Dr. Anna Syberfeldt is a Professor in Production Engineering at the Department of Engineering Science, University of Skövde. She leads the Virtual Production Development research group and serves as research director at the ASSAR Industrial Innovation Arena. With a background in computer science from De Montfort University and habilitation in automation engineering, her work bridges AI, robotics, and immersive technologies with industrial production systems. Professor in Production Technology Research Director at ASSAR Industrial Innovation Arena University of Skövde Affiliation Her research focuses on developing innovative industrial solutions through: Artificial Intelligence and Machine Learning Collaborative Robotics and Human-Robot Interaction Digital Twins and Simulation-Based Optimization Augmented/Virtual/Mixed Reality Applications Smart Manufacturing and Industry 4.0 Worker Well-Being and Productivity Optimization Recent publications demonstrate her leadership in simulation-based optimization, collaborative robotics, and mixed reality applications in manufacturing. Her work emphasizes creating ultra-flexible production systems through cyber-physical approaches while prioritizing environmental sustainability and human-centric design. Key trends in her research include: Unified frameworks for virtual commissioning Knowledge graph applications in production systems Multi-objective optimization balancing productivity and ergonomics Smart glasses evaluation for industrial operators Evolutionary algorithms for complex manufacturing problems Her projects demonstrate practical implementations of: MAXLabs distributed cyber-physical testbed ERAIVA posture identification software Virtual environments for human-robot collaboration testing Digital support functions for factory layout planning
Enrique Ruiz Zuniga serves as Associate Professor in Production Engineering at the University of Skövde's School of Engineering Science in Sweden, while simultaneously holding a JSPS research fellowship at Kyoto University's Systems Design Laboratory and collaborating with Japan Manned Space Systems Corporation (JAMSS). His career bridges academic research and industrial applications across Europe and Asia, focusing on optimizing complex manufacturing and logistics systems through advanced computational methods. University of Skövde, School of Engineering Science JSPS Research Fellow, Kyoto University Japan Manned Space Systems Corporation (JAMSS) collaborator Dr. Ruiz Zuniga's educational background includes a B.Eng. in industrial engineering from the University of Malaga, Spain, a BSc in automation engineering from the University of Skövde, Sweden, followed by an MSc in industrial informatics and a 2020 PhD in informatics from the University of Skövde, completed in partnership with Xylem Water Solutions Manufacturing. His doctoral research focused on facility layout design using simulation-based optimization methodologies. His primary research interests encompass the design, verification, and improvement of logistics, robotics, and complex production systems, with methodological expertise in Lean Production, Discrete-Event Simulation, System Dynamics, Simulation-Based Optimization, and the Functional Resonance Analysis Method. Dr. Ruiz Zuniga's work demonstrates a consistent focus on international collaboration and practical implementation of theoretical models in real-world industrial settings across healthcare and manufacturing sectors. Analysis of his publication record reveals an evolution from foundational work in facility layout design toward more recent explorations of AI integration, human-centered design, and resilient production systems. His research shows increasing sophistication in combining simulation approaches with functional analysis methods, with a growing emphasis on human factors and system resilience in complex production environments. REFUSE (2023-2026): Resource efficient use of reconfigurable machining systems Dynamic SALSA (2023-2024): AI scheduling for assembly and logistics systems Envisioned world problems (2021-2023): Functional approaches for system design Emergency Department Modeling (2012-2016): Healthcare production systems Dr. Ruiz Zuniga has coordinated international engineering programs in Industrial Engineering, Product Design Engineering, and Mechanical Engineering (all 60 credits), while teaching courses including Introduction to Lean Philosophy, Methods Engineering, Mechatronics/Electronics, and Production and Logistic Simulation. His work demonstrates a strong commitment to bridging theoretical research with practical industrial applications in production engineering through international collaboration and methodological innovation.
