Dr. Asma Atamna is a postdoctoral researcher in the Theory of Machine Learning group led by Prof. Tobias Glasmachers at Ruhr-Universität Bochum’s Institute of Neuroinformatics (INI). Her research bridges numerical optimization and machine learning (ML), focusing on adaptive hyperparameter mechanisms for optimizers like Adam and SGD. She emphasizes real-world applications, contributing to the ecoKI project to aid SMEs in digitalization via ML platforms. Notably, she developed ContainerGym —a reinforcement learning benchmark addressing real-world resource allocation challenges. Her work critiques academic ML benchmarks for lacking real-world complexity, advocating for practical evaluation frameworks. Research interests include reinforcement learning, evolutionary algorithms, convergence analysis, and constrained optimization. She collaborates closely with industrial partners to ensure applied relevance. Affiliated with the INI (part of the Faculty of Computer Science), her work intersects disciplines like neuroscience-inspired AI, robotics, and data science. Contact: asma.atamna@ini.rub.de .
Dr. Robin Schiewer is a researcher at the Institute of Neuroinformatics (INI) , part of the Ruhr-Universität Bochum . His work focuses on understanding neural systems and applying biological insights to artificial cognitive systems. Research Interests: Robin specializes in Machine Learning , Reinforcement Learning , and Neural Networks , with a particular emphasis on hierarchical world models, latent representation prediction, and catastrophic interference in multi-task learning. His research bridges computational neuroscience and AI. Teaching: He has taught courses such as Introduction to Python and Machine Learning: Unsupervised Methods across multiple semesters (2019–2023) in both lab and seminar formats. Publications: Recent articles highlight his contributions to physical reasoning benchmarks, Hebbian learning frameworks, and modular reinforcement learning architectures. Labs & Teams: Robin collaborates with the INI’s interdisciplinary team, integrating insights from experimental psychology, neurophysiology, and robotics into his AI research.
Dr. Andrew Bradley is a Reader in the School of Engineering, Computing and Mathematics at Oxford Brookes University, leading the Autonomous Driving and Intelligent Transport group. He sits on the steering committee of the AI and Data Analytics Network (AIDAN) and is a member of the Artificial Intelligence, Data Analysis and Systems (AIDAS) institute. His research focuses on autonomous vehicles, intelligent transport systems, and AI-driven solutions for adverse conditions. Key projects include the CLAIMOR initiative (2025-2025) with £54k funding and the £966k Epistemic AI project (2021-2025). Research interests span autonomous driving datasets (e.g., ROAD), adversarial weather perception, trajectory prediction, and cybersecurity for connected vehicles. He collaborates with industry partners like Oxfordshire County Council. Notable outputs include the ROAD: The ROad event Awareness Dataset (2023) and work on real-time simulation tools for vehicle control. Bradley’s work emphasizes bridging AI theory and real-world applications, with a focus on safety-critical systems. Current projects aim to enhance autonomous systems’ robustness through scenario-based testing and self-supervised learning techniques.
Francesco De Natale is a Full Professor at the Department of Information Engineering and Computer Science, University of Trento. His expertise spans Computer Vision, Digital Forensics, Signal Processing, and Multimedia Systems. He teaches courses including 'Fondamenti di Comunicazioni', 'Fondamenti di Elaborazione dei Segnali', and 'Tecnologie Multimediali', focusing on signal analysis, communication systems, and multimedia processing. Research Interests: Digital forensics (e.g., deepfake detection, image provenance), computer vision applications (3D haptic modeling, crowd behavior analysis), and embedded systems for IoT/AAL solutions. Key Projects: Developed the AUSILIA platform for assisted living and Neuroberry for pervasive EEG signaling. Contributed to datasets like WILD for synthetic image analysis. Recent Focus: Combining machine learning with forensic techniques for detecting media manipulation, and energy-efficient edge computing for drones/UAVs. His work bridges theoretical research with practical applications in healthcare, cybersecurity, and robotics. Over 50+ publications since 2012 highlight contributions to signal processing, multimedia forensics, and human behavior analysis.
