Dr. Alberto Carnicero López is a Senior Associate Professor at the School of Engineering (ICAI) of Comillas Pontifical University, affiliated with the Institute for Research in Technology (IIT). He holds a Ph.D. in Engineering from Comillas and has been active in academia since 1999. His research focuses on numerical methods in engineering, railway systems, catenary-pantograph dynamics, fire safety, thermal/fluids engineering, and bioengineering. He has supervised multiple Ph.D. theses and led over 40 research projects funded by entities like the Spanish Ministry of Science and international organizations. His educational background includes degrees in Mechanical Engineering and Industrial Engineering from Comillas. Key research contributions include advancements in catenary-pantograph interaction modeling, structural analysis using FE methods, and bioengineering applications like subperiosteal implant designs. He has authored 42+ journal articles, with recent works addressing e-scooter rider safety, CO2 emissions during the pandemic, and crack propagation analysis. Dr. Carnicero has received awards such as the X Premio Talgo for Innovation and has served on doctoral committees and as a reviewer for journals like Vehicle System Dynamics . His work bridges engineering disciplines, emphasizing practical solutions for railway systems, biomechanics, and sustainable mobility.
Bernd Simeon is a Professor of Mathematics at Technische Universität Kaiserslautern (TUK), leading the Differential-Algebraic Systems group within the Department of Mathematics. His research focuses on applied and numerical mathematics, with particular emphasis on coupled systems of differential-algebraic equations (DAEs) and partial differential equations (PDEs). Key areas of expertise include computational mechanics (especially multibody dynamics), interdisciplinary collaborations in materials science and fluid mechanics, isogeometric finite elements, and biomechanical applications such as hemodynamics and musculoskeletal systems. Before joining TUK in 2010, he held professorships at TU München (2002–2010) and the University of Karlsruhe (2000–2002). He obtained his habilitation in mathematics from Karlsruhe in 2000 and his PhD from TU München in 1994. His academic career includes acting professorships and research roles across multiple German universities. Research Interests: Applications of DAEs and PDEs in engineering and biology Isogeometric analysis for structural and fluid-structure interaction problems Model reduction techniques for dynamic systems Numerical methods for nonlinear dynamics and biomechanics Publications reflect his contributions to computational mechanics, including influential works on differential-algebraic systems (e.g., multibody dynamics) and isogeometric methods. Recent trends emphasize interdisciplinary applications in biomechanics and material science. His work bridges theoretical mathematics with practical engineering challenges, with a focus on advancing numerical simulation techniques. Current projects explore fiber-based muscle modeling and mean-field particle systems. Labs/Teams: Head of the AG Differential-Algebraic Systems research group at TUK.
Xinfeng Liu serves as Professor and Undergraduate Director in the Department of Mathematics at the University of South Carolina. His academic journey includes progression from Assistant Professor (2009-2013) to Associate Professor (2013-2017) and finally to full Professor (2018-present), following a Visiting Assistant Professor position at UC Irvine (2006-2009). His research spans Computational Biology with focus on cancer stem cell modeling and signal transduction mechanisms, alongside Numerical PDEs specializing in front tracking methods and integration factor techniques. Liu's work bridges mathematical theory with biological applications, particularly in morphogen gradient formation and MAPK cascade regulation. His computational approaches address complex phenomena like Rayleigh-Taylor instability in fluid dynamics. Liu's recent publications demonstrate strong trends in developing numerical methods for free boundary problems in biological systems, with significant contributions to cancer stem cell modeling. His work increasingly integrates stochastic elements and time-delay systems in therapeutic response modeling. NSF Mathematical Biology Division grant DMS1853365 (2019-2022) NSF Mathematical Biology Division grant DMS1308948 (2013-2017) NSF Mathematical Biology Division grant DMS1019544 (2010-2014) University of South Carolina ASPIRE I grant (2013-2014) South Carolina EPSCoR/IDeA GEAR program (2011-2013) Liu has mentored three doctoral students to completion, all securing competitive postdoctoral positions at Los Alamos National Lab, University of Waterloo, and University of North Texas. His teaching portfolio spans computational mathematics, differential equations, and advanced analysis courses since 2009, including honors sections and specialized topics like Modeling of Complex Biological Systems.
