جستجوی استاد راهنمای دانشگاهی

Emmanuel Alejandro Flores Johnsonمشاهده پروفایل

دانشگاهی

• Composite and hybrid materials
• Metals and alloy materials
• Numerical modelling
• +۹ مورد دیگر

Dr. Emmanuel Alejandro Flores Johnson is an Adjunct Senior Lecturer at the School of Mechanical and Manufacturing Engineering, Faculty of Engineering at the University of New South Wales (UNSW), and a Senior Structural Modeller at the Australian Nuclear Science and Technology Organisation (ANSTO). His research focuses on the performance of advanced structural materials under extreme conditions for applications in the nuclear industry and non-nuclear energy-generation systems using numerical modelling. Dr. Flores Johnson has extensive expertise in developing finite element simulations of non-linear material behaviour, including plasticity, damage and fracture mechanics, impact, and shock behaviour. His work spans various engineering materials and structures, including composites, metal alloys, polymers, concrete and bioinspired materials. He has been involved in international multi-disciplinary collaborations, industry-oriented projects, and defence projects requiring numerical modelling and experimental testing expertise. His research interests include: Composite and hybrid materials Metals and alloy materials Numerical modelling and mechanical characterisation Modelling and simulation Finite element analysis of non-linear material behaviour Plasticity, damage and fracture mechanics Dr. Flores Johnson's publication record demonstrates a strong focus on applying numerical methods to solve complex structural engineering problems, particularly in extreme environments. His recent work shows increasing emphasis on nuclear applications and non-nuclear energy systems, reflecting the growing importance of these sectors in materials research. Dr. Flores Johnson has received recognition for his contributions to structural modelling and materials science through various research collaborations and projects. His work bridges theoretical modelling with practical applications in critical industries. With over a decade of experience in both academic and research organization settings, Dr. Flores Johnson has developed a robust research program that combines advanced numerical techniques with experimental validation to address challenging problems in structural materials science.

Iris Yoonمشاهده پروفایل

استادیار

• Topological Data Analysis
• Algebraic Topology
• Network Theory
• +۵ مورد دیگر

Iris Yoon is an Assistant Professor of Mathematics at Wesleyan University, specializing in topological data analysis with applications in biological systems. Her research bridges algebraic topology, network theory, and machine learning to address complex problems in neuroscience, cancer research, and genomics. Her research interests focus on developing novel topological methods for analyzing biological data. She creates mathematical frameworks that extract meaningful patterns from high-dimensional datasets, particularly in tumor microenvironments and neural systems. Her work combines persistent homology, relational topology, and multiscale analysis to reveal hidden structures in biological systems. Yoon's publication record shows consistent contributions to computational topology with increasing biological applications since 2018. Her recent work (2023-2025) demonstrates sophisticated applications of topological methods to tumor-immune interactions, neural manifolds, and cellular spatial patterns, reflecting an evolving research trajectory toward complex biological systems. Outside traditional academic publications, Yoon has developed practical applications including a classical musician recommendation system based on collaboration networks using node2vec embeddings, and a COVID-19 hospital bed shortage prediction model. These demonstrate her commitment to applying topological methods to real-world data science problems.

• Quantum Computing
• Hybrid Quantum-Classical Algorithms
• Workflow Scheduling
• +۳ مورد دیگر

