جست‌وجوی استاد و پژوهشگر

Ioan Andricioaeiمشاهده پروفایل

استاد

• Theoretical and Computational Physical Chemistry
• Molecular Biophysics
• Enhanced Sampling Techniques
• +۴ مورد دیگر

Ioan Andricioaei is a Professor in the Department of Chemistry at the University of California, Irvine (UCI), affiliated with the School of Physical Sciences. His research focuses on theoretical and computational molecular biophysics, particularly in developing novel sampling techniques and simulating biomolecular processes. Key areas include enhanced sampling in trajectory space, contractile tail dynamics, DNA-binding machines, and connections between molecular dynamics and NMR relaxation. Positions: Faculty member at UCI since his appointment, leading the Andricioaei Group. Education: Ph.D. in Theoretical Chemistry (not explicitly stated but inferred from research trajectory). Research Interests : His work bridges theoretical chemistry and biophysics, addressing challenges such as simulating rare events in biomolecules, understanding viral infection mechanisms via contractile tails, and modeling protein-DNA interactions. Techniques include weighted ensemble milestoning, stochastic path integrals, and massively parallel molecular dynamics. Advising & Collaborations: Supervises students like Dhiman Ray (Postdoc) and Moises Romero (PhD candidate). Collaborates with institutions like the Italian Institute of Technology and TeraGrid. Alumni include Gianmarc Grazioli (Assistant Professor at San Jose State) and Aaron T. Frank (Assistant Professor at University of Michigan). Labs/Teams: Directs the Andricioaei Group, which develops computational tools and applies them to problems in virology, drug delivery, and fundamental biophysical processes.

Shengwen Wangمشاهده پروفایل

مدرس

• Geometric Analysis
• Partial Differential Equations
• Differential Geometry
• +۴ مورد دیگر

Dr. Shengwen Wang is a Lecturer in Geometric Analysis at Queen Mary University of London (QMUL), affiliated with the School of Mathematical Sciences and the Centre for Geometry, Analysis and Gravitation. He holds a PhD from Johns Hopkins University (2018) and has held postdoctoral positions at SUNY Binghamton, QMUL, and the University of Warwick. His research focuses on geometric analysis and partial differential equations, emphasizing regularity theory and singularity analysis of geometric flows like mean curvature flow and Allen-Cahn equations, with applications in geometric topology and mathematical relativity. Research Interests: His work explores the formation of singularities in geometric PDEs, including minimal surfaces, Ricci flows, and their applications. He has contributed to entropy stability, topological rigidity of self-shrinkers, and curvature estimates in geometric flows. Publications: Recent work includes studies on entropy stability (J. Reine Angew. Math., 2020), topological rigidity (Int. Math. Res. Not., 2020), and mean curvature flow singularities (Nonlinear Anal., 2025). His articles often address energy quantization, transition layers, and curvature estimates in geometric contexts. Awards: George E. Owen Fellowship (2012-2015), Grad Student Travel Grant (2018). Teaching: Courses include Partial Differential Equations, Differential Geometry, and Mean Curvature Flow. Professional Activities: Referee for leading journals (e.g., Journal of Differential Geometry), organizer of QMUL’s Internal School Colloquium, and active in geometric analysis conferences. He is part of the Centre for Geometry, Analysis and Gravitation, fostering interdisciplinary research in geometric flows and their applications.

• Heavy Ion Collisions
• Quark-Gluon Plasma
• General Relativity
• +۴ مورد دیگر

Dr. W. van der Schee is an Assistant Professor at Utrecht University's Faculty of Science, within the Department of Theoretical Physics (ITF). He also holds a staff position at CERN. His research focuses on heavy ion collisions, quark-gluon plasma dynamics, and gravitational wave studies using holographic methods. Key areas include numerical relativity, neutron star equations of state, and tensor networks. He received the NWO Veni award in 2017. Educational background: PhD in Theoretical Physics (cum laude, Utrecht University, 2014), postdoctoral work at MIT (2014-2018) and Utrecht University (2016-2020), supported by a VENI grant. He teaches courses on quantum chromodynamics and particle physics field theory. Research highlights include Bayesian analysis of heavy-ion data using the Trajectum framework, gravitational wave studies from neutron star mergers, and quantum null energy condition explorations. His group includes PhD students Olaf Massen and Maaike Bakker, and postdoc Javier Subils. Ongoing projects involve holographic jet dynamics and neutron star merger simulations. Awards: NWO Veni award (2017). Active in both university and CERN-based collaborations, advancing theoretical frameworks for high-energy and gravitational physics phenomena.

