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Ludger BeckerView profile

Researcher

• Database Systems
• Geographic Information Systems
• Spatio-Temporal Data Management
• +3 more

Ludger Becker is an Academic Director at the University of Münster, where he has been working since December 2003. He currently serves as Head of the Information Security Unit since June 2023 and as CISO of the University of Münster since April 2023. Previously, he was Head of IVV5 from January 1998 to December 2023 and held positions as Senior Academic Councillor and Academic Councillor at the Institute of Computer Science at the University of Münster. Dr. Becker received his Doctor of Engineering (Computer Science) from the University of Siegen in July 1992 and completed his Diploma in Computer Science from the University of Dortmund in April 1989. His research spans several key areas in computer science, with particular expertise in database systems , geographic information systems , and software engineering . Dr. Becker has made significant contributions to spatio-temporal data management, developing frameworks for representing moving objects and efficient index structures for spatial queries. More recently, his work has expanded into health informatics, particularly in clinical data generation and literature database analysis. His teaching focuses on software engineering principles, databases, and object-oriented programming, as evidenced by his leadership of the Software Internship course. Dr. Becker's publication record shows a clear evolution from foundational work in spatial databases and GIS in the 1990s and early 2000s, through educational technology applications in the early 2000s, to more recent contributions in health informatics. His research consistently demonstrates expertise in data management challenges across multiple domains. Dr. Becker regularly teaches Software Internship, Databases, and Advanced Database Concepts courses at the University of Münster. His teaching materials show particular attention to software design processes, object-oriented principles, and modern development tools and frameworks, including detailed documentation on implementing @pre, @post, and @inv tags in Javadoc for specifying software contracts. As Head of the Information Security Unit and CISO, Dr. Becker oversees critical information security infrastructure and policies for the University of Münster, applying his technical expertise to protect academic data and systems across the institution's extensive network.

• Intelligent Robotics
• Multi-Robot Systems
• Human-Robot Interaction
• +3 more

Sérgio Paulo Carvalho Monteiro is an Assistant Professor in the Department of Industrial Electronics at the School of Engineering, University of Minho, and a Senior Researcher at Centro Algoritmi. He serves as Director of the Doctorate in Electronics and Computer Engineering since 2021 and previously led the Integrated Masters program (2011-2013), with core expertise in robotics and signal processing education. His academic credentials include a Doctor of Engineering (DEng), Master of Science (MSc), and PhD. Monteiro's research centers on intelligent robotics with dual focus on single-robot autonomy and multi-robot coordination . His work pioneers dynamic neural field approaches for human-vehicle interaction, particularly in learning driver routines through machine learning. Key application domains span industrial logistics , autonomous transportation , and collaborative workstations , emphasizing safety in human-robot shared environments. Analysis of his 42 publications reveals a clear trajectory from foundational formation control (2002-2010) toward contemporary AI-driven solutions for intelligent cockpits (2020-2024), with increasing emphasis on human-centered design and real-world industrial implementation. He has secured funding through major European initiatives including Horizon 2020 (iFACTORY), FP6 (CoopDyn), and national FCT projects, focusing on autonomous logistics and multi-robot collaboration. His academic leadership extends to directing doctoral programs and shaping curriculum for over 500 engineering students. As core member of the Mobile and Anthropomorphic Robotics Lab within Centro Algoritmi's CAR R&D Group, he develops bimanual manipulators (RAMBO) and safety-critical systems for human-robot collaboration, with recent expansion to CCG/ZGDV Institute affiliation (2023).

Céline HelbertView profile

Senior Lecturer

• Statistics
• Numerical Experimental Designs
• Functional Data Analysis
• +6 more

Céline Helbert is a Senior Lecturer at the École Centrale de Lyon, affiliated with the Department of Mathematics and Computer Science and the Camille Jordan Institute (UMR CNRS 5208). Her research focuses on statistics, numerical experimental design, and functional data analysis. Education: DEA in Financial and Actuarial Sciences (ISFA, Lyon), ICM at École des Mines de Saint-Étienne. Research: Expertise in kriging (Bayesian, high-dimensional), Gaussian process optimization, metamodelling for complex systems, and sensitivity analysis for functional inputs. Projects: Led the CIROQUO consortium (2021-2024) and contributed to initiatives like Chaire OQUAIDO (2016-2020) and ANR Pepito (2014-2019). Students: Supervised PhD candidates including Martin Buisson (automotive design), Reda El Amri (functional uncertainty analysis), and Simon Nanty (stochastic sensitivity analysis). Software: Maintainer of R packages DiceDesign and DiceEval for computer experiment design and validation. Conferences: Active in organizing events like JDS2022 and GDR Mascot Num workshops.

