (UPC). He is actively engaged in teaching and research, with a strong focus on Artificial Intelligence, Cloud Computing, and sustainable computing practices. He leads innovative educational initiatives and is affiliated with the CROMAI research group and the Barcelona Supercomputing Center (BSC-CNS). Research Interests: Artificial Intelligence and Deep Learning Cloud and High-Performance Computing Resource Orchestration and Management Sustainable and Ethical AI AI Education and Pedagogy His recent research and teaching projects center on integrating sustainability and ethical responsibility into AI education, using active learning methodologies. The trend in his work shows a consistent focus on optimizing computing resources through AI, particularly in cloud and HPC environments, with increasing emphasis on environmental impact and responsible innovation. Scientific Awards: UPC Award for Quality in University Teaching 2025 (Teaching Initiative for Newly Recruited Professors) Advising and Grants: While specific students are not listed, his leadership in competitive R&D+i projects and innovation initiatives indicates active supervision and grant-funded research. His involvement in multiple competitive and non-competitive R&D projects demonstrates sustained funding and research leadership. Labs and Teams: He is a key member of the CROMAI (Computing Resources Orchestration and Management for AI) research group at UPC and maintains a strong collaborative link with the Barcelona Supercomputing Center (BSC-CNS), leveraging the MareNostrum supercomputing infrastructure for AI and systems research.
Dr. Asif Karim is a Research Active Lecturer in the Department of Information Technology at the Faculty of Science and Technology, Charles Darwin University, Australia. He has been a full-time lecturer since August 2021, following a sessional role from 2018 to 2021. Prior to his current position, he served as a lecturer at Daffodil International University and Uttara University in Bangladesh. His research focuses on the application of machine intelligence in health informatics and blockchain technologies. He has significant industry experience in Software Engineering and actively supervises postgraduate research students. Machine Learning Health Informatics Blockchain Applications Smart Contracts Deep Learning Anomaly Detection The recent publications of Dr. Karim span a diverse range of applications in artificial intelligence, particularly in healthcare and secure computing. His work includes developing efficient deep learning models for medical diagnosis, privacy-preserving classification of diseases from medical images, and anomaly detection in cybersecurity. He also explores applications in mobile cloud computing and agricultural technology, demonstrating a broad interdisciplinary approach to solving real-world problems using machine learning. Dr. Karim actively contributes to research projects and supervises postgraduate students. He led the project "Machine Learning Diagnostics System for Bronchiectasis" and has extensive experience in teaching undergraduate and postgraduate courses in computer science, including Machine Learning, Operating Systems, and Software Engineering. He is involved in organizing academic events, such as a digital awareness workshop for rural indigenous communities, reflecting his commitment to community engagement and technology outreach.
Mª Carmen Carrión Espinosa is a Full Professor at the University of Castilla-La Mancha (UCLM) with a career spanning over 25 years. She teaches Computer Architecture in undergraduate and postgraduate programs and coordinates the bilingual Computer Engineering degree, which holds the Euro-Inf Bachelor quality award. Her research focuses on Fog computing, blockchain integration, and distributed systems management. University of Castilla-La Mancha - Full Professor (Computer Architecture) University of Cantabria - PhD in Physical Sciences Her research explores low-cost Fog infrastructures, container-based virtualization, and secure distributed architectures. Recent work includes federated learning on constrained devices and Kubernetes scheduling innovations. She has supervised 5 PhD and 15 Master's theses and contributed to 25 JCR-indexed publications. Key research trends include: Blockchain for fault-tolerant IoT systems Hybrid cloud-Fog resource orchestration Energy-efficient scheduling algorithms Educational technology innovations Scientific Awards: Extraordinary Degree Prize (1992) Euro-Inf Bachelor Quality Certification Active IEEE membership She leads the High Performance Networks and Architectures research group and has participated in 24 major projects, including CONSOLIDER-INGENIO 2010 as Task Leader. Her work bridges technical innovation with pedagogical excellence in cloud computing education.