Associate Professor Daniel McCrum is a faculty member at the School of Civil Engineering, University College Dublin (UCD). He holds a PhD from Trinity College Dublin and has held academic positions at Queen's University Belfast and UCD. His research focuses on modern methods of construction, structural steel, resilience of infrastructure, and cyber-physical threats. He leads the Modern Methods of Construction Research Group (MMCRG) and is involved in projects like Construct Innovate and the Horizon 2020-funded PRECINCT initiative. Education: BEng (1st class) from Trinity College Dublin (2005), MSc in Structural Engineering (2006, University of Glasgow), PhD in Civil Engineering (Trinity College Dublin, 2008). Research interests include modular construction, cold-formed steel design, bridge aerodynamics, and critical infrastructure resilience. He has supervised over 15 postgraduate researchers and led numerous funded projects including EU-funded research on construction standards and predictive modeling tools. Awards include the 2022 Teaching Excellence Award and the 2016 Fulbright Scholarship. Professional roles include Programme Director for Civil Engineering programs at UCD, Head of Teaching and Learning (2019–2020), and member of Construct Innovate's Centre Executive Management Committee. He is a Chartered Structural Engineer with the Institution of Structural Engineers and an active peer reviewer for journals like Engineering Structures and Structure and Infrastructure Engineering. Key contributions include advancing CFS structural analysis, developing robustness frameworks for cyber-physical infrastructure, and aerodynamic studies on long-span bridges like the Queensferry Crossing. His work integrates computational methods with experimental testing to address challenges in construction safety and disaster resilience.
Prof. Jörn Behrens is a Professor of Numerical Methods in Geosciences at the University of Hamburg's Department of Mathematics. His work focuses on computational geoscience, including adaptive numerical methods, tsunami modeling, and multiscale simulations. He leads the Numerical Methods in Geosciences research group and is affiliated with the Climate, Climatic Change, and Society (CLICCS) cluster of excellence. Education & Positions: Since 2009: Full Professor at University of Hamburg 2012: Visiting Scientist at Isaac Newton Institute, Cambridge 2007–2009: Privatdozent at University of Bremen Extensive experience at Alfred-Wegener-Institut and TU Munich Research Interests: Behrens develops numerical algorithms for geophysical flows, emphasizing adaptive mesh refinement, discontinuous Galerkin methods, and tsunami hazard modeling. His work bridges computational mathematics and geoscience applications, with a focus on environmental challenges like climate modeling and disaster risk assessment. Recent efforts include cloud computing for instant hazard simulations and interdisciplinary projects like the Global Tsunami Model (GTM). Key Publications: Recent work includes bathymetry reconstruction via PDE-constrained optimization, low-rank preconditioning for fluid flow, and tsunami hazard frameworks within EPOS. These reflect his expertise in computational methods and geophysical problem-solving. Grants & Projects: Lead on EU-funded DT-GEO (Digital Twin for Geophysical Extremes) Co-chair of COST Action AGITHAR (Tsunami Hazard Research) Contributions to CLICCS and EPOS infrastructure development Labs & Teams: Leads the Numerical Methods in Geosciences group, collaborating internationally on tsunami modeling, climate systems, and high-performance computing.
Bo Zhu is an Assistant Professor at Georgia Tech's School of Interactive Computing. His research focuses on computer graphics, computational physics, and scientific machine learning, particularly in fluid dynamics, vortical structures, and computational design. He holds a Ph.D. in Computer Science from Stanford University and completed postdoctoral training at MIT CSAIL. Prior to Georgia Tech, he served as an Assistant Professor at Dartmouth College. His work is supported by grants such as the NSF CAREER Award (2022). Education: Ph.D. in Computer Science, Stanford University Postdoctoral Training: MIT CSAIL Dr. Zhu's research bridges physics simulation and machine learning, addressing challenges in fluid dynamics and material design. His group develops high-performance algorithms for fluid-structure interaction, topology optimization, and biomedical modeling. Recent projects include real-time fluid simulation techniques and neural network-based approaches for cardiac cell analysis. His publications span topics like fluid reconstruction from videos, particle-based methods, and biomimetic engineering. The NSF CAREER Award recognizes his innovative contributions to computational science and engineering. Advising and Grants: Zhu's grants support interdisciplinary projects in fluid dynamics and machine learning. He advises students in computational physics and design, though specific advisee names are not listed. His lab collaborates with industry and academic partners to advance simulation-driven engineering solutions.
