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Andrew ChildsView profile

Professor

• Quantum Algorithms
• Quantum Information Processing
• Quantum Simulation
• +2 more

Andrew Childs is a Professor at the University of Maryland, affiliated with the Department of Computer Science and the Institute for Advanced Computer Studies (UMIACS). He serves as Director of the NSF Quantum Leap Challenge Institute for Robust Quantum Simulation (RQS) and is a Fellow at the Joint Center for Quantum Information and Computer Science (QuICS). His research focuses on quantum algorithms for simulating physical systems, algebraic problems, and quantum walk protocols, with applications in quantum computing and computational complexity. University of Maryland Institute for Advanced Computer Studies (UMIACS) Joint Center for Quantum Information and Computer Science (QuICS) NSF Quantum Leap Challenge Institute for Robust Quantum Simulation Childs' research spans quantum simulation, quantum Fourier transform, phase estimation, and Hamiltonian dynamics. He has developed techniques to reduce quantum computational resources for simulating quantum systems and explored limitations of quantum computers through hidden subgroup problems and non-unitary dynamics. His publications cover diverse areas including quantum walk optimization, Hamiltonian simulation methods, and applications to cryptography and condensed matter physics. Recent works address spatial search algorithms, product formulas for commutators, and quantum routing protocols. As an educator, Childs has taught courses on quantum algorithms and information processing at both the University of Maryland and University of Waterloo, with lecture notes and materials spanning multiple years. Contact: amchilds@umd.edu | Office: ATL 3359 | Affiliated with University of Maryland's quantum research institutes.

John EvansView profile

Associate Professor

• Computational Mechanics
• Fluid Dynamics
• Fluid-Structure Interaction
• +3 more

John Evans is an Associate Professor and Jack Rominger Faculty Fellow in the Department of Aerospace Engineering Sciences at the University of Colorado Boulder, affiliated with the Applied Mathematics program. He serves as Associate Chair for Undergraduate Curriculum and is part of the Aerospace Mechanics Research Center (AMREC). His research focuses on computational mechanics, particularly fluid dynamics, fluid-structure interaction, and turbulence modeling using high-order and structure-preserving methods. Evans holds a PhD (2011) and MS (2008) in Computational and Applied Mathematics from the University of Texas at Austin, and dual BS/MS degrees in Mathematics and Applied Mathematics from Rensselaer Polytechnic Institute (2006). Before joining CU Boulder, he was a postdoctoral fellow at the Institute for Computational Engineering and Sciences (ICES). His research interests include isogeometric analysis, immersed methods, and data-driven turbulence modeling. Notable contributions include development of divergence-conforming discretizations for incompressible flows, stabilized collocation methods, and invariant subgrid stress models. He leads the AMREC lab and collaborates on plasma-fueled propulsion systems and geometrically sensitive simulations. Key Awards: 2021: Rocky Mountain AIAA Educator of the Year 2021: Gallagher Young Investigator Medal 2019-2021: Clarivate Highly Cited Researcher Professional Activities: Editor of Engineering Computations, Senior AIAA Member, Simons Visiting Professor (2019) Evans' work bridges advanced numerical methods with real-world engineering challenges. His lab develops open-source tools like XIGA for multi-material problems and focuses on immersive simulation environments. Current projects explore turbulence closure models, plasma propulsion, and topology optimization with B-spline-based approaches.

• Nonlinear partial differential equations
• Singularity formation
• Mathematical analysis
• +2 more

Manuel Del Pino is Professor at the University of Bath's Department of Mathematical Sciences and Royal Society Professor specializing in nonlinear partial differential equations. His research focuses on singularity formation, geometric evolution equations, and asymptotic analysis in fluid dynamics and mathematical physics. His investigations encompass blow-up phenomena in heat equations, vortex dynamics in Euler flows, and minimal surface theory. Current projects examine infinite-time singularity formation in parabolic equations and asymptotic properties of vortex configurations. Del Pino has received the Royal Society Professorship and leads multiple grants including 'Asymptotic patterns in nonlinear evolution problems' (EPSRC). He maintains collaborations with researchers globally through projects on singularity formation in PDEs.