• Compilers
• Computer Architecture
• High-Performance Storage
• +5 more

Mahmut Taylan Kandemir is a Professor in the Department of Computer Science at Pennsylvania State University. His research spans optimizing compilers, runtime systems, embedded systems, I/O, high-performance storage, and power-aware computing. He leads the Microsystems Design Lab and collaborates with institutions like Argonne National Lab, Microsoft, and Intel. Education: B.S. Computer Engineering, Istanbul Technical University (1988) M.S. Computer Engineering, Istanbul Technical University (1992) Ph.D. Computer Science, Syracuse University (1999) Research Focus: Dr. Kandemir's work integrates hardware-software co-design to address challenges in multi-core architectures, non-volatile memory systems, and energy efficiency. His recent projects explore 3D NAND flash optimization, near-data computing, and reliability in heterogeneous systems. Key themes include reducing data movement overhead, enhancing storage longevity, and developing compiler-directed solutions for emerging hardware. Publications: His articles frequently appear in premier venues (PLDI, ASPLOS, HPCA) and emphasize memory hierarchy optimization, security, and parallelism. Trends include hardware-assisted resilience, approximate concurrency, and energy-efficient VR streaming. Awards: Premier Research Award, Penn State Engineering Society (2017) IEEE Fellow NSF CAREER Award (2000) Best Paper Awards (IPDPS 2008, ICPADS 2006) Top ACM Digital Library Download (2006) Advising & Grants: He has graduated 32 Ph.D. and 20 master's students, with 15 Ph.D. and 5 master's students currently advised. His research is funded by NSF, DARPA, DOE, and industry partners. Active projects include: NSF/OAC: Re-Engineering Galaxy for Performance (2019-2023) NSF/SHF: 3D NAND Flash Optimization (2019-2022) NSF/SPX: Heterogeneous Intermittent Accelerators (2018-2022) Labs & Teams: Leads the Microsystems Design Lab, focusing on architecture-compiler co-design. Collaborates with the Storage Systems Research Center and heterogeneous computing groups at Penn State.

• Computer Vision
• Pattern Recognition
• Human Behavior Understanding
• +5 more

Anis KACEM is a Researcher at the Interdisciplinary Centre for Security, Reliability and Trust (SnT) within the University of Luxembourg, part of the Signal Processing and Satellite Communications (SIGCOM) research group. His work focuses on Computer Vision and Pattern Recognition, particularly in Human Behavior Understanding from visual data. He received his PhD in Computer Science from the University of Lille (France) in 2018. Research interests include advanced topics such as Earth Observation via multi-modal autoencoders, domain adaptation for image classification, vulnerability-aware deepfake detection, and CAD system reverse engineering. His contributions span neural network pruning, 3D shape analysis, and generative models for medical imaging. Publications highlight innovations in spatio-temporal learning for deepfake detection, hybrid attention mechanisms for pedestrian detection, and tool-augmented CAD task solvers. His work bridges theoretical advances with practical applications in autonomous systems and space technology. Notably, he has contributed to challenges like the SHARP 2023 Challenge on CAD history recovery and developed frameworks like Picasso for CAD sketch inference using self-supervised learning.

Frederic CordierView profile

Associate Professor

• Computer Graphics
• 3D Modeling
• Texture Mapping
• +5 more

Frederic Cordier is an Associate Professor (HDR) at the University of Haute-Alsace, affiliated with the LMIA department within the Faculty of Science and Technology (FST). His research focuses on computer graphics, 3D modeling, and geometric algorithms. He holds a PhD in Computer Science from the University of Geneva (2004) and advanced degrees from the University of Lyon. His work spans sketch-based interfaces, cloth simulation, medical modeling, and texture mapping. Key projects include inferring mirror symmetry from sketches, compressing 3D mesh sequences, and reconstructing organ models from medical data. His contributions to real-time cloth simulation and dressed virtual humans have been influential in interactive systems and virtual garment design. Publications emphasize geometric algorithms for shape reconstruction, symmetry detection, and medical applications. He has held visiting roles at KAIST (South Korea) and conducted postdoctoral research in computational geometry. Teaching includes graduate-level computer science courses in Geneva and Haute-Alsace.