Praveen Kumar Donta is an Associate Professor (Docent) and Senior Lecturer at the Department of Computer and Systems Sciences, Stockholm University, Sweden. His research focuses on distributed computing continuum systems, learning-driven approaches for IoT and edge computing, and intelligent data protocols. He leads the Distributed Immersive Participation research group which investigates how humans and things can be more connected and exchange information in real and virtual societies. Education: Ph.D. in Computer Science & Engineering from Indian Institute of Technology (Indian School of Mines), Dhanbad (2021) Visiting Ph.D. Fellow at Mobile&Cloud Lab, University of Tartu, Estonia (2019-2020) Master in Technology from JNTUA, Ananthapur (2014) Bachelor in Technology from JNTUA, Ananthapur (2012) Dr. Donta's research centers on distributed computing continuum systems that integrate cloud, edge, and IoT devices to deliver scalable and low-latency computing resources. His work explores learning techniques in IoT, AI/ML for computing systems, cognition and causality in computing systems, and cyber-physical continuum applications. He investigates how human body analogies can inform the design of more resilient and efficient distributed systems, as well as developing frameworks for privacy enforcement, equilibrium in computing continuum systems, and energy-efficient user interactions with smart environments. His research has significant applications in smart city management, satellite services, and intelligent transportation systems. Dr. Donta's publication record demonstrates a strong focus on the intersection of distributed systems, machine learning, and privacy-preserving technologies. His recent work shows an increasing emphasis on human-inspired approaches to distributed computing, with particular attention to making these systems more interpretable, efficient, and adaptable. His research spans theoretical foundations of computing continuum systems to practical implementations in areas like satellite services, smart environments, and anomaly detection. Scientific Awards and Recognition: IEEE Senior Member ACM Professional Member Dr. Donta serves as an editorial board member for several prestigious journals including IEEE Internet of Things Journal, Computing (Springer), Transactions on Emerging Telecommunications Technologies (Wiley), Measurement, and Computer Communications (Elsevier). He actively mentors the next generation of researchers, currently supervising PhD student Alfreds Lapkovskis and co-supervising Shubham Vaishnav. His research is supported by projects such as the Heterogeneous Computing Continuum for a Sustainable Smart City Management (HCSCM), which aims to develop scalable, secure solutions for urban environments by integrating IoT, edge, and cloud computing. As part of the Distributed Immersive Participation research group, Dr. Donta collaborates with researchers across disciplines to explore how technological advances enable humans and things to be more connected. The group focuses on application areas such as culture, transport, intelligent vehicles and e-health, developing solutions that enhance participation in both real and virtual societies.
Professor Christian Stummer is a Chair Holder in the Faculty of Economics and Business Administration at Bielefeld University, Germany. His academic career spans over two decades with a consistent focus on innovation and technology management. As an active faculty member, he contributes significantly to the university's research profile, particularly in the area of Economic Implications of Smart Products and Smart Systems through the Institute for Technological Innovation, Market Development and Entrepreneurship (iTIME). Position: Chair Holder in Economics and Business Administration Research Focus: Innovation and Technology Management Key Collaborators: M. Günther, E. Kiesling, W.J. Gutjahr Academic Network: Active contributor to Computational Economics research focus Professor Stummer's research interests center on business administration with a specialized focus on innovation management and technology management. His work bridges theoretical frameworks with practical applications in R&D investment planning and market launch of innovations. His research approach is characterized by an analytical-quantitative methodology that aligns with the faculty's overall research profile. Stummer has made significant contributions to the understanding of multi-criteria decision problems in innovation contexts, particularly through the development of decision support systems that help organizations navigate complex innovation management challenges. His publication record reveals a clear trajectory of research evolution from foundational work in R&D project selection in the late 1990s to sophisticated agent-based simulation approaches for innovation diffusion in recent years. The publications demonstrate a consistent focus on decision support methodologies applied to innovation management problems, with increasing sophistication in modeling approaches over time. His work frequently addresses the challenge of balancing multiple objectives in innovation management decisions, reflecting the complexity of real-world business environments. Most Innovative Paper Award: IEEE EDUCON 2010 for game-based learning in technology management education Best Applied Paper Award: Winter Simulation Conference for agent-based simulation of biofuel market diffusion Nomination: Best Paper Award at ECMS 2010 conference Professor Stummer's research has practical applications across various industry sectors, particularly in technology-intensive industries where innovation management is critical. His work on multi-channel management and disruptive technology strategy provides valuable frameworks for businesses navigating digital transformation. Through his involvement with the Bielefeld Graduate School of Economics and Management (BiGSEM), he contributes to training the next generation of researchers in quantitative economic approaches. His decision support methodologies have potential applications in both public policy contexts (particularly regarding sustainable technology adoption) and private sector innovation management.
