جست‌وجوی استاد و پژوهشگر

Erika Tsingosمشاهده پروفایل

استادیار

• Computational Biology
• Animal Developmental Biology
• Multiscale Modeling
• +۴ مورد دیگر

Dr. Erika Tsingos serves as an Assistant Professor in the Department of Theoretical Biology and Bioinformatics at Utrecht University's Faculty of Science. She leads the Computational Animal Development Group focusing on computational approaches to developmental biology questions. Her research spans animal developmental biology with emphasis on Computer modeling of cell fate decisions Pattern formation in tissues Cell migration mechanics Thymus development and leukemogenesis C. elegans developmental precision She employs diverse modeling approaches including ordinary differential equations and multiscale cell-based models using cellular Potts or center-based formalisms. Analysis of her 15 most recent publications reveals strong focus on computational modeling of developmental processes, particularly examining extracellular matrix effects on cell migration, thymic niche architecture in leukemia development, and precise cell fate specification in C. elegans. Her work consistently bridges computational modeling with experimental validation through collaborations. Scientific recognition includes: NWO grant VI.Veni.222.323 for research on extracellular matrix effects on cell migration Dr. Tsingos actively mentors the next generation of computational biologists through supervision of postdoctoral researchers and students. Current team members include Benjamin Planterose Jiménez (post-doc modeling C. elegans development), Saber Shakibi (post-doc developing hybrid migration models), and Nikki Landzaat (Master's student studying mesoderm cell state transitions). Former students include Bidayatul Masulah (Master's in Applied Mathematics) and Heleen van Osch (bachelor researcher). Her research group operates within Utrecht University's Department of Theoretical Biology and Bioinformatics, collaborating extensively with the Ten Tusscher group and experimental labs. Current projects investigate how extracellular matrix affects cell migration phenotypes in cancer and the molecular control mechanisms behind C. elegans' precise cell division patterns.

• Food Science
• Biophysics
• Lipid Oxidation
• +۵ مورد دیگر

John van Duynhoven is a Professor of Biophysics at Wageningen University and Research. His work bridges advanced imaging techniques and food science, focusing on lipid oxidation, protein processing, and emulsion structure. Affiliation: Wageningen University and Research Academic Rank: Professor Research Interests: He investigates food structure and stability using NMR spectroscopy, super-resolution microscopy, and magnetic resonance imaging. Key areas include lipid oxidation pathways, plant-protein extrusion, and granular flow dynamics. Scientific Contributions: Supervised multiple PhD projects on multiscale protein modeling, emulsion oxidation, and infant nutrition protein digestion. His recent articles highlight innovations in quantifying anisotropic food structures and oxidation products. Collaborations: Partnerships span imaging techniques, X-ray scattering, and food technology. Active projects include lipid oxidation mapping and protein extrusion modeling.

• Biophysics
• Food Science
• Biochemistry
• +۸ مورد دیگر

Johannes Hohlbein is an Associate Professor in Biophysics at Wageningen University & Research , with a focus on interdisciplinary research bridging food science, molecular biology, and advanced microscopy techniques. His work spans structural analysis of protein-based food materials and single-molecule studies of biomolecular processes. Interdisciplinary Research : Food Science, Biophysics, Molecular Biology Methodologies : Super-resolution microscopy, cryo-CLEM, Fourier transform analysis, single-particle tracking Scientific Contributions include: Structural anisotropy in soy protein extrudates Heterogeneity in food emulsions Lipid oxidation mechanisms DNA-targeting enzyme dynamics Statistical tools for nanoparticle tracking Supervised PhD Projects : Measurement and Modeling of Multiscale Protein Products (MP3) Biomolecule Localization in Food Matrices Food Oxidation Studies via Cryo-CLEM Single-Nanoparticle Diffusometry Plant Hormone Response at Single-Molecule Level

Hai Linمشاهده پروفایل

استاد

• Theoretical Chemistry
• Computational Chemistry
• Molecular Modeling
• +۴ مورد دیگر