Dylan Nelson is an Emmy Noether Research Group Leader at the Institute for Theoretical Astrophysics within Heidelberg University's Center for Astrophysics (ZAH). His work focuses on computational models of galaxy formation, circumgalactic medium dynamics, and cosmological simulations using the AREPO hydrodynamics code. He leads the IllustrisTNG Project and co-leads TNG50 and TNG-Cluster simulations, advancing understanding of galactic feedback, gas accretion, and magnetic fields. Education: PhD in Astrophysics from Harvard University (2015), postdoctoral fellowship at the Max Planck Institute for Astrophysics (MPA). Research interests span cosmological magnetohydrodynamics, galaxy evolution, and computational methods. Awards include the MERAC Prize (2023) and Clarivate Highly Cited Researcher (2021-2023). Research highlights include resolving small-scale CGM structures, studying X-ray emission from hot halos, and simulating galaxy clusters' intracluster medium. His group is funded by DFG, Hector Fellow Academy, and the STRUCTURES Cluster of Excellence. Supervised over 20 students, including PhDs and MSc/BSc researchers. Technical contributions include the IllustrisTNG public data release, visualization tools like ArepoVTK, and Python package scida for big data analysis. His work bridges theory and observation, informing next-generation missions like the Lynx X-ray telescope and Giant Magellan Telescope.
Philip Mocz is a computational research scientist at the Flatiron Institute's Center for Computational Astrophysics, part of the Simons Foundation. His work focuses on developing scalable, high-performance multiphysics simulation software with a growing emphasis on integrating modern AI techniques and automatic differentiability. Previously, he was a Computational Physicist at Lawrence Livermore National Laboratory, where he specialized in designing high-order Arbitrary Lagrangian-Eulerian (ALE) finite element methods for magnetohydrodynamics (MHD) simulations on heterogeneous computing architectures as part of the Multiphysics on Advanced Platforms Project (MAPP). Dr. Mocz earned his Ph.D. in Astrophysics from Harvard University in 2017 under Lars Hernquist, where he developed a finite-volume moving mesh magnetohydrodynamics algorithm applied to study structure formation and magnetic field amplification, integrating his solvers into the Arepo simulation code. Prior to his doctorate, he received an A.B. in Mathematics and Astrophysics from Harvard in 2012. His research spans multiphysics simulations, cosmology, galaxy formation, black hole physics, turbulence, numerical methods, and AI integration in computational astrophysics. A significant focus involves cosmological simulations of alternative dark matter candidates, particularly fuzzy dark matter. His work bridges theoretical astrophysics with high-performance computing, developing novel simulation frameworks that incorporate modern computational techniques. Dr. Mocz's publication record reveals a strong trajectory in computational methods for astrophysical problems, with increasing integration of AI techniques in recent years. His research demonstrates expertise across multiple domains including quantum mechanics applications to cosmology, turbulence modeling, and the development of advanced simulation algorithms. The interdisciplinary nature of his work connects astrophysics with computer science and applied mathematics. He maintains an active educational presence through his blog featuring approximately 100-line Python tutorials on scientific computing at the undergraduate level, published on Medium and followed on Twitter. His educational materials cover fundamental computational methods including finite difference approaches, Riemann solvers, and differentiable simulations using JAX. Dr. Mocz has served as a Teaching Fellow for Harvard courses including Astronomy 151 (Astronomical Fluid Dynamics), Applied Computation 274 (Computational Fluid Dynamics), and Applied Mathematics 205 (Advanced Scientific Computing). His outreach activities include mentoring for the LLNL DSTI Research Program, NASA Cosmic Origins Transitional Leadership Team, and Princeton Astrophysics Undergraduate Summer Research Program. Originally from Hawaii, he enjoys outdoor activities when not working. His professional presence includes active GitHub repositories (pmocz), a personal website (pmocz.github.io), and engagement on Bluesky (@philipmocz.bsky.social), where he shares insights about computational physics and scientific software development.
