جست‌وجوی استاد و پژوهشگر

• Calculus of Variations
• Continuum Mechanics
• Nonlinear Elasticity
• +۵ مورد دیگر

Prof. Dr. Marco Cicalese is a Professor of Mathematical Continuum Mechanics at the Technical University of Munich (TUM), holding a position in the Department of Mathematics within the TUM School of Computation, Information and Technology. He has been at TUM since 2012, following roles as an Assistant Professor at the University of Naples (2005–2012) and a researcher at SISSA (2004–2005). His research focuses on variational analysis of atomistic and continuous systems, multiscale problems, and geometric inequalities. Education: PhD in Applied Mathematics from the University of Naples (2004), MSc in Physics (details not specified). His editorial roles include Associate Editorships at Acta Applicandae Mathematicae and Mathematics in Engineering . Research interests encompass calculus of variations, nonlinear elasticity, and phase transitions, with contributions to discrete-to-continuum limits and stability of geometric inequalities. Teaching includes courses on partial differential equations, calculus of variations, and mathematical modeling. His work often bridges discrete and continuous models, with applications to materials science and continuum mechanics. Recent publications explore topics like Wulff crystal emergence, fractional vortices, and surfactant effects in phase transitions.

• Soft Materials
• Quantum Mechanics
• Machine Learning
• +۳ مورد دیگر

Professor Nicholas E. Jackson is an Assistant Professor of Chemistry at the University of Illinois, affiliated with the College of Liberal Arts & Sciences and the Beckman Institute for Advanced Science and Technology. He holds the Lincoln Excellence for Assistant Professors Scholar distinction. His research focuses on soft materials, quantum mechanics, and machine learning, particularly in developing coarse-grained electronic structure methods and sustainable polymer design. Education: B.A. in Physics from Wesleyan University (2011), Ph.D. in Chemistry from Northwestern University (2016). Postdoctoral work as a Named Fellow and Assistant Scientist at Argonne National Laboratory (2016–2021). Joined the University of Illinois faculty in 2021. Research interests include conjugated materials theory, electronic structure predictions, and interdisciplinary applications of machine learning in materials science. His work bridges computational chemistry, materials engineering, and AI, addressing challenges in renewable energy and sustainable materials. Recent recognition: 2025 Kavli Foundation Emerging Leader in Chemistry Award for innovative research in young scientists under 40. His contributions to AI-driven materials design and thermoset fracture modeling have been highlighted in peer-reviewed journals. Advances include novel approaches to polymer reactivity prediction, molecular-level understanding of conjugated polymers, and machine learning workflows for molten salt mixtures. His lab collaborates across disciplines to advance soft materials design and computational tools for chemistry.

David Smithمشاهده پروفایل

استاد

• Microscale biological fluid dynamics
• Sperm motility and fertility modeling
• Cilia and flagellar mechanics
• +۵ مورد دیگر

David Smith is a Professor of Applied Mathematics at the University of Birmingham and Deputy Director of Research and Knowledge Transfer at the Engineering and Physical Sciences Healthcare Technologies Institute. He is renowned for his interdisciplinary research applying mathematical modeling to medicine and biology, particularly in microscale fluid dynamics of fertility, sperm motility, cilia mechanics, and mathematical endocrinology. Research Interests: Microfluid dynamics of fertility and reproduction, especially sperm motility and embryonic nodal cilia Mathematical endocrinology, including pharmacokinetics of cortisol and thyroid disease Development and application of regularized Stokeslets methods for biological flows Bayesian modeling for spectroscopic biomedical diagnostics Multiscale modeling in reproductive health and cell motility His recent publications span computational tools for viscous flow, dinoflagellate swimming, kinetic modeling of biochemical reactions, and Bayesian diagnostics using Raman spectroscopy, reflecting a broad and impactful interdisciplinary portfolio. Projects & Grants: Principal Investigator, EPSRC project on rapid sperm capture using imaging and machine learning (2016–2022) Co-Investigator, US Army and UK Ministry of Defence projects on traumatic brain injury biomarkers (2021–2028) Alan Turing Institute Turing Fellowship (2019–2020) EPSRC and Proctor & Gamble supported parameter estimation projects Smith chairs the editorial board of Mathematics in Medical and Life Sciences , has organized major conferences on bioactive fluids, and delivered keynote lectures on regularized Stokeslets methods. He currently supervises four PhD students and a postdoctoral fellow, welcoming new doctoral applicants.

