جست‌وجوی استاد و پژوهشگر

• Robot Vision
• Robot Learning
• Computer Vision
• +۴ مورد دیگر

Joni-Kristian Kämäräinen serves as Professor of Signal Processing within the Computing Sciences department at Tampere University, where he leads research in the Vision Group. Previously, he held faculty positions at LUT University's School of Engineering Science for five years before joining Tampere University in 2012 (tenured 2017, promoted to full professor in 2020). His academic journey includes a postdoctoral fellowship at the University of Surrey's Center of Vision, Speech and Signal Processing under Josef Kittler. His research centers on robot vision and robot learning , with significant contributions to computer vision and machine learning. Key focus areas include visual place recognition, RGB-D tracking, color constancy, and anthropometric measurements. His group maintains strong industry collaborations with Huawei, Nokia Technologies, and Business Finland-funded projects. His publication portfolio shows a clear trajectory toward real-world robotic applications, with recent work emphasizing visual place recognition under varying conditions (2022-2024), depth-aware video processing (2023-2024), and reinforcement learning for industrial manipulators (2023-2025). The 2023 textbook Koneoppimisen perusteet (Machine Learning Fundamentals) demonstrates his commitment to education. Expert Statement for Finnish Parliament (2022) on AI solutions Contributor to Finnish Roadmap: Robots and the Future of Welfare Services (2021) Featured in YLE Uutiset (2018), Aamulehti (2021), and multiple technical press outlets He has supervised 20 PhD students since 2007, including Vivienne Huiling Wang (2025), Samu Koskinen (2025), and Fatemeh Shokollahi Yancheshmeh (2024), with alumni placed at Aalto University, Ericsson AB, and Huawei. His group receives funding from the Academy of Finland, EU Horizon 2020, Business Finland, Huawei, and Nokia Technologies. The Vision Group operates from Tampere University's Hervanta Campus, maintaining close ties with industrial partners through applied research projects.

Anas Barakatمشاهده پروفایل

پژوهشگر ارشد

• Multi-Agent Learning
• Reinforcement Learning
• Stochastic Optimization
• +۵ مورد دیگر

Anas Barakat is a Research Fellow at the Singapore University of Technology and Design , where he develops theory and algorithms for learning in strategic, structured, and dynamic environments. His work integrates reinforcement learning, game theory, online learning, stochastic optimization, and dynamical systems to build robust multi-agent learning systems. PhD : Applied Mathematics and Computer Science, 2021, Institut Polytechnique de Paris (Télécom Paris) MSc : Data Science, 2018, Université Paris Saclay MSc : Applied Mathematics and Computer Science, 2018, Télécom Paris His research focuses on multi-agent learning in strategic environments, behaviorally aligned reinforcement learning (e.g., incorporating psychological biases), and adaptive optimization via dynamical systems analysis. He explores feedback loops, non-stationary objectives, and structured games like Markov potential games and zero-sum linear quadratic games. Recent publications (2025–2023) span multi-agent control , symmetric cone games , policy gradient frameworks , and Adam algorithm convergence . His work appears in venues like IEEE CDC , NeurIPS , ICML , and AISTATS , with preprints on arXiv. At ETH Zurich (2022–2024), he taught Optimization for Data Science and Foundations of Reinforcement Learning , and at Télécom Paris (2018–2021), he assisted courses in machine learning and optimization.

Lingzhong Guoمشاهده پروفایل

مدرس

• Signal Processing
• Biomedical Engineering
• Nonlinear Systems Identification
• +۳ مورد دیگر

Lingzhong Guo is a Lecturer in the Department of Automatic Control and Systems Engineering at the University of Sheffield , affiliated with the Insigneo Institute for in silico Medicine and Neuroscience Institute . His research bridges signal processing, nonlinear systems identification, and biomedical applications. PhD from Bristol Robotics Laboratory (UWE Bristol) Specializes in spatio-temporal system identification, PDE control, and machine learning for medical imaging Recent work focuses on deep learning applications for muscle segmentation in MR images and EIS analysis for oral disorder detection. Collaborations with Zilico Ltd. and participation in Horizon2020 projects highlight his translational research. Scientific Recognition SANO Centre grant (H2020, £2.5M) for computational diagnostics SPINe: Numerical and Experimental Repair strategies (H2020 European program, €1.58M) Innovate UK KTP for EIS-based medical diagnosis (£190K) His publications span nonlinear dynamics , multiscale modeling , and biomedical signal processing , with methodological contributions to Volterra series and coupled PDE-ODE systems.