Robert Nürnberg is an Associate Professor in the Department of Mathematics at the University of Trento. His research focuses on numerical analysis, computational fluid dynamics, and geometric evolution equations, with a particular emphasis on finite element methods for multiphase flow, phase-field models, and biomembrane dynamics. He teaches courses such as Numerical methods for PDEs and Scientific computing , covering finite element implementations in MATLAB/Python and programming for mathematical sciences. His work addresses complex phenomena like Willmore flow, anisotropic crystal growth, and surfactant dynamics, often combining rigorous mathematical analysis with computational innovation. Recent contributions include parametric finite element methods for geometric PDEs and structure-preserving discretizations of elastic flow in Riemannian manifolds. Dr. Nürnberg collaborates on interdisciplinary projects involving additive manufacturing, tumor growth modeling, and fluidic biomembrane dynamics. His methods emphasize stability, accuracy, and applicability to real-world problems like void electromigration and phase separation in materials science.
Dr.-Ing. Norbert Hosters is a Research Associate and Chief Engineer at the Chair for Computational Analysis of Technical Systems (CATS), Faculty of Mechanical Engineering, RWTH Aachen University. He has been active since 2020 and serves as General Secretary of the German Association for Computational Mechanics (GACM). His work bridges advanced computational methods with engineering applications. His research focuses on numerical methods for fluid-structure interaction , computational fluid and structural dynamics , isogeometric analysis , and aerothermoelasticity . He also explores applied quantum methods and physics-informed neural networks for solving complex PDEs and optimizing industrial processes. His interdisciplinary work spans mechanical, biomedical, and computational engineering. The recent publications (2023–2025) demonstrate a strong trend toward integrating machine learning with traditional simulation techniques, particularly in partitioned FSI , multiphase flow , shape optimization , and biomedical simulations such as LVAD modeling. His work appears in high-impact journals like Scientific Reports , Computers & Fluids , and International Journal for Numerical Methods in Engineering , as well as major conferences including GACM and CMBE. He is actively involved in teaching courses on Numerical Methods for Fluid-Structure Interaction , Isogeometric Analysis , and Simulation Methods in Mechanical Engineering . He offers student projects and supervises research, though no named advisees are listed. He has no listed scientific awards or fellowships. His research is conducted within the CATS chair, a leading group in computational mechanics, contributing to both fundamental methods and industrial applications. He plays a key role in academic service through GACM leadership.
Felipe Gonzalez Cornejo is a Researcher at the Chair for Computational Analysis of Technical Systems (CATS) at RWTH Aachen University, affiliated since 2019 (PhD student until 2023, postdoc thereafter) and a member of NHR4CES's Simulation and Data Lab Fluids since 2023. Education: Bachelor's and Master's in Mechanical Engineering, University of Santiago of Chile (Usach) His research develops numerical methods for extrusion-based additive manufacturing, focusing on Computational Fluid Dynamics (CFD) for free-surface flows and non-Newtonian fluids, stabilized finite-element formulations, adaptive mesh techniques, and moving-domain simulations. He integrates High-Performance Computing (HPC) to model thermofluid flow, phase change, and thermoplastic behavior in Fused Deposition Modeling (FDM). No scientific awards were mentioned. He offers thesis supervision on FDM simulation topics: Multi-filament deposition and porosity estimation Nozzle geometry optimization via model order reduction Mesh-update techniques for moving multi-domain simulations HPC tools including load rebalancing and adaptive mesh refinement He collaborates across CATS work groups: production engineering, fluid-structure interaction, and INTERESST.
Serpil Kocabiyik is a Professor of Mathematics at Memorial University of Newfoundland, leading research in fluid mechanics and computational science. She holds a Ph.D. from Western Ontario (1987) and has held academic positions at Manitoba and Western Ontario. Her work focuses on unsteady separated flows, vortex-induced vibrations, and numerical simulation methodologies. Education: B.Sc. & M.Sc. (Middle East Technical University, 1979/1981), Ph.D. in Applied Mathematics (University of Western Ontario, 1987). Postdoctoral fellowships followed before joining Memorial in 1999 as Associate Professor, promoted to Full Professor in 2005. Research Interests: Interdisciplinary applied mathematics, theoretical fluid mechanics, computational science. Specializes in fluid-bluff body interactions, numerical methods for PDEs, and validation across computational/experimental studies. Key Contributions: Over 80 refereed papers, $2M+ in grants, and mentorship of >30 students/postdocs. Pioneered studies on free surface flows with moving bodies and developed parallelized CFD algorithms. First woman in Canada to win Petro-Canada Young Innovator Award (2000) CAIMS Arthur Beaumont Distinguished Service Award (2006) Teaching: Courses include Numerical Algorithms, Fluid Mechanics, Partial Differential Equations. Advocated for STEM education equity, training 17→50 graduate students in her department (1999–2009).
