جست‌وجوی استاد و پژوهشگر

Raoul Bongersمشاهده پروفایل

استادیار

• Motor Control
• Motor Learning
• Prosthetics
• +۶ مورد دیگر

Dr. Raoul Bongers is an Assistant Professor at the Faculty of Medical Sciences, University of Groningen, affiliated with the SMART Movements research group. His expertise focuses on action-perception learning, motor control, and rehabilitation technologies, particularly in upper limb prosthetics and serious game applications for prosthesis training. He investigates motor variability and coordination dynamics using advanced methodologies like Uncontrolled Manifold analysis and multifractal analyses. His research emphasizes the development of clinical tools such as IMU-based calibration algorithms and virtual reality environments to improve prosthetic control and rehabilitation outcomes. Collaborations span biomechanics, neuroscience, and medical engineering, addressing challenges in prosthetic design, user adaptation, and clinical assessment. Bongers has contributed to over 139 publications, with recent work exploring task-specific serious game training, IMU sensor calibration reliability, and motor learning dynamics in novel control tasks. His work aligns with UN Sustainable Development Goals related to health and innovation. Professional activities include keynote lectures on motor control and participation in interdisciplinary research networks. Laboratory affiliations include SMART Movements, where research integrates ecological psychology and dynamical systems theory to advance rehabilitation technologies and motor learning understanding.

• Multimodal signal processing
• Audio-video signal processing
• Biomedical signal processing
• +۱۲ مورد دیگر

Richard M. Dansereau is a Professor in the Department of Systems and Computer Engineering at Carleton University's Faculty of Engineering and Design. He holds a Ph.D. from the University of Manitoba and is a Professional Engineer (P.Eng.) and Senior Member of IEEE. He currently serves as Associate Dean (Graduate Studies) and Clerk of Senate, reflecting his leadership roles within the university. His research interests include: Multimodal and audio-visual signal processing Biomedical and biometric signal processing Image and speech signal processing Compressive sensing and deep learning for reconstruction Fractal and multifractal complexity measures, including Rényi dimensions Applications in medical imaging, speech enhancement, and radar systems Recent publications highlight his lab's focus on advanced deep learning techniques for image reconstruction (e.g., deep equilibrium models for compressive sensing), medical image analysis (e.g., PET reconstruction and cervical cell segmentation), and Riemannian geometry in radar signal processing for drone detection. His work integrates theoretical signal processing with practical applications in healthcare and defense. Scientific awards associated with his research group include: Ontario Graduate Scholarship Alexander Graham Bell Canada Graduate Scholarship (CGS D) John Ruptash Memorial Fellowship NSERC Best Project Award 1st prize in poster competition at hSITE 2012 Finalist for World Congress Award at WSCTS’2006 Dansereau actively supervises graduate students, with a long list of Ph.D. and M.A.Sc. alumni who have worked on topics such as speech separation, ECG analysis, image registration, and radar signal processing. He collaborates with researchers at institutions like the University of Ottawa Heart Institute and Defence Research and Development Canada (DRDC). His lab, the Signal Processing and Machine Learning Lab, continues to publish in top journals and conferences, securing research opportunities for Canadian, American, and British citizens in speech intelligibility research.

Rahul Sharmaمشاهده پروفایل

دانشیار

• Control Systems
• Renewable Energy
• Power Systems
• +۵ مورد دیگر

Associate Professor Rahul Sharma is affiliated with the School of Electrical Engineering and Computer Science at The University of Queensland (UQ), within the Faculty of Engineering, Architecture and Information Technology. His primary research focuses on control systems applications, particularly in solar farm fault detection, grid-connected inverter control, and demand side management. His work emphasizes system modeling, control development, and model-based fault diagnosis, with significant industrial relevance in renewable energy and smart grid technologies. Research Interests: - Solar farm fault detection and diagnosis via control theory to identify underperforming panels. - Advanced control methods for grid-connected inverters to enhance power quality and stability. - Demand side management through novel control algorithms for distributed energy resources. His experimental work includes field trials at Gatton Solar Farm and collaborations with industry partners. Articles Trends: Recent publications emphasize network-aware control of distributed energy resources, dynamic operating envelopes for demand response, and smart grid optimization. Key themes include low-voltage distribution networks, prosumer P2P trading, and fault diagnosis in power systems. Advising & Labs: He supervises prospective PhD students in control systems and renewable energy. Experimental setups at UQ and field trials at Gatton Solar Farm support his research on fault detection and grid systems.

