Naser Hossein Motlagh is an Academy Research Fellow at the Department of Computer Science, University of Helsinki, affiliated with the Helsinki Institute of Sustainability Science (HELSUS) and Helsinki Institute of Urban and Regional Studies (Urbaria). He is an IEEE Senior Member and ACM Professional Member with a D.Sc. in Networking Technology from Aalto University (2018). Education: D.Sc. (Aalto University), M.Sc. & B.Sc. (University of Vaasa) His research focuses on Internet of Things (IoT) , Wireless Sensor Networks , and Environmental Sensing for smart cities and sustainability. Key projects include drone-based air quality monitoring, underwater plastic detection systems, and edge computing architectures for smart environments. Recent publications highlight trends in low-cost sensor networks for urban pollution, AI-driven adaptive systems , and quantum computing integration with IoT. He chairs the annual EnvSys workshop and organizes the IEEE World Congress on Services. Scientific Awards: Academy Research Fellow (Research Council of Finland, 2024–2028) IEEE Senior Member ACM Professional Member He supervises Master’s and Licentiate theses and contributes to teaching networking technologies at the University of Helsinki. His work spans collaborations with institutions like Tokyo Institute of Technology and TU Wien.
Dr. Yingshu Li is a Professor at Georgia State University’s Department of Computer Science and an affiliated faculty member at the INSPIRE Center. Her academic journey includes a B.S. from Beijing Institute of Technology, and M.S. and Ph.D. from University of Minnesota-Twin Cities. Research Interests : Artificial Intelligence of Things (AIoT) Privacy-aware Computing Internet of Things (IoT) Social Networks Wireless Networking Recent Publication Trends focus on generative AI for heterogeneous data, federated learning, blockchain for privacy in IoT, digital twin applications in vehicular networks, and privacy-aware systems. These works span computer science, artificial intelligence, and wireless communication. Scientific Awards : NSF CAREER Award recipient 2021 Outstanding Graduate Director Award 2022 N²Women: Stars in Computer Networking and Communications Included in The World’s Top 2% Scientists (2020–2024) Editorial and Leadership Roles include top-tier journal editorships (ACM Transactions on Sensor Networks, IEEE Transactions) and leadership in international conferences (Steering Committee Chair, General Chair, Program Chair). Her research has secured funding from the National Science Foundation, U.S. Department of State, and global sponsors. Dr. Li has supervised over 20 Ph.D. students, many of whom hold academic positions. She co-develops Eureka Labs, a platform for cybersecurity education.
Johnson Thomas is a Professor in the Department of Computer Science at Oklahoma State University, with affiliations in both the Stillwater and Tulsa campuses. His research focuses on Quantum Computing, Machine Learning, and Computational Neuroscience, with notable work on quantum circuit optimization and spiking neural networks. He holds degrees from the University of Reading (PhD, 1995), University of Edinburgh (MSc, 1983), and University of Wales (BSc, 1982). His teaching spans advanced topics in databases, quantum computing, and programming languages. Recent courses include *Quantum Computing*, *Advanced Topics in Information Systems*, and *Discrete Mathematics for Computer Science*. He has also advised doctoral students and led research in distributed systems and security. Research interests include quantum algorithms, neurocomputational models, and causal inference. His work bridges theoretical foundations with practical applications, such as ROS security frameworks and NIRS-based forage quality prediction. Over 20 funded projects highlight his expertise in big data, autonomous systems, and sensor networks. Publications emphasize quantum circuit design, medical ML interpretation, and computational neuroscience. Collaborations include interdisciplinary projects with healthcare and agricultural sectors. His contributions advance both theoretical computer science and applied technologies.
Eric Heller is the Abbott and James Lawrence Professor of Chemistry and Professor of Physics at Harvard University's Faculty of Arts and Sciences. He holds dual appointments in the Departments of Chemistry and Physics. His research spans quantum mechanics, semiclassical physics, and condensed matter systems, with notable contributions to quantum scars, graphene physics, and acoustics. Heller also teaches a GenEd course on acoustics for non-scientists and authored the textbook Why You Hear What You Hear . He is a member of the American Academy of Arts and Sciences and the National Academy of Sciences. His work explores wave phenomena in diverse contexts, including superwires in high Brillouin zones, Planckian diffusion, and relativistic quantum scars in graphene quantum dots. Education: PhD in Chemical Physics from Harvard University. Research Interests: Time-dependent quantum mechanics, semiclassical methods, tunneling in complex systems, dynamical systems, and applications of quantum mechanics to acoustics and astrophysics. His lab investigates phenomena such as branched flow, Anderson localization, and chaos-assisted tunneling in flat band systems. Recent Research Trends: Recent work focuses on relativistic quantum scars (e.g., in graphene quantum dots), Planckian diffusion in dynamic disordered systems, and superwires in periodic lattices. His publications bridge theoretical quantum physics with experimental observations in materials science and astrophysics. Awards: Member of the American Academy of Arts and Sciences Member of the National Academy of Sciences Grants & Advising: While specific grants are not detailed, his research group actively publishes in top journals and collaborates internationally. He advises students through Harvard’s physics and chemistry programs. Labs/Teams: Leads the Heller Research Group, focusing on interdisciplinary quantum studies with experimental and theoretical collaborations.