Katarzyna Rycerz is a Lecturer at the Institute of Computer Science within the Faculty of Computer Science at AGH University of Science and Technology in Kraków, Poland. She holds the academic rank of Lecturer as part of the assistant professors group, with her office located at D-17, ul. Kawiory 21, room II, 3.54. Her research spans quantum computing, hybrid quantum-classical algorithms, and distributed systems, with significant contributions to workflow scheduling and complex network optimization. Dr. Rycerz's primary research focuses on quantum optimization techniques, particularly quantum annealing and variational algorithms applied to combinatorial problems. Her work bridges theoretical quantum computing with practical high-performance computing challenges, including load rebalancing in HPC systems and community detection in complex networks. She has developed software tools like QHyper for hybrid quantum-classical optimization and explored quantum solutions for thermoelectric properties in graphene and quantum game theory paradoxes. Her 15 most recent publications (2019-2025) reveal a dominant trend toward quantum-classical hybrid methods, with 80% focused on quantum annealing applications for workflow scheduling, network analysis, and optimization problems. Key themes include D-Wave system implementations, quantum game theory extensions, and thermoelectric material simulations, demonstrating a consistent shift from earlier multiscale simulation work toward cutting-edge quantum computing applications. No scientific awards are documented in the available information. Details regarding student advising, research grants, laboratory affiliations, or collaborative teams are not provided in the source materials.

Xavier Antoineمشاهده پروفایل

استاد

• Mathematical analysis of wave scattering problems
• Numerical analysis of PDEs
• Scientific computing
• +۲ مورد دیگر

Xavier Antoine is a Full Professor in applied and computational mathematics at the Institut Elie Cartan de Lorraine (IECL) , Université de Lorraine, and a member of the Inria team SPHINX . He leads the associated team BEC2HPC focused on Bose-Einstein Condensates simulation. Research Focus : Mathematical and numerical analysis of wave scattering problems (Helmholtz, Maxwell, Klein-Gordon), quantum mechanics (Schrödinger/Gross-Pitaevskii), and fractional/nonlocal PDEs. Methodologies : Absorbing boundary conditions, perfectly matched layers, high-order approximation schemes, domain decomposition methods, and structure-preserving algorithms. His recent work includes advancements in fractional gradient flows for nonlinear Schrödinger equations and domain decomposition solvers for acoustic problems. He collaborates on multidisciplinary projects involving computational physics and engineering. Current Roles : Attaché pour la Science et la Technologie at the French Embassy in Beijing (since 2022), and former Head of IECL (2015-2020). He also served as a Scientific Advisor at the French National Agency for Research (ANR).

• Battery Components
• Lattice-Boltzmann Method
• Nanoscale Engineering
• +۴ مورد دیگر

Martin Lautenschläger is an Associate Professor at the Institute of Mechanical and Electrical Engineering, University of Southern Denmark (SDU Mechatronics), specializing in computational modeling and simulation of advanced engineering systems. His work bridges mechanical engineering and material science through high-performance computing, focusing on battery technologies, fluid dynamics, and nanoscale interactions. Academic Rank: Associate Professor Institution: University of Southern Denmark Research Focus: Battery microstructures, Lattice-Boltzmann methods, nanoscale lubrication, and multiphase flow simulations Research Trends: Recent publications emphasize pore-scale modeling of battery components, electrolyte filling processes, and multiscale approaches to optimize battery performance. His simulations span from molecular dynamics to continuum-level predictions, particularly in lithium-ion and lithium-sulfur systems. Collaborations: Active in interdisciplinary projects with institutions across Europe, focusing on energy storage, computational microstructure analysis, and manufacturing optimization. Teaching: Supervises master's and bachelor's theses in mechanical design, fluid dynamics, and battery technologies, while teaching core courses in mechanical engineering.

Hao Liمشاهده پروفایل

پژوهشگر ارشد

• Topology Optimization
• Mechanical Engineering
• Thermal Systems
• +۲ مورد دیگر

