Professor Mahdi Mahfouf holds a Chair in the School of Electrical and Electronic Engineering at the University of Sheffield. He has held academic roles since 1997, progressing from Lecturer to Professor in 2005. His research focuses on Fuzzy Logic, Control Systems, and their applications in biomedical and industrial contexts. Mahfouf leads the Intelligent Systems Research Laboratory and has contributed over 370 publications, including influential work on fuzzy modeling and predictive control. Education: Ing.Dipl. (Hons) in Control Systems MPhil in Control Systems (University of Sheffield, 1988) PhD in Control Systems (University of Sheffield, 1991) Research Interests: Fuzzy Logic applications, Artificial Intelligence, Neural Networks, Model-Based Predictive Control, Biomedical Engineering (e.g., ICU Decision Support Systems), and Manufacturing Systems (e.g., granulation processes, surface metrology). Key Achievements: Recipient of the IEE Hartree Premium Award (1992) and MEDIPEX Innovation Award. His work integrates fuzzy logic into real-time systems for aviation, healthcare, and robotics. Grants & Labs: Leads the Intelligent Systems Research Lab. Active in collaborative projects with industry (e.g., pharmaceuticals, aerospace). His research bridges theory and practice, emphasizing data-driven solutions for complex systems.
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Andreas C Cangellaris is a Professor at the University of Illinois at Urbana-Champaign, affiliated with the College of Engineering and the Department of Electrical and Computer Engineering. He leads research in the Coordinated Science Lab, focusing on electromagnetic modeling, signal integrity, and high-speed interconnects. His work spans nanotechnology, stochastic analysis, and machine learning applications in electromagnetics. Education details are not explicitly provided in the text, but his academic career includes significant contributions to computational electromagnetics, including the development of PEEC methods and neural network-based models for high-speed systems. Research interests emphasize modeling uncertainties in electromagnetic systems, metamaterials, and power delivery networks. He has pioneered techniques like the Stochastic Latency Insertion Method (LIM) and FFT-based macromodeling for stochastic systems. Key awards include the Humboldt Research Award (2004) and IEEE Fellow designation (2000). His grants and advising activities are not detailed here, but his lab focuses on advancing electromagnetic compatibility and high-speed link design. Notable collaborations involve stochastic modeling of fiber-weave effects in PCBs and inverse design methodologies using tandem neural networks.
Jie Liمشاهده پروفایل
استادیار
Jie Li is a Clinical Assistant Professor (Part-Time) in Molecular Microbiology & Immunology at USC. His research applies remote sensing and spatial data science to environmental health challenges, with emphasis on urban nighttime light analysis, air quality monitoring, and climate-health interactions. Research Focus: Key investigations include: Urban light pollution characterization using satellite data Applications of VIIRS and Luojia-1 satellite systems Fine-scale particulate matter mapping Green space impacts on cognitive health Environmental justice dimensions of climate exposures Recent methodological innovations include deep ensemble learning for temperature reconstruction and hyperspectral data processing for air quality assessment. Case studies frequently examine Hong Kong and Los Angeles as urban laboratories. His work demonstrates strong integration of remote sensing, epidemiology, and urban planning perspectives. Studies of Earth Hour participation effects provide evidence for lighting policy interventions, while temperature variability research highlights climate justice concerns.
- Robotics
- Mechatronics
- Control Systems
- +۵ مورد دیگر
Pieter van Goor is a Researcher in Robotics and Mechatronics at the Digital Society Institute. He holds a PhD from the Australian National University (2018–2022), awarded in July 2023, and served as a Postdoctoral Research Fellow there from May 2022 to June 2024. His research focuses on geometric methodologies in control systems, state estimation, and navigation, with applications to Lie groups, inertial systems, and multi-agent data fusion. Recent work emphasizes fusion of Gaussian distributions on Lie groups, observer design for navigation systems with delayed measurements, and equivariance in tracking controllers. Key research areas include robotics, mechatronics, and mathematical control theory, with publications addressing topics such as parallel transport in Kalman filtering, collaborative attitude estimation, and geometric approaches to data fusion. His work integrates theoretical mathematics (e.g., Lie group theory) with practical engineering challenges in navigation and robotics. Collaborations span institutions globally, with notable contributions to inertial navigation systems and multi-agent systems. Despite no explicitly listed awards, his h-index of 62 and 4 citations (as of 2023) reflect significant scholarly impact. Advising and grant details are not provided in the text.