Prof. Dr. Dilek Tüzün Aksu is a faculty member at Yeditepe University's Faculty of Engineering, Department of Industrial Engineering. She has held various academic positions including Professor (2021-present), Associate Professor (2015-2021), and Assistant Professor (2006-2015). Her career spans institutions like Sabancı University and Lehigh University, with administrative roles as Department Head and Institute Deputy Director. Current role: Professor at Yeditepe University Research focus: Operations Research, Logistics, Optimization Industry collaborations: United Airlines, Obase Bilgisayar Education B.Sc. in Industrial Engineering, Boğaziçi University (1988-1992) Integrated Ph.D., Lehigh University (1993-1998) Research Interests center around Operations Research and Optimization , particularly in disaster response and logistics. Her work includes metaheuristics for post-disaster road clearance , dynamic programming for manufacturing optimization , and network modeling for transportation logistics . She combines mathematical programming with real-time decision systems in applications ranging from glass cutting to airline crew pairing . Scientific Contributions include 12+ peer-reviewed publications across disciplines like disaster management, urban planning, and production systems. Her 2017 Journal of Industrial Management Optimization paper introduced heterogeneous flow routing in constrained networks, while the 2022 IISE Transactions article developed stochastic models for debris clearance. Advising has involved 13+ theses across Yeditepe University and Sabancı University, including Elifcan Yasa's 2022 Ph.D. on earthquake road clearance and Bahadir Durak's 2018 dissertation on glass cutting optimization. Industry Engagement includes technical consulting for TÜBİTAK projects worth over 1.7M+ Turkish Lira between 2011-2023. She served as Principal Investigator for real-time glass cutting optimization and as Consultant for retail demand forecasting systems, container shipping scheduling, and workforce optimization platforms.
Professor Jun Liu is a Professor of Artificial Intelligence and Director of the Artificial Intelligence Research Centre (AIRC) at the School of Computing, Ulster University. With over 270 publications and more than £18 million in research funding, he is a leading figure in artificial intelligence, particularly in trust and explainable AI systems and logic-based reasoning methods. Dr. Liu received his BSc and MSc degrees in Applied Mathematics, and PhD degree in Information Engineering from Southwest Jiaotong University, Chengdu, China, in 1993, 1996, and 1999, respectively. Prior to joining Ulster University, he held postdoctoral positions at The University of Manchester, UK (Feb. 2002 - Dec. 2004) and the Belgian Nuclear Research Centre (SCK*CEN) (Mar. 2000 - Feb. 2002). Professor Liu's research focuses on trust and explainable data-knowledge integrated AI decision models with applications in safety and risk analysis, policy decision making, security/disaster management, and healthcare; and logic and automated reasoning methods for intelligent systems, including resolution-based automated reasoning and lattice-valued logics for handling incomparability, inconsistency, and imprecision. His work spans theoretical foundations to practical applications in smart homes, healthcare, and industrial settings. His recent publications demonstrate a strong trend toward developing more trustworthy and explainable AI systems, with particular emphasis on belief rule-based approaches for handling uncertainty in decision-making. The research spans multiple domains including smart home activity recognition, medical imaging, food quality analysis, and environmental monitoring, showing the versatility and applicability of his methodologies. Ulster University best computer science paper award for 2016 IEEE Senior Member including IEEESMC and IEEECI Fellow of the UK Higher Education Academy Associate Editor of IEEE Transaction on Fuzzy Systems Current Chair of IEEE CIS Emergent Technologies Technical Committee As Director of the Artificial Intelligence Research Centre, Professor Liu has secured significant research funding as principal investigator and co-investigator. His current projects include "The use of Agentic AI in judicial decision-making" funded by EPSRC and "Adaptive Modeling Method for Deep Belief Rule Base" for smart home applications. He serves on editorial boards of multiple high-impact journals and organizes international conferences including the 23rd UK Workshop on Computational Intelligence. The Artificial Intelligence Research Centre under Professor Liu's leadership focuses on developing cutting-edge AI methodologies with practical applications. The center collaborates extensively with industry partners including BT through the BTIIC Phase 2 initiative and PwC through their Advanced Engineering and Research Centre, ensuring research has real-world impact across multiple sectors.
