جست‌وجوی استاد و پژوهشگر

David Mowbrayمشاهده پروفایل

استاد

• Semiconductor Physics
• Quantum Dots
• Nanowire Growth
• +۵ مورد دیگر

Professor David Mowbray is a Professor of Physics at the University of Sheffield, affiliated with the School of Mathematical and Physical Sciences and the Department of Physics. His research focuses on III-V semiconductors, quantum dots, and nanostructures, emphasizing applications in high-efficiency lasers and light emitters. He has pioneered studies on AlGaInP band structures and quantum dot-based devices, including lasers on Si substrates for silicon integration. Qualifications: B.A. in Physics (Hertford College, Oxford, 1984) and D.Phil. in Physics (Hertford College, Oxford, 1989). Research interests include optical spectroscopy of wide band gap materials (AlGaInP, AlInGaN) for visible/UV emitters, quantum dot lasers with low threshold currents, and nanowire quantum dots for single-photon sources. Current projects involve quantum dots in quantum wires with UCL and Warwick, aiming for nanoscale lasers and efficient photon sources. Teaching includes courses on Fourier Techniques, Electromagnetism, and nanotechnology. He has held leadership roles, such as Head of Department and Senior Tutor, and serves on professional committees like the Institute of Physics Degree Accreditation Committee. Grants include EPSRC funding for quantum dot lasers on Si (2012–2016) and nanowire quantum dots for silicon-based emitters (2016–2020). His work bridges semiconductor physics, nanostructure engineering, and optoelectronic device applications.

Neil Stricklandمشاهده پروفایل

استاد

• Algebraic Topology
• Stable Homotopy Theory
• Formal Groups
• +۳ مورد دیگر

Professor Neil Strickland is a faculty member at the University of Sheffield's School of Mathematical and Physical Sciences, holding the rank of Professor. He earned his PhD from the University of Manchester in 1992 and held positions as a C.L.E. Moore Instructor at MIT and a Research Fellow at Trinity College Cambridge before joining Sheffield in 1998. He received the prestigious Whitehead Prize from the London Mathematical Society in 2005. His research focuses on stable homotopy theory, exploring connections between topology, algebraic geometry, and category theory. Key areas include formal group laws, chromatic homotopy theory, and equivariant cohomology. Prof. Strickland emphasizes translating topological problems into algebraic frameworks, leveraging category theory for structural insights. Grants: He has led EPSRC-funded projects, including 'Symmetric Powers of Spheres' and 'Equivariant Elliptic Cohomology and Class Field Theory,' and collaborated on grants like 'Higher Structures on Elliptic Cohomology.' Teaching: Prof. Strickland instructs courses such as MAS334 Combinatorics and MAS435 Algebraic Topology. His homepage provides further academic resources and materials. Awards: His recognition includes the Whitehead Prize, reflecting contributions to algebraic topology and homotopy theory.

• Data Science
• Machine Learning
• Harmonic Analysis
• +۷ مورد دیگر

Mauro Maggioni is a Professor in the Departments of Mathematics and Applied Mathematics and Statistics at Johns Hopkins University. His research focuses on the mathematical foundations of Data Science, with applications in molecular dynamics, hyperspectral imaging, and reinforcement learning. He employs techniques from Harmonic Analysis, Approximation Theory, and Probability to develop scalable algorithms, particularly multiscale methods for analyzing high-dimensional data. Maggioni’s work bridges theoretical mathematics and practical applications, including cardiac electrophysiology modeling, unsupervised segmentation of hyperspectral images, and reduced-order modeling of complex systems. Education: B.S. in Mathematics from Università degli Studi in Milan, Italy; Ph.D. in Mathematics from Washington University in St. Louis. He held a Gibbs Assistant Professorship at Yale before moving to Duke University and later becoming a Bloomberg Distinguished Professor at JHU. Research Interests: Mathematical foundations of Data Science, Machine Learning, Partial Differential Equations, and their applications in physical and biological systems. Notable contributions include diffusion wavelets, interaction kernel learning, and multiscale geometric analysis of molecular dynamics data. Scientific Awards: Popov Prize in Approximation Theory (2007), NSF CAREER Award and Sloan Fellowship (2008), Fellow of the American Mathematical Society (2013), Simons Fellowship (2020). Advising & Grants: Maggioni mentors postdocs and students in areas like stochastic systems and signal processing. His group’s work is supported by Simons Foundation grants, NSF funding, and collaborations with institutions like MINDS and CIS at JHU. Labs/Teams: Leads a research group focused on data-driven discovery in mathematics and applied sciences, emphasizing interdisciplinary collaboration across computational methods, statistics, and domain-specific applications.