Natalia Jurgaمشاهده پروفایل

مدرس

• Dynamical Systems
• Ergodic Theory
• Random Matrix Products
• +۱ مورد دیگر

Natalia Jurga is a Lecturer in Pure Mathematics at the University of St Andrews School of Mathematics and Statistics. Her research explores dimension theory, dynamical systems, and fractal geometry, particularly through iterated function systems and ergodic methods. Funded by a Leverhulme Trust Early Career Fellowship, Jurga investigates the dimension theory of invariant sets, box dimension anomalies, and applications to parabolic fractals. She develops innovative approaches to compute Hausdorff dimensions and analyze chaotic systems. Awards & Grants: Leverhulme Trust Early Career Fellowship She advises PhD students including Luke Derry, Saeed Shaabanian, and Yunlong Xu, and serves as an editor for the Proceedings of the Royal Society of Edinburgh.

Bhupesh Kumarمشاهده پروفایل

پژوهشگر ارشد

• Optics
• Photonics
• Lasers
• +۷ مورد دیگر

Bhupesh Kumar is a Research Fellow at the School of Physics and Astronomy, University of St Andrews. His work focuses on advancing optical and photonic technologies through disorder engineering, particularly in random lasers and spectrometers. He has contributed to studies on solid-state polymer lasers, temperature-controlled spectral tuning, and multifractal scattering media applications. His research bridges fundamental physics with practical engineering solutions. His research interests include developing high-throughput optical devices, exploring localized modes in disordered systems, and applying light-based techniques to biomaterials like silk. These interests span Optics, Photonics, Lasers, and Materials Science, with a strong emphasis on interdisciplinary applications. Recent articles highlight advancements in tunable lasers, disorder-enhanced spectrometers, and the mechanics of silk. Collaborations with international researchers have been active in the last five years, though specific details are not provided here. No scientific awards are mentioned in the provided texts. His advising and grant activities are not detailed, but his research outputs include datasets related to speckle spectrometers. He is affiliated with the University of St Andrews’ School of Physics and Astronomy, contributing to both experimental and theoretical research.

• Ergodic Theory
• Multifractal Analysis
• Dynamical Systems
• +۳ مورد دیگر

Meng Wu is a professor at the Department of Mathematical Sciences, University of Oulu, Finland. Previously, he held postdoctoral positions at the Einstein Institute of Mathematics, Hebrew University of Jerusalem (2017), and at the University of Oulu (2013-2016), followed by a University Researcher role there (2017-2018). His work bridges ergodic theory, dynamical systems, and fractal geometry. Research interests include multifractal analysis, self-similar sets, and geometric measure theory. Recent work explores Furstenberg-type slicing theorems, scaling limits of self-conformal measures, and projection theorems with applications to exact overlaps conjectures. His publications often employ ergodic theory and dynamical systems to analyze fractal dimensions and geometric properties of sets. Trends in his articles highlight connections between number theory, probability, and fractal geometry, focusing on Hausdorff and Assouad dimensions, oriented random walks, and renormalization techniques. Collaborations include A.H. Fan, J. Schmeling, L.M. Liao, and A. Algom.

• Data Analytics
• Statistical Learning
• Computational Science
• +۴ مورد دیگر

Giuseppe Brandi is an Assistant Professor in Data Science at Northeastern University London and an Honorary Research Fellow at Imperial College London's Centre for Climate Finance & Investment. His research focuses on DataMetrics, a discipline blending Data Analytics, Statistical Learning, and Computational Science to analyze complex systems, particularly in finance and climate contexts. He emphasizes adapting models to data rather than forcing data into pre-existing theoretical frameworks. Dr. Brandi's work spans Climate Data Science, Econometrics, and Quantitative Finance. He critiques rigid theoretical models and advocates for interdisciplinary methods to extract insights from multidimensional data. His recent publications explore multiscaling in financial systems, tensor-based decomposition for multidimensional data, and liquidity contagion in interbank markets. His articles highlight trends in financial risk assessment, systemic risk modeling, and the application of tensor methods to complex datasets. Notably, he develops tools like the DisaggregateTS package for time-series analysis and visual warning systems for financial anomalies. Despite these contributions, no scientific awards are explicitly mentioned in the provided texts. Dr. Brandi's advising and grant activities remain undocumented here, though his academic roles suggest involvement in student mentorship and collaborative research projects. His affiliation with Northeastern's global network positions him to engage with interdisciplinary teams across multiple campuses.

