جست‌وجوی استاد و پژوهشگر

• Aerospace Engineering
• Materials Science
• Mechanical Engineering
• +۳ مورد دیگر

Michele Pasquali is an Associate Professor at the Department of Mechanical and Aerospace Engineering of Sapienza University of Rome. His research focuses on advanced materials for aerospace and high-energy physics applications, structural health monitoring, Industry 4.0 integration in aerospace manufacturing, and preliminary design methodologies for satellite systems and collimation systems in particle accelerators. His work often intersects with experimental facilities at CERN, particularly the HiRadMat facility for material testing under extreme conditions. Key research themes include: Development of advanced composite materials for aerospace and accelerator components Integration of AI and immersive technologies (AR/VR) in manufacturing and assembly processes Optimization of beam collimation systems for high-luminosity particle accelerators like the HL-LHC and FCC Economic analysis of space infrastructure concepts such as lunar propellant mining Systems engineering approaches for Earth observation satellite constellations His recent projects include: Design of a Cyber-Physical System for AR-assisted assembly of aerospace components Material characterization studies for vacuum beam windows at CERN facilities Preliminary analysis of moon-mined propellant viability for on-orbit refueling Collaborations involve major scientific institutions such as CERN and contributions to Future Circular Collider (FCC) conceptual design reports. His work bridges theoretical modeling with practical implementation in cutting-edge engineering systems.

Arif Perdanaمشاهده پروفایل

دانشیار

• Data Science and Analytics
• Digital Strategy
• Responsible AI
• +۴ مورد دیگر

Arif Perdana is an Associate Professor specializing in digital strategy and data science at Monash University. He holds a PhD in Business Information Systems from the University of Queensland (2016) and serves as Director of the Action Lab Indonesia since December 2022. His research focuses on responsible AI, blockchain, machine learning, and digital strategy applications in finance, healthcare, and education sectors. He has secured significant funding from entities like Bank Indonesia and Monash Data Future Institute. Research Interests: - Blockchain for supply chain sustainability - Ethical AI governance - FinTech and RegTech innovations - Digital transformation strategies - Data governance frameworks Awards: - Lybrand Award 2022-2023 Key Projects: - ASEAN Disinformation Index (2024-2025) - Algorithmic Systems Governance in Finance (2023-2024) - Inclusive P2P Lending Models (2023) - Digital Health Governance in Indonesia (2022-2023) Media Contributions: - Over 50 media features discussing AI ethics, cybersecurity, and tech governance - Featured in international outlets like RMIT Vietnam and Swinburne University

Mohamed Atiaمشاهده پروفایل

پژوهشگر

• Modular Robotics
• Soft Robotics
• Path Planning Algorithms
• +۳ مورد دیگر

Mohamed Atia is a Researcher in the Department of Electrical and Electronic Engineering at the University of Nottingham. His academic role focuses on robotic systems, with a PhD awarded in 2023 for his work on reconfigurable soft robots. His research aligns with UN Sustainable Development Goals related to innovation and education. Education: Doctor of Philosophy (2017–2022): Reconfigurable Soft Robots by Building Blocks , University of Nottingham Research Interests: Atia specializes in modular robotics, soft robotics, and path planning algorithms. His work integrates advanced materials with control systems, particularly in exoskeleton development. Recent studies explore machine learning applications in material science and 3D-printed composites. Research Trends: His publications emphasize reconfigurable robot design (2022–2024), path optimization for mobile systems (2020–2018), and exoskeleton control systems (2019–2017). The 2024 paper on machine learning for composites marks an interdisciplinary expansion. Impact & Collaborations: Participated in the CRADLE center, focusing on robotic autonomy in demanding environments. His work has been cited over 30+ times and reached 40+ readers on Mendeley. Labs/Teams: Part of the CRADLE research group, collaborating with experts in robotics, materials science, and biomedical engineering.