Dr. Panqiu Xia is a Lecturer in the School of Mathematics at Cardiff University since Autumn 2024. His research focuses on theoretical and applied probability, with expertise in stochastic partial differential equations (SPDEs) and chemical reaction networks (CRNs). Previously, he held postdoctoral positions at Auburn University (USA) and the University of Copenhagen (Denmark). He earned his Ph.D. in Mathematics from the University of Kansas (USA) in 2020 under the supervision of Professors Yaozhong Hu and David Nualart. His research interests include Malliavin calculus, Stein's method, and the analysis of SPDEs such as the parabolic Anderson model and super-Brownian motions. In CRN studies, he investigates stationary distributions and stochastic modeling of biochemical systems. His work bridges pure probability theory with applications in mathematical biology and statistical physics. Dr. Xia has taught courses in statistics, stochastic analysis, and numerical methods at institutions including Auburn University and the University of Copenhagen. His educational background includes a BSc from Wuhan University (China) and an MSc from the University of Macau. Key research themes include the long-term behavior of SPDE solutions, intermittency phenomena, and ergodic properties. His contributions span both theoretical advancements in stochastic analysis and applied problems in chemical reaction systems.
Zhang Pinمشاهده پروفایل
استادیار
Dr. Zhang Pin is a Presidential Young Professorship (PYP) Assistant Professor in the Department of Civil and Environmental Engineering at the National University of Singapore (NUS). He previously held roles at the University of Cambridge as a Royal Society Newton International Fellow, Department Teacher, and Wolfson College Research Associate. His research group focuses on explainable AI for science, intelligent computational mechanics, uncertainty quantification, granular mechanics, coastal infrastructure resilience, and digital twins. Dr. Zhang earned his PhD from The Hong Kong Polytechnic University (2022), with a visiting scholar year at the University of Oxford. He holds a Master of Engineering (2019) and a Bachelor of Engineering (2016) from Hunan University and Hefei University of Technology, respectively. His research interests include: Explainable AI for scientific modeling Physics-informed machine learning Uncertainty quantification in geotechnical systems Micro/macro-scale granular mechanics Resilient coastal infrastructure design Digital twin technologies Notable achievements include recognition as a World’s Top 2% Scientist (2023-2024), PolyU PhD Thesis Merit Award, and the prestigious Royal Society Newton International Fellowship. His work bridges computational methods with geotechnical engineering challenges, emphasizing practical AI applications. Dr. Zhang has advised students like He GF and Wu HN, and his research has been published in top-tier journals such as Computer Methods in Applied Mechanics and Engineering and Géotechnique . He actively contributes to professional bodies including the British Geotechnical Association and International Society for Soil Mechanics (ISSMGE).
Marten Wegkamp is a Professor of Mathematics and Professor of Statistics & Data Science at Cornell University, located in Ithaca, NY. He holds dual affiliations within the College of Arts and Sciences, contributing to both the Department of Mathematics and the Department of Statistics & Data Science. His research focuses on applied mathematics, probability, and statistics, with a strong emphasis on high-dimensional statistics, statistical learning theory, and empirical process theory. He has developed methodologies in latent factor regression, sparse topic models, and interpretable statistical frameworks. His work frequently addresses challenges in high-dimensional data analysis and machine learning. Education: PhD in Mathematics from Leiden University (1996). Research Interests: Wegkamp’s research spans mathematical statistics, empirical process theory, and the development of novel statistical learning techniques. He explores areas such as latent factor models, high-dimensional inference, and the theoretical foundations of machine learning algorithms. His contributions include advancements in prediction methods, discriminant analysis, and optimal estimation strategies for complex data structures. Publications: His recent work includes studies on latent factor regression, sparse topic models, and interdisciplinary applications in genomics and multi-omic data analysis. Key themes across his publications involve high-dimensional data analysis, latent structure discovery, and algorithmic optimization for statistical models. Professional Contributions: He is affiliated with the Statistical Learning and High Dimensional Inference Group at Cornell, and his research has led to software packages like STRS, LOVE, and LoveER, which implement his methodologies. He teaches advanced courses such as Statistical Learning Theory (MATH 7740) and supervises research projects.