Mawuli Segnonمشاهده پروفایل

استاد

• Econometrics
• Financial Economics
• Time Series Analysis
• +۲ مورد دیگر

Dr. Mawuli Segnon is a Professor at the University of Münster , affiliated with the Department of Econometrics and Economic Statistics and the Center for Quantitative Economics (CQE) . His research focuses on econometrics, financial economics, and energy economics, with a specialization in volatility forecasting and nonlinear dynamics. Current affiliations: University of Münster, CQE Research areas: Financial volatility modeling, Cryptocurrency markets, Energy economics His recent work explores regime-switching models for stock market volatility, multifractal processes in electricity price forecasting, and the impact of geopolitical risks on financial markets. He has contributed extensively to journals like Energy Economics and Renewable and Sustainable Energy Reviews , emphasizing quantitative methods in economic forecasting.

Peter Sollichمشاهده پروفایل

استاد

• Non-equilibrium statistical physics
• Soft matter rheology
• Active matter
• +۷ مورد دیگر

Prof. Dr. Peter Sollich is a Professor of Theoretical Physics at Georg-August-Universität Göttingen, affiliated with the Institute for Theoretical Physics. His research spans non-equilibrium statistical physics with applications to soft matter, active systems, and complex networks. He maintains a small part-time appointment at King's College London. His primary research interests focus on non-equilibrium statistical physics , particularly soft and active matter rheology, jamming transitions, glassy dynamics, dynamical phase transitions, and inference from dynamical data. His work bridges theoretical physics with applications in materials science and network theory, emphasizing both fundamental mechanisms and quantitative modeling approaches. Analysis of his recent publications reveals strong thematic consistency in studying glassy dynamics and active matter systems , with increasing integration of machine learning techniques for network analysis. Key methodological threads include coarse-grained modeling, spectral analysis of complex systems, and non-equilibrium thermodynamics frameworks. His 2023-2025 work shows growing emphasis on nonreciprocal interactions in active mixtures and physics-inspired machine learning applications. Prof. Sollich actively supervises Bachelor's, Master's, and PhD students, welcoming thesis inquiries in theoretical physics. His group develops analytical and computational approaches to complex dynamical systems, with recent grants likely supporting work on network dynamics and active matter modeling (specific grants not detailed in source text). His research group operates within the Institute for Theoretical Physics at Göttingen, focusing on computational and analytical modeling of disordered systems. Current projects involve elastoplastic modeling of amorphous solids, spectral analysis of heterogeneous networks, and theoretical frameworks for active matter phase separation.

• Data Mining
• Database Systems
• Spatial Databases
• +۵ مورد دیگر

Yubao Liu is a Professor at Sun Yat-sen University's School of Data and Computer Science, Department of Computer Science, with a prolific research career spanning over two decades in computer science. His work demonstrates significant contributions to database systems, data mining, and spatio-temporal analysis. Professor Liu's research interests focus on Data Mining , Database Systems , Traffic Flow Prediction , and Graph Neural Networks . His recent work has concentrated on developing advanced techniques for large-scale traffic flow prediction, crowd flow analysis, and spatio-temporal modeling using deep learning approaches. His research bridges theoretical computer science with practical applications in transportation systems and urban computing. Liu's publication record shows consistent high-impact contributions, with recent work emphasizing graph-based neural network architectures for traffic forecasting problems. His research demonstrates a clear evolution from foundational database work to cutting-edge applications of deep learning in transportation and social network analysis. Professor Liu has collaborated extensively with researchers including Weiyang Kong, Kaiqi Wu, Sen Zhang, Genan Dai, and Youming Ge, indicating a strong research group focused on spatio-temporal data analysis and deep learning applications. His academic advising is evident through publications where his students appear as first authors, suggesting an active mentorship role in training the next generation of computer scientists specializing in data-intensive applications.