Jean-Marc VannobelView profile

Lecturer

• Brain-Computer Interfaces
• Signal Processing
• Human-Computer Interaction
• +4 more

Jean-Marc Vannobel serves as a Lecturer at the University of Lille, where he is a permanent member of the Brain-Computer Interfaces (BCI) research team within the CRIStAL laboratory. His office is situated in room S4.06 at ESPRIT, Scientific City, Lille, with contact telephone number 03 20 43 40 14. His research spans Brain-Computer Interfaces, Signal Processing, Human-Computer Interaction, Virtual Reality, Neurotechnology, Multi-sensor Fusion, and Data Classification. The BCI team focuses on developing interfaces that translate neural activity into external device commands, evolving from clinical applications for motor disability palliation to broader public use cases. This work involves multidisciplinary collaboration with Lille University Hospital's clinical neurophysiology and rehabilitation services, national networks like CORTICO, and international bodies including the BCI Society and NeurotechEU. Dr. Vannobel currently co-supervises PhD students Sharham Bahrmai and Mehena Loudahi on projects involving neuromarker detection for VR user experience enhancement. His research integrates spatio-temporal signal processing, constrained programming, and multimodal human-machine interaction, supported by institutional partnerships though no specific grants are detailed in the source text.

Tomaso PoggioView profile

Professor

• Computational Neuroscience
• Artificial Intelligence
• Machine Learning
• +5 more

Tomaso Poggio is the Eugene McDermott Professor in the Department of Brain & Cognitive Sciences at MIT. He is a member of both the Computer Science and Artificial Intelligence Laboratory (CSAIL) and the McGovern Brain Institute. Poggio previously served as director of the NSF Center for Brains, Minds and Machines (CBMM) at MIT, a multi-institutional Science and Technology Center dedicated to studying intelligence. Poggio's research spans computational neuroscience, artificial intelligence, and machine learning. His work focuses on the mathematics of deep learning and the computational neuroscience of the visual cortex. He introduced regularization as a mathematical framework for addressing ill-posed vision problems and made foundational contributions to understanding learning from data. His research has always been interdisciplinary, bridging brains and computers, with applications ranging from biophysical studies of the visual system to computational analyses of vision and learning in humans and machines. His recent publications reveal a strong focus on theoretical deep learning, particularly compositional sparsity, regularization methods, and the mathematical foundations of neural networks. Poggio's work demonstrates how deep networks overcome the curse of dimensionality through compositionally sparse structures present in most practically relevant functions. Laurea Honoris Causa from the University of Pavia for the Volta Bicentennial 2003 Gabor Award Okawa Prize 2009 2014 Swartz Prize for Theoretical and Computational Neuroscience 2017 Rosenfeld Lifetime award International Scientific Award 'Ratio et Spes' 2022 Kampe de Fériet award 2021 Helmholtz Prize for 'HMDB: A large video database for human motion recognition' Poggio has mentored several leaders in AI and neuroscience, including Christof Koch (President of Allen Institute), Amnon Shashua (CTO and founder of Mobileye), and Demis Hassabis (CEO and founder of DeepMind). His research group at MIT has received significant funding from NSF through the CBMM center, enabling interdisciplinary work that combines neuroscience, cognitive science, and artificial intelligence. At the Center for Brains, Minds and Machines, Poggio leads research on visual recognition, metric learning, and spatio-temporal convolutional networks. His team investigates how visual cortex represents actions and how to create robust representations that handle transformations like pose, scale, and rotation without affecting semantic categories.

Jean Hyaejin OhView profile

Research Professor

• Artificial Intelligence
• Robotics
• Human-Robot Interaction
• +7 more

Jean Hyaejin Oh is an Associate Research Professor at Carnegie Mellon University's Robotics Institute, where she leads the Bot Intelligence Group. Her research focuses on developing persistent robots capable of coexisting with humans through advances in AI, language understanding, multimodal perception, and navigation systems. She investigates how robots can interpret verbal commands, describe environments, generate semantic navigation plans, and explain their actions using natural language. Her work spans autonomous systems, human-robot interaction, computer vision, and machine learning, with emphasis on translating information between vision, language, and planning. Current projects include: DARPA ALIAS program (co-PI): Developing cockpit automation through semantic perception and pilot observation learning ARL RCTA program (PI): Intelligence architecture for language-guided robot navigation US DoD-Korea MOTIE collaboration (co-PI): Disaster response robotics using social media data Awards include: Best Cognitive Robotics Paper Award at ICRA 2015 She advises 10 PhD students and 1 master's student, with research outputs spanning robotic art, trajectory prediction, 3D vision, embodied AI, and autonomous exploration. Her recent publications demonstrate strong interdisciplinary focus across robotics, computer vision, and human-centered AI.