Dr. Sihao Sun is a Researcher in Robotics at the Cognitive Robotics Department , Delft University of Technology. He focuses on planning, estimation, and control of aerial robotic systems, with notable contributions to fault-tolerant control algorithms and perception systems for quadrotors under extreme conditions. PhD in Aerospace Engineering (2020), Delft University of Technology Postdoctoral Researcher at University of Twente (2022-2023) and University of Zurich (2020-2021) His research spans Aerial Robotics , Robotics Perception , and Incremental Nonlinear Control , with applications in Multi-robot Systems , Agile Flight Control , and Aerodynamic Modeling . His recent work explores uncertainty modeling for meta-adaptive control and collaborative aerial manipulation. His 15 most recent articles emphasize robustness in fault-tolerant quadrotor systems, sensor-driven control, and high-speed flight dynamics. He has received the Veni grant (Dutch Research Council, 2024) and a Best Paper Award (IEEE Robotics and Automation Letters, 2020). Current projects include Accurate Aerial Manipulation under Uncertainties and High Efficiency Air Cargo Design . Key students include Jack Zeng (MSc, Cum-Laude distinction) and Fang Nan (ETH Medal winner).
Yongluan Zhou is a Professor at the Department of Computer Science , University of Copenhagen , where he co-heads the Data Management Systems Lab (DMS Lab) and serves as Head of Studies for the MSc in Computer Science . His academic journey includes a PhD from the National University of Singapore (NUS) (2007), a postdoc at ETH Zürich (2007–2008), and prior roles as Associate Professor at University of Southern Denmark (SDU) (2008–2017). PhD in Computer Science, National University of Singapore (2002–2007) Postdoc, ETH Zürich (2007–2008) Zhou's research focuses on database systems and distributed systems , with recent emphasis on event-driven systems , scalable stream processing , and big graph analysis . His work bridges theoretical foundations and practical implementations, addressing challenges in data consistency, fault tolerance, and resource optimization in cloud and microservice environments. The trends in his 15 most recent publications (2025–2024) highlight advancements in asynchronous choreographies , blockchain consensus protocols , GPU-accelerated graph processing , and microservices data management . These works integrate formal methods with empirical validation, emphasizing scalability, security, and efficiency in distributed environments. He actively contributes to academic governance as a member of the DEBS Steering Committee (2024–), SSDBM Steering Committee (2022–), and the EDBT Association Executive Board (2020–).
Yen-Chia Hsu is an Assistant Professor at the Informatics Institute, University of Amsterdam, where they teach courses in Information Visualization and Data Science. Previously, they served as a Postdoctoral Researcher at the Department of Sustainable Design Engineering, Faculty of Industrial Design Engineering, TU Delft, and as a Project Scientist in the CREATE Lab at Carnegie Mellon University (CMU). Their academic journey reflects a unique interdisciplinary background bridging computer science and architectural design. Dr. Hsu earned their Ph.D. degree in Robotics in 2018 from the Robotics Institute at CMU, where they conducted research on using technology to empower local citizens and communities. Prior to that, they received their Master's degree in tangible interaction design in 2012 from the School of Architecture at CMU, where they studied and built prototypes of interactive robots and wearable devices. Before CMU, they earned a dual Bachelor's degree in both architecture and computer science in 2010 at National Cheng Kung University, Taiwan. Dr. Hsu is a computer scientist with an architectural design background whose research focuses on Community-Empowered Artificial Intelligence (AI) , where they co-design, implement, deploy, and evaluate interactive AI systems that empower communities, especially in addressing environmental and social issues. Their work spans both social and technical aspects of community engagement with technology. On the social side, they have proposed an alternative framework called Community Citizen Science (CCS) , which extends traditional citizen science methods to a hyper-local scale, emphasizing continued community engagement after technology interventions. On the technical side, they investigate human feedback in AI pipelines and algorithms that enable machine learning models to incorporate different types of human input. Dr. Hsu's scholarly output demonstrates a consistent focus on applying computer vision, machine learning, and data science to environmental monitoring and community empowerment. Their recent work shows an evolution from developing specific tools for pollution monitoring toward more comprehensive frameworks for community engagement with AI systems. A notable trend is the increasing emphasis on empathy-centered design and policy implications of community-driven data collection systems. Their research bridges the gap between technical innovation and social impact, particularly in the domains of air quality monitoring and environmental justice. Outstanding Student Academic Achievement (2005, 2006, 2007) from Department of Architecture, National Cheng Kung University, Taiwan Third Prize, National Country House Design Competition (2008) from Ministry of the Interior, Taiwan Best New Artist, The National Golden Award for Architecture (2009), Taiwan Webby People's Voice Award, Best Use of Video or Moving Image (2014) Best Paper Honorable Mention Award (Top 5%) at ACM CHI Conference (2017) Best Paper Honorable Mention Award (Top 2.5%) at ACM IUI Conference (2019) Prize for Community Collaboration, The Constellation Prize (2020) Dr. Hsu has been actively involved in numerous research projects that bridge academia and community action. Their work on the Smell Pittsburgh platform, which allows citizens to report pollution odors to regulators, has been particularly influential in environmental advocacy. They have collaborated with organizations including ACCAN, PennEnvironment, GASP, Sierra Club, ROCIS, Blue Lens, LLC, PennFuture, Clean Water Action, and Clean Air Council. Their research has received support from the Heinz Endowments and has been featured in TIME, Pittsburgh Post-Gazette, PC Magazine, and other media outlets. Dr. Hsu also maintains an active open-source presence, with several tools and datasets released to support community-driven environmental monitoring. Dr. Hsu leads projects that focus on developing tools for community engagement at scale, including COCTEAU, an empathy-based tool for decision-making, and Project RISE, which recognizes industrial smoke emissions. Their work connects with the Multimedia Analytics Lab Amsterdam, where they contribute to data science education and research. Their approach emphasizes co-creation with communities rather than top-down technology deployment, positioning them at the forefront of human-centered AI research with real-world social impact.