Dr. Ron Lin is a Visiting Assistant Professor in Computer Science at Clark University, specializing in semiconductor device design using machine learning and AI. His research bridges materials science, electrical engineering, and computer science with applications in photonics and electronics. Publication trends (2021-2024) demonstrate consistent focus on semiconductor optimization through ML: 60% cover UV LEDs/photodetectors with AlGaN/Ga2O3 materials, 27% address ML algorithm development for device design, and 13% explore neuromorphic/memristor applications. Recent work shows increased emphasis on multimodal AI and large language models. Education: PhD in Electrical Engineering (KAUST), MBA in Data Science (Santa Clara University). Prior experience includes Principal Data Scientist at Capital One. Serves as reviewer for multiple computational science journals.
Marina Lardou is a Researcher affiliated with the Computation-based Science & Technology Research Center (CaSToRC) at The Cyprus Institute. Her research focuses on advanced computational methodologies and technologies, including quantum computing, machine learning, and high-performance computing applications. She contributes to interdisciplinary projects leveraging computational modeling and artificial intelligence in scientific contexts. Research Interests: Her work spans cutting-edge areas such as quantum algorithms, computational physics simulations, and AI-driven solutions for complex scientific challenges. She collaborates on initiatives related to computational infrastructure development and its application in material science and environmental modeling. No awards, student advisees, or specific grants are listed in the provided information. Her professional activities are centered within CaSToRC's core mission areas.
Dr. Marco Caminati is a Lecturer in Computer Science at Lancaster University, affiliated with the Security Lancaster research group, specializing in Distributed Systems and Software Security. His research focuses on formal methods, theorem proving, and their applications in areas like auction theory, quantum computing, and cybersecurity. He has contributed to verified algorithms, conflict resolution frameworks, and formalizations in Isabelle/HOL. His work bridges theoretical foundations with practical implementations, addressing challenges in software verification, healthcare informatics, and distributed systems. Key research interests include automated theorem proving, formal verification of concurrent systems, and applying formal methods to real-world problems such as medical guidelines and combinatorial optimization. He actively publishes in venues like the Journal of Automated Reasoning and has explored topics ranging from quantum information theory to SMT-based concurrency analysis. His publications often emphasize rigorous validation through tools like Isabelle and Mizar, ensuring correctness in complex systems. While no specific awards are listed, his contributions to formal methods have advanced applications in both academia and industry.
Hui Guan is an Assistant Professor at the Manning College of Information and Computer Sciences (CICS) at the University of Massachusetts Amherst. Currently on leave, she works at Amazon AWS developing LLM systems. She holds a PhD in Electrical Engineering from North Carolina State University (2020) and is a member of the PLASMA lab at UMass. Education: PhD in Electrical Engineering, North Carolina State University, 2020 Research Interests: Focuses on machine learning systems, optimizing deep multitask and graph learning. Aims to improve efficiency, scalability, and reliability of ML through system innovations. Leverages principles like composability and locality awareness to democratize ML applications. Awards: NSF CAREER Award (2024) Amazon Research Award (2022) NCSU ECE Distinguished Dissertation Award (2020) IBM PhD Fellowship (2015-2018) Grants and Projects: Includes NSF support for projects like Adaptive Deep Learning Systems and Memory-Driven Collaboration for Embedded Systems. Received grants from Adobe and Dolby. Students and Advising: Advises PhD students including Lijun Zhang, Kunjal Panchal, and Qizheng Yang. Collaborates with students from other groups like Sohaib Ahmad. Labs and Teams: Active member of the PLASMA lab, focusing on programming languages and systems research.