Christos Likosمشاهده پروفایل

استاد

• Soft Matter Physics
• Theoretical Physics
• Computational Physics
• +۴ مورد دیگر

Univ.-Prof. Christos N. Likos is a world-leading researcher at the University of Vienna , holding the chair in Multiscale Computational Physics since 2010. Affiliated with the Faculty of Physics and directing the Computational and Soft Matter Physics group, he bridges scales from microscopic to macroscopic in soft matter systems. Education: Dipl.-Ing. in Electrical Engineering (NTUA Athens), M.Sc. & Ph.D. in Physics (Cornell University) Honors: Fellow of the Royal Society of Chemistry (2013), University of Vienna Teaching Award (2025), Outstanding Referee Award (2009) His research in Soft Condensed Matter focuses on polymers, colloids, and biomolecular systems through coarse-graining , density functional theory , and Monte Carlo simulations . Key collaborations include institutions in Rome, Heraklion, San Sebastian, and Princeton. His work reveals principles of self-organization, non-equilibrium phenomena, and responsive material design with applications in cosmetics, nanotechnology, and biophysics. Recent publications highlight active matter , topological polymers , and electric field-responsive microgels . The group trains 18 current students (Ph.D., M.Sc., B.Sc.) and maintains partnerships with experimental teams across Europe. As Associate Editor of Soft Matter and member of Journal of Colloid and Interface Science Open editorial board, he shapes scientific discourse in his field. Teaching excellence is a hallmark, with courses on Advanced Statistical Physics and Soft Matter Principles . His group website details ongoing projects, while lab facilities in Vienna's Kolingasse campus enable interdisciplinary research.

Bo Chenمشاهده پروفایل

دانشیار

• Biomacromolecular assemblies
• Nuclear Magnetic Resonance (NMR) techniques
• Viral capsid structure and dynamics
• +۳ مورد دیگر

Bo Chen is an Associate Professor in the Department of Physics at the University of Central Florida (UCF), part of the College of Sciences. He earned his PhD in Physics from Northwestern University in 2007 and held research positions at the NIH before joining UCF in 2011. His research focuses on structural characterization of biomacromolecular assemblies using NMR, TEM, and computational modeling, particularly HIV capsid proteins and antimicrobial peptides. Education: PhD in Physics, Northwestern University (2007). Research Interests: Solid-state NMR techniques, self-assembly mechanisms of viral capsids, nano particle catalysis, and antimicrobial peptide dynamics. His lab develops novel coarse-grain models and integrates experimental and computational approaches to study biomolecular systems. Awards: 2012 UCF Inhouse Award, 2013 AFOSR Young Investigator Award, 2016 COS Dean’s Rising Star Award, and 2020 Mid-Career Refreshment Award. Labs/Teams: Leads the Bo Chen NMR Lab at UCF, focusing on interdisciplinary approaches to structural biology and biophysics. Current students include Tyrone Thames (PhD), with notable alumni like Xin Qiao and Jaekyun Jeon.

• Graph Representation Learning
• Deep Learning
• Reinforcement Learning
• +۷ مورد دیگر

