Dr. Ignatius Ezeani is a Research Fellow in the Department of Computing and Communications at Lancaster University, specializing in Low-Resource Natural Language Processing (LowResNLP). His work focuses on developing tools and methods for under-resourced languages, particularly African languages such as Igbo, and minority languages like Welsh. He leads projects such as the Igbo-English Machine Translation initiative and the Welsh Summary Creator, advancing computational linguistics in these domains. His research interests include machine translation, part-of-speech tagging, named entity recognition, and corpus-based approaches to language processing. He is affiliated with the University Centre for Computer Corpus Research on Language (UCREL), contributing to data science and linguistic resource development. Ezeani supervises PhD student Chiamaka Chukwuneke and collaborates on initiatives like the National Corpus of Contemporary Welsh (CorCenCC). His projects emphasize participatory research, ensuring technologies address real-world linguistic needs in underrepresented communities. Key contributions include the IgboAPI Dataset, IgboBERT models, and frameworks for spatial narrative analysis. Ezeani’s work bridges computational linguistics with societal impact, fostering multilingualism through technology.
Michalis Matthaiou is a Professor of Communications Engineering and Signal Processing at Queen’s University Belfast (QUB), UK, and Deputy Director of the Centre for Wireless Innovation (CWI). He holds a Diploma in Electrical and Computer Engineering from Aristotle University of Thessaloniki (2004), an M.Sc. (Distinction) from the University of Bristol (2005), and a Ph.D. from the University of Edinburgh (2008). Prior roles include Assistant Professor at Chalmers University of Technology (Sweden) and postdoctoral research at TU Munich (Germany). His research focuses on signal processing for wireless communications, massive MIMO, intelligent reflecting surfaces, and AI-driven systems. He leads a team of 20 researchers under multi-million-pound grants, including the ERC Consolidator Grant BEATRICE (2021–2026). **Affiliations**: Centre for Wireless Innovation, Institute of Electronics, Communications & Information Technology **Awards**: IEEE Fellow (2023), Leonard G. Abraham Prize (2017), EURASIP Early Career Award (2019) His work spans over 340 publications (140+ journal papers) and includes groundbreaking contributions to wireless systems. He supervises PhD students in areas like edge computing, reconfigurable intelligent surfaces, and millimeter-wave systems. He serves as Editor-in-Chief of Elsevier’s Physical Communication and Senior Editor for IEEE journals.
Dr. Kieran McLaughlin is a Reader at Queen's University Belfast, leading research in cyber security for operational technologies (OT), including smart grids and industrial control systems (ICS). He focuses on threat analysis, intrusion detection/prevention, and digital twins for incident response. His work addresses SCADA systems, ICS protocols (e.g., IEC61850), and cyber-physical resilience. He leads projects like XANDAR (H2020) and CAPRICA, and collaborates with UK's RITICS institute. He teaches Network Security (CSC3064) and actively publishes in top-tier venues. His research spans over 100 publications, with recent focus on digital twins, AI-driven malware detection, and secure IoT communications. Projects include EU-funded initiatives targeting critical infrastructure security and embedded system safety. He advises on cybersecurity for energy sectors and contributes to global standards like DO-326A for avionics systems. Key areas of innovation include deception platforms using digital twins, lightweight AI for IoT security, and blockchain-based predictive maintenance in vehicular networks. His work integrates machine learning for automated response and explores novel networking architectures (e.g., Named Data Networking) to enhance security.