Hai Lin serves as Professor and Departmental Chair in the Department of Chemistry at the University of Colorado Denver. His academic career spans over two decades with progressive appointments from Assistant Professor (2005-2011) to Associate Professor (2011-2017) and ultimately to full Professor and Department Chair (2017-present). Dr. Lin received his academic training at the University of Science and Technology of China, earning a BSc. in Engineering (1993), M.S. in Physics (1995), and Ph.D. in Chemistry (1998). His postdoctoral experience includes positions as a Max-Planck Postdoctoral Fellow in Germany (2001-2003) and a Minnesota Supercomputing Institute Scholar at the University of Minnesota (2003-2005). Dr. Lin's research program focuses on the development and application of computational methods to study chemical and biological processes in complex environments. His Group of Theoretical/Computational Chemistry has made significant contributions to quantum mechanics/molecular mechanics (QM/MM) methodologies, particularly in adaptive partitioning techniques that improve the accuracy of molecular simulations. His work spans enzymatic reactions, protein-ligand binding, ion transport mechanisms, and membrane protein dynamics. The theoretical frameworks developed in his laboratory bridge computational chemistry with practical applications in biochemistry and biophysics. An analysis of Dr. Lin's recent publication record reveals a consistent focus on advancing QM/MM methodologies with particular emphasis on adaptive partitioning schemes, proton transfer reactions, and ion channel mechanisms. His interdisciplinary research connects computational chemistry with structural biology, physical chemistry, and biophysics, demonstrating both theoretical innovation and practical applications to biological systems. Dr. Lin has received numerous prestigious awards that recognize both his research excellence and teaching capabilities: Cottrell Scholar, Research Corporation for Science Advancement (2015) Henry Dreyfus Teacher-Scholar Award, Camille & Henry Dreyfus Foundation (2014) Research Corporation Cottrell College Science Multi-Investigator Award (2013) NSF CAREER Award (2010) Excellence in Research & Creative Work Award (2007) Research Corporation Cottrell College Science Award (2006) As an educator, Dr. Lin teaches across the chemistry curriculum including General Chemistry, Physical Chemistry, and specialized graduate courses in Computational Chemistry and Molecular Modeling. His leadership as Departmental Chair reflects his administrative capabilities and commitment to the academic mission of the Department of Chemistry at the University of Colorado Denver.

Miroslav Grmelaمشاهده پروفایل

پژوهشگر

• Thermodynamics
• Transfer processes
• Mesoscopic dynamics
• +۳ مورد دیگر

Miroslav Grmela is a Researcher at the Department of Chemical Engineering in Polytechnique Montréal , and a member of the Research Center for High-Performance Polymer and Composite Systems (CREPEC) . His work spans thermodynamics, transfer processes, and multiscale modeling of complex fluids. Grmela’s research focuses on non-equilibrium thermodynamics , contact geometry in kinetic dynamics , and mesoscopic theories of polymer suspensions, superfluids, and nanocomposites. He has extensively explored the role of energy and entropy in multiscale systems, with recent publications addressing geometric formulations of thermodynamics and neural network applications to non-symplectic mechanics. Analysis of his 15 most recent articles reveals trends in multiscale thermodynamics , non-Fourier heat conduction , GENERIC formalism , and quantum hydrodynamics . His work often bridges geometric mechanics with thermodynamic consistency. Grmela has supervised 10 graduate students (7 PhD, 3 Master’s) in projects involving nanocomposite thermal conductivity , polymer rheology , and powder suspension simulations . He has no listed scientific awards. His research intersects with fluid dynamics , polymer science , and statistical mechanics , emphasizing mathematical structures like Poisson brackets and Hamiltonian formulations . The CREPEC laboratory provides institutional support for his studies on polymers and composites.