• Industrial Automation
• Control Systems
• Machine Learning Applications
• +۵ مورد دیگر

Valeriy Vyatkin is a Professor at the Department of Electrical Engineering and Automation, Aalto University. His research focuses on advancing industrial automation, control systems, and their integration with emerging technologies like machine learning and digital twins. He specializes in standards such as IEC 61499, addressing interoperability, formal verification, and performance optimization in distributed automation systems. Key research interests include: Physics-informed machine learning for industrial processes (e.g., steel rolling, reservoir engineering) Formal methods for control system validation and safety-critical applications Development of adaptive automation frameworks for Industry 5.0 challenges, including human-robot collaboration and energy systems Interoperability between legacy and modern industrial standards (OPAS, OPC UA) Recent work emphasizes real-time simulation, FPGA-based control prototyping, and AI-driven solutions for energy efficiency and sustainability in manufacturing, horticulture, and process industries. Publications span topics like robotic walker design, probabilistic model checking, and decentralized learning management systems. He collaborates on EU and industry-funded projects, focusing on digital twin implementation, edge computing, and virtual commissioning. His team develops tools for automated code generation, system migration, and anomaly detection in complex industrial settings.

Pankaj Mehtaمشاهده پروفایل

استاد

• Statistical Physics
• Systems Biology
• Ecological Dynamics
• +۳ مورد دیگر

Pankaj Mehta is a Professor in the Department of Physics at Boston University, with additional affiliations in the Department of Biomedical Engineering. His research bridges statistical physics, theoretical biology, and interdisciplinary systems approaches. Key areas include ecological dynamics, synthetic biology, and the application of machine learning principles to biological systems. His work focuses on understanding emergent phenomena in biological systems, such as cell fate decisions, ecosystem stability, and signal processing in cellular networks. He has pioneered methods combining physics-based modeling with computational tools to study complex systems, including gene circuits, microbial communities, and cancer dynamics. Recent contributions highlight the use of order parameters for interpreting cellular states, geometric frameworks for ecological niches, and machine learning analogies to ecological principles. His interdisciplinary approach integrates experimental data with theoretical models to address questions in biomedicine, environmental science, and fundamental physics.

• Algebraic Geometry
• Hyperkähler Manifolds
• Rozansky-Witten Invariants
• +۳ مورد دیگر

Prof. Dr. Marc Nieper-Wißkirchen is a University Professor (W3) for Algebra and Number Theory at the Institute of Mathematics, University of Augsburg, since 2008. He has held visiting positions at the Max Planck Institute for Mathematics in Bonn and the University of Cambridge, and served as a Junior Professor at Johannes Gutenberg University Mainz (2004–2008). Since October 2021, he has been Vice Dean of the Faculty of Mathematics, Natural Sciences and Technology (MNTF). Education: Diploma in Mathematics (2000) from the University of Cologne, preceded by preliminary diplomas in physics (1997) and mathematics (1997). His doctoral studies (2000–2002) under Prof. Daniel Huybrechts focused on characteristic classes and Rozansky-Witten invariants of hyperkähler manifolds. His research spans algebraic geometry, differential geometry, and mathematical physics, with a focus on hyperkähler manifolds, Hilbert schemes of points, irregular singular connections, and Stokes phenomena. He has contributed to the understanding of Rozansky-Witten invariants, Chern numbers, and topological aspects of differential equations. Recent publications include works on Betti structures of hypergeometric equations (2022) and Stokes matrices for confluent hypergeometric equations (2022), reflecting his ongoing exploration of differential equations' geometric underpinnings. Earlier works address Hochschild homology, equivariant cohomology, and operads in algebraic contexts. His computational contributions include software demonstrations for elliptic curve arithmetic, Collatz sequences, and Google Matrix simulations. Technical expertise includes programming languages (TeX, PHP, HTML) and systems (Unix/Linux, Zope/Plone).

