جست‌وجوی استاد و پژوهشگر

• Neural Network Dynamics
• Computational Neuroscience
• Theoretical Physics
• +۳ مورد دیگر

Prof. Raoul-Martin Memmesheimer is a Professor at the University of Bonn's Institute of Genetics , contributing to the Transdisciplinary Research Area (TRA) - Life and Health . His research focuses on understanding neural network dynamics across microscopic, mesoscopic, and large-scale phenomena, integrating mathematical approaches with neurophysiological insights. Key interests include the dynamics of precise spiking activity, collective network behavior, and computational principles underlying neural systems. Education & Background : While specific educational details are not listed, his position as a Professor indicates advanced academic qualifications in theoretical neuroscience or related fields. Research Interests : Memmesheimer's work bridges theoretical physics and computational neuroscience, addressing topics like spiking neuron models, learning in neural networks, and the emergence of complex behaviors. His group employs methods from computer science and applied mathematics to study how neural systems perform computations through their dynamic properties. Publications : Recent work explores topics such as gradient descent learning in spiking networks, STDP-based assembly dynamics, and oscillatory phenomena in hippocampal regions. These publications highlight his focus on both foundational theory and applications to biological systems. Awards & Collaborations : While no specific awards are listed, his participation in TRA Life and Health underscores collaborative efforts in transdisciplinary health-related research. His work is supported by the University of Bonn's strong focus on transdisciplinary innovation. Labs & Teams : His research group operates within the Institute of Genetics, leveraging interdisciplinary resources at the University of Bonn to advance theoretical neuroscience and computational biology.

Gaurav Aryaمشاهده پروفایل

استاد

• Viral DNA Packaging
• Polymer-Nanoparticle Composites
• Virus Assembly
• +۷ مورد دیگر

Gaurav Arya is a Professor in the Thomas Lord Department of Mechanical Engineering and Materials Science at Duke University, with additional appointments in the Department of Chemistry and Biomedical Engineering. His research laboratory employs physics-based computational tools to investigate biological and soft-material systems at the molecular scale. Ph.D. from University of Notre Dame (2003) B.Tech. from Indian Institute of Technology Delhi (1998) Research focuses on: Molecular modeling and simulations Statistical mechanics DNA nanotechnology Viral DNA packaging Chromatin biophysics Polymer-nanoparticle composites Recent research trends emphasize AI-driven materials discovery and programmable DNA nanostructures. Key grants include: NSF DMREF: Architecting DNA nanodevices (2023-2027) DOE Mesoscale Self-Assembly (2020-2027) UC San Diego Ligand-Nanocrystal Interlayer Studies (2024-2027) Scientific contributions: Featured in 2025 Nature Communications on DNA superstructures 2022 Science Advances on DNA nanodevice reconfiguration 2021 PNAS on viral DNA packaging motors

Zhen Liمشاهده پروفایل

استادیار

• Multiscale/Multiphysics modeling
• Soft matter and Smart materials
• Biophysics and Collective dynamics
• +۷ مورد دیگر

