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Mohsen AminiView profile

Associate Professor

• Cloud Computing
• Artificial Intelligence (AI)
• Domain-Specific Cloud Computing
• +7 more

Mohsen Amini is an Associate Professor in the Department of Computer Science and Engineering at the University of North Texas (UNT). He previously held tenured positions at the University of Louisiana Lafayette (UL Lafayette) and completed postdoctoral research at the University of Miami and Colorado State University. His research focuses on Cloud 2.0 systems, AI-friendly serverless architectures, and domain-specific computing for Industry 4.0, multimedia, and edge-to-cloud integration. He leads the High Performance Cloud Computing (HPCC) Lab and has attracted over $2.7 million in research funding, including an NSF CAREER Award (2021). His work emphasizes democratizing cloud-native development and enhancing system resilience through heterogeneous computing frameworks. Education: Ph.D. in Computing and Software Engineering, University of Melbourne (2012) M.Sc. in Software Engineering, Ferdowsi University (2006) B.Sc. in Software Engineering, Azad University (2003) Research Interests: Cloud+AI Integration Fluid Computing across Edge-to-Cloud Continuum Domain-Specific Cloud Platforms (e.g., Industry 4.0, Multimedia) Confidential Computing Security Awards: NSF CAREER Award (2021) Best Service Award at IEEE/ACM CCGrid 2023 Francis Patrick Clark/BORSF Endowed Professorship (2021–2024) Grants & Projects: NSF IRES Track 1: Wireless Federated Fog Computing for Remote Industry 4.0 ($400k grant) NSF CAREER: Special-Purpose Cloud for Serverless Multimedia Streaming ($513k grant) SmartSight: AI-Based Platform for Visually Impaired Assistance ($499k grant) Teaching & Advising: Current advising 5 PhD students and leading a team of 25+ researchers Teaches courses on Cloud Computing, Distributed Systems, and Software Engineering Lab & Collaborations: HPCC Lab develops open-source tools like the E2C simulator and OaaS framework. Active collaborations with institutions like IMDEA Networks and industry partners like Hub Enterprise Inc.

Mario Di FrancescoView profile

Professor

• Wireless Networking
• Mobile and Ubiquitous Computing
• Internet of Things
• +4 more

Mario Di Francesco is a Professor at the Department of Computer Science, Aalto University. He holds the position of Professor in Computing Systems (ComputingSystems) Research Area and leads multiple research projects in networking and IoT. Previously, he was a research associate and adjunct faculty at the University of Texas at Arlington (2012–2014), and completed his PhD in Information Engineering at the University of Pisa under Prof. Giuseppe Anastasi. He also served as a research fellow at Scuola Superiore Sant'Anna in 2009. Research Interests : His work focuses on wireless networking, mobile and ubiquitous computing, and the Internet of Things (IoT). Key areas include edge computing, fog networks, virtual/augmented reality, and AI-driven resource management. His research contributes to UN Sustainable Development Goals related to innovation and infrastructure (SDG 9), sustainable cities (SDG 11), and industry/infrastructure development (SDG 9). Publications : His recent work spans topics like private virtual reality conversations, cloud-based network measurement frameworks (CLAIM), microservice connectivity analysis, and edge-DNN optimization. These publications reflect a trend toward integrating AI with distributed systems for real-time, low-latency applications in IoT and edge computing. Awards : Recipient of the Google Internet of Things Technology Research Award (2016) and MobileHCI Honorable Mention (2020). Grants & Projects : CRUISE (2023–2025): Cross-system architecture for autonomous wireless networks using lifelong machine learning. MeXICO (2020–2024): Mobile cross-reality through immersive computing. ASPECT (2016–2018): Spectrum utilization for mobile video. Labs & Teams : Leads research groups focused on edge computing, IoT systems, and immersive technologies. Collaborates with international partners on projects like fog radio networks and autonomous system design.