Jian Tang is an Assistant Professor at HEC Montreal and the Montreal Institute for Learning Algorithms (MILA), as well as an Associate Professor at the Department of Computer Science and Operations Research (DIRO) at Université de Montréal. He is also affiliated with IVADO (Institut de valorisation des données) as a member. His research spans multiple institutions including collaborations with leading biology labs worldwide and access to extensive computational resources through industry partners. Ph.D. in Computer Science, Peking University (2009-2014) Visiting Ph.D. student, University of Michigan (2011.10-2013.8) B.S. in Mathematics, Beijing Normal University (2005-2009) Professor Tang's research focuses on the intersection of deep learning and graph theory, with particular emphasis on geometric deep learning, knowledge graph reasoning, and applications in drug discovery. His work bridges symbolic and neural approaches to create robust reasoning systems that can handle complex structured data. He has pioneered techniques in graph representation learning that have significantly advanced the field of molecular property prediction and protein design. His publication record shows a clear trajectory toward applying geometric deep learning to biological problems, with a growing emphasis on protein design, molecular conformation generation, and multi-omics analysis. Recent work demonstrates sophisticated integration of 3D geometry with deep learning architectures to model complex biomolecular interactions. Canada CIFAR Artificial Intelligence Chairs (CCAI Chair) Tencent AI Lab Rhino-Bird Gift Fund Amazon Faculty Research Award Microsoft-Mila collaboration grant National Research Council Canada (NRC) Collaborative Research and Development Grant Professor Tang actively mentors doctoral and master's students, with six recent graduates working on cutting-edge topics including graph neural networks for reasoning, protein design, and molecular representation learning. His research is supported by substantial funding from industry partners including Microsoft, Amazon, and Tencent, as well as government agencies like NRC. He collaborates extensively with biology labs worldwide, applying AI to solve real-world biomedical challenges. He leads a research group focused on geometric deep learning for drug discovery, with active projects in protein design using geometric-aware models and large language models for multi-omics analysis. The group has access to thousands of GPUs through industry collaborations, enabling large-scale experiments in molecular simulation and generative modeling.

David A. Egolfمشاهده پروفایل

دانشیار

• computational physics
• nonequilibrium systems
• spatiotemporal chaos
• +۶ مورد دیگر

David A. Egolf is an Associate Professor in the Department of Physics at Georgetown University, specializing in computational physics with a focus on systems maintained far-from-equilibrium. His research spans fluid dynamics, granular materials, biophysics, and statistical mechanics, employing nonlinear dynamics and large-scale computation to understand complex phenomena. His educational background includes undergraduate and doctoral studies at Duke University, where he earned his PhD in Physics with a thesis on Characterizations of Extensively Chaotic States and Transitions . Prior to Georgetown, he held postdoctoral positions at Cornell University's Cornell Theory Center and Los Alamos National Laboratory's Center for Nonlinear Studies. Egolf's research interests center on spatiotemporal chaos and nonequilibrium systems. He investigates how localized events determine the evolution of complex systems, with applications ranging from fluid convection to fibrillating heart tissue. His work reveals that seemingly chaotic systems often contain predictable elements around specific critical events, providing pathways to develop a statistical mechanics for nonequilibrium phenomena. His publication record shows consistent contributions to understanding dynamical systems, with recent work focusing on granular jamming transitions, biopolymer networks, and QCD calculations. The research demonstrates recurring themes of identifying fundamental building blocks within chaotic systems and establishing connections between nonequilibrium behavior and equilibrium statistical mechanics. Alfred P. Sloan Research Fellow Software of the Year Award (1984) for AtariLab Science Series Egolf has successfully mentored numerous undergraduate researchers at Georgetown, supervising over a dozen senior theses with several students receiving departmental awards and honors. His research has been supported by major funding agencies including the National Science Foundation, Research Corporation, NASA, and the Alfred P. Sloan Foundation. He maintains an active collaboration with experimental physicist Jeffrey Urbach, combining theoretical and experimental approaches to study driven granular systems and biophysics. His laboratory work focuses on computational modeling of nonequilibrium systems, utilizing large computer clusters to simulate complex phenomena across multiple scales. Current projects include studying granular systems driven by both shaking and shearing to introduce multiple time-scales, and investigating biopolymer networks relevant to cellular mechanisms.