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +۴ مورد دیگر

Guangyao Chen is an Assistant Professor in the Department of Computer Science within Cornell University's College of Engineering, where he leads research at the intersection of computer vision, machine learning, and artificial intelligence. His work focuses on advancing open-world visual understanding systems capable of handling unknown classes and real-world complexity. His primary research interests include: Computer Vision and Open-Set Recognition Few-Shot Learning and Cross-Domain Adaptation LLM-Visual Integration and Symbolic Reasoning Neuromorphic Computing and Spiking Neural Networks Multi-Modal Learning and Real-Time Systems Analysis of his 2021-2025 publications reveals a strategic evolution toward solving open-world perception challenges. His recent work demonstrates how large language models can unlock complex event understanding from object detectors, while his G-OSR benchmark establishes new standards for graph-based open-set recognition. Notable contributions include real-time multimodal anomaly detection frameworks, retina-inspired saliency models, and Autoagents for automatic agent generation - all addressing critical gaps in deploying AI systems in dynamic, uncontrolled environments. Though specific awards and advising details aren't documented in available sources, his prolific publication output (including 9 papers in 2025) indicates an active research program with significant community impact. His work bridges theoretical advances in representation learning with practical applications in robotics, medical imaging, and industrial systems where handling unknown classes is critical.

Samira Ebrahimi Kahouمشاهده پروفایل

استادیار

• Deep Learning
• Reinforcement Learning
• Multi-modal Learning
• +۷ مورد دیگر

Samira Ebrahimi Kahou holds the following academic appointments: Assistant Professor, Department of Electrical and Software Engineering, Schulich School of Engineering, University of Calgary Adjunct Professor, McGill University Adjunct Professor, École de Technologie Supérieure (ÉTS) Canada CIFAR AI Chair, Mila Educational background: Ph.D. from Polytechnique Montréal/Mila (2016), supervised by Professor Chris Pal Research focuses on deep learning (generalization and interpretability), reinforcement learning, and multi-modal learning with critical applications in clinical decision support systems, climate modelling, and disaster response. Her foundational work includes computer vision for emotion recognition, object tracking, and knowledge distillation during her doctoral studies. Key recognition: Canada CIFAR AI Chair Prior to her current role, she served as Associate Professor at ÉTS, Postdoctoral Fellow at McGill/Mila with Professor Doina Precup, and Researcher at Microsoft Research Montréal. Prospective students (MSc/PhD/Postdoc/Internship) must contact via email with specific subject-line formatting due to high inquiry volume.

Chengcheng Taoمشاهده پروفایل

استادیار

• Sustainable Construction Materials
• Rheology of Non-Newtonian Fluids
• Multi-Functional Infrastructure Materials
• +۷ مورد دیگر