Daniele Jahier Pagliari is an Associate Professor in the Department of Control and Computer Science (DAUIN) at Politecnico di Torino, where he is also a member of the Interdepartmental Center PEIC - Power Electronics Innovation Center. He is actively involved in teaching and research, focusing on embedded systems, electronic design automation, and machine learning for edge computing. He teaches courses such as Optimized Execution of Neural Networks at the Edge, Machine Learning for IoT, and Hardware/Software Codesign of Flexible Computing Systems for Edge AI across various engineering programs including Computer Science and Systems Engineering, Data Science, and Automotive Engineering. His research interests span electronic design automation, embedded systems, energy-efficient computing, low-power design, and machine learning. He is particularly engaged in applying machine learning techniques to improve the design and performance of digital and analog circuits, with a focus on edge AI applications. His work aligns with key scientific areas including computer architecture, cyber-physical systems, and scientific computing. The recent publications highlight a strong trend in optimizing deep learning models for resource-constrained environments, accelerating neural network inference on ultra-low-power devices, and integrating physics-based models with AI for battery state estimation. There is also significant focus on using machine learning to enhance electronic design automation, particularly for analog and mixed-signal circuits, reflecting a convergence of AI and hardware design. He leads and participates in several high-impact research projects, including EU-funded initiatives like HAL4SDV, ISOLDE, TRISTAN, and AMBEATion, as well as commercial projects such as MASAI and software platform development for production support. He serves as the Scientific Responsible or Director in multiple projects, demonstrating leadership in both academic and industrial research contexts. He supervises multiple PhD students in the Computer and Systems Engineering program, including Luca Benfenati, Mohamed Amine Hamdi, Beatrice Alessandra Motetti, Giovanni Pollo, and Matteo Risso, whose research topics include latency-optimized inference, compiler optimization for edge devices, and hardware-aware deep learning design. He is also involved in patent development, notably for an instrumentation method to dynamically modify circuit precision. He is a member of the College of Computer, Film and Mechatronics Engineering and contributes to various degree programs. His work supports UN Sustainable Development Goals related to good health, affordable and clean energy, industry innovation, and sustainable cities.
Xingyu Zhou is an Assistant Professor in the Department of Electrical and Computer Engineering at Wayne State University. He earned his Ph.D. in ECE from Ohio State University under Prof. Ness Shroff, with prior degrees from BUPT (B.S.) and Tsinghua University (M.S.) in Electrical Engineering. His research bridges machine learning and stochastic systems, focusing on bandits, reinforcement learning, and cloud computing with differential privacy. His work addresses privacy-preserving sequential decision-making and optimal load balancing. Ph.D., Electrical and Computer Engineering, Ohio State University M.S., Electrical Engineering, Tsinghua University B.S., Electrical Engineering, BUPT His research explores machine learning (bandits, reinforcement learning, offline alignment techniques) and stochastic systems (load balancing, cloud computing). Recent work emphasizes differential privacy in federated learning, contextual bandits, and reinforcement learning frameworks. Publications span top venues like ICML , NeurIPS , and ICLR , with a focus on privacy-utility trade-offs and robust optimization. Key scientific awards include the NSF CAREER Award, Best Student Paper at WiOpt 2022, and Ohio State University’s Presidential Fellowship. He actively contributes to teaching , offering graduate courses in online decision-making and undergraduate programming courses in electrical engineering.
Sajid Mohamed is a guest researcher in the Electronic Systems group at the Department of Electrical Engineering, Eindhoven University of Technology. His work focuses on model-based design of image-based control systems tailored for resource-constrained domains like semiconductor manufacturing and automotive industries. PhD, M.Tech, and B.Tech degrees from TU/e, IIT Kharagpur, and NIT Calicut respectively His research combines expertise in Multiprocessor Systems , Deep Neural Networks , and Motion Control to address challenges in real-time performance and optimization. Key contributions include the IMOCO4.E reference framework and methodologies for vision-based DNN deployment on legacy hardware . Recent publications highlight advancements in Motion Control through multi-rate sensor fusion , DNN-driven visual perception , and predictable multi-core implementations . His work aligns with the UN Sustainable Development Goals, particularly in technological innovation for industrial efficiency. Best Paper Award at ECYPS (2019) Professionally, he serves as a Principal Software Engineer at ITEC B.V., integrating academic research with industry applications. Collaborations include projects like FitOptiVis and oCPS , emphasizing cyber-physical systems and edge computing.