• Materials Science
• Magnetic Materials
• High-Entropy Alloys
• +۳ مورد دیگر

Esmat Dastanpour Hosseinabadi is a postdoctoral researcher at the Department of Materials Science and Engineering, KTH Royal Institute of Technology. His research focuses on exploring the relationship between structure and magnetic properties of materials , with an emphasis on developing high- and medium-entropy alloys and green solutions for hard metals . He bridges atomistic calculations with experimental results, particularly targeting Co binder phase alternatives in hard metal products to reduce harmful emissions during manufacturing and use. His research interests include Magnetocaloric effect in alloys for energy applications Nanocrystallization and thermal stability of soft magnetic materials Computational modeling using DFT techniques Environmental engineering for sustainable materials design Recent work highlights include investigations into Al-Cr-Mn-Co high-entropy alloys for room-temperature magnetic refrigeration and phase decomposition in CrFeCoNi alloys. His studies often integrate experimental data with computational simulations to optimize material properties. Publications emphasize alloy design , material characterization , and environmentally conscious material science . Collaborations span experimental and theoretical approaches to advance materials for energy and structural applications .

Jack Tuszynskiمشاهده پروفایل

مدرس

• Biophysics
• Cancer Chemotherapy
• Computational Drug Design
• +۵ مورد دیگر

Jack Tuszynski is a Full Lecturer in the Department of Physics at the University of Alberta's Faculty of Science. He also holds the prestigious Allard Chair in Experimental Oncology at the Cross Cancer Institute. His academic career includes roles as a Professor, Associate Professor, and Assistant Professor across multiple institutions, including the University of Alberta and Memorial University of Newfoundland. He has a PhD in Condensed Matter Physics from the University of Calgary and has held postdoctoral and visiting positions in Germany, Denmark, France, Belgium, Israel, and China. His research focuses on computational biophysics and oncology, particularly the design of targeted anticancer drugs using molecular dynamics simulations. Key projects include studying tubulin and microtubule behavior under electric fields, developing TTFields-based therapies, and exploring the structural and electrical properties of proteins. He has published nearly 400 peer-reviewed papers and serves on editorial boards for multiple journals, including the Journal of Biological Physics and Quantum Biosystems. Tuszynski has received significant recognition, including the Allard Chair appointment and Fellow status with the National Institute for Nanotechnology. His work is funded by grants from Canadian, U.S., and European agencies. He teaches courses like PHYS 124 (Particles and Waves), emphasizing physics applications in life sciences. His research group explores interdisciplinary topics such as p53 protein modeling, microtubule motor proteins, and personalized medicine through entropy analysis. Collaborative efforts include designing novel colchicine derivatives and investigating TTFields' intracellular mechanisms to improve chemotherapy efficacy with reduced side effects.

Vlad Yaskinمشاهده پروفایل

استاد

• Convex Geometry
• Geometric Functional Analysis
• Geometric Tomography
• +۳ مورد دیگر

Vlad Yaskin is a Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta. His research focuses on Convex Geometry, Geometric Functional Analysis, and applications of Harmonic Analysis to convex bodies. He has held academic positions at the University of Alberta since 2008, advancing from Assistant to Associate and eventually Full Professor. His editorial role includes serving as an Associate Editor for the Rocky Mountain Journal of Mathematics since 2022. Yaskin earned his Ph.D. in Mathematics from the University of Missouri-Columbia (2006), advised by Alexander Koldobsky, and an M.Sc. from Kharkiv State University (1997), advised by Yuri Aminov. His teaching accolades include the 2012 Teaching Award from the University of Alberta and a Distinguished Teaching Award from the University of Missouri (2003). His research emphasizes geometric inequalities, convex body properties, and tomographic uniqueness. Notable contributions include work on Busemann-Petty problems, Grünbaum-type inequalities, and Fourier transform applications in convex geometry. He has supervised numerous graduate students and postdoctoral researchers, fostering collaborations in geometric analysis and harmonic analysis. Yaskin has organized major conferences such as the 2024 Summer School on Probability and Machine Learning and the 2023 BIRS workshop on Harmonic Analysis and Convexity. His funding includes multiple NSERC Discovery Grants, reflecting sustained research excellence.

