Prof. Charalambos Makridakis is a Professor of Mathematics at the University of Sussex's School of Mathematical and Physical Sciences, and Director of the Institute of Applied and Computational Mathematics (IACM) at the Foundation for Research and Technology - Hellas (FORTH). He holds a PhD from the University of Crete and has held postdoctoral positions at the University of Maryland and the University of Tennessee. His research focuses on numerical analysis, computational mathematics, multiscale modeling, wave propagation, and fluid mechanics. He coordinates the EU-funded ModCompShock network and serves on the editorial board of the IMA Journal of Numerical Analysis. Education: PhD in Mathematics, University of Crete (1990) Postdoctoral Fellowships: University of Maryland (USA), University of Tennessee (USA) Research Interests: Multiscale Adaptive Modeling (atomistic/continuum coupling, kinetic/continuum coupling) Adaptive Methods for Evolutionary Problems (error control, geometric adaptivity) Wave Propagation (shock dynamics, DG methods) Fluid Mechanics (Navier-Stokes equations, turbulent models) Material Defects (dynamic fracture, dislocation motion) Recent Research Trends: His work integrates machine learning (e.g., Physics-Informed Neural Networks) with traditional numerical methods, advancing computational techniques for PDEs and complex systems. Awards & Grants: Coordinator of EU ModCompShock Network (2015-2019) Recipient of grants from the University of Sussex and European Union Labs & Teams: Leads IACM-FORTH, collaborating with global institutions like UCLA, Oxford, and the Mittag-Leffler Institute.
Greg Buzzard is Professor of Mathematics and Director of the Center for Computational & Applied Mathematics (CCAM) at Purdue University's College of Science. His research develops mathematical foundations for computational imaging systems through collaborative work with electrical engineering and materials science researchers. Dr. Buzzard leads a substantial research group focusing on inverse problems in imaging science. His work combines theoretical advances in optimization with practical applications in electron microscopy, neutron tomography, and medical imaging. Key research areas include: Consensus Equilibrium frameworks for model integration Dynamic sampling algorithms for computational imaging Plug-and-play methods for image reconstruction Multi-agent systems for large-scale inverse problems Recent publications demonstrate leadership in computational tomography innovations, particularly neutron imaging techniques for materials science and spectral CT reconstruction. His team's methods enable faster, higher-resolution imaging across scientific domains from biomedical applications to aerospace materials characterization. The 2020 SIAM Imaging Sciences Best Paper Prize recognized fundamental contributions to Plug-and-Play reconstruction methods. Professor Buzzard maintains an active mentoring program with 9 current graduate students and postdocs. His interdisciplinary collaborations include sustained partnerships with the Purdue School of Electrical and Computer Engineering and materials science research groups.
Prof. Maria Colombo is a Full Professor in the Department of Mathematics at École Polytechnique Fédérale de Lausanne (EPFL), holding positions in the Chair of Mathematical Analysis, Calculus of Variations, and PDEs (AMCV), and the SMA Teaching Unit (SMA-ENS). She is actively involved in academic governance, serving on EPFL's School of Basic Sciences Academic Evaluation Committee (SB-CEA). Her research focuses on advanced topics in analysis, including partial differential equations, calculus of variations, fluid dynamics, and optimal transportation. She has advised multiple doctoral students and teaches courses like Analysis IV and Topics on Euler/Navier-Stokes Equations. Her awards include the 2024 Feltrinelli Prize, EMS Prize, and Frontiers of Science Award, alongside the 2023 Collatz Prize and De Giorgi Prize. Her work explores non-uniqueness in fluid equations, regularity theory, and nonlocal conservation laws. She contributes to mathematical communities through editorial roles and research leadership. Positions: Professeure ordinaire (AMCV, SMA-ENS), Committee Member (SB-CEA) Research Themes: PDEs, Fluid Dynamics, Variational Methods, Nonlinear Analysis Awards: 2024 Feltrinelli Prize, 2024 EMS Prize, 2023 Collatz Prize, 2022 Peter Lax Award Labs/Teams: AMCV Research Group (https://amcv.epfl.ch/), SMA Teaching Group (https://sma.epfl.ch/)
Giacomo Albi is an Associate Professor in Numerical Analysis at the Department of Computer Science, University of Verona. He holds a PhD in Mathematics and Computer Science from the University of Ferrara (2014) and has conducted research at TU München under an ERC project on optimal control. His expertise spans numerical methods for kinetic equations, optimal control of high-dimensional systems, and mathematical modeling of multi-agent systems in socio-economic and biological contexts. Teaching includes modules like Numerical Analysis I, Logistic Optimization, and Foundation of Data Analysis. He leads research in contemporary applied mathematics, focusing on multi-scale particle systems and PDEs. Projects include data-driven control strategies, efficient numerical schemes for PDEs, and computational social dynamics. Active in the INdAM Research Unit, he also contributes to third mission activities and oversees internationalization efforts in Erasmus programs. Research groups involve advanced methods for transport phenomena, high-dimensional control, and computational social dynamics. Key interests include plasma confinement control, opinion-epidemic modeling, and evacuation strategies using multi-scale models.