Kevin Hughesمشاهده پروفایل

مدرس ارشد

• Fuel Combustion
• Fuel Cells
• Process Modelling in Carbon Capture and Storage (CCS)
• +۳ مورد دیگر

Kevin Hughes is a Senior Lecturer in the Energy Engineering Group at the Department of Mechanical Engineering, School of Mechanical, Aerospace and Civil Engineering, University of Sheffield. He holds a PhD and first degree in Chemistry from the University of Leicester (1987) and focuses on fuel combustion, fuel cells, and process modelling in carbon capture and storage (CCS) systems. His research combines experimental and theoretical approaches, including planar laser diagnostics, quantum chemistry, and CFD simulations. Education: PhD and BSc in Chemistry from University of Leicester. Research Interests: Fuel combustion, pollutant chemistry, PEM fuel cells, CCS process modelling, catalyst development, and combustion in supercritical CO2. Grant Projects: FP7-ENERGY-2010-2 (RELCOM), Gas-FACTS (EPSRC), EP/J020788/1, EP/M001482/1 (Selective EGR), TEABPP (Energy Technology Institute). Scientific Contributions Publications: Over 50 papers on fuel combustion mechanisms, fuel cell optimization, CCS systems, and alternative fuels. Collaborations: Regular work with M. Pourkashanian, D.B. Ingham, S. Michailos, and M.S. Ismail. Technical Expertise Chemical Kinetics Validation Quantum Chemistry Applications Gas Diffusion Layer Analysis Surrogate Fuel Development Supercritical Combustion

Rory Cooperمشاهده پروفایل

پژوهشگر ارشد

• Evolutionary Developmental Biology
• Biological Pattern Formation
• Developmental Biology
• +۷ مورد دیگر

Dr. Rory Cooper is an Independent Research Fellow in the School of Biosciences at the University of Sheffield. His work bridges evolutionary developmental biology and biophysics , focusing on skin appendage morphogenesis across diverse vertebrates including sharks, chickens, and crocodiles. Formerly a 5-year postdoc in the LANE lab at University of Geneva, he earned his PhD at Sheffield under Dr. Gareth Fraser and Dr. Nicola Nadeau. Current position: Research Fellow (Sheffield) Alumni: Geneva postdoc (2020-2025), Sheffield PhD Expertise: EvoDevo, mechanochemical patterning, volumetric imaging His research investigates how molecular signals and mechanical forces interact to shape complex patterns in embryonic development. Key findings include: Discovery of compressive forces in crocodile scale sculpting Experimental induction of mechanical skin folding in chickens Identification of dual chemical/mechanical patterning in tortoise headscales Recent publications highlight mechanical forces as fundamental to pattern robustness across species. His 2024 Nature paper demonstrated physical sculpting of crocodile cranial scales, followed by 2025 Open Biology and Cell Reports iScience studies on avian/tortoise systems. Scientific recognitions include: Independent Research Fellowship Three major journal cover features (Nature, Open Biology, PLOS Biology) As a PhD advisor for Alex Liu at UC San Diego, he continues fostering next-generation EvoDevo researchers. Current work explores cross-species mechanosensitive development using advanced imaging and multiscale modeling.

• Wind Energy
• Wind Turbine Engineering
• Condition Monitoring
• +۳ مورد دیگر

Simon J. Watson is a Professor in the Faculty of Aerospace Engineering at Delft University of Technology, specializing in Wind Energy through the TU Delft Wind Energy Institute (DUWIND). His work focuses on advancing wind turbine technology, wind farm optimization, and renewable energy integration within the university's aerospace framework. His research spans wind turbine engineering, condition monitoring systems, atmospheric effects on energy production, and wind farm design. Key investigations include damage detection in turbine components (blades, drivetrains), simulation of atmospheric gravity waves for improved energy forecasting, and hybrid wind-storage systems for grid stability. Recent work emphasizes machine learning applications for predictive maintenance and high-fidelity modeling of boundary layer conditions. Professor Watson's 2025 publications reveal a strong trend toward AI-driven condition monitoring and refined atmospheric simulations, with consistent focus on operational reliability and damage detection across wind energy systems. His work bridges computational fluid dynamics, structural health monitoring, and energy storage integration. He actively supervises students and leads the €4.2M MERIDIONAL project (2022-2026) on multiscale wind farm modeling, developing advanced toolchains for performance assessment and load prediction. This EU-funded initiative involves collaboration with Siemens Gamesa, Vestas, and ENEL. As a core member of DUWIND, he co-develops industry partnerships and experimental facilities including wind tunnel testing and field monitoring systems for offshore wind farms. His team maintains close ties with the Netherlands Wind Energy Association and European Wind Energy Technology Platform.