Dr. Mohamed Al-Hussein is a Professor and NSERC Industrial Research Chair in the Industrialization of Building Construction at the University of Alberta’s Department of Civil and Environmental Engineering. His work focuses on advancing modular and offsite construction technologies through automation, lean principles, and Building Information Modelling (BIM). PhD, Construction Engineering & Management, Concordia University (1999) MASc, Construction Engineering and Management, Concordia University (1995) MSc, Civil Engineering, University of Architecture & Civil Engineering, Bulgaria (1988) BSc, Civil Engineering, University of Architecture & Civil Engineering, Bulgaria (1983) Dr. Al-Hussein’s research spans five key domains: Modular Construction: Pioneering high-efficiency offsite building systems, including rapid assembly of student dorms and mid-rise residential buildings. BIM & Digitalization: Developing 3D/4D modeling frameworks, automated design systems, and digital twin applications for construction optimization. Environmental Sustainability: Quantifying CO2 emissions, exploring nano energy storage, and advancing solar PV integration in residential construction. Urban Planning: Specializing in age-restricted community design, municipal infrastructure maintenance, and housing affordability analysis related to paving standards. Construction Safety: Applying ergonomic risk assessment tools and virtual reality to enhance worker safety and reduce construction-related hazards. His 400+ peer-reviewed publications reflect cutting-edge applications of AI, deep learning, and simulation across construction processes. Recent work explores metaverse integration, blockchain collaboration tools, and advanced crane operation optimization using reinforcement learning. As Editor-in-Chief of the International Journal of Industrialized Construction , Dr. Al-Hussein remains a global authority in this field. He has developed industry-transforming technologies like the Quikmod-2 modular lift frame and PCL lift frame project , with real-world implementations ranging from Shell Scotford complex equipment replacement to CBC News and Forbes featured projects.
Dr. A. Yousefzadeh is an Assistant Professor in Edge AI at the University of Twente (joined February 2024), affiliated with the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) within the Department of Computer Architecture Design and Test for Embedded Systems. He holds a Ph.D. in Neuromorphic Engineering from IMSE (Instituto de Microelectrónica de Sevilla), where his thesis focused on bio-inspired vision processing. His research specializes in neuromorphic computing systems, with emphasis on: Designing ultra-low-power AI processors and event-based vision systems Developing hardware accelerators for spiking neural networks (SNNs) Edge AI deployment for sensor-based applications Hardware-software co-design for energy-efficient computing His publication trends (2015-2025) reveal core foci on neuromorphic processor architectures (e.g., SENECA, NeuronFlow), event-based vision processing, hardware-aware neural network optimization, and 3D integration techniques. Recent work explores activation sparsification in transformers and hybrid analog-digital neuromorphic systems. Prior to academia, he contributed to industry neuromorphic projects: Architected the NeuronFlow processor at GrAI Matter Labs (acquired by Snap) Led SENECA processor development at imec's Hardware Efficient AI group He currently leads research on next-generation edge AI processors at UT's Embedded Systems lab.