Michael Falkمشاهده پروفایل

استاد

• Computational Materials Science
• Metals Processing
• Additive Manufacturing
• +۵ مورد دیگر

Michael Falk is a Professor of Materials Science and Engineering, Mechanical Engineering, and Physics at Johns Hopkins University (JHU), and serves as Vice Dean for Undergraduate Education at the Whiting School of Engineering. He leads initiatives to enhance undergraduate education, curriculum development, and student mentorship. Falk holds a B.S. in Physics (1990) and M.S. in Computer Science (1991) from JHU, followed by a Ph.D. in Physics from the University of California, Santa Barbara. His academic career spans roles at the University of Michigan (2000–2008) and his return to JHU in 2008. Research Interests: Falk’s work focuses on computational materials science, simulating atomic-scale processes in materials undergoing mechanical stress, friction, or thermal changes. Key areas include amorphous/glass materials, metallic glass deformation, and additive manufacturing. He also investigates educational strategies to improve STEM engagement, particularly for underrepresented groups, and advocates for LGBTQ+ inclusion in STEM fields. Notable outreach efforts include partnerships with Baltimore City Schools to boost STEM participation. Articles Overview: Falk’s recent publications explore topics such as socio-environmental factors influencing STEM persistence, structural analysis of disordered materials, and computational models predicting material behavior under extreme conditions. His work bridges fundamental materials science with practical engineering applications, emphasizing predictive modeling and interdisciplinary collaboration. Grants & Partnerships: Falk has secured funding from NSF and DOE for projects like simulation-informed additive manufacturing models and studies on crystal nucleation in composition gradients. His educational research includes NSF-funded initiatives to enhance computing literacy in engineering curricula and improve STEM accessibility in urban schools. Labs & Teams: Falk leads a research group dedicated to computational materials modeling and educational innovation. His collaborations span academic, industrial, and community partners to advance both technical and educational missions.

Ivar Ståle Ertesvågمشاهده پروفایل

استاد

• Combustion Engineering
• Thermodynamics
• Exergy Analysis
• +۵ مورد دیگر

Professor Ivar Ståle Ertesvåg is a faculty member in the Department of Energy and Process Engineering at the Norwegian University of Science and Technology (NTNU). He holds a Dr.ing. (1991) and MSc (1985) in Mechanical Engineering from the Norwegian Institute of Technology (NTH). His research focuses on turbulent combustion, exergy analysis, fire modeling, and energy utilization. He leads the FRIC Fire Research and Innovation Centre and participates in projects like OxyFUN and nCO₂PP. Teaching includes courses on thermodynamics, heat and combustion engineering, exergy analysis, and sustainable energy systems. He advises doctoral and master’s students and has authored a textbook on turbulent flow and combustion. His work spans computational fluid dynamics (CFD), turbulence modeling, and energy efficiency in industrial processes. Key contributions include exergy analysis of Norwegian societal energy use, development of the Eddy Dissipation Concept (EDC) for combustion modeling, and studies on CO₂ capture technologies. He serves on NTNU’s Central Appeals Committee and is a member of academic boards for energy and environmental engineering programs.

• Ferroic Materials
• Battery Materials
• Oxygen Absorption and Storage
• +۷ مورد دیگر

Professor Sverre Magnus Selbach leads the Functional Materials and Materials Chemistry (FACET) research group at NTNU's Department of Materials Science and Engineering, under the Faculty of Natural Sciences. He has held roles as Professor since 2018 and Associate Professor from 2012–2018. His academic journey includes a PhD in Solid State Chemistry (2009) and MSc (2005) from NTNU, alongside visiting positions at NIMS (Japan), ETH Zürich, and UC Santa Barbara. His research focuses on functional materials, including ferroic materials (domain walls, defects), battery materials, oxygen absorption/storage, and inorganic-organic hybrids. Techniques employed include DFT calculations and synchrotron/neutron total scattering. Notable awards include the 2020 Fridtjof Nansen Award and memberships in the Norwegian Academy of Technological Sciences (NTVA) and Royal Norwegian Society of Sciences and Letters (DKNVS). Publications emphasize domain wall dynamics, oxygen absorption mechanisms, and novel material design. Recent work includes high-entropy manganites and conductive domain walls in LiNbO₃. He has supervised 7 PhD graduates, 4 postdocs, and 28 MSc students, with 2 current PhD candidates. Teaching includes courses on nanomaterials and solid-state chemistry.