• Computer Systems Performance
• Cloud Computing
• Edge Computing
• +4 more

Dr. Isaac Lera Castro is a Full Professor (Catedrático) at the University of the Balearic Islands (UIB) since 2021, with a PhD in Computer Science (2012) and Computer Science Engineering degree (2006) from the same institution. He teaches core and elective subjects including Computer Architecture, Cloud Computing, and Data Processing at undergraduate and graduate levels. Deputy Director of LAIA (Artificial Intelligence Applications Laboratory) Former co-director of the Master's Degree in Big Data Analysis in Economics and Business (MADM) 2018-2021 His research focuses on computer systems performance , cloud computing , and edge computing through semantic web applications and IoT simulation tools. The work includes availability-aware fog service placement and YAFS simulator development for IoT scenarios. He is actively involved in R&D&I groups: Arquitectura i Comportament de Sistemes Informàtics i de Comunicacions (ACSIC) Laboratori d’Aplicacions de la Intel·ligència Artificial de la UIB (LAIA@UIB)

• Combinatorial Optimization
• Natural Language Processing
• Wireless Communication
• +4 more

Dr. Meghana Navnath Satpute is an Assistant Professor of Instruction in the Department of Computer Science at the University of Texas at Dallas, affiliated with the Erik Jonsson School of Engineering and Computer Science. She holds a PhD in Computer Science and has been actively contributing to research in algorithmic applications for natural language processing and wireless communication systems since 2019. Her research focuses on combinatorial optimization , particularly in solving complex problems in natural language processing and wireless network efficiency . Key areas include multidocument summarization, edge computing task allocation, and UAV trajectory planning for sensor data collection. These efforts reflect a strong interdisciplinary approach combining theoretical algorithms with real-world system design. The recent publications demonstrate a consistent trend in applying attention-based models and fine-grained optimization techniques across diverse domains such as NLP and IoT-enabled aerial networks. Her work bridges algorithm development with practical deployment challenges in distributed and mobile computing environments. Scientific Awards: No awards listed in the provided text. Dr. Satpute is involved in academic instruction and research supervision, though specific details about graduate student advising or funded grants are not mentioned in the available information. Her role emphasizes both teaching and research within the computer science curriculum at UTD. She is associated with ongoing research in intelligent systems and optimization algorithms, likely contributing to collaborative projects within the Erik Jonsson School of Engineering and Computer Science, particularly in areas related to data science, networking, and autonomous systems.

Adrian SpătaruView profile

Lecturer

• Cloud Computing
• Distributed Systems
• Edge Computing
• +4 more

Adrian Spătaru serves as a Lecturer at the Faculty of Mathematics and Computer Science, West University of Timisoara, Romania, with his office located in room 058. He maintains active academic engagement through direct contact channels including email (adrian.spataru@e-uvt.ro) and phone ((0256) 592 157), reflecting ongoing institutional affiliation and research activities within computer science. His research centers on the integration of edge, cloud, and high-performance computing resources within the cloud continuum framework. Key focus areas include service orchestration, resource management, blockchain applications for decentralized systems, and AI-driven optimization of cloud infrastructures. His work addresses critical challenges in heterogeneous platform integration, fault tolerance, and predictive maintenance across large-scale distributed environments, with notable contributions to container deployment and accelerator-aware application specification. Analysis of his publication trends reveals sustained emphasis on the edge-cloud-HPC continuum since 2018, evolving toward intent-based AI orchestration and heterogeneous hardware integration in recent works. His research bridges theoretical distributed systems concepts with practical applications in environmental monitoring (solar forecasting, freshwater quality assessment) and industrial cloud reliability, demonstrating both academic rigor and real-world impact. No scientific awards are documented in the provided institutional information. Details regarding student supervision, research grants, or laboratory affiliations are not specified in the available materials. Current research directions appear focused on advancing the edge-cloud-HPC continuum through heterogeneous platform integration, with 2025 publications indicating active development in this domain.

Dominic HenzeView profile

Professor

• Cyber-Physical Systems
• Smart Environments
• Machine Learning Applications
• +1 more

Dominic Henze is a Professor at the Technical University of Munich (TUM), affiliated with the Faculty of Informatics and the Chair of Software Engineering . He leads research initiatives in Cyber-Physical Systems , Smart Environments , and Machine Learning Applications , with a particular focus on Fog Computing architectures. His research spans theoretical frameworks and real-world implementations, including collaborations with institutions like Carnegie Mellon University and industry partners such as Siemens AG and Zeiss IMT . His work addresses challenges in resource allocation , predictive maintenance , and blockchain integration within industrial contexts. Dr. Henze's publications reveal a consistent focus on Fog Computing architectures, IoT resource management, and educational software engineering. Key trends include self-organizing network systems , decentralized supply chain traceability , and smart environment coordination . He has advised numerous students on topics ranging from QoS negotiation to autonomous drone coordination . As an educator, he has taught multiple iPraktikum courses and seminars on iOS development and Agile Project Management since 2014, with publications exploring team composition strategies and distributed programming pedagogy.