Professor Hyung Seok Kim is a distinguished academic at Sejong University, currently serving as Professor in the Department of AI and Robotics. He also holds significant administrative positions including Dean of the College of Software Convergence at Sejong University. Professor Kim leads the MINES LAB (Mobile Intelligent Embedded Systems Lab), located in Room 211, Chungmu Hall at Sejong University, where he directs research in cutting-edge AI and embedded systems technologies. Professor Kim's educational background includes: Bachelor of Engineering: Department of Electrical Engineering, Seoul National University Master of Engineering: Department of Electrical and Computer Engineering, Seoul National University Doctor of Engineering (Ph.D.): Department of Electrical and Computer Engineering, Seoul National University Professor Kim's research spans multiple domains at the intersection of artificial intelligence and embedded systems. His work focuses on AI robots, wearable AI devices, Large Language Models (LLMs), and on-device AI technologies . His research group develops innovative solutions for emotion recognition, medical imaging analysis, and IoT applications. The MINES LAB specifically targets the integration of AI with embedded systems to create efficient, low-latency solutions for real-world problems ranging from healthcare monitoring to industrial applications. Analysis of Professor Kim's recent publications reveals a strong focus on medical AI applications, federated learning for IoT networks, and multimodal emotion recognition . His work demonstrates consistent innovation in applying deep learning techniques to medical imaging (particularly ophthalmology and cardiology), developing efficient edge-AI solutions for wearable devices, and creating novel network optimization approaches for industrial IoT. The publications show a clear trajectory toward more integrated, privacy-preserving AI systems that can operate effectively on resource-constrained devices. While specific awards to Professor Kim aren't detailed in the provided information, his research group has achieved notable recognition: Dr. Song Seung-hwan, a Ph.D. candidate at the lab, received the Presidential Industrial Service Medal Professor Kim has mentored an extensive number of students throughout his career, with alumni pursuing diverse career paths at leading organizations worldwide. His former students have secured positions at major technology companies including Samsung Electronics, LG Electronics, Kakao, and Amazon, as well as academic positions at universities globally. The MINES LAB currently supports multiple graduate students, post-doctoral researchers, and research assistants working on various AI and embedded systems projects. Professor Kim's research appears to be well-funded, with connections to industry partners including Hyundai Motor Company and Samsung Electronics, though specific grant details aren't provided in the text. The MINES LAB serves as the central hub for Professor Kim's research activities, focusing on AI robots, wearable AI devices, and LLM applications. The lab maintains active collaborations with industry partners and has produced numerous commercial applications through its alumni network. Current research directions include developing low-latency emotion recognition systems, medical imaging analysis tools, and efficient network protocols for IoT applications. The lab environment appears highly collaborative, with both full-time and part-time researchers contributing to various projects across the AI and embedded systems spectrum.
Marc Geilen is an Associate Professor at the Electronic Systems group of Eindhoven University of Technology (TU/e) . He leads the Model-Based Design Lab within the CompSOC Lab and High Tech Systems Center .
Andrew Nelson is a part-time Assistant Professor at the Electronic Systems department, College of Engineering , Eindhoven University of Technology . He also serves as a founder and R&D lead at Verintec Solutions B.V. . Research Focus : Predictable and composable embedded systems, real-time robotics, multi-sensor fusion for industrial positioning, multi-core processor optimization. Key Contributions : Development of the CompSOC platform, CompROS architecture for ROS2, and novel multi-rate control strategies. Recent Article Trends : His work emphasizes predictable execution on multi-core platforms, sensor fusion techniques (linear encoders + vision systems), and real-time robotics. Articles span 2015–2025, highlighting collaborations with institutions like TU/e and ECSEL JU grant projects IMOCO4.E (2021–2023) and COMP4DRONES (2021).