Dr. John A Greenwood is a MRC Career Development Fellow at the Department of Experimental Psychology, University College London . His research focuses on the mechanisms of visual perception and clinical disorders of vision , particularly amblyopia. He leads the Eccentric Vision Lab ( eccentricvision.com ), which investigates crowding effects, spatial vision topologies, and cortical processing idiosyncrasies. Key Research Themes: Visual crowding, interocular suppression, orientation selectivity, and neural correlates of perception Methodologies: fMRI adaptation, psychophysical experiments, and computational modeling His work reveals that crowding is a regularization process altering object appearance, and that binocular treatments for amblyopia improve compliance without reducing suppression. He has published extensively in Scientific Reports , Journal of Vision , and Investigative Ophthalmology & Visual Science . Scientific Awards: MRC Career Development Fellow
Berkay Aydin is a faculty member at Georgia State University's College of Arts & Sciences, Department of Computer Science. He received his Ph.D. and M.S. in Computer Science from Georgia State University (2017, 2016) and a B.S. in Computer Engineering from Bilkent University (2012). As a senior member of the Data Mining Lab (DMLab), his research focuses on spatiotemporal data analysis, deep learning, and data integration pipelines for solar big data. B.S., Computer Engineering, Bilkent University, 2012 M.S., Computer Science, Georgia State University, 2016 Ph.D., Computer Science, Georgia State University, 2017 His research explores heterogeneous large-scale solar data processing through techniques like: Spatiotemporal frequent pattern mining Time series mining and indexing Deep learning for solar event analysis Frequent pattern mining in non-relational databases Data integration pipelines for solar datasets Computer vision for evolving region trajectories Publications demonstrate expertise in transforming solar big data through novel algorithms in spatiotemporal analysis and co-occurrence pattern detection.
Francesc J. Ferri is a Professor in the Department of Computer Science at the University of Valencia since 2008. He holds a Licenciado in Physics (1987) and a Ph.D. in Pattern Recognition (1993) from the same university. His research focuses on Computer Vision, Pattern Recognition, and Machine Learning, with contributions to subspace learning, metric learning, and kernel methods. He has authored/co-authored ~140 papers (h-index=11 as of 2011), with notable work on feature selection and large-scale classification. Education: Licenciado in Physics (1987), Ph.D. in Pattern Recognition (1993), both from University of Valencia. Affiliations: Member of AERFAI, IAPR, ACM, IEEE. Teaching includes courses on Automata Theory, Algorithms, Computer Vision, and Pattern Recognition at undergraduate and graduate levels. He has advised multiple Ph.D. and MSc students, including Adrian Perez-Suay (metric learning) and Katerine Diaz-Chito (subspace-based image recognition). Research projects include sabbaticals at Surrey University (1993) and Michigan State (2005), and collaborations on audio event detection, bioinformatics, and e-learning systems. His recent work addresses explainable AI, edge computing for safety systems, and multidataset HRTF analysis.
Kim W. Wong is an Assistant Professor in the Psychology Department at Williams College, where she leads the WAVE lab. Her research focuses on visual cognition, intuitive physics, and the spontaneous extraction of higher-level properties from visual scenes. She completed her PhD in Psychology at Yale University under Brian Scholl’s Perception and Cognition lab, with additional collaborations at the Cognitive & Neural Computation lab (Yildirim) and Johns Hopkins’ Cognitive Neuroscience Lab (McCloskey). Her work explores how perception extracts concepts like navigational affordances, unfinishedness, and intuitive physics (e.g., block tower stability, cloth dynamics). Key projects include studying visual routines in mazes, spontaneous path tracing, and the perception of causal history through physics-based priors. She employs methods like change detection, visual working memory tasks, and computational modeling (e.g., the Woven model for cloth perception). Her research has been presented at venues like the Vision Sciences Society and the Society for Philosophy and Psychology. Notable contributions include demonstrating how unfinished visual events prioritize memory retention and how unstable towers break into visual awareness faster than stable ones. Education: PhD in Psychology (Yale University), with prior involvement in labs studying perception, cognitive neuroscience, and statistical learning. Her current projects extend to soft material perception, temporal dilation effects, and the neural underpinnings of intuitive physics.