Hao Li is a MSCA Postdoc Fellow and Visiting Scholar at LIP6 , affiliated with the University of Southern Denmark in the Department of Mechanical Engineering . His research focuses on advanced computational methods for topology optimization in thermal, fluid, and structural engineering systems. Education: Not explicitly stated in the provided text. Current Projects: Leading EU-funded research on heat exchanger design using multiscale models and machine learning. Dr. Li's work spans multiscale topology optimization, level-set methods, and fluid-structure interaction, with applications in microchannel cooling, compliant mechanisms, and biodegradable composites. His recent publications highlight advancements in 3D conjugate heat transfer, adaptive meshing, and eigenfrequency maximization. The trends in his research output (2017–2025) emphasize thermal-fluid systems , high-resolution structural optimization , and manufacturable composite designs . Notable subfields include triply periodic minimal surfaces for cooling channels, nonlinear buckling analysis, and phasor-based dehomogenization techniques. Teaching & Supervision: Currently supervising projects on topology optimization frameworks for heat sinks and high heat flux cooling. His past projects (2018–2023) include research on piezoelectric transducers and thermal-fluid system design. Labs & Collaborations: Collaborates with institutions in Japan and France, focusing on experimental validation and industrial applications. His network includes partnerships with researchers in structural mechanics, computational fluid dynamics, and additive manufacturing.

• Environmental Geochemistry
• Biogeochemistry
• Contaminant Transport and Remediation
• +۳ مورد دیگر

Chongxuan Liu is Chair Professor at the Southern University of Science and Technology (SUSTech) , affiliated with the School of Environmental Science and Engineering and the Department of Environmental Science and Engineering. Previously, he served as Chief Scientist at the U.S. Department of Energy's Pacific Northwest National Laboratory (PNNL) from 2006–2016 and as a part-time scientist there since 2015. His research integrates theoretical, experimental, and simulation approaches to study pollutant kinetics, multi-scale reactive transport, microbial community evolution, and nanomaterials for environmental remediation. Ph.D. in Environmental Engineering, Johns Hopkins University M.S. in Low-Temperature Aqueous Geochemistry, University of Alabama M.E. in Hydrogeochemistry and B.S. in Geology, Zhejiang University Liu’s research focuses on environmental geochemistry and biogeochemistry , particularly the kinetics of contaminant transformation , multi-scale reactive transport theories , and microbial/nanomaterial-based remediation technologies . His work addresses uranium, arsenic, and nitrate contamination in groundwater-sediment systems, employing cybernetic modeling, pore-scale simulations, and watershed-scale hydro-biogeochemical studies. His recent publications (2014–2016) emphasize uranium adsorption mechanisms , nanoparticle transport dynamics , and coupled biogeochemical-hydrological processes . Articles span journals like Environmental Science & Technology , Geochimica et Cosmochimica Acta , and Water Resources Research , reflecting expertise in contaminant transport, microbial remediation, and nanotechnology. Scientific accolades include Fellow of the Geological Society of America (2011) U.S. Department of Energy Outstanding Contribution Award (2004) He has authored over 170 peer-reviewed articles, with an H-index of 51 (8,000+ citations), and served as Associate Editor-in-Chief for ACS Earth and Space Chemistry . His lab at SUSTech trains undergraduate, master’s, and Ph.D. students, with recent publications by Li Qianqian (master’s student) and collaborations across environmental molecular sciences, computational modeling, and nanomaterial synthesis.

• Additive Manufacturing
• Computational Mechanics
• Topology Optimization
• +۴ مورد دیگر

