جست‌وجوی استاد و پژوهشگر

Clélia de Mulatierمشاهده پروفایل

استادیار

• Statistical Physics
• Information Theory
• Computer Science
• +۵ مورد دیگر

Clélia de Mulatier is an Assistant Professor at the University of Amsterdam , affiliated with both the Institute for Theoretical Physics and the Informatics Institute . She leads research at the intersection of statistical physics, information theory, and computer science , focusing on theoretical and numerical methods for complex systems . Her work spans collaborations with experimentalists in neuroscience and biology , and she actively participates in educational programs across multiple Dutch universities. Research Labs : Computational Soft Matter Lab, Computational Science Lab Affiliations : Dutch Institute for Emergent Phenomena (DIEP), Netherlands Platform Complex Systems (NPCS) Her research develops minimally complex spin models for high-order data analysis , applying exact Bayesian model selection to uncover hidden variable communities in binary datasets. This work has produced open-source tools like MinCompSpin and MinCompSpin_Greedy for different system sizes. Publications demonstrate expertise in tensor networks for dimensional reduction , epidemic modeling , and branching random walks in confined environments . Teaching includes Python programming , complex systems theory , and statistical inference for physics students across multiple institutions. She serves as program committee member for International Conference on Computational Science and organizes academic discussions through initiatives like Behind the CV: story from a Physicist .

Piotr Jan Garbaczewskiمشاهده پروفایل

استاد

• Mathematical Physics
• Quantum Physics
• Theoretical Physics
• +۷ مورد دیگر

Piotr Jan Garbaczewski is a Professor of Physics at the University of Opole, Poland, where he serves as the Head of the Institute of Physics. Previously, he held professorial positions at the University of Zielona Góra (1998-2006) and the University of Wrocław (1971-1998). His research spans multiple areas of theoretical and mathematical physics, with particular focus on: Stochastic processes and random dynamics Quantum mechanics and fractional quantum mechanics Non-equilibrium statistical mechanics Lévy flights and stable processes in bounded domains Diffusion processes with non-conservative drifts Mathematical aspects of quantum potentials Garbaczewski's recent publication record shows consistent output in prestigious journals, with notable work on killing versus branching processes, non-conservative diffusion, and Lévy processes in bounded domains. His research demonstrates a strong connection between stochastic processes and quantum mechanical systems, particularly through the lens of non-Hermitian quantum mechanics and fractional calculus. His scientific achievements include: h-index (WoS Citations): 7 Total Impact Factor: 31.499 Total SNIP: 15.42 Total CiteScore: 28.41 Total ministerial score: 1,210 Throughout his career, Garbaczewski has maintained active collaborations with researchers across Poland and internationally, contributing significantly to the field of mathematical physics through both theoretical developments and practical applications of stochastic processes in physical systems.

• Spatial Audio
• Machine Learning for Audio
• Environmental Soundscapes
• +۳ مورد دیگر

Dr. Marc C. Green serves as a Postdoctoral Research Fellow within the School of Science, Engineering & Environment at the University of Salford, affiliated with the Acoustics Innovation Institute. His research bridges acoustic engineering, machine learning, and environmental psychology with focus on unmanned aircraft systems noise impact assessment. PhD in Music Technology, University of York (2020) Dr. Green's research investigates how drone noise interacts with urban soundscapes through innovative methodologies including soundwalk assessments in controlled environments, psychoacoustic laboratory testing , and deep learning model development . His work specifically examines time-variant noise characteristics, flight operation impacts, and environmental interactions (such as pine tree barriers in urban parks) to understand human annoyance responses. The integration of spatial audio techniques with machine learning enables novel approaches to predicting UAV sound affect beyond traditional sound pressure level metrics. Analysis of his 11 recent publications (2024-2025) reveals a strong focus on practical solutions for urban drone integration, with 55% dedicated to psychoacoustic modeling, 27% to field assessment methodologies, and 18% to technical noise characterization. His work consistently addresses the tension between societal benefits of drone technology and community noise impacts. EPSRC connected nation pioneers prize for 'Creative Computing for the Digital Economy' (for EigenScape database development) Currently leading the RefMap project to reduce UAS environmental impact, Dr. Green's research receives direct institutional support through the Acoustics Innovation Institute. His EigenScape database continues to serve as a foundational resource for global soundscape researchers. The systematic review framework he developed for UAS noise impact testing provides regulators with evidence-based assessment protocols. Dr. Green operates within the Acoustics Innovation Institute's collaborative ecosystem, leveraging specialized recording equipment and computational resources. His research group maintains comprehensive drone noise datasets across multiple platforms and develops open methodologies for community noise assessment in evolving urban air mobility scenarios.

• Cyber-physical Systems Security
• Attack and Fault Detection
• Robust Control
• +۴ مورد دیگر

Dr. Hunmin Kim serves as Assistant Professor in the Department of Electrical and Computer Engineering at Mercer University's School of Engineering, specializing in security and operational resilience of cyber-physical systems with applications in autonomous vehicles, UAVs, and smart grids. His educational background includes: PhD in Electrical Engineering from Pennsylvania State University (2018) BSE in Mechanical Engineering from Pusan National University (2012) Dr. Kim's research centers on developing attack/fault detection mechanisms, robust control frameworks, and path planning algorithms for cyber-physical systems operating in dynamic environments. His work addresses critical security vulnerabilities and coordination challenges in multi-agent systems, with emphasis on verifiable safety guarantees for autonomous operations. Analysis of his 2023-2025 publications reveals strong thematic convergence in resilient control under adversarial conditions, motion planning in complex environments, and assistive technology applications. His work consistently bridges theoretical control systems with practical implementations in autonomous vehicles and UAVs, featuring increasing integration of machine learning techniques for adaptive security. Professional recognition includes: Nomination for 2024 Clayton R. Paul Teaching Excellence Award Dr. Kim actively mentors undergraduate researchers through Mercer's Bear Day events and honors projects, guiding student teams in developing gesture-based drone controls, EMG signal processors, and assistive navigation devices for visually impaired individuals. He maintains extensive peer review commitments across 45+ journals/conferences annually including IEEE Transactions on Automatic Control and Automatica. He contributes to the Collaborative Center for Computing at Mercer University and serves on the IEEE CSS Technology Conferences Editorial Review Board.