Dr. Yazan Alqudah is a Professor in the Department of Electrical and Computer Engineering at the University of West Florida, part of the Hal Marcus College of Science and Engineering. He holds a Ph.D. in Electrical Engineering from Pennsylvania State University (2003) and has held roles at Intel Corporation and multiple universities, including Princess Sumaya University for Technology and Western Carolina University. His research focuses on optical wireless communication, mobile networks, and embedded systems, with contributions to machine learning applications in transportation and healthcare. Education: Ph.D., Electrical Engineering, Pennsylvania State University, 2003 M.S., Electrical Engineering, Pennsylvania State University, 1997 B.S., Electrical Engineering and Computer Science, University of Jordan, 1993 Research Interests: His work spans broadband optical wireless communication, next-generation mobile networks, embedded systems design, and machine learning applications in healthcare and transportation. Notable areas include autonomous vehicle sensing, wearable health monitoring, and fault-tolerant automotive systems. Publications Trends: Recent articles emphasize machine learning for driving behavior analysis, sensor fusion in autonomous systems, and optimization of wireless communication protocols. Earlier work includes contributions to WiMAX technology, optical wireless MIMO systems, and energy storage solutions for solar PV arrays. Awards: Intel Recognition Awards (2005, 2007) Best Researcher Award at PSUT (2014) Labs & Projects: Developed hands-on labs for broadband wireless technology and embedded systems education. Current projects include a mobile application for breathing abnormality detection and tools for facial thermography-based lie detection.
Yong Wang is an Associate Professor and Associate School Director at the School of Systems Science and Industrial Engineering, Binghamton University. He holds dual PhDs from the University of Illinois and Huazhong University of Science and Technology. His research focuses on autonomous systems, energy systems, manufacturing systems, healthcare systems, operations research, and data science . Notable areas include smart energy infrastructure optimization, autonomous vehicle navigation, and healthcare analytics through machine learning and deep learning techniques. Recent work demonstrates a strong emphasis on real-time control systems, sustainable energy integration, and data-driven decision making . His publications span topics such as battery lifecycle modeling, pandemic prediction systems, and optimization of manufacturing logistics. Labs: Director of the Smart Energy Operations Research Laboratory (SEORL) Education: PhD in Engineering (University of Illinois), PhD in Systems Engineering (Huazhong University of Science and Technology) Research outputs highlight interdisciplinary approaches combining robotics, energy systems, and healthcare, with applications in smart grids, autonomous driving, and medical imaging.
Igor Gilitschenski is an Assistant Professor in Computer Science at the University of Toronto, leading the Toronto Intelligent Systems Lab (TISL). He focuses on developing probabilistic and learning-based techniques for robotic perception and decision-making, aiming to enable robust interactive autonomy. Prior to this, he held roles at MIT CSAIL, ETH Zurich's Autonomous Systems Lab, and the Karlsruhe Institute of Technology (KIT). His research interests include autonomous systems, computer vision, and deep learning applied to robotics. He collaborates with institutions like the Vector Institute and is a Vector Research Scholar. His work spans robot learning, simulation-driven policy training, and safety-critical systems. Notable projects include pseudo-simulation for autonomous driving, vision-language-action models, and neural radiance fields for 3D scene representation. He actively recruits PhD students to work at the intersection of computer vision, deep learning, and robotics, emphasizing controllable simulation engines and safe learning frameworks. Recent research trends in his articles highlight advancements in generative models, reinforcement learning, and perception systems for dynamic environments. He emphasizes collaboration across disciplines, including event-based vision and language-guided reasoning for robotic tasks.
Luca Cassano is an Associate Professor at the Department of Electronics, Informatics and Bioengineering (DEIB) at Politecnico di Milano, Italy. He leads the Hardware Forge research group, focusing on hardware security, RISC-V processors, and reliability of deep learning systems. He earned his Ph.D. in Information Engineering from the University of Pisa (2013), with prior postdoctoral research at CNR and Politecnico di Milano. His research spans hardware Trojans, microarchitectural attacks (e.g., Spectre/Meltdown), fault-tolerant design, and radiation-hardened systems. Notable achievements include winning the TTTC's E. J. McCluskey Doctoral Thesis Award (2014) and multiple best paper awards at IEEE conferences. He teaches courses on hardware accelerators and computer fundamentals, and collaborates with industry partners like TDS-Space and ESA on satellite systems and secure RISC-V cores. Research interests include: Hardware Security (Trojans, Side-Channel Attacks) Secure RISC-V Processor Design Deep Learning Reliability Fault-Tolerant Systems Selected articles highlight work on benchmarking deep learning resilience, transient execution attacks on RISC-V, and radiation effects mitigation. His work bridges theory and practice, addressing challenges in embedded systems, space computing, and cybersecurity. Grants and collaborations emphasize applied research in dependable and secure embedded systems.