• Remote Sensing
• Hyperspectral Image Processing
• Spatial Data Analysis
• +۶ مورد دیگر

Associate Professor Xiuping Jia is a faculty member at the School of Engineering and Information Technology, The University of New South Wales at Canberra, Australia. She has been with the institution since 1988, holding the position of Associate Professor with expertise in remote sensing and image processing. Xiuping Jia earned her B.Eng. degree from the Beijing University of Posts and Telecommunications in January 1982. She later completed her Ph.D. in electrical engineering and a Graduate Certificate in Higher Education from The University of New South Wales, Australia, in 1996 and 2005, respectively, through part-time study. Her primary research interests focus on remote sensing , hyperspectral image processing , and spatial data analysis for remote sensing applications. Her work spans various aspects of image processing including image registration, image correction, feature mining and learning, and spectral-spatial based classification. She has made significant contributions to subpixel mapping through spectral unmixing techniques and super resolution reconstruction approaches. Professor Jia is the coauthor of the widely recognized textbook Remote Sensing Digital Image Analysis (Springer-Verlag, 3rd and 4th editions) and the author of Field Guide to Hyperspectral/Multispectral Image Processing (SPIE, 2022). Her recent publication record shows a strong focus on deep learning applications for hyperspectral image analysis, with numerous papers in top journals including IEEE Transactions on Geoscience and Remote Sensing, IEEE Transactions on Neural Networks and Learning Systems, and IEEE Transactions on Pattern Analysis and Machine Intelligence. Her work demonstrates a progression from traditional image processing techniques to cutting-edge deep learning approaches for remote sensing applications. Professor Jia has made substantial contributions to the field of remote sensing with highly cited publications (as evidenced by her Google Scholar profile). Her research bridges theoretical developments with practical applications in remote sensing data analysis. She has supervised numerous research projects and students in the areas of remote sensing, hyperspectral image processing, and spatial data analysis. Her work has been supported by various research grants focusing on advanced image processing techniques for remote sensing applications. Professor Jia's research activities involve collaboration with multiple research teams working on remote sensing applications, hyperspectral data analysis, and geospatial information systems. Her work contributes significantly to the advancement of remote sensing technologies and their practical implementation in various environmental and urban monitoring applications.

Stefano Curtaroloمشاهده پروفایل

استاد

• Artificial Intelligence Materials Science
• Autonomous Materials Design
• Computational Materials Science
• +۴ مورد دیگر

Stefano Curtarolo is the Edmund T. Pratt Jr. School Distinguished Professor of Mechanical Engineering and Materials Science at Duke University’s Pratt School of Engineering. He also holds professorships in the Department of Physics and Electrical and Computer Engineering, and serves as Director of the Center for Extreme Materials (2025–present). Education: M.S. in Physics, University of Padua (1995, 1998) M.S. in Physics, Pennsylvania State University (1999) Sc.D. in Physics, Massachusetts Institute of Technology (2003) Research Interests: Curtarolo’s work bridges Materials Science , Physics , and Artificial Intelligence , focusing on Autonomous Materials Design , High-Entropy Disordered Systems , and Materials for Extreme Environments . His methodologies integrate Quantum Mechanics , Machine Learning , and High-Throughput Computing . Recent Research Trends: His 2024–2025 publications highlight advancements in High-Entropy Ceramics , including Soliquidy (a geometric descriptor for disorder), Dual-Phase Ceramic Synthesis , and Machine Learning Interatomic Potentials . These works emphasize disorder engineering , plasticity enhancement , and AI-driven material discovery for Aerospace and Deep Space Exploration applications. Scientific Awards: European Academy of Sciences (2024) Clarivate Highly Cited Researcher (2021) Friedrich Wilhelm Bessel Research Award (2016) DOD MURI Awards (2013, 2015) NSF CAREER (2008) ONR Young Investigator (2008) PECASE (2007) Grants & Leadership: Current projects include DETER (2024–2027, Missouri S&T) and Transition States Algorithms (2024–2027, ONR). He co-leads NRT-HDR (2020–2026, NSF). Labs & Teams: Curtarolo directs the Center for Extreme Materials and leads the AFLOW consortium , a global materials design ecosystem. His lab combines machine learning , quantum simulations , and experimental validation for multiscale materials discovery .