Albert To is a Professor at the Swanson School of Engineering, University of Pittsburgh, where he holds the William Kepler Whiteford Professorship. He serves as Director of both the MOST-AM Consortium and the ANSYS Additive Manufacturing Research Laboratory. Since joining Pitt in 2008, he has advanced from assistant to associate (2014) and full professor (2019). Education: BS, MS, and PhD from UC Berkeley; MS from MIT Postdoctoral Research: Northwestern University with Wing Kam Liu Dr. To's primary research interests center around design optimization for additive manufacturing, multiscale methods, and computational mechanics. His work focuses on fast process modeling and topology optimization for metal additive manufacturing. He directs the ANSYS Additive Manufacturing Research Laboratory, which houses advanced metal 3D printers including EOS DMLS, Optomec LENS, and ExOne binder jetting systems. In 2016, he founded the MOST-AM Consortium, which now includes over 30 member companies and research labs collaborating on additive manufacturing research. His research has been consistently supported by major funding agencies including NASA, DOD, DOE, NSF, America Makes, and industry partners like ANSYS. The recent publications demonstrate a strong focus on addressing key challenges in metal additive manufacturing processes, particularly laser powder bed fusion and wire-arc directed energy deposition technologies. His work spans from fundamental process modeling to practical applications in materials science and mechanical engineering. NSF BRIGE Award (2009) Air Force Summer Faculty Fellowship (2009) Board of Visitors Faculty Award (2016) Carnegie Science Award (2018) Best Student Paper Award, 46th Acoustic Emission Working Group Meeting (2003) Dr. To has secured substantial research funding from government agencies and industry partners to advance additive manufacturing technologies. His MOST-AM Consortium facilitates collaboration between academia and industry, accelerating the translation of research findings into practical applications. He has advised numerous graduate students who have contributed to his extensive publication record in top journals. His laboratory at the University of Pittsburgh is equipped with state-of-the-art metal 3D printing systems, enabling both fundamental research and applied development in additive manufacturing. The MOST-AM Consortium provides a framework for industry collaboration, ensuring research addresses real-world challenges in the field.

Eran Treisterمشاهده پروفایل

استادیار

• Computational Science
• Scientific Computing
• Numerical Methods
• +۸ مورد دیگر

Eran Treister is an Assistant Professor at the Ben Gurion University of the Negev in the Department of Computer Science. He completed his postdoctoral fellowship at the University of British Columbia (2014-2016) and earned his PhD from the Technion in 2014 under Prof. Irad Yavneh. His research spans computational science, numerical methods, and machine learning, with a focus on: Scalable algorithms for inverse problems Graph Neural Networks (GNNs) optimization Seismic and optical imaging via PDE solvers Low-precision deep learning acceleration Multilevel preconditioning techniques Recent work explores: Graph neural networks for PDEs with adaptive meshes Deep learning approaches to Helmholtz equation modeling 3D shape reconstruction via parametric level sets He serves on editorial boards: SIAM Journal on Scientific Computing (2024-) Copper Mountain Conference on Multigrid Methods (2025) International Conference on Machine Learning (ICML) as Area Chair (2025) Current teaching: Optimization Methods for Data Science (Spring 2025) Deep Learning Mini-Project (Winter 2024/5) Advanced Numerical Optimization (Spring 2025)

Florian De Vuystمشاهده پروفایل

استاد

• Fluid-Structure Interaction
• Scientific Machine Learning
• Reduced-Order Modeling
• +۶ مورد دیگر

Florian De Vuyst is a Full Professor at the Department of Computational Engineering, University of Technology of Compiègne (UTC), where he conducts research at the Biomechanical Bioengineering Laboratory (BMBI UMR 7338). Previously affiliated with the Applied Mathematics Laboratory (LMAC), his work spans Fluid-Structure Interaction (FSI) , Scientific Machine Learning , GPU Computing , and Reduced-Order Modeling (ROM) . His research focuses on multimaterial flows , automotive crash dynamics , and bioengineering applications like microcapsule deformation in Stokes flows. His recent publications highlight machine learning integration with FSI simulations and GPU-accelerated solvers for compressible flows. Collaborations include Renault , Michelin , and ENS Paris-Saclay on projects like tsunami coastal impact modeling (DIGISCOPE EquipEx) and vehicle drag estimation . He has supervised 15+ PhD students, including Azzedine Tiba (non-intrusive ROM) and Vincent Mahy (multimaterial methods). Scientific Awards: Recipient of the Best Applied Paper Award at EGC 2008 Grants & Collaborations: Involved in CNRS Editions' interdisciplinary volume on tsunamis, ERCOFTAC symposia, and industrial partnerships

Yi-Lung Moمشاهده پروفایل

استاد

• Structural Engineering
• Seismic Engineering
• Composite Materials
• +۶ مورد دیگر

Yi-Lung Mo , Ph.D., P.E., is a Moores Professor at the University of Houston in the College of Engineering , Department of Civil and Environmental Engineering. With a Ph.D. in Structural Engineering from the University of Hannover (1982), he has held academic and leadership roles, including Director of the Thomas T.C. Hsu Structural Research Laboratory (2003–2016) and Advisor to the National Center for Research on Earthquake Engineering (2003–2009, 2011–2016, 2019–Present).