Dr. Zhen Li is an Assistant Professor in the Department of Mechanical Engineering at Clemson University's College of Engineering, Computing and Applied Sciences. He joined Clemson in August 2019 after serving as a research associate professor at Brown University and a postdoctoral research associate at University of California, Merced. Education: Ph.D. in Fluid Mechanics, Shanghai University, 2012 MS in Fluid Mechanics, Shanghai University, 2008 BS in Engineering Mechanics, Wuhan University, 2005 Dr. Li's research focuses on multiscale modeling of soft matter, complex fluids, biophysics, and collective dynamics using both bottom-up (coarse-grained molecular modeling) and top-down (from continuum descriptions to fluctuating hydrodynamics) approaches, along with high-performance computing. His work spans mathematical theory for coarse-graining and model reduction, statistical methods and machine-learning approaches applied to multiscale modeling, memory effects in complex fluids, and concurrent coupling of heterogeneous solvers for scale-bridging. Analysis of Dr. Li's recent publications reveals a strong trend toward integrating machine learning with traditional computational methods, particularly neural operators for multiscale problems. His work spans diverse applications from bubble dynamics and blood flow to materials science and bioprinting, demonstrating the versatility of his computational approaches across multiple disciplines in engineering and physics. Awards and Recognition: CECAS Dean's Professor Award (2024) Award of Excellence - Junior Faculty (2021-2022) Best Research Poster Award at SC19 (2019) 2nd Place Award of Best Poster Presentation at DOE/EFRC AIM for Composites meeting (2024) Dr. Li actively mentors PhD students including Miles Lu, Ryan Wan, Haizhou Wen, and Ali Mohammadi, who have published significant research in computational mechanics. His research is supported by multiple grants including an NSF Elements grant as PI for 'SciMem: Enabling High Performance Multi-Scale Simulation on Big Memory Platforms', an NSF CDS&E grant as co-PI for 'HAM3R: Heterogeneous Automated Management of Multiscale Methods and Resources', a DOE/EFRC grant as Thrust lead co-PI for 'AIM for Composites', and a NASA EPSCoR grant as Science-PI. Dr. Li leads the MuthComp (Multiscale theory and Computation) research group, which focuses on developing interfaces between Engineering, Applied Mathematics, Physics-based Machine Learning, and High Performance Scientific Computing. The group has active collaborations with institutions including Idaho National Laboratory, University of Tokyo, and Brown University, and has developed open-source software including USERMESO for GPU-accelerated DPD simulations.

• statistical physics
• computational physics
• soft matter
• +۵ مورد دیگر

Kurt Kremer serves as Director and Scientific Member of the Theory Department at the Max Planck Institute for Polymer Research (MPI-P) in Mainz, Germany, a position he has held since 1995. His leadership oversees a multidisciplinary research group focused on computational soft matter physics, with close collaboration from senior scientists including Dr. D. Andrienko, PD Dr. Kostas Daoulas, and Dr. R. Cortes-Huerto. His research spans statistical physics , computational methods , and multiscale modeling of soft matter systems, with emphasis on polymers, biopolymers, membranes, organic electronics, and glassy dynamics. The group develops advanced simulation techniques like adaptive resolution methods and coarse-grained modeling to study structure-process-property relationships in synthetic and biological materials. Analysis of his co-authored publications (2015–2019) reveals dominant trends in free energy calculations , finite-size effects in simulations , and mesoscale self-assembly phenomena , particularly in conjugated polymers and nematic systems. Keywords consistently include computational physics, thermodynamics, and polymer science with subfields spanning adaptive resolution frameworks, solvation thermodynamics, and charge transport mechanisms. Key recognition includes membership in the German National Academy of Sciences (Leopoldina). Notable alumni from his research group include Prof. B. Baumeier (TU Eindhoven) and Prof. D. Donadio (UC Davis), who have established independent careers in biophysics and materials science. Kremer mentors a team of senior scientists and postdoctoral researchers, driving projects funded through Max Planck Society resources and collaborative grants. Current work emphasizes bridging atomistic simulations with continuum models for organic electronic materials and nonequilibrium molecular dynamics.

Yoram Alhassidمشاهده پروفایل

استاد

• Theoretical Nuclear Physics
• Many-Body Quantum Systems
• Nuclear Many-Body Problem
• +۱۵ مورد دیگر