Dr. Chengcheng Tao is an Assistant Professor in the School of Construction Management Technology within Purdue University's Polytechnic Institute. She holds a Ph.D. in Civil Engineering from the University of Florida and previously served as an ORISE Postdoctoral Research Fellow at the DOE National Energy Technology Laboratory. Her research spans sustainable construction materials, rheology of non-Newtonian fluids, and AI-driven infrastructure resilience. Dr. Tao's research focuses on sustainable construction materials and manufacturing, rheology of non-Newtonian fluids (including fresh cement, concrete, and liquid epoxy), multi-functional infrastructure materials, hazard-resilient infrastructure, computational mechanics of materials, and AI-driven multi-objective optimization. Her work integrates computational modeling with experimental validation to address critical challenges in infrastructure sustainability and resilience, particularly in flood-prone regions and pipeline systems. She leads the Sustainable Infrastructure and ManUfacturing Lab (SIMULab) at Purdue. Her publication portfolio demonstrates strong focus on infrastructure resilience modeling, cement rheology, and AI applications in civil engineering. Recent work shows increasing integration of machine learning with traditional computational mechanics, particularly in pipeline rehabilitation, flood resilience, and sustainable cement manufacturing. Key publication venues include Applied Energy, Structures, Construction and Building Materials, and Fluids. National Academies of Sciences, Engineering, and Medicine - Gulf Research Program Early-Career Research Fellow (2023-2025) American Chemical Society - Petroleum Research Fund Doctoral New Investigator Award (2023) Purdue Polytechnic Institute Outstanding Faculty in Discovery Award (2023-2024) SCMT Outstanding Faculty in Discovery Award (2022-2023 & 2023-2024) Illinois-Indiana Sea Grant Faculty Scholars Award (2022) DOE ORISE Postdoctoral Fellowship (2018-2021) Dr. Tao has secured significant research funding as PI or Co-PI from NSF, USDOT, DOE, ACS, NASEM, IISG, and INDOT. Current projects include multi-objective optimization of precast concrete, flood resilience modeling for Great Lakes infrastructure, and AI-assisted sustainable cement manufacturing. She serves as a panel reviewer for NSF, DOE, and NCHRP, and as guest editor for multiple journal special issues. Her professional service includes voting membership in ACI Committees 135, 238, and 241, and participation in ASCE, AIChE, and SPE working groups focused on cementing challenges. Dr. Tao leads the Sustainable Infrastructure and ManUfacturing Lab (SIMULab), which develops data-driven approaches for infrastructure resilience. The lab focuses on integrating computational mechanics with machine learning for infrastructure assessment, developing sustainable construction materials using industrial byproducts, and creating physics-based models for hazard resilience. Current projects involve Great Lakes dredged material utilization, pipeline rehabilitation technologies, and AI-driven cement manufacturing optimization.

Georgios Th. Papadopoulosمشاهده پروفایل

استادیار

• Computer Vision
• Machine Learning
• Deep Learning
• +۷ مورد دیگر

Georgios Th. Papadopoulos is an Assistant Professor at the Department of Informatics and Telematics at Harokopio University of Athens. He holds a Diploma and PhD in Electrical Engineering from Aristotle University of Thessaloniki and completed postdoctoral research at CERTH and FORTH. With over 50 peer-reviewed publications, he actively contributes to European research projects, serving as Technical Coordinator for Anti-FinTer and Ceasefire projects. His research focuses on: Computer vision and deep learning for security applications (X-ray analysis, gesture recognition) Federated learning systems for intrusion detection and cybersecurity Human-robot collaboration frameworks Explainable AI methodologies Multi-agent systems for IoT and edge computing His recent publications demonstrate strong emphasis on federated learning architectures, security applications of computer vision (especially X-ray threat detection), and human-centered AI systems. Research trends show growing focus on cybersecurity countermeasures, multimodal learning systems, and deployment of AI in public safety domains. He has led significant EU-funded initiatives including: Technical Coordinator: Anti-FinTer, Ceasefire, DANTE, ANITA Deputy Coordinator: HR-Recycler, LASIE Contributor: aceMedia, K-Space, MESH, Vidi-video, GLOCAL, CEEDs, REVERIE, RePlay

• Sustainable Manufacturing
• Manufacturing Systems
• Industrial Automation
• +۱۰ مورد دیگر

John Patsavellas is a Senior Lecturer in Manufacturing Management at Cranfield University, specializing in sustainable manufacturing systems. He is affiliated with the Sustainable Manufacturing Systems Centre and has extensive industry experience across multiple sectors including steel, food, paper products, pharma, fashion, building products, and sanitaryware ceramics. His educational background includes: Honours degree in Mechanical Engineering Design from the University of Huddersfield MSc in Manufacturing Systems and Management from the University of Bradford MBA from the University of Kingston Dr. Patsavellas' research focuses on sustainable manufacturing, industrial automation, and the integration of people in modern industrial systems. His work bridges theoretical frameworks with practical implementation, emphasizing lean manufacturing, circular economy practices, and digital transformation. He explores how behavioral economics intersects with manufacturing efficiency and sustainability, particularly in high-value manufacturing contexts. His recent publications (2023-2025) demonstrate a strong focus on sustainable manufacturing practices, particularly in Middle Eastern contexts. There's a clear trend toward digital transformation in manufacturing, with significant work on AI applications, digital twins, and metaverse technologies for sustainable production. His research also emphasizes organizational culture, change management in lean implementation, and carbon accounting in manufacturing supply chains. Scientific awards and recognitions: 2014 IMechE Manufacturing Excellence award for innovation in products and services Dr. Patsavellas has held significant leadership positions including serving as president of the European Resilient Flooring Manufacturers' Institute (ERFMI) in Brussels and as an active member of the All-Party Parliamentary Manufacturing Group (APMG). He is also an industrial visiting fellow at the University of Hertfordshire and a member of the IET's Manufacturing Policy Panel and Production and Design Sector. His industry experience as a boardroom director since 2004 provides practical grounding for his academic work. His research activities involve collaboration with various teams focused on sustainable manufacturing systems, with particular attention to the integration of advanced technologies with human factors in production environments. Current projects examine the application of behavioral economics principles to manufacturing operations and the development of frameworks for sustainable digital transformation.