Howard ElmanView profile

Professor

• Scientific Computing
• Numerical Analysis
• Computational Fluid Dynamics
• +6 more

Howard Elman is a Professor in the Department of Computer Science at the University of Maryland, with affiliations to the Institute for Advanced Computer Studies (UMIACS) and as an Affiliate Professor in the Department of Mathematics. His research spans numerical analysis, computational fluid dynamics, and uncertainty quantification, focusing on iterative solvers for partial differential equations. Education: PhD in Computer Science, Yale University (1982); BA in Mathematics, Columbia University (1975); Stuyvesant High School (1971) Elman's research integrates Scientific Computing with Numerical Linear Algebra , Computational Fluid Dynamics , and Uncertainty Quantification . His work addresses Stochastic Galerkin Methods , Reduced-Order Modeling , and Low-Rank Approximations for PDEs with random data. Recent publications emphasize Surrogate Models and Deep Learning in Bayesian inverse problems. His scientific awards include SIAM Fellowship (2009) and roles as Associate Editor for journals like Mathematics of Computation and SIAM Journal on Scientific Computing . He served as SIAM Editor-in-Chief (1998-2004) and Vice President for Publications. Contact: helman@umd.edu | Office: 4210 Iribe Center | Courses: AMSC/CMSC 460 Computational Methods

Xu JinchaoView profile

Professor

• Multigrid methods
• Machine learning
• Finite element methods
• +5 more

Xu Jinchao is a Professor of Applied Mathematics and Computational Sciences at King Abdullah University of Science and Technology (KAUST) and the Verne M. Willaman Professor of Mathematics at Penn State University. He has held distinguished roles, including Director of the Center for Computational Mathematics and Applications at Penn State since 1997 and is an Affiliated Faculty member of the College of Information Sciences and Technology at Penn State. His research focuses on numerical partial differential equations (PDEs), multigrid methods, machine learning, finite element methods, and domain decomposition methods. He is renowned for pioneering contributions such as the Bramble-Pasciak-Xu (BPX) preconditioner, Hiptmair-Xu (HX) preconditioner, Xu-Zikatanov (XZ) identity, and Morley-Wang-Xu (MWX) element. His work bridges computational mathematics and machine learning, including the development of MgNet, which unifies multigrid methods with convolutional neural networks. Xu has been recognized with numerous awards, including Fellowships from SIAM, AMS, AAAS, and the European Academy of Sciences. Notable accolades include the 2008 DOE Top 10 Breakthroughs for his HX preconditioner and the 1995 Feng Kang Prize for Scientific Computing. He has organized over 100 conferences and serves on editorial boards of top journals such as Mathematics of Computations and Numerische Mathematik . His leadership includes directing research centers and advancing computational science through collaborative efforts.

• Applied Analysis
• Inverse Problems
• Scientific Machine Learning
• +7 more

Rima Alaifari is currently an Assistant Professor for Applied Mathematics at ETH Zürich , where she works on applied analysis, inverse problems, and scientific machine learning. Her research emphasizes stability analysis and regularization of inverse problems, applied harmonic analysis, phase retrieval, and operator learning. She is an associated member of the ETH AI Center and will transition to a full professorship at RWTH Aachen University in 2025 as Chair of Analysis and its Applications. Education : PhD in Mathematics (2010–2014, Vrije Universiteit Brussel); MSc in Applied and Industrial Mathematics (2005–2010, Johannes Kepler University) Research Focus : Stability estimates for inverse problems, phase retrieval in wavelet/Gabor transforms, operator learning with neural networks, and deep learning robustness. Article Trends : Her recent work bridges harmonic analysis with machine learning, focusing on phase retrieval stability, adversarial perturbations in imaging, and mathematically grounded neural operator frameworks like ReNO and CNO. Advising : She has supervised PhD students like Tandri Gauksson and Matthias Wellershoff. Former postdoctoral researchers include Francesca Bartolucci (now at TU Delft) and Jesse Railo (Finnish Inverse Prize winner).