Minh-Quan Tran is a University Researcher at Eindhoven University of Technology (TU/e) within the Department of Electrical Energy Systems, Faculty of Electrical Engineering. His work focuses on modern power distribution systems, renewable energy integration, and grid stability solutions, contributing to UN Sustainable Development Goals in sustainable energy. He maintains an ORCID profile (0000-0001-6430-7333) and has accumulated 99 Scopus citations for his 20 research outputs, with publication activity increasing from 2 works in 2019 to 7 in 2023. His key research interests include: Distribution System Engineering (100% fingerprint strength) Distributed Energy Resource Engineering (71%) State Estimation (64%) Voltage Stability (51%) Microgrid Control (40%) System State Monitoring (47%) Recent publications (2022-2024) reveal strong trends in AI-driven grid management, particularly Model Predictive Control and Graph Neural Networks for voltage stability and state estimation. Real-world case studies feature campus networks (e.g., Chalmers University), emphasizing practical validation of control strategies for low-voltage systems with high renewable penetration. Scientific Awards: No awards or honors are documented in the provided materials Advising and Grants: Tran lists no formal PhD/Master's students in the repository. His research involves IoT platforms, digital twins, and real-life demonstrations for flexibility optimization but lacks explicit grant details or funding acknowledgments in the extracted text. Labs and Teams: He operates within TU/e's Electrical Energy Systems research group, specializing in active distribution network monitoring, microgrid stability, and distributed resource control. Collaborations span international institutions as visualized in the repository's network map, with recent joint work involving Vietnamese and European universities.
Andrew McDonald is an Associate Professor at Cornell University, holding dual appointments in the School of Integrative Plant Science (Soil and Crop Sciences Section) and the Department of Global Development. His work focuses on cropping systems ecology, climate change adaptation, and agricultural sustainability with a strong emphasis on South Asia. He previously led CIMMYT's sustainable intensification program and currently develops decision frameworks for technology scaling in agri-food systems. He earned his B.Sc., M.Sc., and Ph.D. from Cornell University between 1994 and 2003. His research integrates agronomy, hydrology, and social sciences to address food security challenges, particularly in water resources management and policy. Recent work includes applying remote sensing for crop yield estimation and machine learning for precision agriculture in resource-constrained environments. Key research themes include climate-smart agriculture practices, residue management, and irrigation strategies. His interdisciplinary approach addresses both biophysical and socio-economic dimensions of agricultural systems. He has contributed to frameworks like the PAiCE tool for climate adaptation and the LCAS-APT protocol for prioritizing agronomic interventions. His lab's work has been supported by grants such as the Cornell Atkinson Academic Venture Fund (2024). Education history includes a Doctorate (2003), Master of Science (1998), and Bachelor of Science (1994) all from Cornell University. He advises on courses like PLSCI 4140 (Global Cropping Systems) and PLSCI 6017 (Cropping Systems Ecology). His lab's website highlights ongoing projects in systems agronomy and global development contexts.
Ning Xiong is a Professor at Mälardalen University, affiliated with the School of Innovation, Design and Engineering and the Division of Intelligent Future Technologies. His research focuses on advanced artificial intelligence, machine learning, optimization algorithms, and cyber-physical systems. He explores applications ranging from digital twin frameworks in distributed systems to predictive maintenance using explainable AI and anomaly detection in timeseries data. His work integrates techniques like federated learning, Bayesian classifiers, and bio-inspired computing (e.g., membrane clustering) to address challenges in smart systems and data science. Key research areas include: Machine Learning & Deep Learning Cyber-Physical Systems Optimization Algorithms Smart Systems & IoT Data Science & Big Data Recent publications highlight advancements in digital twin frameworks for resilient distributed systems, ensemble learning for imbalanced data, and lightweight object detection methods for UAV imagery. His contributions emphasize practical applications in energy grids, predictive maintenance, and industrial automation while addressing theoretical challenges in model explainability and scalability. His research is characterized by interdisciplinary collaboration, combining software engineering, systems architecture, and domain-specific expertise to develop innovative solutions for dynamic environments.
