Lourenço Beirão da Veiga (University of Milano, Italy / lourenco.beirao@unimib.it)Web 

Lourenço Beirão da Veiga is Full Professor of Numerical Analysis at the University of MilanoBicocca, Italy. After a PhD in Mathematics at the University of Pavia (2005) he won an Assistant position at the University of Milano, where he later became Associate Professor (2010). In 2015 he moved as Full Professor to the University of MilanoBicocca.He initially worked on Finite Elements for plates and shells, plasticity, aposteriori and apriori error estimation, with particular attention on robustness to physical coefficients. More recently his research focused on the development and theoretical analysis of novel numerical technologies for partial differential equations. He strongly contributed to the development of Mimetic Finite Differences and Isogeometric Analysis, with results on the theoretical foundations of the methodology, on the approximation properties of nurbs and Tsplines, on the development and analysis of Galerkin and collocation schemes, on domain decomposition approaches. More recently, he is among the founders of the Virtual Element Method (VEM), which constituted his main area of research in these last years. Some noteworthy achievements are an ERC Consolidator Grant (on the VEM technology) and the J. L. Lions Award of the ECCOMAS. He is author of more than 100 publications and a Highly Cited Author for the last three years according to Web of Science. 
Yongyong Cai (Beijing Normal University, PRC / yongyong.cai@bnu.edu.cn) 

Yongyong Cai is a Professor at Beijing Normal University since 2019. He received his BS (2004) degree and MS (2007) degree in Pure Mathematics from Peking University, and PhD degree in Applied and Computational Mathematics from National University of Singapore in 2012. His research focuses on computational and applied mathematics including modeling, analysis and numerical simulation with applications in quantum physics and chemistry, BoseEinstein condensation (BEC), complex fluids, etc. In particular, he has been working on mathematical analysis, numerical analysis and scientific computing for nonlinear Schrödinger equations (also known as GrossPitaevskii equation) from BoseEinstein condensates, highly oscillatory dispersive PDEs, and phase field equations. Most recently, his research interests have been focused on high order numerical methods for nonlinear dispersive equations in the highly oscillatory/long time regime and phase field equations. 
MiYoung Kim (Inha University, Korea / mikim@inha.ac.kr) 

MiYoung Kim is a professor at Inha University. She received her B.S. degree (1985) and M.S. degree (1987) in Mathematics from Yonsei University. She received her Ph.D. degree (1993) in Applied Mathematics from Purdue University, West Lafayette, IN, USA. She has been a postdoctoral researcher at the University of Trento (19951996) in Italy and a J. Tinsley Oden Faculty Fellow (20062007) at University of Texas at Austin in the US.MiYoung worked on population dynamics, epidemiology, and their finite element approximations. MiYoung’s recent research concerns the development of high order discontinuous Galerkin finite element methods for the PDEs with a focus on the design of an efficient iterative linear solver for the high order discontinuous Galerkin approximation with Lagrange multiplier. 
Misun Min (Argonne National Lab, USA / mmin@mcs.anl.gov) 

Misun Min is a Computational Mathematician in Mathematics and Computer Science Division at Argonne National Laboratory. She received a Ph.D. from Brown University in Applied Mathematics and joined Argonne National Laboratory in 2003. Her research focuses on challenging application problems governed by PDEs using state of the art highorder methods. A major part of her work in recent years is directed towards performance and scalability, whether using millions of CPUs or tens of thousands of GPUs. She is the Argonne PI for the U.S. Department of Energy (DOE)’s Exascale Computing Project (ECP), Center for Efficient Exascale Discretizations (CEED). Dr. Min’s software development effort includes NekCEM, NekLBM, and Nek5000/RS. She is the lead developer of NekCEM, which is an opensource highorder multiphysics code that supports computational electromagnetics, driftdiffusion, Schrodinger, and quantum density matrix equation solvers. She has also developed NekLBM, which is a spectralelement discontinousGalerkin (SEDG) based lattice Boltzmann solver for the NavierStokes equations. She developed the first GPUenabled variant of the Nek code suite using OpenACC for NekCEM simulations on Oak Ridge Leadership Computing Facility, Titan. This work laid the foundation for subsequent OpenACC variants of Nek5000 and for the current developments in NekRS, which is the OCCAbased version of Nek5000 that is targeted for DOE’s exascale platforms. Dr. Min has led the scaling efforts for NekRS, with problems exceeding two billion spectral elements running on DOE’s leadership computers. Major application areas include reactor thermal hydraulics and wind energy. 
Fabio Nobile (EPFL, Switzerland / fabio.nobile@epfl.ch) 