Piotr Luszczek is a Research Professor and Adjunct Associate Professor at the University of Tennessee, Knoxville's Tickle College of Engineering, affiliated with the Department of Computer Science and the Innovative Computing Laboratory. He holds a Ph.D. and M.S. from the University of Tennessee, Knoxville, and a B.S. from AGH University of Science and Technology in Kraków, Poland. Affiliations : Innovative Computing Laboratory (ICL), Tickle College of Engineering. Roles : Research and teaching in high-performance computing, numerical linear algebra, and performance optimization. Research Interests focus on benchmarking, numerical linear algebra for HPC, automated performance tuning for modern hardware, and stochastic models for performance analysis. His work emphasizes scalable algorithms, GPU acceleration, and efficient use of hybrid architectures. Grants and Collaborations include projects on batched linear algebra, sparse matrix operations, and energy-efficient AI frameworks. He contributes to software libraries like PLASMA and MAGMA, optimizing for exascale computing. Labs/Teams : Active in the Innovative Computing Laboratory (ICL), developing tools for HPC benchmarking (e.g., HPCG) and parallel linear algebra libraries. Engaged in international collaborations for exascale computing initiatives.
Kevin Godin-Dubois is a Researcher at the Faculty of Science, Vrije Universiteit Amsterdam (VU), with affiliations to the Network Institute. His work focuses on advancing artificial intelligence (AI) through interdisciplinary research, particularly in reinforcement learning, human-AI interaction, and evolutionary robotics. He leads projects involving modular frameworks for AI experiments and benchmark generators for agent prototyping. Research Interests: His primary areas include reinforcement learning frameworks, human-AI collaboration systems, neuroevolutionary techniques, and embodied evolution in robotics. His work bridges theoretical AI advancements with practical applications in socially adept agents and modular system design. Recent Trends: His 2024-2025 publications emphasize modular frameworks (e.g., SHARPIE and AMaze), which enable scalable experimentation and benchmarking of AI agents. Earlier work (2017-2020) explored long-term evolutionary dynamics in artificial ecosystems and plant communities, showcasing adaptability in changing environments. Collaborations: Active in international workshops (e.g., ALIFE 2024) and open-source projects (ci-group/revolve2), emphasizing reproducibility and community-driven AI development.
Dr. Osman Tursun is a Postdoctoral Research Fellow in Computer Vision and Machine Learning at QUT’s School of Electrical Engineering & Robotics, affiliated with the Signal Processing, Artificial Intelligence and Vision Technologies (SAIVT) research group. He holds a PhD in Computer Vision from QUT. His research focuses on large-scale image retrieval, semantic segmentation, natural language processing, and machine vision applications in mining through collaboration with Orica Digital Solutions. Education: PhD in Computer Vision (QUT). Advanced Queensland Industry Research Fellow (2023) with a $240k grant for AI-driven mining software development. Current projects include real-time fragmentation analysis and ore intelligence systems. Research interests emphasize cross-domain applications of AI, particularly in trademark retrieval, scene text editing, and explainable AI via heatmap analysis. His work bridges computer vision with natural language processing, as seen in projects like TUMLU (Turkic Language Benchmark) and PDV (Prompt Directional Vectors). Publications span image retrieval (zero-shot, sketch-based), deep learning techniques (MTRNet++, attention mechanisms), and benchmark datasets (METU). His recent work explores explainability in neural networks using heatmaps and LLMs. Awards include the 2023 Advanced Queensland Fellowship. Active in supervising postgraduate research students in AI, computer vision, and machine learning.
Sebastian Gerard is a Researcher and PhD student at the Division of Robotics, Perception and Learning, KTH Royal Institute of Technology. His work focuses on applying machine learning and computer vision to address environmental challenges, particularly wildfire prediction and disaster response through remote sensing. He has contributed to the development of datasets like WildfireSpreadTS and TS-Satfire, advancing multimodal time-series analysis for wildfire spread prediction and disaster management. Education details: While specific academic credentials are not explicitly listed, his role as a PhD student indicates ongoing advanced studies in Robotics, Perception, or related fields. Research interests include wildfire prediction using satellite imagery, climate change mitigation via machine learning, and improving geospatial data analysis for disaster response. His work bridges computer vision techniques with environmental science, aiming to create actionable insights from remote sensing data. Publications reflect contributions to wildfire modeling, semantic segmentation robustness, and smart grid automation. Collaborations include work with Josephine Sullivan and Paul Borne-Pons, addressing challenges in domain-specific pretraining and dataset validation. Labs/Teams: Active in KTH's Robotics and Perception research groups, contributing to projects involving wildfire datasets and remote sensing applications.