Yoram Alhassid is the Frederick Phineas Rose Professor of Physics in the Department of Physics at Yale University, where he leads a research group focused on theoretical nuclear and many-body physics. His work bridges nuclear physics, mesoscopic systems, and ultracold atomic gases, using advanced computational methods such as quantum Monte Carlo and the configuration-interaction shell model. His research interests include the nuclear many-body problem, femtoscience and nanoscience (nuclei, quantum dots, nanoparticles), and cold atomic Fermi gases. He has developed and applied the shell model Monte Carlo (SMMC) method to study statistical and collective properties of nuclei, such as level densities, deformation, and pairing correlations. He has extended these methods to study cold Fermi gases, particularly in the unitary regime, where he has investigated pseudogap phenomena, heat capacity, and pairing gaps. The recent publications highlight a strong focus on nuclear level densities, γ-ray strength functions, deformation effects, and quantum Monte Carlo methodologies. There is a clear trend toward microscopic, ab initio calculations of nuclear and many-body properties, with increasing attention to odd-mass and deformed nuclei, as well as the interplay between pairing and collective phenomena. Scientific Awards: Frederick Phineas Rose Professor of Physics Alhassid advises students and postdoctoral researchers, as evidenced by numerous co-authored publications. His group has developed advanced computational tools and codes (e.g., HF-SHELL) for finite-temperature mean-field and shell model calculations. The research is supported by high-performance computing and has implications for nuclear astrophysics, radioactive beam facilities, and quantum simulation with cold atoms. His lab focuses on theoretical and computational modeling of finite-size quantum many-body systems, with close collaborations across nuclear theory, condensed matter, and atomic physics. The group emphasizes method development, benchmarking against mean-field theories, and extracting model-independent signatures of physical phenomena such as deformation and pairing.

Danesh Taftiمشاهده پروفایل

استاد

• Computational Fluid Dynamics
• Fluid Mechanics
• Heat Transfer
• +۷ مورد دیگر

Danesh Tafti is the William S. Cross Professor and Associate Department Head for Graduate Studies in the Department of Mechanical Engineering at Virginia Tech. He holds a PhD from Pennsylvania State University (1989) and has held roles at institutions including the University of Illinois at Urbana-Champaign and West Virginia Institute of Technology. His research focuses on computational fluid dynamics (CFD) and heat transfer, with applications in gas turbines, biomedical systems, and renewable energy. Key research areas include turbulence modeling, fluid-structure interaction, and biofluid mechanics, leveraging high-performance computing. His work spans aerodynamics of flapping flight, cardiovascular flows, and particle-laden flows. Tafti leads the High Performance Computational Fluid-Thermal Science and Engineering Lab, developing tools like GenIDLEST software for complex fluid-thermal systems analysis. Education: PhD (Penn State, 1989), MS (Texas Tech, 1983), BE (Bombay University, 1980) Awards: ASME Fellow (2014), Virginia Tech Dean’s Research Award (2012) Labs: High Performance Computational Fluid-Thermal Science and Engineering Lab Publications emphasize CFD advancements, machine learning integration, and multiphase flow modeling. Tafti’s work bridges computational methods with real-world applications in energy systems, aerospace, and biomedicine.

Markus Desernoمشاهده پروفایل

استاد

• Biological Physics
• Theoretical Biophysics
• Computational Biophysics
• +۵ مورد دیگر

Markus Deserno is a Professor of Physics at Carnegie Mellon University's Mellon College of Science, Department of Physics, where he serves as Director of Graduate Affairs. His research focuses on theoretical and computational biological physics, investigating mesoscopic phenomena in lipid membranes, proteins, and other biological systems through the lens of fundamental physical principles. Educational background: Ph.D. from Max-Planck-Institute for Polymer Research (2000) Habilitation in Computational Physics from Mainz University, Germany (2006) Deserno's work centers on how thermal fluctuations, cooperativity, self-assembly, and elasticity govern biological systems at scales between atomic resolution and whole cells. He employs continuum elastic theories, differential geometry, and coarse-grained simulations to develop simplified models that capture essential physics without atomic detail. His research group actively explores membrane mechanics, phase behavior, and curvature elasticity, with particular emphasis on asymmetric lipid bilayers and their biological implications. Analysis of his 15 most recent publications reveals a dominant focus on membrane asymmetry (appearing in 8/15 papers), Gaussian curvature mechanics (6/15), and computational methodology development (12/15). His work increasingly integrates DNA nanotechnology with membrane systems and addresses thermodynamic consequences of lipid composition. Honors include: Thomas E. Thompson Award from Biophysical Society (2021) Otto Hahn-Medal from Max Planck Society (2001) As Section Editor for The Biophysical Journal and active member of four professional societies (American Physical Society, Biophysical Society, American Chemical Society, German Physical Society), Deserno maintains significant scholarly engagement. His research group, featuring members Nick, Mert, Martina, Muhammed, Nishchay, and Amirali, receives funding from federal agencies supporting computational biophysics research. The Deserno group operates within Carnegie Mellon's high-performance computing environment, developing specialized simulation tools for mesoscale membrane phenomena while maintaining strong collaborations with experimental biophysics laboratories worldwide.