• Embedded Systems
• Hardware/Software Co-design
• Energy-efficient AI Accelerators
• +7 more

Oliver Bringmann is a full Professor and head of the Chair of Embedded Systems at the University of Tübingen, Germany, and a member of the board of directors at the FZI Research Center for Information Technology. His research integrates embedded-system design, energy-efficient AI accelerators, dependable automotive perception, and medical AI for capsule endoscopy. Education & Career Ph.D. in Computer Science, University of Tübingen, 2001 Diploma in Computer Science, University of Karlsruhe (KIT) Head, Chair of Embedded Systems, University of Tübingen (since 2012) Deputy spokesperson & spokesperson, Dept. of Computer Science, University of Tübingen (2014-2022) Board of Directors, FZI Research Center for Information Technology Research Interests Bringmann’s group pioneers hardware/software co-design for ultra-low-power Edge-AI , developing RISC-V based accelerators, compiler-aware neural-architecture search, and real-time perception systems for autonomous driving and medical devices. Key topics include: Energy-efficient AI architectures (“Edge AI”) and custom accelerator generation Robust collective perception under adverse weather (LiDAR, camera, V2X fusion) Timing/power-predictable embedded software and system-on-chip design automation Hardware-assisted security and safety for automotive & IoT systems AI-driven capsule endoscopy localization and anomaly detection Recent Publication Trends His 2024-2025 articles reveal a strong shift toward robust multimodal perception for automated driving (snow, fog, collective LiDAR fusion) and Edge-AI medical devices (capsule endoscopy with multi-task CNNs). Core contributions span dataset generation (SCOPE, SnowyLane), safety metrics (LSM), and fast performance modeling for DNN accelerators. Professional Service & Projects Executive/Steering Committees: IEEE/ACM DATE, CODES+ISSS, CASES, ITSS conferences EU CATRENE EDA roadmap chapter lead (Embedded Software & ESL-to-RTL) Principal investigator in Scale4Edge, OCEAN12, enerDAG and other national projects on energy-efficient sensorics and secure energy trading. His group maintains extensive collaborations with automotive and semiconductor industry, focusing on dependable, energy-aware embedded intelligence.

Richard YuView profile

Professor

• Machine learning and artificial intelligence
• Connected and autonomous vehicles
• Blockchain and distributed ledger technology
• +2 more

Dr. Richard Yu is a Professor at the School of Information Technology, Carleton University, Canada, with academic roles spanning since 2006. His research bridges machine learning, blockchain, and wireless networks, focusing on emerging technologies like vehicular systems and edge computing. Ph.D. in Electrical Engineering (UBC, Canada) M.A.Sc. in Computer Engineering (Beijing University of Posts & Telecomm.) B.A.Sc. in Electrical Engineering (Dalian University of Technology) His research explores machine learning and blockchain integration in connected vehicles , mobile networks , and IoT , emphasizing security and resource optimization . His work often employs deep reinforcement learning to address challenges in distributed systems. Recent publications focus on blockchain-enabled edge computing , vehicular networks , and IoT optimization , with methodologies centered on reinforcement learning , interference alignment , and secure transmission . 2023: Electronics and Electrical Engineering Leader, Research.com 2022: IEEE ICC Best Paper Award 2021: Fellow, Canadian Academy of Engineering 2020: IEEE GLOBECOM Best Paper Award 2019: IEEE TCGCC Distinguished Service Award 2017: IEEE VTC Best Paper Award 2017: IEEE Fellow 2016: IET Fellow 2011: Ontario Early Researcher Award His grants include the Canada Foundation for Innovation’s Leadership Opportunity Fund Award (2009). Collaborative work with students and colleagues underpins his contributions to wireless systems and blockchain.