Fulvio Giovanni Ottavio Risso is a Full Professor in the Department of Control and Computer Engineering (DAUIN) at Politecnico di Torino, where he is a member of the NETGROUP Computer Networks Research Group and leads research in cloud, edge, and software-defined networking. He is the Scientific Advisor of the European EIT Digital Partnership and serves as a representative for Politecnico di Torino in EIT Digital. He teaches core courses in Computer Engineering, including Cloud Computing Technologies, Enterprise Network Technologies, and Software Networking, and supervises several PhD students in advanced distributed systems. His research focuses on cloud computing, edge computing, network functions virtualization (NFV), software-defined networking (SDN), and high-speed packet processing. He has pioneered work in eBPF-based network functions through the Polycube framework and in computing continuum orchestration via the Liqo project. His interests extend to Kubernetes networking, real-time data plane optimization, and privacy-preserving infrastructure. He has led numerous EU, national, and industry-funded projects, including FLUIDOS (PNRR), NEWTON, RESTART, TOSHI, ASTRID, and NFV@EDGE. His recent work emphasizes liquid computing, borderless data spaces, and secure, scalable network services for 5G/6G. The recent publications reflect a strong trend in edge-to-cloud orchestration, secure and efficient data plane processing, and real-time performance optimization. Key themes include the use of reinforcement learning for scheduling in the computing continuum, Kubernetes-based edge orchestration, eBPF for in-kernel networking, and energy-aware task distribution. Projects like Liqo and Polycube are central to his vision of a programmable, fluid infrastructure. The integration of machine learning, real-time monitoring, and open-source frameworks underscores a commitment to practical, scalable solutions in modern distributed systems. Scientific Awards and Recognitions: No explicit awards listed in the provided text. Advising and Grants: Fulvio Risso supervises multiple PhD students including Attilio Oliva, Daniele Cacciabue, Davide Miola, Jacopo Marino, Stefano Galantino, Carlos Mateo Risma Carletti, and Federico Parola, whose research spans cloud-edge continuum, vehicular micro-clouds, and Kubernetes networking. He has led over 30 competitive and commercial research projects, including EU-funded initiatives (H2020, EIT), national PRIN projects, PNRR missions, and industry contracts with Rakuten Mobile. These grants focus on network programmability, edge computing, 5G/6G observability, anomaly detection, and secure orchestration, reflecting strong industry-academia collaboration. Labs and Research Groups: He is a key member of the NETGROUP - Computer Networks Group (DAUIN) and leads research activities in LAB 9 - Research Laboratory (DAUIN). He is also associated with the Interdepartmental Center PIC4SeR - PoliTO Interdepartmental Center for Service Robotics. His work is deeply integrated with open-source development, particularly through Liqo and Polycube, which are actively used in both research and industrial deployments.
Joyce Chai is a prominent academic researcher in computational linguistics and AI with extensive contributions to grounded language learning, embodied agents, and human-machine collaboration. Her work spans vision-language models, theory of mind implementation, and task guidance systems. Key research themes: Language grounding in physical/social contexts Embodied AI and situated reasoning Interactive learning frameworks Zero-shot and continual concept acquisition Recent publications demonstrate her leadership in: Developing TRAVER for coding tutoring agents Creating W2W grounded language model Advancing theory of mind evaluation in LLMs Establishing HAR reasoning strategies for coherent physical reasoning She has mentored numerous students including Ziqiao Ma, Shane Storks, and Yuwei Bao. Her work appears in top venues like ACL, EMNLP, and NAACL with focus on practical applications like cake-making guidance systems (WTaG) and autonomous driving dialogue (DOROTHIE). Technical contributions include: MetaReVision retrieval-enhanced meta-learning EpiCA network for compositional concept recognition Neuro-symbolic DANLI agent architecture Pragmatic Rational Speaker framework
Professor Emma Hart is a leading academic at Edinburgh Napier University , affiliated with the School of Computing Engineering and the Built Environment and the Centre for Algorithms, Visualisation and Evolving Systems . Her research focuses on evolutionary computation , swarm robotics , and optimisation algorithms , with applications in ecology, cybersecurity, and AI-driven design. Education: 1 st Class Honours in Chemistry (Oxford), MSc and PhD in AI (Edinburgh) Leadership: Former Editor-in-Chief of Evolutionary Computation (MIT Press, 2017–2023), ACM SIGEVO Board member, and key contributor to Scotland’s AI Strategy (2021). Her research spans evolutionary robotics, lifelong learning systems, and explainable AI. Recent work includes using novelty search to engineer diverse robot swarms, XAI for algorithm configuration, and LLM-evolved heuristics for bin packing. She explores fitness landscapes and attractor analysis to improve optimisation model robustness. Scientific Awards include the ACM SIGEVO Outstanding Contribution Award (2023) Fellow of the Royal Society of Edinburgh (2022) Bronze Humies Award (2018) Best Paper Award@GECCO 2019 She supervises PhD students such as Grant Anderson Kirsty Montague Kelly Hunter Magnus Janson Fiona Stewart and has led over £2M in grants, including EPSRC , Innovate UK , and EU projects on autonomous robot evolution and lifelong learning systems.