Enrique Ruiz Zunigaمشاهده پروفایل

دانشیار

• Production Engineering
• Logistics Systems
• Robotics
• +۷ مورد دیگر

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.

• Robotics
• Machine Learning
• Computer Vision
• +۱۱ مورد دیگر

Dr. Hamidreza Kasaei is an Associate Professor in the Department of Artificial Intelligence at the University of Groningen, Netherlands. He leads the Interactive Robot Learning Lab (IRL-Lab) , focusing on robotics, machine learning, and computer vision. Develops algorithms for lifelong interactive robot learning Specializes in 3D perception and multi-arm manipulation Actively involved in IEEE RAS as Associate Editor His research directions include: Perception systems for dynamic environments Dual-arm manipulation techniques Lifelong learning architectures Multimodal integration for object understanding Dynamic motion planning for reactive systems Recent work trends show emphasis on: Vision-language model integration for open-world grasping Dual-arm reinforcement learning frameworks Neural ODE applications for video generation Ensemble methods in continual learning Imitation learning for agricultural robotics Transformer-based view planning systems Scientific awards include: Google Research Scholar Award (2023) Outstanding Associate Editor - IEEE RAS (2023) His lab actively trains students through PhD and Master's projects in domains like 3D object perception, dual-arm coordination, and multimodal learning. The lab has successfully graduated multiple PhD candidates including Zhenxing Zhang and Hamed Ayoobi .

Aneta Neumannمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Machine Learning
• Evolutionary Computation
• +۷ مورد دیگر

Aneta Neumann is a Researcher at the School of Computer and Mathematical Sciences within the University of Adelaide. Her work bridges bio-inspired computation with machine learning and computational creativity , focusing on dynamic and stochastic multi-objective optimization for real-world applications in mining, renewable energy, cybersecurity, and public health. Education : B.Sc. in Computer Science from Christian-Albrechts-University of Kiel, Germany; Ph.D. from University of Adelaide, Australia. Her research explores evolutionary diversity optimization to generate innovative solutions for complex problems like the Traveling Thief Problem , Chance-Constrained Knapsack , and Time-Use Planning . She investigates theoretical foundations through runtime analysis and applies these insights to industrial software integration in mining and energy sectors. Recent work emphasizes AI-based optimization trends , particularly in quantum computing benchmarks (e.g., Maximum Cut) and health outcomes via time-use scheduling. Her publications span top venues like GECCO , AAAI , NeurIPS , and Algorithmica . Scientific Awards : ACM-W Scholarship (2018), Hans-Juergen and Marianna Ohff Research Grant, Best Paper Award at GECCO 2024, multiple Best Paper Nominations at GECCO (2019, 2021, 2022). She co-organizes key conferences ( AI-OPT 2025 , EMO 2025 ) and serves as track co-chair for Genetic Algorithms at GECCO. Her teaching includes the Big Data Fundamentals course in the University of Adelaide's MicroMasters program.

• Modular Construction
• Stochastic Programming
• Construction Logistics
• +۷ مورد دیگر

Pei-Yuan (Leo) Hsu is a Lecturer specializing in modular construction, stochastic programming, and construction logistics. His research focuses on optimizing modular construction supply chains through stochastic programming and robust optimization, addressing uncertainties in off-site and hybrid construction methods. He also applies Design for Manufacturing and Assembly (DfMA) and lean principles to enhance sustainability. Current research includes reducing embodied carbon in modular supply chains, optimal wireless charging facilities for public transport, and reinforcement learning for humanitarian logistics. Completed projects include risk-averse modular supply chain design, root cause analysis for construction schedule deviations, and BIM integration for progress tracking. His work bridges theoretical optimization models with practical applications in construction management, emphasizing sustainability and efficiency. Key contributions include multi-stage stochastic programming frameworks for logistics planning and fault tree analysis for schedule deviation diagnosis.