• Educational Technology
• eLearning
• Artificial Intelligence in Education
• +12 more

Marc Alier Forment is an Associate Professor at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Services and Information Systems Engineering within the Faculty of Informatics of Barcelona (FIB). He is also associated with the Institut de Ciències de l'Educació and serves as Coordinator of the Doctoral Program in Engineering, Science, and Technology Education. His research is conducted through the UPC EduSTEAM - STEAM University Learning Research Group. His research interests span Educational Technology , Artificial Intelligence in Education , Learning Management Systems , Open Source in Education , Ethics in Computing , Sustainability in Education , Mobile Learning , Learning Analytics , and Privacy in EdTech . He emphasizes ethical, secure, and sustainable applications of technology in higher education, particularly in engineering contexts. The recent scholarly output highlights a strong focus on the integration of AI in education (especially through the LAMB framework), ethical implications of generative AI, privacy in learning analytics using edge and fog computing, and innovative pedagogical methods in computer science education. His work increasingly bridges technical computing with humanistic concerns such as ethics, privacy, and social responsibility. Best Paper Award TEEM'22 Premis de Programari lliure 2005 de l'AGAUR VI Premi Davyd Luque a la innovació en les TIC Best interoperability innovation: Moodle simple learning tools for interoperability consumer – Spain Marc Alier Forment has led and participated in numerous educational innovation and R&D+i projects, particularly focused on Moodle/LMS integration, mobile learning, open-source educational tools, and the development of ethical and privacy-preserving technologies. He has mentored and collaborated extensively with colleagues on curriculum development, particularly in embedding sustainability and ethics into computing education. His work is central to UPC’s digital education strategy, especially through the Atenea platform. He leads and contributes to the UPC EduSTEAM research group and has been instrumental in developing STEAM-based lecturer training programs. His projects often involve interdisciplinary collaboration across computing, education, and social sciences, aiming to create holistic, responsible technological solutions for learning.

• Decentralized Machine Learning
• Federated Learning
• Industry 4.0
• +3 more

Alessio Mora is a postdoctoral researcher at the Department of Computer Science and Engineering (DISI), University of Bologna, specializing in decentralized learning techniques such as Federated Learning. His work focuses on communication efficiency, heterogeneous data handling, and distributed AI systems. PhD in Computer Science and Engineering, University of Bologna (2023) Master in Computer Engineering, University of Bologna (2019) Bachelor in Computer Engineering, University of Bologna (2016) His research spans decentralized learning, federated learning, and their applications in Industry 4.0, IoT, and edge computing. Key themes include model optimization, communication efficiency, and heterogeneous data distribution in cross-device settings. Recent publications highlight trends in federated learning infrastructure, including unlearning methods, knowledge distillation, and sparse compression techniques. His work integrates theoretical analysis with practical implementations in industrial and IoT contexts. Summer of Reproducibility Award – Flower Labs (2023) Awarded $2,000 USD for implementing and reproducing the FedMLB baseline in Flower, advancing reproducibility in federated learning. Mora has contributed to European projects like OntoTrans and SimDOME, focusing on ontology-based systems for materials modeling and industrial data translation. His technical expertise includes Docker, Stardog triplestore, FastAPI, and Python wrappers for chemical simulation tools.

• Internet of Things (IoT)
• Fog Computing
• Digital Twins
• +3 more

Dimitrios Georgakopoulos is a researcher at Swinburne University of Technology and National Measurement Institute, focusing on Internet of Things (IoT), digital twins, fog computing, and smart manufacturing. His work bridges theoretical advancements with real-world applications in urban infrastructure, precision agriculture, and industrial IoT. Research Areas: IoT, Digital Twins, Fog Computing, Smart Cities, Precision Agriculture Key Contributions: Sensor sharing marketplaces, 5G-enabled smart city frameworks, metadata-assisted IoT data classification Recent Articles (2023-2025): Explore topics like digital manufacturing consistency, AI-powered roadside asset management, and deep learning for heterogeneous IoT data. Trends emphasize autonomic systems, sensor integration, and contextual data analysis. Collaborations: Frequently works with Prem Prakash Jayaraman, Ali Yavari, Abhik Banerjee, and Anas Dawod on IoT security, sensor networks, and time-sensitive applications.

Samee U. KhanView profile

Professor

• Quantum Computing
• Edge Computing
• Fog Computing
• +4 more

Samee U. Khan is a Professor in the Department of Electrical & Computer Engineering at Mississippi State University (MSU), affiliated with the Bagley College of Engineering. He holds a Ph.D. from the University of Texas (2007) and a B.S. from GIK Institute of Engineering & Technology (1999). His research focuses on optimization, robustness, and security of computer systems, with recent emphasis on quantum computing, edge/fog computing, and machine learning applications in neuroscience and cybersecurity. Key research interests include quantum algorithm development, edge computing architectures, and neuro-inspired systems. His work integrates quantum computing with traditional machine learning to address challenges in visual perception modeling and medical imaging analysis. He also explores fog computing frameworks for IoT and distributed systems, emphasizing security and efficiency in heterogeneous environments. Publications highlight advancements in quantum noise mitigation, hybrid quantum optimization, and brain-computer interface models. His contributions span simulation toolkits like iFogSim and frameworks for vehicular networks and blockchain-based resource management. Khan’s research bridges theoretical computer science with practical applications in smart homes, healthcare, and autonomous systems.