• Transportation and Freight Services
• Adaptive Agents and Intelligent Robotics
• Building Construction Management and Project Planning
• +5 more

Panagiotis Angeloudis is a Professor of Transport Systems & Logistics at Imperial College London's Department of Civil and Environmental Engineering, Faculty of Engineering. He leads the Transport Systems & Logistics (TSL) Laboratory, focusing on autonomous systems, multi-agent modeling, and network optimization for transportation and logistics. His research spans freight distribution, passenger transport, and the integration of AI and robotics in mobility systems. He holds a PhD (2009) and MEng (2005) in Civil & Environmental Engineering from Imperial College London. His professional roles include Programme Director of the MSc in Transport and Data Science, Director of Teaching for the Transport Section, and transport champion for the Institute for Security Science and Technology. He also serves as an Associate Editor for the Journal of Maritime Policy and Management and on the UK Government's Future of Mobility review team. Research interests include autonomous vehicles, logistics optimization, and resilience engineering. His work addresses challenges in urban mobility, energy networks, and infrastructure resilience through advanced modeling and AI-driven solutions. Recent projects focus on deployment strategies for autonomous systems, maritime policy, and critical infrastructure resilience in geopolitical disruptions. His funded projects involve EPSRC, InnovateUK, and industry partners. Key areas of application include smart cities, emergency logistics, and sustainable transport systems. TSL Lab's innovations include digital twin simulations, trajectory prediction algorithms, and decision-support frameworks for autonomous systems.

Nicholas M. BlauchView profile

Research Fellow

• Neural Computation
• Artificial Intelligence
• Cognitive Science
• +3 more

Nicholas M. Blauch is a Postdoctoral Fellow at Harvard University, affiliated with the Harvard Vision Sciences Lab, Psychology Department, and the Kempner Institute for Natural and Artificial Intelligence. His research focuses on understanding how biological neural systems learn and represent visual information, and how these principles can inform the development of energy-efficient artificial intelligence. He holds a Ph.D. in Neural Computation from Carnegie Mellon University, advised by David C. Plaut and Marlene Behrmann. His work bridges computational neuroscience and AI, particularly in modeling topographic organization of the brain. Key interests include foveation's role in visual processing, spatial embedding in neural networks, and aligning AI models with biological brain structures. His postdoctoral projects aim to develop vision models that emulate human perception while advancing insights into neural mechanisms. Recent publications highlight innovations like the Topoformer (2024), a Transformer variant with topographic organization mirroring brain structures, and TopoLM (2025), a language model with spatio-functional brain-like organization. Collaborations span Harvard's Vision Sciences Lab and Visual Computing Group, emphasizing interdisciplinary approaches. Education includes a Ph.D. from Carnegie Mellon (2023) and prior teaching roles as a Teaching Assistant for computational neuroscience courses. His long-term vision is to create large-scale, spatially-embedded models of the human brain, integrating biological constraints to achieve sustainable AI.

• Neural Networks
• Adaptive Resonance Theory
• Machine Learning
• +5 more

Professor Ah-Hwee Tan is a distinguished faculty member at Singapore Management University's School of Computing and Information Systems, Department of Information Systems. With over 30 years of academic contributions since 1991, his research has significantly advanced neural network architectures, particularly Adaptive Resonance Theory (ART), with applications spanning multiple domains of artificial intelligence. His primary research interests include Neural Networks , Adaptive Resonance Theory , Machine Learning , Reinforcement Learning , Knowledge Graphs , Natural Language Processing , and Multi-Agent Systems . Professor Tan's work bridges theoretical neural computation with practical applications, developing novel approaches for knowledge representation, semantic understanding, and intelligent decision-making systems. His recent publications (2022-2025) demonstrate continued innovation across multiple AI subfields, with particular emphasis on hierarchical reinforcement learning, knowledge graph refinement, sentiment analysis, and federated learning architectures. The research shows a clear trajectory from foundational neural network theory toward increasingly complex real-world applications in healthcare, social media analysis, and multi-agent coordination. Professor Tan has mentored numerous researchers who have become significant contributors in their own right, including Budhitama Subagdja, Shubham Pateria, and Di Wang. His collaborative work spans international institutions, reflecting his standing in the global AI research community. His research has been consistently published in top-tier venues including IEEE Transactions, Neural Networks, ACM journals, and major AI conferences (AAAI, IJCAI), demonstrating both theoretical rigor and practical impact across computer science and interdisciplinary applications.