Rastko Sknepnekمشاهده پروفایل

استاد

• Pattern formation in complex geometries
• Physics of biological membranes
• Physics of artificial membranes
• +۵ مورد دیگر

Rastko Sknepnek is a Chair of Biological Physics at the University of Dundee , affiliated with both the School of Science and Engineering (Physics department) and the School of Life Sciences (Computational Biology). His research focuses on pattern formation in complex geometries, physics of biological/artificial membranes, active matter systems, and computational biophysics. PhD in Physics (2004, Missouri S&T) Postdoctoral training at McMaster, Iowa State/Ames Lab, Northwestern, and Syracuse University Joined University of Dundee in 2013 as Lecturer and Dundee Fellow Current work explores cellular homeostasis , actomyosin dynamics , and collective cell behavior , with recent publications analyzing epithelial monolayers, active matter models, and developmental mechanics. His 2025 projects include AI applications for drug resistance and cell shape quantification. Scientific Awards : Distinguished University Postdoctoral Fellowship (Syracuse, 2012) Dundee Fellow (2013) He leads the Computational Soft Condensed Matter and Biophysics Group , collaborating with institutions like University of Oxford, UCL, and University of Bristol. Grants include £2.1 million from UKRI for embryonic self-organization research and BBSRC funding for cell dynamics studies.

• Fluid Mechanics
• Aeroacoustics
• Computational Fluid Dynamics
• +۳ مورد دیگر

Antonio Alguacil Cabrerizo is an Assistant Professor (starting 2025) at Université de Sherbrooke, where he currently serves as a Postdoctoral Fellow (2023-2025). His academic trajectory includes dual doctoral degrees in Mechanical Engineering from Université de Sherbrooke and École Nationale Supérieure d'Aéronautique et de l'Espace, complemented by aerospace engineering degrees from ENSEEIHT and Universidad Politécnica de Madrid. His research integrates computational fluid dynamics with machine learning, focusing on: Aeroacoustic prediction and noise source identification Deep learning surrogates for fluid and acoustic systems Turbomachinery and airfoil aerodynamics Data-driven modeling of spatiotemporal physical systems This work advances computational efficiency in simulating complex wave propagation, turbulence effects, and fluid-structure interactions. Publication analysis reveals consistent focus on developing neural network-based computational methods for aeroacoustics and fluid dynamics. His 15 most recent works demonstrate progressive refinement in applying convolutional architectures to predict acoustic scattering, refraction phenomena, and turbomachinery noise with increasing physical accuracy and computational efficiency. Awards and recognition include: Top 5 in AIAA Best Student Paper in Aeroacoustics (2024) Graduate Scholarship Award from CFD Society of Canada (2022) Eureka Scholarship from Université de Sherbrooke (2021) Best Poster Prize at CRASH Day (2021) He secured a $70,000 CAD startup grant (2025-2028) from Université de Sherbrooke for establishing his research program. No student advising relationships or laboratory affiliations are currently documented.

Efstathios (Stathis) Michaelidesمشاهده پروفایل

استاد

• Advanced Energy Systems
• Energy Conversion and Conservation
• Geothermal Energy
• +۹ مورد دیگر

Efstathios (Stathis) Michaelides is the W.A. "Tex" Moncrief, Jr. Founding Chair of Engineering at Texas Christian University (TCU). He holds a Ph.D. and M.S. in Engineering Science from Brown University (1980, 1979) and a B.A. in Engineering Science and Economics from Oxford University (1977). His research focuses on advanced energy systems, multiphase flow, and renewable energy transitions. Ph.D. , Engineering Science, Brown University, 1980 M.S. , Engineering Science, Brown University, 1979 B.A. , Engineering Science and Economics, Oxford University, England, 1977 Michaelides' work spans energy conversion , geothermal systems , nanofluidics , and particle-fluid dynamics . His recent publications analyze energy storage requirements for renewable transitions, drag force correlations in complex flows, and thermodynamic implications of carbon sequestration. His research trends include decarbonization strategies , nanoparticle-enhanced phase transitions , and smart microfluidic systems . He has also contributed to foundational texts like Particles, Bubbles and Drops (2006) and the Multiphase Flow Handbook (2017).