Jonathan Blackمشاهده پروفایل

استاد

• Space Systems Engineering
• Autonomous Vehicles
• Structural Dynamics
• +۵ مورد دیگر

Jonathan Black is a Professor in the Kevin T. Crofton Department of Aerospace and Ocean Engineering at Virginia Tech. He serves as Director of the Aerospace and Ocean Systems Laboratory within the Ted and Karyn Hume Center for National Security and Technology, and Co-Director of the Center for Space Science and Engineering Research (Space@VT). Additionally, he holds the Northrop Grumman Senior Faculty Fellowship in C4ISR. His work bridges mission platforms and payloads across multiple research centers at Virginia Tech. Black's academic background includes a PhD from the University of Illinois at Urbana-Champaign. Prior to joining Virginia Tech, he was a faculty member at the Air Force Institute of Technology (AFIT), where he founded and directed the Center for Space Research and Assurance, overseeing a $2M annual research portfolio and leading five spaceflight experiments. His research focuses on autonomous systems, sensor data processing in low-SWAP environments, mission-driven payload control, and distributed resource management in space systems. Key areas include structural dynamics of deployable space structures, micro UAV development, and taskable satellite systems for defense, commercial, and scientific applications. Black's leadership roles include directing the Aerospace and Ocean Systems Laboratory and contributing to the Hume Center’s national security initiatives. His work emphasizes cross-disciplinary collaboration and real-world application of advanced technologies in space systems engineering.

Almuatazbellah Bokerمشاهده پروفایل

استادیار

• Autonomous Systems
• Robotics
• Machine Learning
• +۲ مورد دیگر

Almuatazbellah Boker is an Assistant Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech. He earned his PhD in Electrical Engineering from Michigan State University in 2013. His research focuses on autonomous systems, machine learning for robotic decision-making, and distributed control optimization. Key research areas include Koopman operator-based control, sparse controller design, and human-supervised autonomous systems. His research explores: Nonlinear control strategies for robotic and motion systems Data-driven approaches for optimal control Human-robot interaction in autonomous navigation Publication analysis shows strong focus on Koopman operator applications for system modeling and control, with recent extensions to digital twins and fault detection. Work consistently addresses practical implementation challenges in robotic systems and industrial automation. Dr. Boker has supervised PhD candidate Muhammad Al Saaideh to graduation. His research receives support from the US Department of Energy and Siemens USA, including a $300,000 subaward for sensor placement optimization in solar systems (2024-2027).

• Computational Fluid Dynamics
• Chemical Engineering
• Optimisation
• +۴ مورد دیگر

Nausheen Basha is a Researcher in the Department of Chemical Engineering at Imperial College London, part of the Faculty of Engineering. Previously, she held a Research Assistant position at City, University of London. She earned her PhD in Chemical Engineering from City, University of London (2017–2021) and an MSc in Aerospace Engineering from the University of Manchester (2013–2014). Education: PhD in Chemical Engineering, City, University of London (2017–2021) MSc in Aerospace Engineering, University of Manchester (2013–2014) Research Interests: Nausheen’s work focuses on integrating machine learning and computational fluid dynamics (CFD) to enhance engineering design and optimization. Her research spans computational fluid dynamics, chemical engineering optimization, and the application of machine learning techniques such as deep learning and Bayesian optimization. She explores multiphase systems, reactor design, and the development of surrogate models to improve simulation efficiency and accuracy in fluid dynamics and mechanical engineering contexts. Research Trends: Her recent work emphasizes multi-fidelity methods, Bayesian optimization, and machine learning-driven approaches to solve complex fluid dynamics problems. She has contributed to advancements in flow reactor design, laminar mixing systems, and the analysis of dispersed oil-water flows, often leveraging CFD simulations combined with data-driven frameworks. Advising & Grants: No formal advisees or grant information is listed in the provided text. Her contributions are primarily through research publications and academic positions.