• Quantum chaos
• Nonlinear Dynamics
• Chaos in higher dimensional systems
• +۳ مورد دیگر

Prof. Dr. Arnd Bäcker is a Professor at Technische Universität Dresden (TU Dresden) in Germany, specializing in quantum chaos and nonlinear dynamics. His research focuses on the complex interplay between classical and quantum mechanics in chaotic systems. His primary research interests include: Quantum chaos Nonlinear Dynamics Chaos in higher dimensional systems Dynamical tunneling Resonance states in open systems Classical and quantum billiards Prof. Bäcker's recent publications reveal a strong emphasis on quantum phenomena in higher-dimensional chaotic systems. His work consistently explores entanglement dynamics, quantum transport through barriers, and resonance states in complex systems. A distinctive feature of his research is the detailed analysis of 4D symplectic maps and the behavior of quantum systems with mixed regular-chaotic phase space structures. His approach combines rigorous mathematical analysis with computational methods to unravel quantum manifestations of classical chaos. His research has significant implications across multiple domains including quantum information processing, optical cavity physics, and ultracold atomic systems. The interdisciplinary nature of his work bridges theoretical physics, mathematics, and quantum information science, contributing to fundamental understanding while suggesting potential applications in emerging quantum technologies.

• Functional Magnetic Resonance Imaging (fMRI)
• Functional Connectivity
• Resting-state fMRI
• +۵ مورد دیگر

Bharat Biswal is a Distinguished Professor in the Department of Bio-Medical Engineering at New Jersey Institute of Technology (NJIT), serving as Director of the Center for Brain Imaging. His primary affiliation is with the College of Engineering, where he leads neuroimaging research initiatives. Biswal’s work focuses on functional magnetic resonance imaging (fMRI), brain connectivity analysis, and translational applications in neuropsychiatric disorders. Research Interests: Functional and Resting-State fMRI Methodology Neurovascular Coupling Mechanisms Connectomics and Network Neuroscience Clinical Applications in ADHD, OCD, and Neurodegenerative Diseases Post-COVID Neuroimaging White Matter Function Grant Activity: NIH-funded projects on laminar-specific connectivity (2022–2025) National Science Foundation MRI infrastructure grants (2019–2021) Longitudinal HIV brain studies (2015–2018) Recent Articles Highlight: Biswal’s 2025 work advances understanding of cocaine use disorder neurobiology, obsessive-compulsive disorder network dysregulation, and standardized PET nomenclature. His lab also innovates in AI-driven defect detection and transcriptomic-neuroimaging integrations. Awards: Recipient of NJIT’s 2024 Excellence in Research Award for pioneering contributions to resting-state fMRI and brain connectivity research. Labs/Teams: Leads the Center for Brain Imaging at NJIT, collaborating internationally on neuroimaging standards and translational neuroscience projects.

Martin Rypdalمشاهده پروفایل

استاد

• Climate Modeling
• Stochastic Processes
• Complex Systems
• +۶ مورد دیگر

Professor Martin Rypdal is a faculty member at the Faculty of Science and Technology , UiT The Arctic University of Norway, where he has served as Professor and Head of Department of Mathematics and Statistics since 2017. He holds a PhD in mathematics (2008) and a Cand. Scient. in mathematics (2004) from the University of Tromsø. Research Focus: Climate modeling, long-range dependent climate variability, stochastic processes, complex systems, multiscale analysis, and applications in public health (e.g., juvenile idiopathic arthritis, dengue dynamics). Leadership: Leads UiT's initiative on "The Dynamics of the Climate in the Arctic" and co-manages projects funded by the Norwegian Research Council. Outreach: Active science communicator with media appearances on climate and health research, including articles in Science Advances , Nature , and Ecology Letters . His recent publications (2025-2018) span climate science, public health, and statistical physics, emphasizing deep learning applications in paleoclimatology, Arctic sea ice loss, pandemic dynamics, and nonlinear climate responses. Scientific Awards: Recipient of the Faculty Teaching Price (2007, 2015). Supervises 3 PhD students and co-supervises 2, with 16 completed master's students.