Corinne LUCET-VASSEUR is a University Professor at Université de Picardie Jules Verne (UPJV), leading Research Unit UR 4290 (OCIA - Optimisation Combinatoire, Images et Applications). Her office (Room 302, Tel: 5900) serves as the hub for her research group focused on combinatorial optimization and artificial intelligence applications. Her research spans: Combinatorial Optimization : Developing metaheuristics for NP-hard problems Healthcare Logistics : Patient flow optimization, facility location, simulation training Logistics Engineering : Parcel distribution, vehicle routing with time windows Algorithm Design : Ant Colony Optimization, Adaptive Large Neighborhood Search, portfolio methods She applies these methodologies to solve complex real-world problems, particularly in healthcare systems where resource constraints and scheduling complexity demand innovative optimization approaches. Her work bridges theoretical advances with practical implementation through industrial partnerships. Current research projects include: SMILE PICK UP (CIFRE industrial partnership) Simusanté (healthcare simulation) LORH (logistics optimization) These projects secure ongoing funding and provide doctoral training opportunities through industry collaboration. Her publication record demonstrates consistent methodological innovation applied to healthcare and logistics challenges across multiple European conferences and journals. Professor Lucet-Vasseur actively mentors junior researchers through co-authorship on conference papers and journal articles. Her supervision style emphasizes practical problem-solving with industry relevance, preparing students for both academic and industrial careers in optimization. The OCIA research unit provides a collaborative environment for tackling complex combinatorial problems with real-world impact. The OCIA laboratory serves as UPJV's center for combinatorial optimization research, specializing in metaheuristic development for healthcare and logistics applications. The lab maintains strong industry connections through CIFRE contracts and applied projects, ensuring research relevance while providing students with exposure to real business challenges. Current focus areas include adaptive algorithm selection using reinforcement learning and fitness landscape analysis for optimization problems.
Bryan Donyanavard is an Assistant Professor in the Department of Computer Science at San Diego State University's College of Sciences. His research focuses on self-aware computing systems and cyber-physical systems optimization. Ph.D. in Computer Science from UC Irvine B.S. & M.S. in Computer Engineering from UC Santa Barbara Research interests span self-aware systems, embedded systems, and machine learning applications in resource-constrained environments. Current projects explore runtime optimization for autonomous vehicles and cyber-physical systems management. Recent publications analyze reversible neural network pruning for safety-critical systems, hybrid learning models for edge-cloud networks, and cross-layer optimization for mobile devices. Key trends include machine learning integration with hardware systems and performance maximization in embedded environments. Actively advising graduate and undergraduate researchers, with past advisees working on topics like lane following system optimization, SLAM algorithms, and sensor perception in platooning vehicles. Email: bdonyanavard@sdsu.edu Lab: DRG-Lab LinkedIn: https://linkedin.com/in/bryandony
Dr. Bernardi Pranggono serves as Associate Professor in Cyber Security and Computer Networks at Anglia Ruskin University's School of Computing and Information Science, Cambridge. With over 20 years of combined academic and industry experience, he previously held positions at Sheffield Hallam University, Glasgow Caledonian University, Queen's University Belfast, and the University of Leeds, alongside industry roles at Oracle, PricewaterhouseCoopers, Accenture, and Telstra. Education: PhD in Electronics and Electrical Engineering, University of Leeds, UK Master in Digital Communications, Monash University, Australia BEng in Electronics and Telecommunications Engineering, Waseda University, Japan PGCert in Learning and Teaching in Higher Education, Glasgow Caledonian University, UK Research Focus: His work centers on cybersecurity for resource-constrained IoT ecosystems, lightweight cryptographic protocols, and sustainable ICT solutions. Current investigations span IoMT security frameworks, AI-driven intrusion detection systems, green networking for smart grids, and Industry 4.0 security architectures. His approach integrates theoretical rigor with practical industry applications, particularly in healthcare and critical infrastructure protection. Publication Trends: Recent works (2021-2023) demonstrate concentrated efforts in IoT security mechanisms (35%), healthcare cybersecurity (25%), and energy-efficient computing (20%). Key patterns include development of lightweight cryptographic primitives for LoRaWAN, anomaly detection in medical IoT, and AI-enhanced security for smart grid infrastructure, reflecting strong interdisciplinary collaboration with engineering and medical researchers. Scientific Recognition: Fellow of the Higher Education Academy (FHEA) Senior Member of IEEE (SMIEEE) Research Leadership: As Principal Investigator for British Council's Going Global Partnerships project (2024) on AI data science skills for women's employability and Engineering/Mathematics Research Grant on IoMT Security (2022), he directs internationally funded initiatives. His EPSRC/FP7 project participation (INSTANT, HIPNet, PRECYSE) demonstrates sustained grant acquisition capability across cybersecurity and IoT domains. Professional Engagement: Chairs IEEE's SIG-Industrial IoT Networks, serves as Associate Editor for Frontiers journals, and provides extensive peer review services for IEEE, Elsevier, and Springer publications while maintaining active industry consultation roles.