Andrea Kaimمشاهده پروفایل

پژوهشگر

• Landscape ecology
• Ecosystem services
• Biodiversity and trade-offs
• +۳ مورد دیگر

Dr. Andrea Kaim is the Head of the Junior Research Group AgriScape at the Helmholtz Centre for Environmental Research (UFZ) since 2022. She holds a PhD from the UFZ (2015–2022) and has extensive experience in landscape ecology, ecosystem services, and spatial conservation planning. From 2019–2022, she worked as a Research Scientist in the Professorship of Ecological Services at the University of Bayreuth. Her current research focuses on modeling farmer decisions in alpine grassland management using agent-based models, integrating biophysical, economic, and policy data. She has collaborated on projects like SusAlps and AgriScape, addressing climate change and policy impacts on land-use decisions. Education includes a German Diploma in Business Mathematics (2008–2015) from Universität Leipzig, with additional studies in Mathematics and Economics at the Universidad Complutense de Madrid (2012) and a traineeship at The University of Queensland’s Centre for Biodiversity and Conservation Science (2013–2014). Her research interests span landscape ecology, spatial conservation planning, environmental economics, and mathematical optimization. Key publications address agent-based models for nitrogen management, ecosystem service trade-offs in alpine regions, and multi-objective land-use allocation techniques. Dr. Kaim’s work bridges ecological modeling with policy analysis, emphasizing stakeholder preferences and sustainable resource management.

• Maritime Engineering
• Mechanical Engineering
• Oceanography
• +۵ مورد دیگر

Dr. Stephan Kramer is an Advanced Research Fellow in the Department of Earth Science & Engineering at Imperial College London's Faculty of Engineering. He holds affiliations with the Applied Modelling and Computation Group and focuses on interdisciplinary research at the intersection of computational geophysics, renewable energy systems, and numerical methods. His work integrates advanced computational techniques with geodynamic and oceanographic processes. Education: MSc Theoretical Physics and Mathematics (University of Utrecht, 1994–2001), PhD (TU Delft, 2002–2007). Research Interests: Maritime Engineering, Mechanical Engineering, Oceanography, Geophysics, and Computational Fluid Dynamics. His research emphasizes numerical modeling of geophysical flows, tidal energy systems, and mantle dynamics. Recent work explores mesh adaptation techniques for ice flow and mantle convection, optimization of tidal turbine arrays, and inversion methods for reconstructing Earth's mantle evolution. Kramer has contributed to advancing open-source frameworks like Firedrake for geodynamic simulations. Publications from 2023–2024 highlight advancements in mesh movement networks, tidal energy optimization, and geodynamic inversion. His studies address challenges in energy extraction efficiency, environmental impact mitigation, and computational efficiency in large-scale geophysical models. No scientific awards or grants are explicitly listed, but his extensive publication record reflects sustained academic engagement. He is affiliated with the Applied Modelling and Computation Group, contributing to interdisciplinary research teams at Imperial College.

Amy N. Myersمشاهده پروفایل

مدرس ارشد

• Combinatorics
• Probability Theory
• Enumerative Combinatorics
• +۳ مورد دیگر

Amy N. Myers serves as Senior Lecturer in Mathematics and Math Program Coordinator at Bryn Mawr College's Department of Mathematics, where she teaches core courses including Calculus I-II, Multivariable Calculus, Probability Theory, and specialized graduate seminars in Enumerative Combinatorics and History of Mathematics. Her academic credentials include a Ph.D. in Mathematics from Dartmouth College (1999), establishing her foundational expertise in discrete mathematical structures. Dr. Myers' research demonstrates exceptional depth in combinatorial mathematics , particularly in pattern avoidance phenomena and probabilistic enumeration . Her work on interval orders bridges order theory with topological combinatorics, while her contributions to the Coupon Collector's Problem reveal novel asymptotic behaviors with applications in computational biology. The SIGEST recognition of her probability research by SIAM Review underscores its interdisciplinary significance across theoretical computer science and stochastic modeling. Analysis of her publication trajectory shows consistent innovation in multiset permutation statistics and forbidden substring constraints, with recent focus on shellability properties in ordered structures. Her combinatorial frameworks increasingly incorporate computational topology, reflecting evolving methodological sophistication. No scientific awards are documented in the available institutional records. As Math Program Coordinator, Dr. Myers oversees curriculum development and student advising for mathematics majors, though specific mentorship details remain undisclosed. Grant funding information is not publicly accessible through current university channels. Research infrastructure details including laboratories or collaborative teams are not referenced in her official academic profile.