Muhammad Awaisمشاهده پروفایل

پژوهشگر

• Wood Science and Technology
• Hyperspectral Imaging
• Chemical Modification of Wood
• +۳ مورد دیگر

Muhammad Awais is a Postdoctoral Researcher at Aalto University's Bioproducts and Biosystems department, specializing in wood material science and chemical imaging techniques. His work focuses on advanced characterization of wood modification processes and their impact on physical properties. Primary affiliation: Aalto University, Finland Research group: Wood Material Science Research interests include: Chemical modification of wood Hyperspectral imaging for material analysis Fungal biodeterioration mechanisms Moisture interactions in modified wood Non-destructive testing methods Wood-polymer composite development Publication trends (2022-2025) show expertise in: Chemical imaging techniques Wood-water relations Fungal decay analysis Resin treatment optimization Hyperspectral data modeling Laboratory work involves: Wood degradation testing Chemical distribution mapping Moisture content prediction algorithms Acoustic material characterization

• Materials Science
• Mechanical Engineering
• Nanotechnology
• +۳ مورد دیگر

Dr. Xuliang Li is a Postdoctoral Research Fellow at the School of Mechanical and Mining Engineering, The University of Queensland, within the Faculty of Engineering, Architecture and Information Technology. His work focuses on advanced materials processing, particularly in precision machining of brittle solids and nanoscale surface engineering. Research Themes: Brittle material machining, graphene oxide applications, machine learning integration in manufacturing, and multiscale modeling of deformation mechanisms Recent Publications: 14 studies (2019-2024) covering nanoscratch mechanics, laser 3D-printed ceramics, and innovative polishing techniques Funding: Currently developing ductile grinding techniques for dental prostheses applications (2025-2026) Expertise: Available for supervision in advanced manufacturing and materials science domains Key areas include computational modeling of grinding processes, hybrid nanoparticle suspensions, and ductile-to-brittle transition analysis. His collaborations span multiple disciplines in mechanical engineering and materials science.

• computational multiscale modeling
• digital twin development
• applied artificial intelligence
• +۷ مورد دیگر

Davood Pourkargar is an Assistant Professor in the Tim Taylor Department of Chemical Engineering at Kansas State University. He is also a Graduate Faculty Member at the Food Science Institute and a Faculty Researcher at the Johnson Cancer Research Center. His work focuses on integrating data with first-principle models to understand complex systems across multiple scales. Ph.D. in Chemical Engineering from Pennsylvania State University (2015) M.S. in Process Simulation and Control from Sharif University of Technology (2010) B.S. in Chemical Engineering from Sharif University of Technology (2008) His research interests span computational multiscale modeling, digital twin development, applied artificial intelligence, and optimization-based control of complex process networks. Dr. Pourkargar's work integrates process systems engineering with artificial intelligence to address challenging problems in chemical, biological, energy, and food systems. He develops intelligent frameworks for controlling complex process networks, designing cyber-physical architectures for smart manufacturing, and advancing system identification using machine learning and process data analytics. A significant aspect of his research involves physics-informed machine learning applied to cancer dynamics modeling and drug distribution in the human body. Dr. Pourkargar's publication record shows a strong focus on predictive modeling and control of chemical processes, particularly ammonia synthesis systems, polysilicon reactor systems, and food extrusion processes. His recent work increasingly incorporates machine learning techniques, especially transformer architectures and physics-informed approaches, applied to both traditional chemical processes and emerging areas like organ-on-a-chip systems for drug discovery. 2024 Carl R. Ice College of Engineering Outstanding Assistant Professor Award NSF EPSCoR Research Fellowship 2023 Kansas EPSCoR First Award AFOSR Faculty Fellowship Big XII Faculty Fellowship Robert F. Smith School Distinguished Junior Researcher Award from Cornell University (2017) O. Hugo Schuck Best Paper Award (2014) Dr. Pourkargar has successfully mentored numerous graduate students through their master's and doctoral research, with several receiving departmental and college-level awards. His research has been supported by significant grants from the National Science Foundation, Kansas EPSCoR, and K-State's Global Food Systems initiative. His lab has presented extensively at major conferences including AIChE Annual Meetings and American Control Conferences. The Intelligent Systems and Process Systems Laboratory (ISPSL) led by Dr. Pourkargar operates computational and experimental facilities in Durland Hall. The lab is expanding into robotic additive manufacturing and autonomous biomanufacturing, supported by research infrastructure grants. The group maintains active collaborations with the Johnson Cancer Research Center and the Terasaki Institute for Biomedical Innovation.