Guosheng Fuمشاهده پروفایل

استادیار

• Numerical Solution of Differential Equations
• Scientific Computing
• Finite Element Methods
• +۵ مورد دیگر

Guosheng Fu is the Robert and Sara Lumpkins Assistant Professor at the University of Notre Dame's Department of Applied and Computational Mathematics and Statistics (ACMS), part of the College of Science. His research focuses on numerical methods for partial differential equations, scientific computing, and high-order finite element techniques. He holds a Ph.D. from the University of Minnesota (2016), an M.S. from the same institution (2014), and a B.S. from Nankai University, China (2011). His education includes: Ph.D. in Applied Mathematics, University of Minnesota, 2016 M.S. in Applied Mathematics, University of Minnesota, 2014 B.S. in Mathematics, Nankai University, 2011 His research interests emphasize numerical solution strategies for complex systems, including fluid-structure interaction, optimal transport, and reaction-diffusion processes. Recent work highlights high-order discontinuous Galerkin (DG) methods, HDG schemes, and preconditioning techniques for incompressible flows and elasticity problems. His publications explore topics like divergence-free formulations, energy stability, and adaptive algorithms for nonlinear PDEs. Notable contributions include advancing hybridizable DG methods for fluid dynamics, developing robust numerical schemes for porous media flow, and creating efficient solvers for mean field control problems. His work bridges computational mathematics with applications in engineering and physical sciences. He currently holds no listed scientific awards but has an active research program supported by his academic position and collaborations. No advising or grant details are explicitly provided in the text.

• Partial Differential Equations
• Approximation Theory
• Nonlinear Approximation
• +۳ مورد دیگر

Guergana Petrova is a Professor in the Department of Mathematics at Texas A&M University, affiliated with the College of Arts & Sciences. She holds a Ph.D. from the University of South Carolina (1999) and earlier degrees from Sofia University, Bulgaria. Her research focuses on Partial Differential Equations, Approximation Theory, Nonlinear Approximation, and Numerical Methods, with notable contributions to hyperbolic PDEs, conservation laws, and numerical quadrature. Her academic career includes roles as Assistant Professor at the University of Michigan (1999–2001) and Texas A&M University (2001–2006), followed by promotion to Associate Professor (2006–2011) and Full Professor (2011–present). Her work bridges theoretical analysis and computational methods, emphasizing nonlinear approximation, neural network approximation, and reduced modeling techniques. Key research trends in her publications include advancing numerical schemes for PDEs, analyzing approximation theory in high-dimensional spaces, and exploring machine learning applications. She has collaborated extensively with researchers like DeVore, Kurganov, and Wojtaszczyk on topics ranging from greedy algorithms to fluid dynamics. Petrova’s contributions include foundational work on central-upwind schemes for hyperbolic systems, optimal recovery methods, and entropy number analysis. Her teaching includes advanced courses in numerical analysis and applied mathematics.

• Approximation Theory
• Mathematical Theory of Neural Networks
• Statistics
• +۳ مورد دیگر

Jonathan W. Siegel is an Assistant Professor in the Mathematics Department at Texas A&M University. He holds a PhD in Mathematics from UCLA (2018) under Prof. Russel Caflisch and was a postdoc at Penn State (2018–2022) under Prof. Jinchao Xu. His research focuses on approximation theory, mathematical foundations of neural networks, numerical methods for PDEs, and applications in materials science. He is supported by NSF grants (DMS-2424305, CCF-2205004) and an ONR MURI grant (N00014-20-1-2787). Education: PhD in Mathematics, UCLA, 2018 Postdoctoral Researcher, Penn State University, 2018–2022 Research interests span theoretical machine learning, numerical analysis, and computational materials science. He explores topics such as neural network expressivity, greedy algorithms for PDEs, and optimization techniques for scientific computing. His work emphasizes the development of rigorous mathematical frameworks for deep learning methods and their application to real-world problems like materials property prediction. Grants and Awards: NSF DMS-2424305, NSF CCF-2205004, ONR MURI N00014-20-1-2787. Advising: Supervised 6 students, including collaborations with Jinchao Xu and Stephan Wojtowytsch. Courses taught include advanced graduate-level topics like the Mathematical Theory of Deep Learning and Approximation Methods.