• Molecular Computer Simulations
• Force Field Development
• Coarse-Grained Modeling
• +۷ مورد دیگر

Alexander Lyubartsev is a Professor at the Department of Chemistry in Stockholm University, specializing in Molecular Computer Simulations . His research bridges fundamental physical chemistry with applied computational modeling, focusing on biomembranes, DNA-chromatin structures, inorganic surfaces, and multiscale method development. He teaches courses like Physical Chemistry (15 hp) and Chemical Modeling (7.5 hp) , mentoring students in computational approaches for molecular systems. Education: PhD in Molecular Biophysics (1988), St. Petersburg State University Academic Positions: 1988-1992: Researcher, St. Petersburg State University 1993-2001: Research Associate, Stockholm University 2001-2007: Associate Professor, Stockholm University 2007-present: Professor, Stockholm University His research interests center on developing computational methods for molecular systems. Key areas include: Force Field Development : Slipids for lipids, inorganic surfaces (TiO2, ZnO), zeolite interactions Coarse-Grained Modeling : DNA, nucleosome core particles, and multiscale approaches using inverse Monte Carlo Advanced Sampling : Expanded ensemble techniques, metadynamics for free energy calculations Software Development : MDynamix for molecular dynamics, MagiC for coarse-graining The articles reflect his focus on: Modeling G-quadruplex elasticity under variable ionic conditions (2017) Extending Slipids force field to polyunsaturated lipids (2016) Analyzing polyelectrolyte conformational transitions with Monte Carlo methods (2017) Investigating ion-DNA binding specificity through molecular dynamics (2016) Developing transferable potentials for gas adsorption in zeolites (2016) Studying TiO2 nanoparticle wetting mechanisms via ab initio simulations (2016) His methodological contributions include inverse Monte Carlo coarse-graining, systematic force field optimization, and multiscale approaches for chromatin components. The research directly impacts drug design, nanotoxicity modeling, and materials science applications.

• Hydrogeology
• Pore Scale Modelling
• Reactive Transport
• +۱۲ مورد دیگر

Dr. Amir Raoof serves as an Associate Professor in the Department of Earth Sciences within the Faculty of Geosciences at Utrecht University. He leads the Multiscale Porous Media Lab and the Structures of Strength (SoS) platform for Unusual Collaborations, and is affiliated with the University of Stuttgart. As a member of the Utrecht Young Academy and the Sustainability strategic themes, he bridges disciplines to address health, food, energy, cultural, and environmental challenges through porous materials research. His research expertise spans hydrogeology, pore scale modeling, reactive transport, two-phase flow, and precipitation processes. Amir applies multi-scale and multi-disciplinary approaches to integrate microscopic experimental observations with numerical methods, developing the pore-scale modeling platform Poreflow to simulate multi-component reactive transport under saturated and multi-phase flow conditions. His work extends beyond traditional geosciences to biological tissues, industrial materials, and environmental applications. Amir has received significant recognition including the 2020 Procter and Gamble Award for Porous Media Research, STW Open-Mind 2016 Award, NWO Veni award, and Teacher Talent Award. He was selected for the Utrecht Young Academy for his energetic, active, independent and interdisciplinary fellowship. 2020 Procter and Gamble Award for Porous Media Research, International Society for Porous Media Teacher Talent Award STW Open-Mind 2016 Award: Bacteria could save us from extreme drought NWO Veni award Utrecht Young Academy selection As an advisor, Amir supervises multiple PhD candidates and postdoctoral researchers including Enno de Vries, Priyanka Agrawal, and Janou Koskamp. His research is supported by prestigious grants including an NWO Veni award for his work on bridging pore and macro scales in solute transport modeling, the NanoRem project on nanotechnological remediation processes, and the HPT NAM Project on fault friction properties. Amir directs the Multiscale Porous Media Lab with cutting-edge computational and experimental facilities, and leads the Structures of Strength (SoS) platform that brings together researchers from biology, medicine, culture, history, engineering and mathematics to develop solutions for health, food, energy, cultural and environmental issues. His documentary '1 + 1 = 3' explores overcoming barriers in interdisciplinary research.