Martin BurgerView profile

Professor

• Inverse Problems
• Applied Mathematics
• Mathematical Modelling
• +15 more

Prof. Dr. Martin Burger is a leading scientist at DESY and a Full Professor in the Department of Mathematics at Universität Hamburg, where he leads the Computational Imaging Group. His research bridges applied mathematics, imaging sciences, and machine learning, with a focus on inverse problems, mathematical modeling, and partial differential equations. He has held professorial positions at Universität Münster and FAU Erlangen-Nürnberg prior to his current dual appointment. Full Professor, Universität Hamburg (2023–present) Leading Scientist, DESY, Hamburg (2023–present) Full Professor, FAU Erlangen-Nürnberg (2018–2023) Full Professor, Universität Münster (2006–2018) His research interests include inverse problems, variational regularization, optimal transport, kinetic models, and mathematical modeling in biology and social sciences. He has made significant contributions to imaging reconstruction, sparse neural networks, and the analysis of transformer architectures. His work often integrates theoretical analysis with computational methods, influencing both pure and applied mathematics. The most recent articles reflect a strong trend toward interdisciplinary applications, combining deep learning with PDE-based modeling, analyzing social and biological systems via kinetic and mean-field models, and advancing mathematical imaging through graph-based and optimal transport methods. His publications span high-impact venues in applied mathematics and computational science. Calderon Prize, Inverse Problems International Association (IPIA) ERC Consolidator Grant (2014) Invited speaker at ECM (2021), ICM (2022), and ICIAM (2023) Editor-in-Chief, European Journal of Applied Mathematics (since 2017) Prof. Burger has supervised numerous PhD students and postdoctoral researchers, many of whom appear as co-authors in his publications. His research is supported by major grants, including funding from the German Federal Ministry of Education and Research (BMBF). He is actively involved in collaborative projects across mathematics, physics, and engineering disciplines. He leads the Computational Imaging Group at DESY, fostering a collaborative environment for developing novel mathematical tools in imaging science. The group works on both theoretical foundations and practical implementations, contributing to advancements in tomography, machine learning, and data analysis.

• Applied Dynamical Systems
• Data Science
• Computational Biology
• +3 more

Bjorn Sandstede is the Alumni-Alumnae University Professor of Applied Mathematics at Brown University. His research focuses on applied dynamical systems, nonlinear waves, pattern formation, and computational biology. He holds a PhD from the University of Stuttgart and has held faculty positions at The Ohio State University and the University of Surrey before joining Brown in 2008. Sandstede has received numerous awards, including the SIAM J.D. Crawford Prize and the Royal Society Wolfson Research Merit Award. He served as Department Chair at Brown and directed the Data Science Initiative. His work involves interdisciplinary collaborations, such as modeling zebrafish stripe formation and developing computational tools like SCOT for single-cell data integration. Sandstede also mentors extensively, advising over 30 PhD students and postdoctoral researchers. He leads the NSF-funded Institute for Computational and Experimental Research in Mathematics (ICERM) and contributes to initiatives promoting diversity and inclusion in STEM. Education: PhD in Mathematics, University of Stuttgart Undergraduate Degree, University of Heidelberg Research Interests: Applied Dynamical Systems Nonlinear Waves and Pattern Formation Computational Biology Data Science PDE Analysis Awards and Recognition: Alfred P. Sloan Research Fellowship SIAM J.D. Crawford Prize Royal Society Wolfson Research Merit Award Elsevier Jack Hale Award Teaching Excellence Awards from Brown University Fellow of the AMS and SIAM Grants and Leadership: Principal Investigator of NSF grant establishing ICERM Director of Brown's Data Science Initiative Member of Research Advisory Board and Tenure Committees Labs and Teams: Leads the Sandstede Lab at Brown, focusing on computational biology and dynamical systems. Collaborates with the Volkening Lab on zebrafish pattern modeling and the Singh Lab on optimal transport methods.

Julie ClutterbuckView profile

Associate Professor

• Geometric Analysis
• Partial Differential Equations
• Spectral Theory
• +3 more

Julie Clutterbuck is an Associate Professor in the School of Mathematics at Monash University. Her research focuses on geometric analysis, partial differential equations, and spectral theory. She has led major projects funded by the Australian Research Council, including studies on curvature flows, spectral estimates, and optimal shapes. Clutterbuck has been recognized with the Gavin Brown Prize (2014) for outstanding mathematical research. She contributes to academic activities as a conference organizer and speaker, including roles at the AMSI Winter School and the New Zealand Mathematical Society Colloquium. Her teaching commitments include advanced courses in metric spaces and multivariable calculus. Her research explores geometric evolution equations, eigenvalue problems, and capillary surfaces. Notable projects include analyzing the fundamental gap conjecture and studying ancient solutions to curvature flows. Collaborations span international institutions, reflecting her contributions to global mathematical research networks.