Prof. Dr. Abuzer AKGÜN is a full Professor at Adıyaman University's Faculty of Education, Department of Mathematics and Science Education. He holds a PhD in Chemistry from Dicle University (2002) and has over 30 years of academic experience, progressing from Research Assistant (1990) to Lecturer (2000), Assistant Professor (2004), Associate Professor (2012), and Professor (2019). His career includes administrative roles such as Head of Department (2013–present) and Deputy Director of the Institute (2007–2010). His research focuses on science education, particularly student misconceptions in chemistry and physics, active learning strategies, and curriculum development. Notable contributions include studies on concept cartoons, analogical reasoning in science teaching, and environmental education effectiveness. Prof. AKGÜN has authored/co-authored 55 peer-reviewed articles, 9 books, and contributed to 39 conference proceedings. He serves as an editor for educational journals and has reviewed manuscripts for international publications. His work emphasizes bridging theory and practice in science education through innovative teaching methods and curriculum reforms.
Eric Brewer is a Professor in the Department of Computer Science at the University of California, Berkeley, part-time since July 2014 while also serving as a leader at Google. He is renowned for his work in operating systems, networking, and technology for developing regions. His academic journey includes a B.S. from UC Berkeley (1989) and a Ph.D. from MIT (1994), both in EECS. Brewer's research focuses on scalable systems, security, and energy-efficient technologies for underserved areas. He co-founded Inktomi Corporation and pioneered projects like FirstGov.gov (now USA.gov). His awards include the ACM Prize in Computing (2009) and membership in the National Academy of Engineering. Education: B.S. in EECS from UC Berkeley (1989), M.S. and Ph.D. in EECS from MIT (1994). Research interests span developing region technologies, programming languages, and networking. His work on DC microgrids and rural cellular networks highlights efforts to bridge the digital divide. Collaborative projects in Cambodia, India, Ghana, and other regions emphasize practical, sustainable solutions. Recent publications address energy systems, cloud computing, and low-cost connectivity. Awards include ACM Fellow, SIGOPS Mark Weiser Award, and Global Leader for Tomorrow recognition. His contributions to education include courses like CS169 (Software Engineering) and CS262A (Advanced Topics in Computer Systems).
Sanglu Lu is a Professor in the Department of Computer Science at Nanjing University's School of Computer Science and Engineering. With over 400 publications spanning from 1998 to 2025, Professor Lu has established a significant research presence in mobile computing, edge computing, and wireless sensing technologies. Their work frequently appears in top-tier conferences including INFOCOM, IEEE Transactions journals, and AAAI. Professor Lu's research focuses on cutting-edge areas in computer science, particularly in edge AI systems, millimeter-wave and RFID-based sensing, graph neural networks, and time series analysis. Their work bridges theoretical advances with practical applications, often addressing resource-constrained environments and real-world deployment challenges. Recent publications show a strong emphasis on privacy-preserving sensing techniques, efficient model deployment at the edge, and multimodal approaches to human activity understanding. Analysis of publication trends reveals Professor Lu's work has evolved from traditional mobile networking topics toward more AI-centric research, particularly in the last five years. The research demonstrates strong interdisciplinary connections between networking, sensing, and machine learning, with increasing focus on practical applications in healthcare, accessibility technology, and industrial IoT systems. Publications consistently show collaboration with researchers across China and internationally, indicating an active research group and extensive professional network. Professor Lu has mentored numerous researchers as evidenced by the extensive publication record with various co-authors, though specific student names aren't detailed in the provided information. Their work has received significant attention in the research community, as reflected by the substantial number of publications in high-impact venues. Current research directions include innovative approaches to edge AI, advanced wireless sensing techniques using commercial hardware, and novel neural network architectures for time series and graph data. Professor Lu's laboratory appears to be actively working on real-world applications of these technologies, particularly in health monitoring, accessibility solutions, and industrial automation contexts.