• Polymer Chemistry
• Stimuli-Responsive Materials
• Biomaterials
• +۳ مورد دیگر

Adam E. Smith is an Associate Professor in both the Department of Biomedical Engineering and the Department of Chemical Engineering at the University of Mississippi, serving as Associate Dean for Undergraduate Programs in the School of Engineering and Co-Director of the Center for Nano-Bio Interactions. He holds a B.S. in Chemical Engineering (2002), M.S. in Engineering Science (2004) from the University of Mississippi, and a Ph.D. in Polymer Science and Engineering from the University of Southern Mississippi (2010). His research focuses on stimuli-responsive polymers for drug delivery, including RAFT polymerization techniques and nanocarrier design. Notable awards include the 2016 Faculty Service Award and 2020 Outstanding Engineering Faculty Award. Dr. Smith’s expertise spans polymer chemistry, biomaterials, and targeted drug delivery. His research emphasizes the synthesis of stimuli-responsive block copolymers for applications in cancer therapy, immunotherapy, and controlled release systems. He has pioneered methods for enhancing nanoparticle stability and tumor targeting through glycopolymer engineering and shell-crosslinked micelles. His recent work explores gas separation membranes using ionic liquid-infused block copolymers and ROS-responsive nanoparticles for macrophage targeting. These studies leverage molecular simulations and experimental validation to advance material design. His contributions have been recognized through multiple teaching and research awards, reflecting his dual role as an educator and researcher. Awards: 2016 Faculty Service Award, 2016 Junior Faculty Research Award, 2018 Faculty Teaching Award, 2020 Outstanding Engineering Faculty Award. Labs/Teams: Center for Nano-Bio Interactions, Biomedical Engineering Research Group. Grants: Not explicitly listed, but his research aligns with NIH and NSF priorities in biomaterials and drug delivery.

• Multiphase Flows
• Computational Fluid Dynamics
• Turbulence
• +۴ مورد دیگر

Dr. Lina Baroudi is an Associate Professor in Mechanical Engineering at Manhattan University. Her research focuses on multiphase flows, computational fluid dynamics, and turbulence, with applications in nuclear reactor thermal hydraulics and mesoscopic simulation techniques. She holds a Ph.D. from The City College of New York and a B.S. from Damascus University. Education: Ph.D., The City College of New York MPhil, The City College of New York M.S., The City College of New York B.S., Damascus University Her research interests include studying surface-tension-driven flows, particle-laden flows, and developing predictive models using advanced computational methods like Lattice Boltzmann (LB), Molecular Dynamics (MD), and parallel high-performance computing. She has contributed to understanding Taylor-Couette flow dynamics, bubble rising mechanics, and droplet coalescence phenomena. Dr. Baroudi has been awarded prestigious fellowships including the Nuclear Regulatory Commission Graduate Fellowship (2015-2016), MRSEC PREM Fellowship (2011-2015), and NSF Graduate Research Fellowship (2009-2010). Her work bridges fundamental fluid dynamics with industrial and nuclear engineering applications. She teaches courses such as Engineering Mathematics, Applied Heat Transfer, and Finite Element Analysis. Her lab focuses on multiphase flow modeling and high-performance computing, collaborating with ASME, APS, and other professional societies.