Taegyo Kimمشاهده پروفایل

عضو هیئت علمی

• Neural Prosthetics
• Neural Interfaces
• Biomedical Engineering
• +۳ مورد دیگر

Taegyo Kim is an Instructor in the Department of Neurobiology & Anatomy at Drexel University College of Medicine. His research focuses on developing advanced neural interface technologies and computational models of brain circuits involved in motor control. Key areas include braided microelectrode probe design, neural prosthetics, and understanding neurobiological mechanisms underlying motor adaptation. His work combines biomedical engineering with computational neuroscience to address challenges in chronic neural implant biocompatibility and neuroprosthetic functionality. Recent studies emphasize minimizing inflammatory responses through innovative probe materials and exploring cerebellar-basal ganglia interactions in learning processes. Publications span topics from electrode array fabrication to computational modeling of reinforcement learning systems, demonstrating interdisciplinary approaches to neurological solutions.

• Network Security
• Software Defined Networking
• Traffic Engineering
• +۷ مورد دیگر

Xingang Shi is a Professor in the Department of Computer Science and Technology at Tsinghua University's School of Information Science and Technology. With over 126 publications spanning from 2006 to 2025, he maintains an active and influential research program in computer networking, with recent expansion into quantum network optimization. His work consistently appears in top-tier venues including INFOCOM, IEEE/ACM Transactions on Networking, and SIGCOMM. Dr. Shi's research focuses on network security, software defined networking, traffic engineering, and network anomaly detection. His recent work shows increasing emphasis on quantum network optimization and advanced security verification techniques. He has developed innovative approaches to routing protocols, congestion control, and inter-domain routing analysis, with practical applications in real-world network infrastructure. His publication trends reveal a steady evolution from traditional networking topics toward more sophisticated security and optimization frameworks. The 15 most recent publications demonstrate strong integration of machine learning techniques with networking fundamentals, particularly in anomaly detection and traffic engineering. His work increasingly addresses quantum networking challenges while maintaining strong contributions to conventional network security and performance optimization. As a leading researcher in his field, Dr. Shi has made significant contributions to network verification methodologies and security protocol testing frameworks. His recent papers on model checking-based security testing and proactive network policy verification represent important advances in ensuring network reliability and security. Through his extensive collaboration network (particularly with Zhiliang Wang, Xia Yin, and Han Zhang), Dr. Shi has built a productive research ecosystem that bridges theoretical networking concepts with practical implementation challenges. His work on quantum network optimization represents an emerging frontier in his research portfolio.

Alexander Carôtمشاهده پروفایل

استاد

• Networked Music Performance
• Real-Time Audio Streaming
• Telecommunication Systems
• +۳ مورد دیگر

Prof. Dr. Alexander Carôt is a Professor of Media Informatics at Anhalt University of Applied Sciences, leading the Faculty of Computer Science and Languages as its Dean. His work bridges physics, computer science, and music, with a focus on networked music performance and low-latency audio systems. He developed the SoundJack software, enabling remote collaborative music-making, and contributed to projects like FAST-MUSIC and 5GUK trials for distributed music sessions. His research spans telemedicine applications, real-time streaming optimization, and quantum effects in telecommunications. Education: Ingenieursdiplom (2004) Doctorate in Engineering (2009) Research Interests: Prof. Carôt explores interdisciplinary fields combining technology and music. Key areas include: Real-time audio networking for live performances 5G-enabled tele-music systems Medical tele-rehabilitation using music feedback Low-latency multimedia streaming protocols Grants & Projects: Led initiatives like the FAST-MUSIC project and 5G trials, focusing on distributed music collaboration. Active in EU-funded research on telematic systems and networked audio. Labs/Teams: Directs the SoundJack development team and collaborates with industry partners on 5G infrastructure for arts and healthcare.