Blesson VargheseView profile

Associate Professor

• Distributed Systems
• Cloud Computing
• Edge Computing
• +3 more

Dr. Blesson Varghese is an Associate Professor and Reader in the School of Computer Science at the University of St Andrews, leading the Edge Computing Hub funded by Rakuten, Japan. He holds an honorary faculty position at Queen's University Belfast. His research focuses on distributed systems, edge computing, and federated learning, addressing challenges in scalability, resource efficiency, and real-time applications. He has received prestigious awards, including the 2021 IEEE Rising Star Award and a Royal Society Industry Fellowship. Teaching includes CS5052: Data-Intensive Systems, emphasizing large-scale distributed systems design. He advises PhD students in edge computing and collaborative machine learning. Notable publications include DNNShifter (2024), EcoFed (2024), and NeuroFlux (2024), advancing edge computing and federated learning techniques. Awards include the IEEE Rising Star Award, Best Paper Award (IEEE Edge Conference 2022), and Finalist at GSMA Mobile World Congress 2019. His work bridges academia and industry, with collaborations across continents and contributions to open-source projects like FedAdapt and DNNShifter.

• Cloud Computing
• Distributed Systems
• IoT Service Composition
• +3 more

Dr. Abdessalam Elhabbash is a Lecturer in Distributed Systems at the Department of Computing and Communications, Lancaster University. His research focuses on IoT service composition, cloud computing, and self-adaptive systems. He is affiliated with the Lancaster Intelligent, Robotic and Autonomous Systems (LIRA) Centre and leads research groups such as LIRA - Environmental Modelling and LIRA - Extreme Environments. His work emphasizes ontological architectures for system composition, cloud migration optimization, and AI-driven IoT frameworks like the IoT Whisperer and TRANSITIVE. Elhabbash advises PhD student Qianhui Lin. He is contactable at a.elhabbash@lancaster.ac.uk. Research interests include: IoT service composition using large language models (LLMs) Multi-cloud performance optimization and pricing strategies Autonomous system design through ontological frameworks Predictive maintenance in Industry 4.0 environments Recent publications highlight advancements in cloud migration tools (PricingTheCloud), edge computing architectures (HolonCraft), and self-aware system development. His work bridges theoretical foundations with practical applications in smart home automation and industrial IoT. Labs/Teams: Active member of LIRA's environmental and extreme environment groups, focusing on resilient distributed systems in challenging conditions.

Ming XiaoView profile

Associate Professor

• Wireless Communications
• Network Coding
• Machine Learning
• +10 more

Ming Xiao is an Associate Professor in the Division of Information Science and Engineering at KTH Royal Institute of Technology's School of Electrical Engineering and Computer Science (EECS). He is affiliated with the Digital Futures Faculty and leads research in wireless communications, machine learning, and network coding. His work focuses on 6G networks, distributed learning, and secure communications. Affiliations: KTH EECS, Digital Futures, Swedish Research Council, EU Horizon Europe projects Roles: Editor for IEEE Transactions on Wireless Communications, TPC Co-Chair for VTC Fall Research Interests: Dr. Xiao's expertise spans wireless communication systems (e.g., mmWave, NOMA), network coding, machine learning applications in communications, and energy-efficient distributed systems. He has pioneered work on intelligent reconfigurable surfaces (RIS), federated learning in edge computing, and integrated sensing-communications (ISAC). Projects: Ongoing EU-funded projects include ASCENT (autonomous vehicular networks) and COVER (unmanned aerial vehicles for emergency response). Past projects include 6G channel coding and intelligent energy management in smart communities. Grants: Over 12 active grants from VR, EU Horizon Europe, FORMAS, STINT, and VINNOVA Awards: IEEE Vehicular Technologies Society Best Paper Award (2023), World Top 2% Researcher (2020–2023), Highly Cited Researcher in Computer Science/Engineering. Labs/Teams: Leads the KTH Digital Futures initiative, collaborates with RISE Research Institutes, and manages a team of 12 PhD students/postdocs focusing on 6G, distributed ML, and secure IoT.