Sjoukje Goldman is a Senior Lecturer at the Faculty of Business and Economics (FBE) at Hogeschool van Amsterdam. She is affiliated with the Centre for Applied Research on Economics & Management. Her research focuses on sustainable marketing, cross-border e-commerce, consumer behavior, and pricing strategies, with a particular emphasis on 'true pricing' which integrates environmental and social costs into product pricing. Goldman holds an MSc and PhD and has been actively involved in pioneering true pricing initiatives. She has conducted research on consumer acceptance of true pricing, cross-border e-commerce dynamics, and the impact of sustainability on business models. Her work bridges academic research with practical applications, often collaborating with industry partners. She has received awards including the AUAS Research Award 2023 (3rd place) and an NWO grant for educators. Her research emphasizes effective communication strategies to enhance consumer trust in transparent pricing models and sustainability practices. Goldman has organized conferences and workshops on true pricing and cross-border e-commerce, and her activities include supervising student theses on topics like the political preferences influencing sustainable living. She advocates for policy changes to standardize true pricing and reduce barriers for sustainable businesses.
Xosé Ramón Fernández Vidal is a Professor at the University of Santiago de Compostela, affiliated with the Department of Applied Physics within the Higher Polytechnic School of Engineering. His research focuses on Computer Vision, Image Processing, and Machine Learning, with applications in robotics, medical imaging, and environmental analysis. He holds a Doctorate from the same university (1996), specializing in geometric recognition methodologies. His work bridges computational models of visual attention and practical systems like UAV navigation, wireframe modeling for 3D reconstruction, and synthetic image generation for AI training. He is part of the Artificial Vision group at the Center for Research in Intelligent Technologies (CITIUS). Key research themes include visual saliency modeling, robust feature matching in low-textured environments, and algorithmic approaches to scene recognition. His contributions span over 40 publications since 1997, emphasizing interdisciplinary applications such as flour quality assessment via neural networks and environmental variable analysis in agricultural settings. While no formal awards are listed, his sustained output reflects impactful contributions to computational vision systems. His current projects involve developing biologically inspired vision systems (e.g., BIVSEE) and advancing datasets like Sid4vam for attention modeling. He collaborates with industry and academic partners in robotics, medical imaging, and environmental monitoring. No doctoral students are explicitly listed, though his research groups likely involve postgraduate researchers. His address is in Compostela, Galicia, Spain.
José Barbosa is a Senior Researcher at the Research Centre in Digitalization and Intelligent Robotics (CeDRI) and an Invited Professor at the Department of Electrical Engineering, Polytechnic Institute of Bragança, Portugal. He holds a PhD in Automation and Computer Science from the University of Valenciennes (France) and has extensive experience in European-funded projects like ARUM, GRACE, DA.RE, GO0DMAN, and PERFoRM. His expertise spans Cyber-Physical Systems, Multi-Agent Systems, and manufacturing automation, with over 40 international publications. Research focuses on self-organizing manufacturing architectures, digital twins, and bio-inspired mechanisms. He teaches courses including Microcontroller/Microprocessor Systems, Automation, and Electric Propulsion. Projects emphasize Industry 4.0, zero-defect manufacturing, and intelligent products. His work intersects IoT, data analytics, and human-machine integration in industrial systems.
Professor Piotr Rogala is a faculty member at the Wrocław University of Economics, Department of Quality and Environmental Management. His work focuses on quality management systems, business excellence, ISO standards, and organizational development. He has contributed to research on Quality 4.0, audit credibility, and justice in temporary teams within high-tech industries. Key research interests include TQM, continuous improvement, and the integration of quality management with innovation and sustainability. His recent work explores ISO 9001 adaptations, supplier performance metrics, and crisis resilience in Central European quality practices (V4 countries). Publications emphasize practical applications of quality frameworks, such as process automation optimization and educational simulators for inertial navigation systems. No specific awards are listed, but his contributions span academic and industry collaboration through interviews with EFQM leaders and audit system analyses.