• Applied Mathematics
• Computational Mathematics
• Machine Learning
• +4 more

Yue Yu is a Professor in the Department of Mathematics at Lehigh University, specializing in applied mathematics and computational mathematics. Their research focuses on hybrid modeling, physics-informed machine learning, and nonlocal operators for material science and engineering applications. Yu has developed innovative methods for data-driven constitutive law discovery, peridynamic modeling, and causal graph learning. Their work bridges computational mathematics with real-world challenges in materials, biomedical systems, and fluid-structure interactions. Key research directions include operator learning frameworks (e.g., neural operators, nonlocal attention mechanisms), physics-guided smoothing techniques for material modeling, and scalable causal inference algorithms. Yu has pioneered methods like the Peridynamic Neural Operator (PNO) and Embedded Nonlocal Operator Regression (ENOR), advancing nonlocal calculus applications. Their contributions span interdisciplinary areas such as metamaterial design, fracture mechanics, and biomedical tissue modeling using digital image correlation. Publications emphasize methodological advancements in operator networks, DAG learning, and asymptotically compatible numerical methods. Yu has explored meta-learning for physics discovery and developed frameworks for integrating finite elements with deep neural operators. Current work addresses high-dimensional systems, data-scarce scenarios, and uncertainty quantification in complex material systems. Lab activities focus on computational modeling, numerical analysis, and AI-driven scientific computing. Collaborations involve engineering, physics, and biomedical domains. Yu's research is supported by leading journals in computational mathematics and has been recognized in conferences like the ASME Summer Bioengineering Conference.

Abdelmounaam RezguiView profile

Assistant Professor

• Big Data Management
• Cloud Computing
• Data Analytics
• +2 more

Abdelmounaam Rezgui is an Assistant Professor in the Department of Computer Science and Engineering at New Mexico Tech, where he directs the Cloud Computing and Big Data (C2BD) Lab. He holds a Ph.D. from Virginia Tech and specializes in developing efficient cloud computing solutions for big data applications. His research focuses on cloud computing optimization, big data management, and distributed systems design. Key areas include: Efficiency improvements for cloud platforms supporting big data workloads Fault-tolerant architectures for distributed systems Social computing and network analysis techniques Service-oriented computing paradigms Recent publications demonstrate strong emphasis on cloud infrastructure optimization, distributed computing frameworks, and AI-assisted system maintenance. Research consistently addresses practical challenges in large-scale data processing across federated environments. Awards and recognitions include: Best student paper award at IEEE ISCC 2016 Best paper award at ICICS 2016 He has advised multiple graduate students including 9 PhD candidates and 2 MS students, with research often supported through cloud computing infrastructure grants. As director of the C2BD Lab, he leads projects investigating volunteered federated clouds, hardware failure prediction systems, and efficient data processing frameworks. The lab collaborates with industry partners on practical cloud computing implementations.

• Artificial Intelligence
• Machine Learning
• Neural Networks
• +6 more

E. Alonso is a Professor in the Department of Computer Science at City, University of London, with extensive contributions to artificial intelligence, machine learning, and cognitive science. His work spans theoretical foundations of AI, practical applications in power systems, automotive diagnostics, and ethical considerations in generative AI. Faculty member at City, University of London since at least 2010 Active researcher with publications extending through 2025 Collaborates extensively with researchers in AI, power systems, and cognitive science Regular presenter at major AI conferences including IJCAI, AAAI, and IEEE conferences Professor Alonso's research focuses on the intersection of theoretical AI and practical applications. His work in neural networks spans reinforcement learning, time series forecasting, and multi-agent systems. He has made significant contributions to understanding the mathematical foundations of AI systems, particularly in agent representations and learning mechanisms. His recent work increasingly addresses the ethical and legal implications of AI, particularly concerning copyright issues in generative AI training and moral value alignment in democratic AI systems. Analysis of Professor Alonso's recent publications (2023-2025) reveals a strong trend toward practical AI applications with theoretical grounding. His work spans multiple domains including power systems optimization using graph neural networks, automotive diagnostics with specialized neural architectures, and creative AI approaches to generalization. A notable pattern is his ability to bridge theoretical AI concepts with real-world engineering problems, particularly in energy systems and automotive applications. His research increasingly incorporates ethical considerations, reflecting the growing importance of responsible AI development. Professor Alonso has supervised numerous students who have gone on to publish with him in top venues. His collaborative approach is evident in the diverse range of co-authors across different disciplines. While specific grant information isn't detailed in the provided materials, his sustained publication record across multiple domains suggests successful funding acquisition for his research programs. His work appears connected to the Centre for Computational and Animal Learning Research, suggesting involvement in interdisciplinary teams exploring the connections between biological learning systems and artificial intelligence. Recent collaborations with researchers in medical imaging and radiotherapy implementation further demonstrate the breadth of his research impact across different application domains.