Yashar Deldjooمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Recommender Systems
• +۷ مورد دیگر

Yashar Deldjoo is an Assistant Professor specializing in artificial intelligence, machine learning, and their applications in healthcare, user modeling, and recommender systems. His research focuses on explainable AI, ethical AI practices, multimodal learning, and brain-computer interfaces. He has contributed to projects like WeBIUM (Wearable Devices and Brain-Computer Interfaces for User Modelling) and ARIEL (emotional support via BCI and LLMs). His work bridges technical advancements with societal impacts, addressing challenges in healthcare diagnostics (e.g., Alzheimer’s disease prediction), environmental sensing (real-time pollution-aware recommendations), and cybersecurity (data poisoning attacks on recommendation systems). He also explores fairness in large language models and the integration of EEG-based emotion recognition systems. Notable contributions include frameworks like BRAINEX (CNN model evaluation for brain age prediction), Ducho (multimodal feature extraction for recommendation systems), and KGUF (knowledge-aware graph-based recommenders). He chairs conferences like RecSys and contributes to industry-relevant standards for trustworthy AI systems. His research emphasizes interdisciplinary collaboration, combining neural networks, ethical considerations, and human-computer interaction. Current projects involve neurorehabilitation tools, quantum security for smart cities, and AI-driven usability testing.

• Multidisciplinary Design Optimization (MDO)
• Aerospace Engineering
• Surrogate Models
• +۶ مورد دیگر

Nathalie Bartoli is a Senior Research Scientist (DR2) at ONERA and a Professor at ISAE-SUPAERO. She holds a Habilitation degree (HDR) in Applied Mathematics and specializes in Multidisciplinary Design Optimization (MDO), surrogate modeling, and Bayesian optimization. Her work focuses on aerospace applications, particularly aircraft design and optimization algorithms. She leads the Multidisciplinary Methods and Integrated Concepts Unit (DTIS/M2CI) at ONERA and teaches courses on optimization and MDO at ISAE-SUPAERO. Her research interests include surrogate models (Kriging/Gaussian Processes, mixture of experts), Bayesian optimization, and data fusion techniques. She is a member of the AIAA MDO Technical Committee and has organized workshops such as the MDO PhD Day (2019) and European MDO Workshops (2019, 2020). She has supervised over 25 PhD students and co-authored the SMT Python toolbox for surrogate modeling. Recent achievements include keynote speaking at NATO conferences, winning AIAA MDO Best Paper Awards (2022, 2020), and contributions to the AGILE H2020 project. Her work integrates manufacturing, supply chain, and aircraft design through MDO frameworks, addressing challenges in green aviation and hybrid propulsion systems. She collaborates internationally with universities like McGill and TU Delft, and industries like Airbus and DLR. Awards include the 2018 ICAS Award for AGILE project contributions and multiple best paper recognitions. Her research bridges theoretical optimization methods with practical aerospace engineering, emphasizing scalable algorithms for high-dimensional problems and industrial applications.

• Agricultural Systems
• Sustainability Science
• Landscape Planning
• +۵ مورد دیگر