Prof. Werner Martin is a Professor of Big Geospatial Data Management at the Technical University of Munich (TUM), affiliated with the School of Engineering and Design and the Department of Aerospace and Geodesy. His research focuses on georeferenced data processing, distributed computing, quantum algorithms, and machine learning applications in geospatial contexts. Prof. Martin holds a doctorate from LMU Munich and has held academic and research positions at institutions including LMU Munich, Leibniz-University Hannover, the German Aerospace Center (DLR), and UniBW Munich. His work bridges theoretical advancements with practical applications in spatial data analysis, visualization, and high-performance computing. Awards ACM SIGSPATIAL GIS Certificate of Appreciation (2019) 1st place ACM SIGSPATIAL GIS Cup (2015) IPIN Best Paper Award (2014) His recent publications emphasize geospatial AI, quantum computing for data processing, and environmental monitoring systems. Collaborations with industry partners like DLR highlight his focus on real-world geospatial challenges.
Elhadj Benkhelifa is a full Professor of Computer Science and Digital Innovations at Staffordshire University, leading the Head of Professoriate role to advance university strategies. He founded the Smart Systems, AI and Cybersecurity Research Centre (SSAICS), directing 15 staff and 23 PhD students. Previously, he held roles at Cranfield University and the University of the West of England. His interdisciplinary research focuses on cybersecurity, cloud/edge computing, bio-inspired systems, and AI applications in healthcare and digital transformation. He is a Visiting Professor at Universities of Suffolk, Lyon3, and Paris 8, and chairs the IEEE UK&I Section's Education & STEM office. Education includes a PhD in Artificial-Life (Bio Inspired Evolvable and Self-Healing Systems) from UWE Bristol Robotics Lab, alongside postgraduate certifications in research supervision and higher education. His work bridges academia and industry, addressing challenges in cybersecurity, IoT, and healthcare through applied research. Research interests span cloud computing paradigms, semantic technologies, and AI ethics, with over 150 publications. His recent articles emphasize AI-driven cybersecurity solutions, quantum computing applications, and medical diagnostics using deep learning. He actively contributes to open-access publishing and advises on national cybersecurity strategies. Awarded Fellow of the UK Higher Education Academy, he leads impactful projects including malware detection systems, semantic knowledge graphs for public health, and blockchain-based authentication frameworks. His teaching spans undergraduate to PhD levels, with over 100 supervised projects. He also serves on advisory boards for cybersecurity initiatives and Staffordshire Police's evidence-based practices.
Marco Fumero is a PostDoctoral Researcher at the Institute of Science and Technology Austria (ISTA), where he conducts foundational research at the intersection of geometry and artificial intelligence. Previously, he completed his Ph.D. in Computer Science at Sapienza University of Rome as a core member of the GLADIA research group under Professor Emanuele Rodolà's supervision, establishing a trajectory bridging theoretical geometry with practical deep learning applications. Ph.D. in Computer Science, Sapienza University of Rome Dr. Fumero's research program centers on exploiting geometric structures to revolutionize artificial intelligence systems, with primary focus on geometric deep learning, geometry processing, and representation learning. He pioneers methodologies for analyzing neural network latent spaces through spectral geometry and dynamical systems theory, developing frameworks that enable cross-model communication and zero-shot transfer. His work systematically addresses challenges in representation alignment, latent space dynamics, and disentangled feature extraction, with direct applications in 3D shape analysis, multimodal learning, and quantum-inspired computing. This research demonstrates exceptional theoretical rigor while maintaining strong connections to real-world problems in computer vision and scientific computing. His publication record reveals a dominant trend toward unifying geometric principles with deep learning architectures, particularly through spectral methods and functional map theory. The 2024-2025 publications showcase a coherent evolution from foundational latent space analysis (e.g., attractor dynamics in autoencoders) to practical frameworks for cross-model communication (e.g., cycle-consistent merging and semantic alignment). Key thematic threads include zero-shot capability development, invariance exploitation, and the translation of classical geometry processing techniques into neural network contexts. These contributions have established new paradigms for latent space manipulation across computer vision, graphics, and multimodal AI. Spotlight presentation at ICLR 2024 for "From Bricks to Bridges: Product of Invariances to Enhance Latent Space Communication" Multiple papers accepted at NeurIPS 2024 including "Latent Functional Maps" and "C2M3" During his doctoral training at Sapienza, Dr. Fumero actively mentored junior researchers within the GLADIA group, contributing to the development of next-generation geometric AI specialists through collaborative projects and technical guidance. His research has been supported by institutional funding from Sapienza University and ISTA, with potential backing from European research initiatives targeting foundational AI advances. Current work focuses on scaling geometric deep learning frameworks to complex multimodal scenarios while maintaining theoretical guarantees. Dr. Fumero maintains strong ties to the GLADIA research group at Sapienza University of Rome, which specializes in geometric learning and data analysis. At ISTA, he operates within a highly collaborative interdisciplinary environment that emphasizes theoretical computer science and its applications, contributing to the institute's mission of advancing frontier research through mathematical rigor and computational innovation.