Constantin Grigoمشاهده پروفایل

پژوهشگر

• Machine Learning
• Uncertainty Quantification
• Stochastic Differential Equ://grigo.userweb.mwn.de/ueber-mich/
• +۲ مورد دیگر

Constantin Grigo is a PhD researcher at the Technical University of Munich (TU Munich), actively engaged in the Continuum Mechanics group. His work focuses on Uncertainty Quantification (UQ) and Machine Learning (ML), particularly for applications in maritime safety, bicycle traffic modeling, and stochastic systems. He has presented his research at major conferences like SIAM UQ and WCCM, and has been recognized with Student Travel Awards from SIAM UQ 2018 and SIAM CSE 2019. Education: Master of Science in Physics, LMU Munich (2015) Bachelor of Science in Physics, LMU Munich (2012) Year abroad at Grenoble INP (2010-2011) Research Interests: Probabilistic machine learning for coarse-graining high-dimensional systems Bayesian model and dimension reduction Stochastic differential equations in heterogeneous media Microscopic traffic simulation for bicycles and autonomous vehicles Digital twin applications for maritime and urban mobility Reduced-order modeling of random materials Selected Awards: SIAM UQ 2018: Student Travel Award Winner SIAM CSE 2019: Student Travel Award Winner His publications span topics such as data-driven scenario specification for autonomous vehicles, bicycle maneuver prediction using neural networks, and physics-constrained surrogates for UQ. He also contributes to open-source simulation tools like SUMO for traffic modeling.

Joel P Johnsonمشاهده پروفایل

دانشیار

• Geomorphology
• Landscape Evolution
• Sediment Transport
• +۳ مورد دیگر

Dr. Joel P. Johnson is an Associate Professor in the Department of Earth and Planetary Sciences at The University of Texas at Austin's Jackson School of Geosciences. His research focuses on quantifying active surface processes, including geomorphic feedbacks, landscape evolution, and sediment transport dynamics. He explores topics like lithologic controls on topography, tsunami deposit analysis, and morphodynamics in mountain rivers. His work integrates field studies, laboratory experiments, and numerical modeling. Education: BS in Earth Sciences, PhD from MIT, postdoc at USGS. He teaches courses like Earth Wind and Fire, Geomorphology, and Professional Ethics in Geosciences. He co-leads the Jackson Scholars program, fostering student engagement and professional development. Research Themes: (1) Climatic and lithologic influences on landscape evolution, (2) Mountain river morphodynamics using smartrocks and flume experiments, (3) Tsunami and storm surge deposit analysis for hazard assessments. Key projects include Guadalupe Mountains lithology studies, Martian canyon comparisons, and Hawaii climate-erosion feedbacks. Advising: Supervises PhD/Master’s students and undergrad researchers (e.g., Grace Guryan, Morgan Carrington). Collaborates with institutions like Tulane University and Durham University. Publications span 20+ peer-reviewed articles since 2003, focusing on fluvial processes, sediment transport, and geomorphic modeling. Labs/Teams: Active in experimental flume research at UT, smartrock tracer technology development, and comparative planetology studies. His group emphasizes interdisciplinary approaches to address Earth system challenges.

Eric Mayمشاهده پروفایل

دانشیار

• Computational Biophysics
• Theoretical Biophysics
• Biochemistry
• +۷ مورد دیگر

Eric May is an Associate Professor in the Department of Molecular and Cell Biology at the University of Connecticut, with research focused on computational and theoretical biophysics and biochemistry. His work bridges physical mechanisms in biology with applications in nanotechnology and biomedical engineering, particularly studying conformational transitions and mechanical properties of biological materials. May's research explores structural changes in non-enveloped viruses during infection, membrane disruption, protein folding, structural prediction, and mitochondrial peptide interactions. His group utilizes advanced molecular dynamics simulations to connect single-molecule and ensemble experimental data, developing tools like BUMPy for lipid bilayer modeling. Recent computational studies highlight his expertise in pH-dependent viral capsid behavior, mitochondria-targeted peptides, and lipid bilayer interactions. He has published extensively on topics including viral lytic peptides, cardiolipin dynamics, and nanotube architecture, though no specific awards or student advisement details are mentioned in available records. His lab at UConn (maylab.uconn.edu) investigates how environmental stimuli affect viral structures and biomedical systems, combining theoretical approaches with experimental correlations to advance understanding of biological and synthetic material behavior.