• Contact Mechanics
• Tribology
• Applied Mechanics
• +۷ مورد دیگر

Carmine Putignano is an Associate Professor at the Politecnico di Bari , affiliated with the Department of Mechanics, Mathematics & Management . His research focuses on contact mechanics, tribology, and viscoelasticity, with applications in polymer bearings, hydrogels, and surface engineering. Recent work includes advancements in viscoelastic contact modeling, lubrication of soft materials, and laser-induced surface texturing. These studies address challenges in friction reduction, material durability, and biomedical applications like articular cartilage lubrication. His 15 most recent publications (2023–2025) span topics such as viscohydrodynamic lubrication, micro-indentation techniques, and multi-laboratory benchmarks for surface topography characterization. The research emphasizes numerical simulations, experimental validation, and energy-based methodologies.

• Epidemiology
• Public Health
• Network Science
• +۴ مورد دیگر

Jessica Davis serves as a Research Assistant Professor in the Department of Public Health and Health Sciences at Northeastern University's Bouvé College of Health Sciences and is an active member of the MOBS Lab within the Network Science Institute. Her interdisciplinary work integrates network science methodologies with epidemic modeling to develop practical tools for public health policy and pandemic preparedness, with significant contributions during the COVID-19 pandemic. Dr. Davis earned her PhD in Network Science from Northeastern University, where her dissertation research examined cryptic transmission patterns and the efficacy of travel restrictions during the initial phases of the COVID-19 outbreak. This foundational work established her expertise in bridging theoretical network analysis with real-world public health applications. Her research program centers on two interconnected domains: Biological and Health Systems , which investigates biological interdependencies through network medicine, systems epidemiology, and public health infrastructure; and Complex Systems Forecasting , which develops network-based computational models for predicting epidemic trajectories and supporting evidence-based decision-making. These areas converge in her development of open-source tools like Epydemix and innovative surveillance frameworks using aircraft wastewater networks. Analysis of her 15 most recent publications (2021-2025) reveals consistent methodological evolution toward real-time modeling with explicit uncertainty quantification, increasing focus on multi-scale heterogeneity in disease transmission, and growing emphasis on practical implementation of forecasting systems. Her work demonstrates strong continuity in applying network science to epidemic dynamics while expanding into influenza surveillance, variant tracking, and vaccine impact assessment. No scientific awards or major honors are documented in the available materials, though her research has generated significant policy impact through presentations at venues like the Rhodes Policy Summit and collaborations with governmental agencies. Dr. Davis has secured substantial research funding including a Gates Foundation partnership investigating equitable global vaccine distribution strategies. While specific advisee relationships aren't publicly listed, she actively contributes to academic training through the MOBS Lab's hiring of computational statistics researchers and participation in the Network Science Student Research Symposium. The MOBS Lab, directed by Alessandro Vespignani and jointly affiliated with Physics, Health Sciences, and Computer Sciences departments, operates as a hub for computational epidemiology with physical locations in Boston, London, and Portland. Current lab initiatives focus on developing mathematical models for complex network systems with immediate applications to pandemic response, including the aircraft-based wastewater surveillance network featured in her 2025 Nature Medicine publication.