Azita Mayeliمشاهده پروفایل

استاد

• Harmonic Analysis
• Fourier Analysis
• Representation Theory
• +۶ مورد دیگر

Azita Mayeli is a Professor of Mathematics and Faculty Deputy Executive Officer at the City University of New York's Graduate Center. Her academic leadership and research contributions position her at the forefront of mathematical analysis within the CUNY system, with a focus on theoretical and applied harmonic analysis. Her research spans abstract and classical harmonic analysis, Fourier analysis, representation theory, functional analysis, approximation theory, sampling theory, interpolation theory, and signal processing. She investigates fundamental connections between uncertainty principles, eigenvalue distributions, and signal recovery mechanisms across diverse mathematical structures including finite abelian groups, Riemannian manifolds, and fractal geometries. Her work bridges pure mathematics with practical applications in data science and signal processing. Analysis of her 15 most recent publications (2022-2025) reveals a dominant focus on eigenvalue distributions of spatio-spectral operators, uncertainty principles in novel settings, and sparse signal recovery techniques. Her research demonstrates increasing interdisciplinary reach, connecting harmonic analysis with machine learning, geometric measure theory, and time-series analysis while maintaining deep theoretical foundations. Scientific Awards: Feliks Gross Endowment Award (2015) - CUNY's highest honor for assistant professors, recognizing exceptional early-career contributions While specific advising records and grant details are not documented in available sources, her extensive publication record and leadership position suggest active mentorship of graduate students and involvement in significant research funding. Her work on signal recovery mechanisms and mathematical foundations has clear implications for data science applications and computational mathematics. Dr. Mayeli maintains active research laboratories focused on harmonic analysis applications, with particular emphasis on developing mathematical frameworks for signal processing in constrained environments. Her team explores connections between abstract function spaces and practical data recovery problems, contributing to both theoretical advances and algorithmic innovations.

Danijel Grahovacمشاهده پروفایل

دانشیار

• Limit theorems
• Scaling properties of stochastic processes
• Self-similarity
• +۷ مورد دیگر

Danijel Grahovac is an Associate Professor at the School of Applied Mathematics and Informatics, Josip Juraj Strossmayer University of Osijek. His academic career spans multiple research areas in probability theory and applied statistics, with particular focus on stochastic processes and their applications. His educational background includes a PhD in mathematics from the Faculty of Natural Science, Department of Mathematics, University of Zagreb (2015), with a thesis titled Scaling properties of stochastic processes with applications to parameter estimation and sample path properties , supervised by N.N. Leonenko and M. Benšić. He also holds two Master's degrees (2010, 2016) and a Bachelor's degree (2008) in Mathematics from the University of Osijek. Grahovac's research primarily focuses on limit theorems, scaling properties of stochastic processes (including self-similarity and multifractal processes), heavy-tailed distributions, and applied probability and statistics. His work often bridges theoretical developments with practical applications in fields such as image analysis, seismology, and financial modeling. He has developed innovative clustering algorithms and contributed significantly to the understanding of supOU (superpositions of Ornstein-Uhlenbeck) processes and their properties. His recent publications (2023-2025) demonstrate continued productivity across multiple journals including Pattern Analysis and Applications, Bernoulli, and Journal of Theoretical Probability. The articles reveal a consistent research trajectory examining the behavior of stochastic processes, particularly focusing on intermittency, scaling properties, and applications to real-world problems like earthquake analysis and image segmentation. Grahovac leads the project Scaling in stochastic models , funded by the Croatian Science Foundation since December 2023. Previously, he led another project Limiting behavior of intermittent processes and diffusions funded by the University of Osijek (2019/2020). He has served on editorial boards including as Co-Editor of Croatian Operational Research Review and participated in organizing committees for several statistical conferences. As an educator, Grahovac teaches Probability (Winter semesters), Time Series Analysis, and Statistical Learning (Summer semesters). His teaching materials, primarily in Croatian, cover advanced statistical methods with practical implementations in R.