Katya KrupchykView profile

Professor

• Inverse Problems
• Partial Differential Equations
• Microlocal Analysis
• +3 more

Katya Krupchyk is a Professor in the Department of Mathematics at the University of California, Irvine (UCI). Her research focuses on inverse problems, partial differential equations (PDEs), microlocal analysis, and spectral theory. She holds a position in the Analysis and Partial Differential Equations group at UCI. Her work often involves collaborations with leading institutions and researchers globally, addressing challenges in mathematical physics, geometric inverse problems, and nonlinear analysis. Dr. Krupchyk teaches advanced courses in real analysis, functional analysis, and partial differential equations. She has contributed to editorial boards for journals such as Journal of Spectral Theory , SIAM Journal on Mathematical Analysis , and Inverse Problems and Imaging . Her research spans theoretical and applied aspects of inverse problems, including studies on fractional operators, magnetic Schrödinger equations, and anisotropic media. Recent work emphasizes high-frequency analysis, nonlinear perturbations, and reconstruction algorithms for geometric inverse problems.

Wang Li-LianView profile

Professor

• Applied Mathematics
• Numerical Analysis
• Spectral Methods
• +5 more

Professor Wang Li-Lian is a faculty member in the Division of Mathematical Sciences at Nanyang Technological University (NTU), Singapore. He holds the rank of Professor of Applied Mathematics and has been affiliated with NTU since 2006, progressing through roles including Assistant and Associate Professor before his current position. His research focuses on spectral methods, computational acoustics/electromagnetics, and PDE-based image processing. He has supervised multiple PhD and Master's students, including Zhang Jing, Gu Ying, Yang Zhiguo, and others. Education and career highlights include a Postdoctoral Research Associate at Purdue University (2002-2003), followed by a Visiting Assistant Professorship (2003-2005). His academic work spans spectral element methods, fractional differential equations, and high-order numerical techniques for wave scattering problems. Notable contributions include advancements in spectral-Galerkin methods for nonlocal operators and exact nonreflecting boundary conditions for Maxwell's equations. Research interests emphasize high-accuracy numerical schemes, with applications to fractional PDEs, metamaterial simulations, and image processing. His publications (over 100+ articles) reflect expertise in spectral methods, computational physics, and mathematical modeling. Teaching responsibilities include courses on partial differential equations, numerical analysis, and scientific computing.

Rupert FrankView profile

Professor

• Analysis
• Mathematical Physics
• Spectral Theory
• +8 more

Rupert Frank is a Professor of Mathematics at the University of Munich (LMU Munich) . He has held academic positions at Caltech (2013–2021) and Princeton University (2009–2013). His research spans Mathematical Physics , Spectral Theory , and Functional Inequalities , with a focus on quantum many-body systems, stability of matter, and nonlocal operators. Research Themes : Analysis of eigenvalues for Schrödinger and Pauli operators with complex potentials Semi-classical spectral asymptotics and effective theories for quantum systems Matrix inequalities and quantum information theory Calculus of variations in models like the liquid drop problem Geometric inequalities and their applications to quantum mechanics Magnetic field effects on spectral properties Recent Publications : 2025: Sharp stability for Sobolev/log-Sobolev inequalities with dimensional dependence 2025: Endpoint Schatten class properties of commutators 2024: Degenerate stability of Caffarelli-Kohn-Nirenberg inequality 2024: Hardy inequalities for large fermionic systems 2023: Review on Scott conjecture for Coulomb systems Scientific Awards : Young Scientist Prize in Mathematical Physics (2009) Grants and Collaborations : Principal Investigator in CRC TRR 352 (2023–) PI in Munich Center for Quantum Science and Technology (2019–) Multiple NSF grants (2009–2020) DFG and DAAD grants Editorial and Conference Leadership : Editorial boards: Communications in Mathematical Physics , Journal in Mathematical Physics , Journal of Spectral Theory , SIAM Journal on Mathematical Analysis , Springer Lecture Notes Organized conferences/workshops on quantum many-body systems, spectral methods, and functional inequalities (2018–2025)

Lehel BanjaiView profile

Professor

• Numerical Analysis
• Computational Mathematics
• Boundary Element Methods
• +16 more

Professor Lehel Banjai is a faculty member at the School of Mathematical and Computer Sciences, Heriot-Watt University, where he has held the position of Professor in Mathematics since 2022. Previously, he served as Associate Professor (2015–2022) and Assistant Professor (2012–2015) at the same institution. Education: Habilitation (University of Dusseldorf, 2013), D.Phil. (Oxford, 2003), BA(Hons) in Mathematics and Computation (Oxford, 2000) His research spans numerical analysis and computational mathematics, focusing on boundary and finite element methods, space-time formulations, and convolution quadrature for time-domain problems. Key areas of interest include wave scattering (acoustic and electromagnetic), Schrödinger equations, fractional differential operators, and high-order numerical schemes. Recent publications highlight his work on Runge-Kutta convolution quadrature, fractional diffusion, nonlinear impedance boundary conditions, and efficient solvers for wave equations. He has collaborated with researchers at institutions such as Max Planck Institute, University of Zurich, and University of Dusseldorf. Students supervised include Brian Hennessy (joint with Emmanuil Georgoulis), Ebraheem Aldahham, Katherine Baker, Jeta Molla (joint with Gabriel Lord), and Oluwaseun Lijoka, among others.