Natan Rubin is a faculty member in the Computer Science Department at Ben-Gurion University of the Negev, Beer-Sheba, Israel, where he has been conducting research in combinatorial and computational geometry since 2014. He is the principal investigator of a 5-year ERC Starting Grant project titled 'Combinatorial Aspects of Computational Geometry' (CombiCompGeom), which supports graduate students and postdocs in geometric algorithms and structures. Ph.D., Tel Aviv University, 2012 Advisor: Prof. Haim Kaplan and Prof. Micha Sharir His research focuses on fundamental problems in computational geometry, including geometric transversals , epsilon-nets , Voronoi diagrams , Delaunay triangulations , and intersection patterns of geometric objects . He has made significant contributions to the understanding of combinatorial bounds in geometric settings, such as resolving the Richter-Thomassen conjecture for pairwise intersecting Jordan curves and improving long-standing bounds on weak epsilon-nets. The recent publications reveal a consistent trend toward improving asymptotic bounds in high-dimensional and planar geometric configurations, with a strong emphasis on combinatorial methods and topological reasoning. His work often intersects with extremal combinatorics and discrete geometry, particularly in analyzing crossing and touching structures in planar graphs and families of convex sets. His scientific recognition includes: Best Paper Award at FOCS 2013 Best Paper Award at SoCG 2012 Rubin actively contributes to the academic community through service, having organized major workshops such as SODA 2018 and SoCG 2022, and hosting international researchers. He collaborates widely with leading figures in the field, including Pankaj Agarwal, János Pach, Micha Sharir, and Haim Kaplan. Though no formal list of students is provided, his ERC-funded project explicitly advertises multiple graduate and postdoctoral positions, indicating active mentorship. He is also involved in organizing international workshops and fostering collaboration within Israel’s strong computational geometry community, including researchers at BGU, Tel Aviv, and Jerusalem. His research is supported by competitive grants and involves the development of robust kinetic data structures and stable geometric graphs, with applications in dynamic environments and algorithmic stability.
Yulong Xing is a Professor and Vice Chair for Graduate Studies in the Department of Mathematics at The Ohio State University. His research focuses on numerical analysis and scientific computing, particularly in high-order numerical methods for partial differential equations, computational fluid dynamics, and multiscale modeling. He holds a PhD from Brown University (2006) and has authored numerous papers on discontinuous Galerkin methods, well-balanced schemes, and computational approaches for fluid dynamics and geophysical flows. Key research areas include: Discontinuous Galerkin (DG) methods for hyperbolic conservation laws, radiative transfer, and wave propagation Well-balanced schemes for geophysical and astrophysical flows High-order adaptive algorithms for phase field models and nonlinear PDEs Structure-preserving numerical methods for Hamiltonian systems and energy conservation Recent work emphasizes asymptotic preserving methods for multiscale problems, positivity-preserving techniques, and applications to relativistic radiation transport and shallow water equations. Articles often address stability, error analysis, and computational efficiency in complex physical systems. His contributions include pioneering work on DG methods for the Euler equations with gravitational fields and innovative treatments of discontinuous bottom topography in shallow water modeling. Despite extensive publications, no specific awards or grants are explicitly listed in the provided text.
Dr. Yap Yit Fatt is an Associate Professor in the Department of Mechanical & Nuclear Engineering at Khalifa University. He earned his PhD in Mechanical Engineering from Nanyang Technological University (2007), MEng (2002), and BEng (2000) from Universiti Teknologi Malaysia. Education : PhD (NTU, 2007), MEng (UTM, 2002), BEng (UTM, 2000) His research focuses on numerical methods for moving boundary problems in heat, mass, and momentum transfer, particularly modeling multiphase flows, phase-change heat transfer, and particle erosion/deposition using fixed mesh finite volume techniques. Key projects include Droplet Dynamics in Droplet Deposition 3D Printing , Optimization of Fouling-Mitigated Heat Exchangers , and predictive models for Wax/Asphaltene Deposition in pipelines and wellbores. His work combines computational fluid dynamics (CFD) with practical industrial applications. He teaches advanced courses such as Advanced Viscous Flow Analysis (MEEN 612), Computational Methods for Mechanical Engineers (MEEN 360), and Multiphase Flow Engineering (MEEN 615).