Leitao Chen is an Assistant Professor of Mechanical Engineering at the College of Engineering, Embry-Riddle Aeronautical University. His research focuses on multiscale modeling using the Boltzmann equation, thermal management systems for high-power CPUs and electric vehicle batteries, and low-temperature plasma dynamics. He holds a Ph.D. in Mechanical Engineering and has contributed to over 15 peer-reviewed publications since 2016. Dr. Chen is actively involved in professional organizations such as the American Society of Mechanical Engineers (ASME) and chairs the Heat Transfer in Energy Systems Technical Committee under ASME. Education: B.S., Mechanical Engineering M.S., Power Machinery & Engineering Ph.D., Mechanical Engineering Research Interests: Computational modeling of thermal systems Plasma dynamics and fluid simulations Thermal management for electric vehicles Advanced materials for heat transfer enhancement Multiscale modeling using Boltzmann equations Awards: 2023 Tennessee State University Faculty Excellence Award in Research 2017 Outstanding Reviewer Awards from Computers and Fluids, Physica D, and Renewable Energy Courses Taught: ME 409: Vehicle Aerodynamics ME 413: Preliminary Design for High Performance Vehicles with Laboratory ME 433: Senior Design for High Performance Vehicles with Laboratory
Yaohang Li is a Professor in the Department of Computer Science at Old Dominion University (ODU), part of the College of Sciences. His research focuses on computational biology, computational science, Monte Carlo methods, and high-performance computing. He holds a Ph.D. in Computer Science from Florida State University (2003), an M.S. from Florida State (2000), and a B.S. from South China University of Technology (1997). Dr. Li has secured over $15M in research funding, including an NSF CAREER Award (2009) and multiple federal/state grants. His work spans protein structure modeling, bio-inspired algorithms, and parallel computing. Notable contributions include novel sampling approaches for protein modeling and GPU-accelerated optimization methods. He has authored/co-authored numerous peer-reviewed articles in journals like Journal of Computational Biology , IEEE Transactions , and Physical Review . Key Research Areas: Protein Loop Prediction, Parallel Tempering Algorithms, Grid Computing Security Grants: Includes $13.5M NOAA ISET Cooperative Center and $1M NSF Bio-inspired Control Systems Project Awards: 2009 NSF CAREER Award, 2005 Young Researcher Award (NCSU), and 2005 Ralph E. Powe Junior Faculty Enhancement Award. His lab collaborates on interdisciplinary projects such as the Consortium for Research Computing for Sciences (CRCSET) and develops tools like the Variational Autoencoder Inverse Mapper (VAIM) for QCD analysis. Advises on HPC systems and security in distributed computing environments.
Mark Crovella is a Professor and Chair of Academic Affairs in the Faculty of Computing & Data Sciences at Boston University, with a primary appointment in the Department of Computer Science. He holds affiliations with the Department of Electrical and Computer Engineering, Graduate Program in Bioinformatics, Center for Information Systems and Engineering, and Division of Systems Engineering. His research spans networking, data science, bioinformatics, and algorithmic systems. Notable contributions include studies on network traffic analysis, machine learning applications in biology, and greedy routing algorithms in hyperbolic spaces. Education lineage traces back to prominent figures like d'Alembert and Laplace, highlighting a strong academic heritage. Awards include the Applied Networking Research Prize (2013) and Best Student Paper Award (2012). Over 150 peer-reviewed articles and multiple patents reflect his prolific output. His work bridges theoretical computer science with practical applications in bioinformatics and network engineering. Key grants and collaborations involve large-scale network measurement, cybersecurity, and systems biology. Advised doctoral students' work is documented in his CV. Active in curriculum development and academic leadership through his role as Chair of Academic Affairs.
Enrique Zuazua Iriondo holds a prestigious Chair for Dynamics, Control and Numerics as an Alexander von Humboldt Professor at Friedrich-Alexander-Universität Erlangen-Nürnberg (Germany). He simultaneously serves as the Chair of Computational Mathematics at the University of Deusto/Deusto Foundation in Bilbao, Spain, and as a Professor of Applied Mathematics at the Department of Mathematics, Autonomous University of Madrid (UAM). His triple appointment across three major European institutions reflects his international standing in applied mathematics and control theory. Zuazua's research spans a wide range of applied mathematical disciplines, with particular expertise in Partial Differential Equations, Numerical Analysis, Control Theory, and Data Sciences. His work bridges theoretical mathematics with practical applications in engineering, biology, and social sciences. Key research areas include analysis of PDEs, control of diffusion models in biology, multi-agent systems, hyperbolic models in traffic flow, fractional PDEs, optimal design in material sciences, and the emerging turnpike phenomena in long-time horizon problems. His research demonstrates remarkable interdisciplinary reach, connecting pure mathematics with real-world industrial applications. The recent publications reveal a strong trend toward increasingly interdisciplinary work, blending classical control theory with modern machine learning approaches. His research shows particular strength in bridging pure mathematical theory with practical applications, especially in network analysis, fractional calculus, and the intersection of control theory with deep learning architectures. The consistent focus on turnpike phenomena across multiple publications indicates this as a signature research direction that has evolved from pure mathematics to applications in neural networks and optimal control systems. Euskadi Prize for Science and Technology 2006 Spanish National Julio Rey Pastor Prize 2007 ERC Advanced Grant NUMERIWAVES 2010 ERC Advanced Grant DyCon 2016 Honorary member of Academia Europaea Honorary member of Jakiunde (Basque Academy of Sciences) Doctor Honoris Causa from Université de Lorraine Ambassador of Friedrich-Alexander-Universität Erlangen-Nürnberg Zuazua has supervised 24 PhD students and mentored over 25 postdoctoral researchers throughout his career. His research has been substantially funded through prestigious grants including two ERC Advanced Grants (NUMERIWAVES in 2010 and DyCon in 2016). His industrial collaborations have been particularly impactful, including work with Airbus Consortium on aeronautical design that led to the development of the Stanford University Unstructured (SU2) software, and projects with Arteche Group on computational software for electrical networks. He directs the CCM (Chair of Computational Mathematics) at Deusto Foundation, where he leads the DyCon project funded by the European Research Council. Previously, he served as the Founding Scientific Director of the Basque Center for Applied Mathematics (BCAM) from 2008-2012. His research group maintains strong connections with industry through projects in aeronautics, electrical networks, and computational fluid dynamics, demonstrating the practical impact of his theoretical work.