Volker MehrmannView profile

Professor

• Numerical Linear Algebra
• Differential-Algebraic Equations
• Control Theory
• +4 more

Volker Mehrmann is a full professor at the Technical University of Berlin in the Institute of Mathematics , Faculty II - Mathematics and Natural Sciences. He has held academic positions at Chemnitz University of Technology and RWTH Aachen University . His roles include leadership in research centers: Spokesperson for the DFG Research Center Matheon (2008-2016), President of the European Mathematical Society (2017-2022), and committee member of the Cluster of Excellence MATH+. PhD: Bielefeld University (1982) Habilitation: Bielefeld University (1987) His research interests span Numerical Linear Algebra , Differential-Algebraic Equations (DAEs) , Control Theory , and Industrial Mathematics . Recent work focuses on port-Hamiltonian systems and model order reduction for multi-physics applications. Key scientific contributions include: ERC Advanced Grant (2011-2016) on multi-physics systems Hans Schneider Prize (2019) SIAM Fellow (2011) and AMS Fellow (2022) He serves as editor-in-chief of Linear Algebra and Its Applications and contributes to numerous editorial boards. His leadership roles include presidency in the European Mathematical Society and GAMM .

• Geometric Analysis
• Differential Geometry
• Partial Differential Equations
• +3 more

Haotian Wu is a Senior Lecturer in the School of Mathematics and Statistics at The University of Sydney, where he maintains an active research program in geometric analysis. His academic appointments include membership in the Geometry, Topology and Analysis Research Group, and he has been involved in organizing significant mathematical events including the AMSI Summer School 2025 and the Symposium on Geometric Analysis and Non-linear PDEs. Dr. Wu's educational background includes a PhD in Mathematics from The University of Texas at Austin (2013) and dual Bachelor's degrees in Mathematics and Physics from Lafayette College (2007). His research aligns with the University's Faculty of Science Research Strengths in Understanding the Universe, Fundamental Laws of Nature, and Complex Systems. Wu's research focuses on geometric evolution equations, particularly Ricci flow and mean curvature flow. His work investigates singularity formation, stability properties, and asymptotic behavior in these flows, with significant contributions to understanding Type-II singularities. His research bridges pure mathematics and mathematical physics, with applications to general relativity and geometric analysis. He has developed novel analytical techniques for studying nonlinear partial differential equations arising in geometric contexts. Analysis of Wu's publication record shows a consistent trajectory of high-impact research in geometric analysis, with increasing focus on numerical methods for studying geometric flows in recent years. His work demonstrates strong international collaboration, particularly with researchers in the United States, including notable collaborations with Garfinkle, Isenberg, Knopf, and Zhang. The publications reveal a progression from foundational work on Ricci flow to increasingly sophisticated analyses of mean curvature flow and related geometric evolution equations. Australian Research Council (ARC) Discovery Early Career Researcher Award (DECRA) 2018 for 'Singularity Analysis for Ricci Flow and Mean Curvature Flow' Faculty of Science Faculty Startup Scheme 2023 for 'Elliptic and parabolic problems in geometric analysis' Dr. Wu currently supervises research students including Alexander BEDNAREK (working on General Kahler Ricci Flow) and Tiernan CARTWRIGHT (working on Hölder regularity of solutions to degenerate complex Monge–Ampère equations). He has received multiple research grants supporting his work in geometric analysis. Wu has been actively involved in teaching undergraduate mathematics courses including MATH1021 Calculus of One Variable, MATH2061 Linear Mathematics and Vector Calculus, and specialized courses like MATH3968 Differential Geometry. As an active member of the mathematical community, Wu co-organizes significant research events including the AMSI Summer School 2025 and the International Conference on Nonlinear Partial Differential Equations honoring Professor Neil Trudinger's 80th birthday. His research group focuses on geometric analysis problems with connections to mathematical physics and differential geometry.