Mengyan Zhangمشاهده پروفایل
پژوهشگر
Mengyan Zhang is a Researcher at the Department of Computer Science, University of Oxford. Their work focuses on advancing artificial intelligence and machine learning techniques, particularly in applications such as epidemiological modeling, causal inference, and optimization. Mengyan's research bridges theoretical foundations with practical challenges in public health, remote sensing, and synthetic biology. Key research interests include developing AI-driven frameworks for disease surveillance, Bayesian optimization methods with integrated feedback, and uncertainty-aware regression for socio-economic estimation. Their contributions span graph-based algorithms, transformer neural processes, and adaptive recommendation systems with bandit feedback mechanisms. Mengyan’s publications explore cutting-edge topics like causal Bayesian optimization, Gaussian process bandits, and personalized news recommendation. Their work emphasizes interdisciplinary applications, from healthcare analytics to genetic sequence design. While no specific awards or grants are listed, Mengyan’s research demonstrates a strong focus on addressing real-world challenges through innovative machine learning approaches. The lack of student or lab affiliations suggests a primary focus on independent research contributions.
Dr. Masoud Ghalaii is a Lecturer in Quantum Information at the Department of Computing and Mathematics, Manchester Metropolitan University (UK). He holds a PhD in Electrical Engineering (Quantum Communications) from the University of Leeds (2019), and has conducted postdoctoral research at the University of York and the University of Leeds, focusing on quantum technologies. His academic career includes visiting research at Institut Polytechnique de Paris-Telecom ParisTech (2015). Education: PhD, University of Leeds (2019) MSc, Sharif University of Technology (2014) BSc, Isfahan University of Technology (2011) Research Interests: Dr. Ghalaii’s work centers on quantum information science and quantum optics, with applications to quantum communication networks, quantum key distribution (QKD), and quantum repeaters. He has organized workshops (e.g., White Rose Quantum Information Science and Technologies Workshop 2024) and contributed to editorial roles (e.g., Special Issue on Quantum Communication Networks for Entropy Journal). His research emphasizes secure quantum communication protocols, hybrid quantum-classical systems, and turbulence-resistant quantum networks. Publications: His recent articles explore topics like CV-MDI-QKD, satellite-based QKD, and quantum communication in turbulent environments. These contributions highlight advancements in secure quantum communication frameworks and their practical implementation across diverse networks. Affiliations & Roles: He serves as a reviewer for international journals/conferences in quantum information and optics. His expertise bridges theoretical developments with applied quantum technologies, positioning him as a key figure in advancing quantum communication infrastructure.
Orli Herscoviciمشاهده پروفایل
استادیار
- Mathematics
- Combinatorics
- Number Theory
- +۳ مورد دیگر
Orli Herscovici is an Assistant Professor in the Department of Mathematics and Computer Science at St. John’s College of Liberal Arts and Sciences, St. John’s University. Her research focuses on advanced mathematical analysis, combinatorics, and special functions, with notable contributions to fractional calculus, polynomial theory, and combinatorial identities. She has published extensively on topics including Gaussian principal frequencies, deformed fractional transforms, and degenerate polynomials. Her work bridges pure mathematics and applications in probability theory and nonlinear systems. Dr. Herscovici’s academic affiliations include the Mathematics and Computer Science department, where she contributes to teaching and research initiatives. Her publications reflect a deep engagement with interdisciplinary areas such as spectral geometry and umbral calculus. While no specific scientific awards or grants are highlighted in the provided text, her scholarly output demonstrates sustained academic engagement in theoretical and applied mathematics.
Virginie Ehrlacher Galland is a Professor at CERMICS (Centre d'Enseignement et de Recherche en Mathématiques et Calcul Scientifique) within École des Ponts ParisTech. Her expertise lies in applied mathematics, numerical analysis, and computational physics, with a focus on multiscale problems, quantum chemistry, and uncertainty quantification. She holds a PhD from CERMICS (2012) and a Habilitation (2020) from Université Paris-Dauphine. Her research interests include cross-diffusion systems, reduced basis methods, and optimal transport applications. Key contributions involve numerical methods for electronic structure calculations, homogenization techniques, and adaptive algorithms. She leads the ERC Starting Grant HighLEAP (2023-2028) and contributes to major projects like the ERC Synergy project EMC². Awards: Irène Joliot-Curie Prize (2023), Chevalier de l’Ordre National du Mérite (2025). Grants/Projects: ERC Starting Grant HighLEAP (PI), ERC Synergy EMC² (Member), ANR JCJC COMODO (PI). Her work bridges theoretical analysis and computational methods, addressing challenges in materials science, fluid dynamics, and machine learning applications.