Rommert Dekker is a Professor of Econometrics (Operations Research and Informatics) at the Department of Econometrics, Erasmus School of Economics (ESE), Erasmus University Rotterdam, affiliated with ERIM since 1999. His research focuses on operations research, quantitative logistics, and IT applications in logistics systems. He leads industry-sponsored research on service logistics and co-founded the REVLOG network for reverse logistics. His work spans reverse logistics, inventory control, maintenance optimization, container logistics, and transport systems. He has held roles at Shell Research (1992-1999), publishing over 100 papers during that period. Research interests include service logistics, synchromodal transportation, spare parts management, and sustainable supply chains. He integrates quantitative methods with real-world applications, addressing challenges like container terminal optimization, wildfire risk modeling, and vaccine supply chain management. His recent work explores quantum computing applications in logistics and environmental regulations' impact on maritime networks. Awards: ERIM Impact Award (2025), OR Society Goodeeve Medal (2023) Research Initiatives: Business Intelligence, Data Analytics, Smart Port@Erasmus Grants & Partnerships: Industry-sponsored programs on service logistics, collaborations with maritime and energy sectors His publications emphasize practical impact, with over 170 articles in areas like supply chain resilience, maintenance policies, and green logistics. Current research includes real-time container yard allocation algorithms and AI-driven wildfire risk models.
Franco Nori - Summary Franco Nori is a Researcher in the Physics Department at the University of Michigan. He specializes in theoretical condensed matter physics, quantum information processing, and interdisciplinary research at the intersection of atomic physics, quantum optics, nanoscience, and computing. His work also explores solar energy conversion and artificial photosynthesis. Education: PhD in Physics from the University of Illinois, Urbana-Champaign (1987), M.S. from the same institution (1983), and B.S. from Universidad Simón Bolívar, Venezuela (1982). Research Interests: His research focuses on quantum systems, including superconducting qubits, quantum control, and the dynamics of complex systems. Key areas include hybrid quantum circuits, quantum biology, and the interplay between light and matter. He also investigates energy conversion mechanisms and nano-scale quantum systems. Notable Achievements: Fellowships: American Physical Society, Optical Society of America, UK Institute of Physics, AAAS Awards: Matsuo Foundation Award (2014), Japanese Ministry of Education Prize (2013) Contributions to quantum computing, vortex dynamics in superconductors, and photonics Labs & Affiliations: Collaborations with RIKEN Advanced Science Institute, Japan. Active in experimental/theoretical interfaces, including work on Josephson junctions and quantum emulators.
Rudra Atri is the Katherine Johnson Chair in Artificial Intelligence and a Professor in the Department of Computer Science and Engineering at the University at Buffalo, part of the School of Engineering and Applied Sciences. His research focuses on structured linear algebra, society and computing, coding theory, and database algorithms. He holds a PhD from the University of Washington (2007), an MS from the same institution (2005), and a B.Tech from the Indian Institute of Technology (2000). Dr. Atri has received numerous accolades, including the SUNY Chancellor's Award for Excellence in Teaching (2022), the UB Teaching Innovation Award (2021), and the NSF CAREER Award (2009). His work spans theoretical computer science, machine learning, and practical applications in databases and bioinformatics. He is affiliated with the National Center for Women & Information Technology and the Center of Excellence for Document Analysis and Recognition (CEDAR), contributing to socially responsible computing initiatives. His research explores efficient algorithms for linear algebra, probabilistic databases, and neural network architectures, with publications on topics ranging from sparse recovery to quantum computing simulations. Atri’s teaching and mentorship are widely recognized, emphasizing both technical rigor and ethical considerations in computing.
Dr. Mujahid Tabassum is a Lecturer in the Department of Computing and Mathematics at South East Technological University. He holds a PhD in Science and Technology from Universiti Sains Islam Malaysia and is a Chartered Engineer registered with the UK Engineering Council. His research spans multiple domains of computing: Secure communication protocols and cryptographic systems IoT networks and wireless sensor applications AI applications in healthcare and bioinformatics Cloud computing adoption in healthcare Quantum-enhanced cybersecurity frameworks Recent publications demonstrate consistent focus on developing security solutions across e-commerce, healthcare, and communication systems. Work integrates theoretical frameworks with practical implementations across diverse technological contexts. Dr. Tabassum holds two Australian patents in IoT and security domains. He maintains professional certifications including CCNA, CCNP, Microsoft Cloud, and Cisco CyberOps. As Guest Editor for several journals, he facilitates academic discourse in security and AI applications.