Abdullahمشاهده پروفایل

پژوهشگر

• Plant Breeding
• Agriculture
• Genetics
• +۳ مورد دیگر

Abdullah is a researcher in the field of plant breeding and genetics at Texas A&M University's College of Agriculture and Life Sciences. His academic background includes a B.S. and M.S. in Agriculture (Plant Breeding and Genetics) from the University of Agriculture, Faisalabad, Pakistan, completed in 2008 and 2010 respectively. He holds a Ph.D. in Plant Breeding and currently works in the Agronomy Lab, focusing on wheat plant breeding and genetic improvement strategies. His research emphasizes enhancing crop yields through genetic advancements and optimizing breeding techniques for wheat varieties. Notable areas of exploration include genotype-environment interactions, trait selection, and molecular breeding approaches. Abdullah's recent publications span interdisciplinary topics including compiler optimization, cache indexing strategies, and deep learning acceleration techniques—reflecting a unique blend of agricultural and computational research interests. These works address challenges in hardware-software co-design, parallel computing efficiency, and cybersecurity in distributed systems. Though no formal awards are listed, his work demonstrates contributions to both agricultural science and computer systems engineering. His current research program involves collaborations with computational scientists to integrate data-driven methods into plant breeding programs.

Yuncang Liمشاهده پروفایل

دانشیار

• Biomaterials
• Amorphous Materials
• Nanostructured Materials
• +۵ مورد دیگر

Associate Professor Yuncang Li is affiliated with the School of Engineering at RMIT University in Melbourne, Australia. His research focuses on biomaterials, amorphous and nanostructured materials, and their applications in biomedical engineering and renewable energy. Key areas include titanium-based alloys, biodegradable magnesium alloys, shape memory alloys, and surface modification of implant materials. He supervises PhD and master's students in topics such as bulk metallic glasses, corrosion-resistant coatings, and additive manufacturing of biocompatible materials. His research interests span biomaterials engineering, biocompatible metallic materials, and nanocomposites. Recent work emphasizes improving mechanical properties and biocompatibility of materials for orthopedic and dental applications. Over 279 publications reflect his contributions to materials science, including studies on magnesium-graphene composites, zinc-based alloys, and 3D-printed scaffolds. Supervision projects highlight innovative materials for biomedical uses, such as Zirconium-based bulk metallic glasses and Zn-Cu nanocomposites. His work addresses challenges in biodegradation control, corrosion resistance, and osteointegration. Collaborations include Swinburne University and industry partners focusing on advanced manufacturing techniques. Key Projects: Zn-Se alloys, Mg-Zr-Sr composites, and titanium-niobium-zirconium alloys. Techniques: Additive manufacturing, magnetron sputtering, and corrosion testing.

• Surface Engineering
• Corrosion Science
• Materials Science and Engineering
• +۴ مورد دیگر

Associate Professor Dr. Xiaobo Chen is affiliated with the School of Engineering at RMIT University's City Campus. He holds a PhD in Materials Science and Engineering from Deakin University (2009) and has held roles at Monash University and RMIT, including Senior Lecturer and Associate Professor. His research focuses on surface modification and coating technologies for light metals, addressing corrosion and biomedical applications. Key industry collaborations include Baosteel-Australia Joint Research Centre, Mitsubishi Heavy Industry, and BASF Coatings GmbH. He leads the HDR Manager role for Manufacturing, Materials, and Mechatronics disciplines. Research Themes: Corrosion mechanisms, biomaterials, additive manufacturing, antimicrobial coatings Funding: ARC grants (DECRA, DP, LP) as Lead Chief Investigator Industry Projects: 6 completed contracts with industry partners Teaching interests include surface engineering, nanomaterials, and electrochemistry. Current research explores self-cleaning coatings, high-entropy alloys, and 3D-printed biomedical implants.