Dr. Jeroen Groot is an Associate Professor at Wageningen University & Research, specializing in Farming Systems Ecology. He holds a PhD in Agronomy and MSc degrees in grassland science, animal physiology, and tropical animal husbandry. His research focuses on farming systems analysis, model-informed landscape planning, and participatory modeling to address sustainability challenges in agriculture. Dr. Groot has coordinated international projects like EULACIAS and contributed to frameworks such as the COMPASS modeling suite, which integrates biological processes, economic calculations, and optimization algorithms to support decision-making in multifunctional landscapes. His work spans projects in Asia, Africa, and Latin America, emphasizing sustainable intensification of mixed farming systems, trade-off analysis, and stakeholder engagement. Key areas include nutrient cycling, design of mixed farms, and the integration of socio-economic and environmental indicators. Dr. Groot has published widely on topics like participatory gaming for sustainable landscape design, multi-objective optimization in farming systems, and the role of models in bridging disciplinary divides. Recent publications highlight his systematic review of trade-off analysis methodologies in agriculture, emphasizing gaps in multi-scale analysis and stakeholder inclusion. His research also explores gender norms in farming communities and the socio-ecological impacts of agricultural intensification. Dr. Groot’s contributions aim to advance sustainable agriculture through interdisciplinary approaches and policy-relevant insights.

• Algebraic Topology
• Hyperplane Arrangements
• Braid Groups
• +۲ مورد دیگر

Daniel C. Cohen is a Professor in the Department of Mathematics at Louisiana State University. His research focuses on algebraic topology, particularly hyperplane arrangements, braid groups, and configuration spaces. He has organized several major conferences including the 'Topological Complexity and Motion Planning' conference in Mexico and has been recognized as an Honorary Fellow at the University of Wisconsin and Simons Visiting Professor. Research interests include topological robotics, combinatorial arrangements, and geometric group theory, with applications to motion planning algorithms and computational geometry.

Xiaoyu Chenمشاهده پروفایل

استادیار

• Machine Learning
• Advanced Manufacturing
• Cyber Manufacturing Systems
• +۵ مورد دیگر

Xiaoyu Chen serves as an Assistant Professor in the Department of Industrial and Systems Engineering within the School of Engineering and Applied Sciences at the University at Buffalo. His academic foundation includes a PhD in Industrial and Systems Engineering (2021) and an MEng in Computer Science (2021) from Virginia Tech, complemented by a BS in Optical Information Science and Technology from Beijing Institute of Technology (2015). Dr. Chen's research spans machine learning methodology for advanced manufacturing, networked data analytics in cyber manufacturing systems, statistical methods for Bayesian deep learning, smart connected health applications for organ transplantation and perioperative medicine, and human behavior/performance modeling. His work bridges industrial engineering with cutting-edge AI techniques to solve complex problems in manufacturing and healthcare systems. Analysis of recent publications reveals a strong focus on developing interpretable and context-aware AI systems for manufacturing and clinical applications. Key trends include multimodal learning frameworks, distributed data processing for fog manufacturing, human-AI collaboration paradigms, and medical diagnostics through novel data modalities like breath analysis. Dr. Chen actively teaches core engineering courses including Probability & Statistics for Engineers (IE 360/560), Simulation (IE 541), and Predictive Analytics (Deep Learning) (IE 463/663), demonstrating commitment to both foundational and emerging topics in industrial engineering education.

• Computer Architecture
• Parallel Computing
• Embedded Systems
• +۴ مورد دیگر

Sotirios Ziavras is a Professor of Electrical and Computer Engineering at New Jersey Institute of Technology (NJIT), with former affiliations in the Office of the Provost and Graduate Studies. His research focuses on computer architecture, parallel computing, and embedded systems, with notable contributions to wireless sensor networks and structural health monitoring. He has secured multiple federal grants, including projects on network quality of service (NeTS-NR) and scalable parallel computing. Ziavras has led research initiatives funded by the National Science Foundation (NSF), including a 2022-2027 Graduate Research Fellowship Program (GRFP) and earlier work on next-generation network architectures. His publications span interdisciplinary applications of sensor technologies and high-performance computing systems. Grants: 8 federal grants including NSF-funded projects on networks, parallel computing, and graduate fellowships. Awards: Not explicitly mentioned, but his h-index of 16 reflects significant academic impact. His work bridges electrical engineering and computer science, addressing challenges in infrastructure monitoring, parallel processing, and scalable computing systems.