Daniel Barretoمشاهده پروفایل

استاد

• Discrete Element Method (DEM)
• Soil Mechanics
• Geotechnical Engineering
• +۵ مورد دیگر

Daniel Barreto is a Professor at Edinburgh Napier University , specializing in Discrete Element Method (DEM) simulations and soil mechanics . His research emphasizes particle shape and size distribution effects on granular material behavior, with applications in permeability estimation, liquefaction mitigation, and nature-inspired ground engineering. Education : PhD in Soil Mechanics (Imperial College London, 2010), MSc in Soil Mechanics and Engineering Seismology (Imperial, 2005), Civil Engineering (Universidad de los Andes, 2003). Research : Focuses on DEM, grading entropy theory, and particle-scale interactions in sand-rubber mixtures, with implications for seismic isolation and sustainable soil stabilization. Awards : Royal Academy of Engineering Leverhulme Trust Fellowship COST Action Chair for Open Network on DEM Simulations (ON-DEM) Emeritus Member of Royal Society of Edinburgh Young Academy of Scotland Publications : Pioneering studies on DEM-based critical state behavior, particle breakage quantification, and permeability models for granular soils, with a 2023 Transportation Geotechnics paper on hydraulic radius applications. Collaborations : Leads ON-DEM, a global network advancing open-source DEM tools, and contributes to interdisciplinary projects involving synthetic root mimics and microbial soil interactions.

Mehdi Pouraghaمشاهده پروفایل

دانشیار

• Geomechanics
• Constitutive Modeling
• Granular Materials
• +۷ مورد دیگر

Mehdi Pouragha is an Associate Professor in the Department of Civil and Environmental Engineering at Carleton University's Faculty of Engineering and Design in Ottawa, Canada. He holds a BSc and MSc from Sharif University of Technology in Iran and a PhD from the University of Calgary. His academic career focuses on advanced geomechanics research with applications in both theoretical and practical engineering contexts. Dr. Pouragha's research spans multiple interconnected areas within geomechanics, with particular emphasis on constitutive modeling of geomaterials, micromechanics of granular materials, thermo-hydro-mechanical behaviors, and computational geomechanics. His work integrates theoretical approaches with advanced numerical methods to address complex problems in soil mechanics, permafrost engineering, and unsaturated soil behavior. His research program bridges fundamental micromechanical understanding with practical engineering applications, particularly in cold regions and climate change adaptation contexts. Analysis of Dr. Pouragha's recent publications reveals a strong trend toward multiscale modeling approaches that integrate discrete element methods with continuum mechanics. His work increasingly incorporates machine learning techniques to enhance computational efficiency while maintaining physical accuracy. The research spans both fundamental theoretical developments in constitutive modeling and practical applications in permafrost engineering, tailings management, and soil-structure interaction problems. His publications consistently demonstrate a focus on connecting microscale mechanisms to macroscale material behavior. Dr. Pouragha teaches several core civil engineering courses including Geotechnical Engineering, Fundamentals of Geomechanics, Numerical Methods in Geotechnical Engineering, and Professional Practice. His teaching portfolio reflects his research expertise while providing students with both theoretical foundations and practical engineering skills.