Mohammed Nabil EL KORSO is a Professor at CentraleSupélec, part of the University of Paris-Saclay. He is affiliated with the Laboratoire des Signaux et Systèmes (L2S). His research focuses on statistical signal processing, machine learning, detection/estimation theory, and robust signal processing, with applications in radioastronomy, radar systems, and source localization. He has contributed extensively to methodologies like Kalman filtering, covariance estimation, and array processing. His work emphasizes robust techniques for handling non-Gaussian noise and interference, particularly in radio interferometry and SAR imaging. Key contributions include algorithms for array calibration, RFI mitigation, and subspace estimation. His research also addresses challenges in distributed and adaptive signal processing, with applications to vital signs monitoring and robotic systems. He has published over 50 journal articles and conference papers, focusing on performance bounds (Cramér-Rao, Weiss-Weinstein), Bayesian methods, and practical implementations for large-scale systems. His recent work includes advancements in low-cost interferometric imaging and phase estimation for SAR time series. EL KORSO collaborates with international projects like the Square Kilometre Array (SKA), contributing to technological developments in radio astronomy. He supervises research in signal processing labs and actively participates in academic conferences such as EUSIPCO and ICASSP.
Dr. Pradeepa Yahampath is a Professor in Electrical and Computer Engineering at the University of Manitoba's Price Faculty of Engineering, specializing in signal processing and multimedia communications. Research interests include: Next-generation video coding techniques Reliable multimedia transmission in wireless systems Joint source-channel coding methods Adaptive transform coding Recent publications develop novel coding schemes for correlated sources, fading channels, and OFDM systems. Theoretical frameworks address bandwidth constraints and channel uncertainty challenges. Research contributes to efficient data representation and robust transmission for multimedia applications in bandwidth-limited environments.
Maxim L. Yattselev is a Professor in the Department of Mathematical Sciences at Indiana University–Purdue University Indianapolis (IUPUI) . He has held prior positions as a Visiting Assistant Professor at the University of Oregon and a Visiting Scholar at Vanderbilt University. Education: Ph.D. and M.S. in Mathematics from Vanderbilt University (2007, 2004), M.S. and B.S. from Dnepropetrovsk National University (2001, 2000). Research Interests focus on meromorphic approximation, orthogonal polynomials, integrable systems, random polynomials, and spectral theory. He explores connections between these areas and applications to Painlevé equations, random matrix theory, and convergence of rational approximants. Publications highlight advancements in strong asymptotics for Angelesco systems, spectral theory of Jacobi matrices on trees, universality in random polynomial roots, and convergence properties of Padé and Hermite–Padé approximants. His work often employs Riemann–Hilbert techniques and potential theory. Advising: Mentored Ph.D. students Hanan Aljubran and Ahmad Barhoumi on random polynomials and multiple orthogonal polynomials. Teaching includes advanced courses in complex analysis, real analysis, orthogonal polynomials, and differential equations at IUPUI, University of Oregon, and Vanderbilt University.
- System Dynamics
- Control
- Sensing and Automation
- +۷ مورد دیگر
Jiong Tang is a Pratt & Whitney Chair Professor in Design and Manufacturing at the University of Connecticut , where he also serves as Co-Director of the Management and Engineering for Manufacturing Program . He received his B.S. and M.S. in Applied Mechanics from Fudan University, China (1989 and 1992), and his Ph.D. in Mechanical Engineering from Pennsylvania State University (2001). Prior to joining UConn, he worked at the GE Research Center as a research engineer. Research Interests : System dynamics, control theory, smart materials, vibration suppression, uncertainty propagation, computational intelligence, and multi-physics system modeling. Current Projects : Digital twin development for aerospace materials, physics-informed machine learning in manufacturing, adaptive metasurface design, and optimization of cooperative robotics. Methodological Focus : Combines Bayesian deep learning , Gaussian process metamodeling , transformer-based architectures , and multi-fidelity data fusion for industrial applications. His work emphasizes smart sensing , electromechanical integration , and uncertainty-robust inverse analysis . Collaboration : Research funded by federal agencies and industrial partners , with particular emphasis on aerospace and manufacturing technologies. His recent publications highlight generative adversarial networks for defect detection , piezoelectric metamaterials , and physics-guided neural network architectures across mechanical, structural, and composite systems.
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