Arieh Warshelمشاهده پروفایل

استاد

• Theoretical Chemistry
• Biophysics
• Enzyme Catalysis
• +۷ مورد دیگر

Arieh Warshel is a Distinguished Professor of Chemistry and Biochemistry at the University of Southern California’s Dornsife College of Letters, Arts and Sciences. He holds the Dana and David Dornsife Chair in Chemistry and is a Nobel Laureate in Chemistry (2013) for pioneering multiscale simulation methods for biological molecules. His work integrates quantum mechanics and molecular mechanics (QM/MM) to model enzymatic reactions and biological processes. Warshel earned a B.S. from the Technion in Israel (1966), M.S. and Ph.D. from the Weizmann Institute (1967, 1969). He joined USC in 1976 and has since led groundbreaking research in theoretical chemistry, including studies of enzyme catalysis, molecular motors, ion channels, and drug design. His research focuses on computational modeling of biological systems, emphasizing electrostatic effects, proton/electron transfer, and protein dynamics. Key contributions include the EVB (Empirical Valence Bond) method and the QM/MM approach, which revolutionized simulations of enzymatic reactions. Current projects involve multiscale modeling of G-proteins, ion pumps, and molecular machines. Warshel has authored over 500 publications, including seminal works on enzyme catalysis, electrostatic interactions, and molecular dynamics. His honors include membership in the U.S. National Academy of Sciences, the Royal Society of Chemistry’s Honorary Fellowship, and the Biophysical Society’s Founders Award. He oversees a research group with expertise in computational chemistry, mentoring postdocs like Ashim Nandi (plastic-degrading enzymes) and graduate students (e.g., Aoxuan Zhang, Lingfeng Hu). His lab uses advanced computing resources, including 38 dedicated nodes on USC’s HPC cluster, to simulate biological systems at unprecedented scales.

• Systems Biology
• Stress Response
• Stem Cell Differentiation
• +۳ مورد دیگر

Ala Trusina is an Associate Professor at the Niels Bohr Institute , University of Copenhagen , specializing in Biocomplexity and Biophysics . Her research integrates coarse-grained modeling to study complex biological systems. Key Research Areas Stress Response Systems (diabetes, aging, cancer, inflammation) Stem Cell Differentiation (cell fate coordination, reversibility of states) Complex Systems (species coexistence, epidemics, CRISPR-phage interactions) Methodologies Theoretical: Agent-based modeling, in-silico simulations Experimental: Quantitative single-cell imaging, RNA/protein profiling Collaborations Joshua Brickman (ES cells), Anne Grapin-Botton (pancreas), Feroz Papa (diabetes), Else Kai Hoffman (p53 dynamics) Thomas Mandrup-Poulsen (inflammation), Savas Tay (spatio-temporal regulation) Teaching Physics of Molecular Diseases Numerical Methods in Physics

Bjorn Baumeierمشاهده پروفایل

دانشیار

• Multiscale Simulation
• Electronic Transport
• Computational Chemistry
• +۵ مورد دیگر