Peter Vekilovمشاهده پروفایل

استاد

• Bioactive Materials
• Amyloid Fibrillization
• Nonclassical Nucleation
• +۵ مورد دیگر

Peter Vekilov is the Frank Worley Professor and John and Rebecca Moores Professor in the William A. Brookshire Department of Chemical and Biomolecular Engineering at the University of Houston . A Fellow of the American Physical Society and recipient of the Frank Prize (2023), his work spans biocrystallography, pharmaceutical crystallization, and amyloid research. Education: Ph.D. in Chemistry (Russian Academy of Sciences, 1991), M.S. Summa Cum Laude (Moscow University, 1985) Courses: Biological and Physical Chemistry (CHEE 3466), Classical and Statistical Thermodynamics (CHEE 6335) His research focuses on nonclassical nucleation in biological and organic systems, elucidating mechanisms of amyloid-β fibrillization (Alzheimer's), hematin crystallization (malaria), and polymorph control. His lab employs experimental and computational methods, including AFM and machine learning, to design materials and combat diseases. Recent publications highlight nonclassical pathways in hematin and cholesterol crystallization, synergistic drug interactions, and mesoscopic cluster dynamics. Notable awards include the Francqui International Professorship (2014) and Cullen College Faculty Excellence Award (2021). Scientific Awards: Frank Prize (2023) American Association for Crystal Growth Award (2021) Fellow, American Physical Society (2010) Shubnikov Prize (1986) Students: Manasa Yerragunta Huan-Jui Lee Gary Chen Hariharan Annadurai Hamidreza Azargoshasb

• Computational Engineering
• Numerical Methods
• Computational Fluid Dynamics
• +۵ مورد دیگر

Professor Nam Mai-Duy is a faculty member at the University of Southern Queensland (USQ), holding the position of Professor in Computational Engineering within the School of Engineering. His research focuses on advanced numerical methods for fluid dynamics, including integrated radial basis functions (IRBF), finite volume schemes, and dissipative particle dynamics (DPD). He has expertise in computational fluid dynamics (CFD), viscoelastic fluids, and multiphase systems. Qualifications include a Master of Engineering from Ho Chi Minh University of Technology and a PhD from USQ. His work emphasizes high-order numerical techniques for solving partial differential equations in complex geometries and non-Newtonian fluid behavior. Research interests span computational engineering, numerical analysis, and engineering simulations. His publications address topics like boundary-fitted grids, embedded-boundary methods, and microstructure modeling in viscoelastic materials. He collaborates with the Institute for Advanced Engineering and Space Sciences. Advising involves doctoral research in computational methods for fluid flows, such as soliton propagation in waveguides and particulate suspensions. No scientific awards are explicitly listed, though his contributions to numerical methods are widely recognized.

• Physics Education Research
• Teaching Methodologies in STEM
• Conceptual Understanding in Physics
• +۳ مورد دیگر

Claudio Fazio is a Full Professor in the Department of Physics and Chemistry at the University of Palermo. His academic focus lies in physics education research, particularly in innovative teaching methodologies, active learning strategies, and overcoming student misconceptions in physics. He actively engages in designing inquiry-based learning experiences and leveraging technology (e.g., smartphones, AR/VR, simulations) for effective STEM education. Research interests include conceptual understanding in mechanics, thermodynamics, and relativity, with a strong emphasis on pedagogical tools like cluster analysis to evaluate student reasoning patterns. He has pioneered low-cost experimental setups and open-inquiry approaches to enhance learning outcomes, especially in engineering and teacher training programs. Professor Fazio’s work addresses challenges in remote education, pandemic-era pedagogy, and integrating contemporary technologies (e.g., Moodle, MATLAB) into curricula. His contributions span K-12 teacher preparation through university-level educational innovation, emphasizing interdisciplinary collaboration and evidence-based practices.