Fabio Nobile is associate professor in Mathematics at the Ecole Polytechique Federale de Lausanne, Switzerland, since 2011. Prior to joining EPFL, he was an assistant professor in numerical analysis at Politecnico di Milano. He graduated in electronic engineering from Politecnico di Milano in 1998, received his PhD in Applied Mathematics from EPFL in 2001, and has been a postdoctoral fellow at the University of Texas at Austin.
His early research focused on the numerical approximation of fluidstructure interaction problems in haemodynamics. More recently, he has been working on numerical methods for PDEs with random data. In particular he has contributed to the analysis and development of sparse polynomial approximations of the datatosolution map, multilevel monte carlo methods, dynamical low rank techniques and rational approximations. He is author of more than 120 publications and has been invited speaker to several international conferences and workshops, including the 7th European Congress of Mathematics. 
Benjamin Peherstorfer (New York University, USA / pehersto@cims.nyu.edu)Web 

Benjamin Peherstorfer is Associate Professor at Courant Institute of Mathematical Sciences. Until 2016, he was a Postdoctoral Associate in the Aerospace Computational Design Laboratory (ACDL) at the Massachusetts Institute of Technology (MIT), working with Professor Karen Willcox. He received B.S., M.S., and Ph.D. degrees from the Technical University of Munich (Germany) in 2008, 2010, and 2013, respectively. His Ph.D. thesis was recognized with the HeinzSchwaertzel prize, which is jointly awarded by three German universities to an outstanding Ph.D. thesis in computer science. Benjamin was selected for a Department of Energy (DoE) Early Career Award in the Applied Mathematics Program in 2018 and for an Air Force Young Investigator Program (YIP) award in Computational Mathematics in 2020. In 2021, Benjamin received a National Science Foundation (NSF) CAREER award in Computational Mathematics. His research focuses on computational methods for data and computeintensive science and engineering applications, including scientific machine learning, mathematics of data science, model reduction, and computational statistics. 
Xiaofeng Yang (University of South Carolina, USA / xfyang@math.sc.edu)Web 

Xiaofeng Yang is currently a professor in the Department of Mathematics at the University of South Carolina. He received his BS (1998) and MS (2001) in Mathematics from University of Science and Technology of China, and Ph.D. degree in computational mathematics from Purdue University in 2007. After a postdoctoral position at the University of North Carolina at Chapel Hill from 20072009, he joined the Department of Mathematics at the University of South Carolina as the assistant professor (2009), associate professor (2013), and professor (2018). Yang’s research focuses on the mathematical modeling, numerical analysis, scientific computing, for applications ranging from fluids, solids, and soft matter to cell dynamics. His recent work has focused on mathematical modeling and the design of numerical methods with high order, decoupled and energypreserving structure to simulate the dynamics of multiphase fluid flow, multiphase ferrohydrodynamic and magnetohydrodynamic systems, and the growth of multiphase, multicomponent alloys in the mushy region. 
Xiangxiong Zhang (Purdue University, USA / zhan1966@purdue.edu) 

Xiangxiong Zhang got his bachelor’s degree in mathematics and applied mathematics from the University of Science and Technology in China in 2006, and his Ph.D. in mathematics from Brown University in 2011. From 2011 to 2014, he was a postdoctoral associate in Imaging and Computing Group, Mathematics Department, MIT. In 2014, he joined the Department of Mathematics at Purdue University, West Lafayette. He is currently an associate professor of mathematics at Purdue University. His research interests include highorder accurate numerical methods for PDEs, nonsmooth convex optimization, and Riemannian optimization algorithms. 