• Control Technology
• Distributed Optimization
• Machine Learning
• +۴ مورد دیگر

Mikael Johansson is a Professor at Kungliga Tekniska Högskolan (KTH), specializing in Control Technology . He teaches and coordinates courses such as Distributed Optimization (FEL3311) and various advanced-level degree projects in computer science, electrical engineering, and systems engineering. His research spans Control Systems , Machine Learning , and Optimization , with a focus on asynchronous algorithms, federated learning, and applications in energy systems and construction. His work includes 15 recent publications on topics like neural networks, distributed optimization, and battery technology. Notable areas of contribution are in asynchronous learning, federated learning with privacy constraints, and quasi-Newton methods for optimization. His research bridges theoretical advancements with practical applications in urban design, healthcare, and autonomous systems.

• Geometallurgy
• Compositional Data Analysis
• Geostatistics
• +۳ مورد دیگر

Dr. Raimon Tolosana Delgado is a Research Fellow at the Helmholtz-Zentrum Dresden-Rossendorf (HZDR), affiliated with the Helmholtz Institute Freiberg for Resource Technology. He leads research in predictive geometallurgy and statistical analysis of mineral resources, focusing on translating geological data into processing insights. His research integrates geostatistics , compositional data analysis (CoDa) , and machine learning to model ore behavior and resource potential. Key areas include: Predictive geometallurgy for forecasting ore/waste behavior Bayesian statistics for parameter estimation and uncertainty analysis Development of R-based tools (e.g., compositions and gmGeostats packages) for mineral data analysis Particle-based process modelling for mineral separation optimization Recent publications emphasize machine learning integration (e.g., neural networks for geophysical tensor fields), tailings reprocessing (3D geostatistical assessment of resource potential), and advanced statistical methods for compositional data. A consistent trend involves enhancing predictive accuracy in mineral processing through multi-source data fusion. Dr. Tolosana Delgado coordinates the development of technology platforms for geometallurgical data analysis, including databases and interfaces for industrial applications. His work bridges ore geology, mineral processing, and metallurgy to optimize resource efficiency.

Mohammad Piraniمشاهده پروفایل

استادیار

• Resilient Control
• Fault-Tolerant Control
• Networked Control Systems
• +۱۱ مورد دیگر

Mohammad Pirani is an Assistant Professor in the Department of Mechanical Engineering at the University of Ottawa, with a joint appointment at the School of Electrical Engineering and Computer Science. Previously, he held postdoctoral and research assistant professor roles at the University of Waterloo, University of Toronto, and KTH Royal Institute of Technology. Education: Ph.D., Mechanical and Mechatronics Engineering, University of Waterloo (2017) MASc., Electrical and Computer Engineering, University of Waterloo (2014) BASc., Mechanical Engineering, Amirkabir University of Technology (2011) His research focuses on resilient and fault-tolerant control in complex systems, including networked control systems and multi-agent systems . He explores intersections with network science , cybersecurity , and machine learning , addressing vulnerabilities in cyber-physical systems like automotive networks. Notable contributions include publications in IEEE Transactions on Control of Network Systems and Automatica , with recent work on network critical slowing down and graph-theoretic resilience strategies. His research trends emphasize reliable learning , security in distributed systems , and data-driven detection of critical transitions . Scientific Awards: Senior Member, IEEE Mohammad Pirani holds a dual appointment at the University of Ottawa and an adjunct professor position at the University of Waterloo. His work bridges mechatronics , robotics and automation , and networked systems , with future directions targeting cybersecurity in cyber-physical systems.

• Physics
• Computer Science
• Mathematics
• +۱۰ مورد دیگر

Cristopher Moore is a Professor at the Santa Fe Institute, where he conducts interdisciplinary research at the intersection of physics, computer science, and mathematics. His work focuses on understanding phase transitions in computational problems, statistical inference, and network analysis. Moore has made significant contributions to the fields of complex systems, quantum computing, and algorithmic justice. Moore's primary research areas include phase transitions in computational problems and statistical inference, where he investigates how problems suddenly become hard or impossible to solve when certain thresholds are crossed. His work spans social networks, big data analysis, quantum computing, algorithmic transparency, and decarbonization efforts. He is particularly known for applying physics-inspired approaches to computational problems, using techniques from spin glass theory, network theory, and computational complexity. His recent publications reveal a strong focus on community detection in networks, phase transitions in data science problems, algorithmic fairness in criminal justice systems, and quantum computing applications. Moore's work demonstrates consistent patterns across multiple disciplines, with recurring themes of phase transitions, computational limits, and the application of physics concepts to computational problems. Moore actively mentors students and has advised numerous PhD and Master's students who have gone on to successful careers in academia and industry. His work on algorithmic justice has influenced policy discussions in New Mexico and beyond, particularly regarding risk assessment in the criminal justice system.