Upendra Harbolaمشاهده پروفایل

استاد

• Molecular Electronics
• Spectroscopy
• Quantum Transport
• +۳ مورد دیگر

Upendra Harbola is a Professor at the Department of Inorganic and Physical Chemistry, Indian Institute of Science. His research focuses on quantum transport phenomena , non-equilibrium thermodynamics , and statistical physics applied to nanoscale systems. Department: Inorganic and Physical Chemistry University: Indian Institute of Science, Bangalore His work investigates many-body quantum systems using advanced theoretical frameworks like the Quantum Master Equation (QME) and Non-equilibrium Green's Function methods. Key research areas include: Electron/photon current fluctuations in quantum junctions Inelastic electron tunneling spectroscopy via STM Current-induced fluorescence mechanisms Entropic production in non-equilibrium systems Publications highlight applications in nanoelectronics , quantum dot technology , and molecular junctions , with a strong emphasis on fundamental thermodynamic principles like the Fluctuation Theorem.

Samuel Isaacsonمشاهده پروفایل

استاد

• Numerical Analysis
• Mathematical Biology
• Mathematical Physics
• +۴ مورد دیگر

Samuel Isaacson is a Professor at Boston University's Department of Mathematics and Statistics, specializing in numerical analysis, mathematical biology, and mathematical physics. His research focuses on developing and analyzing numerical methods for stochastic reaction-diffusion models in cellular biology, with applications to cell signaling, T cell activation, and antibody-antigen interactions. He emphasizes rigorous coarse-grained modeling, unstructured mesh methods, and parameter inference from experimental data. Recent work includes advancements in reactive Langevin dynamics models, mean-field limits of particle systems, and molecular mechanisms underlying antibody efficacy. His interdisciplinary approach combines computational modeling, experimental collaboration, and mathematical theory to address biophysical questions. Notable contributions include the Catalyst software for reaction network modeling and studies on spatial effects in genetic circuits and chromatin structure. Isaacson's grants and collaborations span computational methods for stochastic systems, parameter estimation in biochemical networks, and the influence of cellular geometry on signaling. His lab focuses on bridging microscopic particle-level models with macroscopic biological observations, with applications in immunology and synthetic biology. Current projects explore the role of molecular 'reach' in antibody-virus interactions and the development of efficient simulation tools for complex biological systems.

Tianbai Xiaoمشاهده پروفایل

پژوهشگر

• Numerical Analysis
• Applied Mathematics
• Kinetic Theory
• +۷ مورد دیگر

Tianbai Xiao is a Researcher at the Karlsruhe Institute of Technology (KIT) within the Department of Mathematics and Steinbuch Centre for Computing. His work spans mesoscopic science , uncertainty quantification , and scientific machine learning , focusing on multi-scale, multi-physics problems in flow transport. His research in kinetic theory addresses nonlinear partial differential equations, hyperbolic conservation laws, and the unified modeling of continuum/rarefied flows. He develops high-performance numerical algorithms like the Unified Gas-Kinetic Scheme (UGKS) and Kinetic.jl (a finite volume toolbox for scientific computing). Current projects include mesoscopic science , stochastic data science , and physics-informed neural networks . He contributes to open-source tools including FluxReconstruction.jl for advection-diffusion methods and Langevin.jl for stochastic kinetic modeling. Publications cover Journal of Computational Physics , Engineering Fracture Mechanics , and Entropy , with preprints on arXiv in 2025 addressing force-driven flows and hybrid peridynamics. Teaching activities include the Introduction to Kinetic Theory lecture at KIT, and mentoring in the CAMMP (Computational and Mathematical Modeling Program) to develop problem-solving skills through real-world modeling tasks. He advocates for problem-based learning where students translate non-mathematical problems into mathematical language.