Dominik Baumann is an Assistant Professor at the Department of Electrical Engineering and Automation at Aalto University in Espoo, Finland. His research focuses on the interplay of systems and control theory with machine learning and communication networks, with a current emphasis on causal inference in control systems. Education: Diploma in Electrical Engineering from TU Dresden, Germany (2016) PhD from KTH Stockholm, Sweden (2020), supervised by Sebastian Trimpe (Max Planck Institute) and Karl H. Johansson Postdoctoral positions at RWTH Aachen University (1 year) and Uppsala University with Thomas Schön (1 year) Dr. Baumann's research bridges theoretical foundations with practical applications in robotics, wireless networks, and decision-making systems. His work spans safe reinforcement learning, event-triggered control systems, causal inference in dynamical systems, and ergodicity economics perspectives on long-term decision-making. He applies mathematical rigor to address challenges in resource-constrained environments, particularly focusing on safety guarantees and computational efficiency for real-world implementation. His recent publication record demonstrates a strong trajectory in safe learning-based control, with increasing focus on ergodicity economics in reinforcement learning, multi-agent coordination, and human-robot interaction. The research consistently balances theoretical guarantees with practical implementation constraints, particularly in bandwidth-limited wireless control systems and robotics applications. Scientific Awards: Best Paper Award for 'Feedback control goes wireless: Guaranteed stability over low-power multi-hop networks' at ACM/IEEE International Conference on Cyber-Physical Systems (2019) Dr. Baumann maintains extensive international collaborations, evidenced by numerous seminar invitations worldwide including Oxford University, ETH Zürich, University College London, and institutions across Asia. His research program addresses fundamental challenges in cyber-physical systems with applications in industrial automation, robotics, and the Internet of Things, securing research funding for projects focused on safe learning in control systems and wireless cyber-physical systems. His research group at Aalto University develops both theoretical foundations of learning-based control and practical algorithms for real-world deployment, with active software repositories on GitHub related to predictive triggering, causal structure identification, and ergodic reinforcement learning.
Andrew Robinson is a Professor at The University of Melbourne, holding dual appointments in the School of BioSciences and the School of Mathematics and Statistics. As Director of the Centre of Excellence for Biosecurity Risk Analysis (CEBRA), he leads research on statistical and risk analysis for biosecurity threats, funded by Australian and New Zealand governments. His work bridges computational tools and natural resource management, with a focus on R programming and data-driven decision-making. Co-authored 4 influential books on R programming and biosecurity Developed icebreakeR, a popular R education resource Conducts workshops for scientists and policymakers Robinson's research spans biosecurity risk modeling, invasive species management, and statistical applications in forestry. His recent work emphasizes climate change impacts on agricultural yields, adaptive sampling strategies, and Indigenous leadership in biosecurity. He maintains active collaborations in data analytics and policy development. Selected Research Trends: Current projects focus on quantitative risk prioritization, climate change adaptation, and automated surveillance systems. Key subfields include computational biosecurity, stochastic modeling, and cross-jurisdictional policy frameworks. Robinson's laboratory, CEBRA, integrates multidisciplinary approaches to biosecurity challenges. His publications demonstrate expertise in statistical ecology, risk analytics, and policy simulation. He continues to develop R-based tools for practical applications in natural resources and biosecurity.
Patrizia Beraldi is an Associate Professor of Operations Research at the Department of Mechanical, Energy and Management Engineering (DIMEG), University of Calabria. She has been with the university since 2001, holding roles from Research Fellow to her current position. With over 80 publications, her work focuses on stochastic programming, probabilistic constraints, and their applications in energy systems, healthcare, finance, and logistics. Education: PhD in System Engineering and Computer Science (2000), University of Calabria Degree in Management Engineering (cum laude, 1995), University of Calabria Her research spans solution methods for network flow problems, stochastic programming with recourse, and probabilistic constraint modeling in diverse fields like energy procurement, drone routing, and financial optimization. Recent publications emphasize stochastic bilevel models for energy tariffs and ML-enhanced optimization frameworks. Scientific Awards: Best paper award, 2015 IMA Journal of Management Mathematics Best presentation award, ICEER Conference 2017 Best poster award, ICORES 2018 She supervises numerous master's and PhD students and leads the Financial Engineering and Risk Management Laboratory at DIMEG. Her editorial roles include Associate Editor for TOP , Algorithms , and IMA Journal of Management Mathematics .