• Molecular Chemistry
• Materials Chemistry
• Photocatalysis
• +۴ مورد دیگر

Professor Richard E Douthwaite is a distinguished academic in the Department of Chemistry at the University of York, where he leads research in molecular and materials chemistry with a focus on photocatalysis and solar energy conversion. His work bridges fundamental inorganic chemistry with practical applications in renewable energy technologies. His research interests span multiple areas of sustainable chemistry: Synthesis and application of materials for photocatalysis using renewable energy Development of metal complexes for catalytic applications Photocatalytic water splitting for hydrogen production Environmental applications of photocatalysis for pollutant degradation Structure-property relationships in catalytic materials Analysis of Professor Douthwaite's recent publications reveals a strong emphasis on materials design for solar energy conversion, particularly focusing on: Advanced characterization of photocatalytic materials using in-situ techniques Development of hybrid materials combining semiconductors with metal complexes Engineering of nanostructured materials for improved photocatalytic efficiency Understanding fundamental mechanisms of light-induced chemical reactions Professor Douthwaite has been recognized with the prestigious FRSC (Fellow of the Royal Society of Chemistry) award in 2010 for his contributions to the field. His research portfolio includes significant grant funding from EPSRC and other organizations, with current projects focusing on environmental electron microscopy of photocatalysts and quantum transport for advanced spintronics. As chair of the graduate school and inorganic section leader, he has mentored over 100 undergraduate students and numerous postgraduates. His teaching spans inorganic and physical chemistry, with a focus on metal-ligand bonding, reaction mechanisms, and materials chemistry. Professor Douthwaite's laboratory is equipped with state-of-the-art facilities including UV-Vis and diffuse reflectance spectrometers, electrochemical workstations, and access to the York JEOL Nanocentre for advanced microscopy. His research group collaborates extensively across disciplines, working at the intersection of chemistry, materials science, and renewable energy technologies.

John E. Taylorمشاهده پروفایل

استاد

• Smart City Digital Twins
• Urban Infrastructure Resilience
• Network Dynamics
• +۵ مورد دیگر

John E. Taylor is the Frederick Law Olmsted Professor and Associate Chair for Faculty Development and Research Innovation at the Georgia Institute of Technology's School of Civil and Environmental Engineering within the College of Engineering. His research focuses on the intersection of human and engineered networks, with particular emphasis on creating resilient infrastructure systems that serve society's needs while creating more livable communities. Taylor's research interests span multiple domains including Smart City Digital Twins , Urban Infrastructure Resilience , Network Dynamics , and Building-Occupant Interaction . His work examines how human behavior, infrastructure systems, and environmental factors interact during normal operations and extreme events. He has developed innovative approaches to understanding urban systems through the lens of network theory and computational modeling. His publication record demonstrates consistent contributions to the fields of urban analytics and infrastructure resilience, with a recent focus on digital twin technologies for urban systems. Taylor's work shows a clear trajectory toward increasingly sophisticated integration of AI, network science, and civil infrastructure engineering to address complex urban challenges. His research has particular relevance for cities facing climate change impacts and seeking to build more equitable and resilient communities. Taylor leads the Network Dynamics Lab at Georgia Tech, where he mentors PhD students and postdoctoral researchers. His lab has produced significant work on human-infrastructure interaction, particularly during disasters and extreme events. The lab's research combines computational modeling, data analytics, and field studies to understand and improve urban systems. His work has been applied to real-world challenges including river emergency response systems, urban heat exposure forecasting, and disaster response optimization. Taylor has collaborated with city officials and agencies to implement systems that have demonstrable community benefits, such as the AI-enabled camera system for drowning prevention on the Chattahoochee River and crime reduction systems using mobile cameras guided by AI algorithms.