Bjorn Baumeier is an Associate Professor in the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e). His research group is part of the Centre for Analysis, Scientific Computing and Applications (CASA) and the Institute for Complex Molecular Systems (ICMS). He also participates in several research groups including Scientific Computing, ICMS Core, Eindhoven Hendrik Casimir institute, and Computational Quantum & Molecular Dynamics. His educational background includes: Diploma in Theoretical Solid State Science from the University of Münster PhD in Theoretical Solid State Science from the University of Münster Baumeier's research focuses on the development and application of multiscale simulation techniques for studying electronic transport processes in soft matter. His work combines approaches from computational chemistry, statistical physics, and mathematics to analyze the interplay between molecular electronic structure and material morphology. Additional research lines include studies of disordered biomolecular assemblies and super-coarse-grained modeling of soft granular materials. His group employs large-scale computer simulations linking quantum chemistry, classical Molecular Dynamics at various levels, and rate-based models. Recent publications (2024-2025) demonstrate a strong focus on charge transport phenomena in complex materials, with particular emphasis on interface effects in polymer composites, trap identification in molecular networks, and embedded many-body Green's function methods. His work bridges fundamental physics with practical applications in energy materials and opto-electronic devices. Scientific awards include: Vidi grant from NWO (The Netherlands Organisation for Scientific Research) in 2017 (€800,000) Baumeier has received significant research funding, most notably the Vidi grant focusing on understanding mechanisms underlying long-distance and spin-selective electronic transport in complex molecular systems. His research is often conducted in collaboration with multiple institutions and research groups within TU/e, indicating a strong interdisciplinary approach. His work has practical applications in opto-electronic devices and bio-molecular processes. His research group operates within the Computational Quantum & Molecular Dynamics group, which is part of several larger research initiatives at TU/e including ICMS and the Eindhoven Hendrik Casimir institute. This positioning allows for strong collaboration across physics, chemistry, and engineering disciplines.

Scott E. Parkerمشاهده پروفایل

استاد

• Plasma Physics
• Magnetic Fusion Energy
• Computational Physics
• +۴ مورد دیگر

Scott E. Parker is a Professor of Physics at the University of Colorado Boulder and a Fellow at both the Renewable and Sustainable Energy Institute (RASEI) and the Center for Integrated Plasma Studies (CIPS). His expertise lies in theoretical and computational plasma physics for magnetic fusion energy. Education: Ph.D. in Engineering Science, University of California, Berkeley B.S. in Nuclear Engineering and Mathematics, University of Wisconsin, Madison His research centers on plasma turbulence simulation using advanced computational methods. Key areas include gyrokinetic theory, tokamak edge physics, and quantum information science applications to plasma modeling. The group develops particle-based simulations in five-dimensional phase space to study transport in magnetically confined plasmas, utilizing massively parallel computing and scientific visualization. Analysis of his publications reveals consistent focus on numerical techniques for plasma turbulence, particularly trapped electron modes and zonal flows. His work bridges fundamental kinetic theory with practical fusion reactor challenges, demonstrating qualitative agreement with experimental transport observations in tokamaks. Awards: RASEI Fellow CIPS Fellow Professor Parker leads the Parker Research Group, mentoring graduate students in computational plasma physics. The group collaborates on extreme-scale computing projects for fusion energy research, though specific grant details are not provided in source materials. Their work is integral to advancing predictive capabilities for next-generation fusion devices. The research group maintains active projects in magnetic fusion energy, utilizing GEM simulations and multiscale kinetic methods to address edge turbulence and transport phenomena critical for reactor viability.

• Biophysics
• Molecular Dynamics Simulations
• Membrane Biology
• +۴ مورد دیگر

Balázs Fábián is a postdoctoral researcher at the Max Planck Institute of Biophysics in the group of Gerhard Hummer. He holds a PhD from the University Bourgogne Franche-Comté (France) and Budapest University of Technology and Economics (Hungary). His research focuses on molecular dynamics simulations of lipid membranes and viral systems, particularly studying viral budding mechanisms in enveloped viruses like HIV. Fábián’s work bridges computational biophysics and membrane biology, with applications ranging from anaesthesia mechanisms to neurodegenerative diseases. Education: BSc and MSc in Chemical Engineering from Debrecen and Budapest Universities (Hungary), followed by a PhD in Biophysics. Previously, he conducted postdoctoral research at the Czech Academy of Sciences and the Max Planck Institute. Research Interests: Computational modeling of lipid bilayers, viral membrane interactions, anaesthetic effects on membranes, and protein aggregation in neurodegenerative contexts. His methods include molecular dynamics simulations and coarse-grained modeling (e.g., Martini force field). Teaching and Mentorship: Guides junior researchers in computational techniques and simulation methodologies. Active in conference presentations and publication preparation. Future Goals: Aims to lead a junior research group via the Emmy Noether program, focusing on advancing computational tools for membrane systems and viral dynamics.