Mengjia Xu is an Assistant Professor in the Data Science department at New Jersey Institute of Technology (NJIT) . Her research focuses on interdisciplinary areas including machine learning, biomedical informatics, and graph embedding techniques applied to healthcare and neuroscience challenges. Research Interests : Development of hyperbolic neural networks for studying aging trajectories and brain networks Graph embedding methods for temporal and biomedical data analysis Automated assessment of sickle cell disease using computer vision and microfluidics Quantum cognition and intrinsic dimension estimation in machine learning Recent Research Contributions : Recent work includes applying hyperbolic neural networks to analyze brain networks in cognitive decline, developing stochastic graph embedding algorithms for temporal data, and creating frameworks for automated sickle cell analysis. These efforts bridge computational methods with biomedical applications. Media Highlights : Featured in discussions on AI limitations and generative AI trends Presented research on physics-informed neural networks and scalable machine learning algorithms Professional Activities : Active in academic collaborations with institutions like MIT and Brown University, focusing on interdisciplinary projects in data science and biomedical engineering.
Anna Wienhard is an Honorary Professor at the University of Leipzig's Mathematical Institute and Director of the 'Geometry, Groups, and Dynamics' division at the Max Planck Institute for Mathematics in the Sciences. Her research focuses on geometric structures, representation varieties, and their applications in mathematics and data science. She leads collaborative initiatives like the International Max Planck Research School and ScaDS.AI, integrating geometric methods with machine learning. Research Interests: Her work spans higher Teichmüller theory , Anosov representations , and geometric structures on manifolds. Recent projects explore applications of Higgs bundles, moduli spaces, and persistent homology in quantum dynamics and data analysis. Publications: Her 2025 papers advance Anosov representation theory and total positivity, while 2021 works apply geometric methods to machine learning. Earlier contributions include foundational studies on maximal surface group representations and Hitchin components. Awards: Membership in the Hector Fellow Academy (2021). Grants: Leads AEI-DFG projects on stability and representation varieties, and coordinates DFG-funded clusters like STRUCTURES and SFB/TRR 191. Teaching: Oversees doctoral training programs and collaborates with Leipzig University’s Mathematical Institute. Labs/Teams: Directs the Max Planck division, collaborates with ScaDS.AI, and chairs international workshops on Teichmüller theory and geometric dynamics.
Fabian Gröger is a Research Associate, Doctoral Student, and Lecturer at Lucerne University of Applied Sciences and Arts (HSLU), affiliated with the Lucerne School of Computer Science and Information Technology. He is a Scientific Collaborator at the Algorithmic Business (ABIZ) Research Lab and teaches in the CAS Machine Learning program. His roles include Data Scientist, Research Assistant, and Teaching Assistant in machine learning and artificial intelligence programs. Education: Ongoing PhD in Biomedical Engineering, University of Basel Master of Science in Data Science/ML, HSLU Bachelor of Science in Computer Science, HSLU His research focuses on Machine Learning , Deep Learning , Audio Data , and Self-Supervised Learning , with applications in digital dermatology, bioaerosol monitoring, and medical anomaly detection. His work emphasizes scalable foundation models, data quality audits, and reducing annotation needs in healthcare AI. Recent publications highlight contributions to hyperbolic space applications in medical AI, audio representation learning for hearables, and benchmarking strategies like CleanPatrick. His work spans conferences like MICCAI, NeurIPS, and ECCV. Awards: Outstanding Reviewer (JEADV, 2024) Best Lightning Talk (AI Medicine Symposium, 2024) Best Paper (MICCAI 2023) Best Master Thesis (HSLU, 2023) Gröger leads projects such as SelfClean for Audio and AI for All , and collaborates with industries like Prepress Media AG. He advises on algorithms for small-hotel revenue management and exhibits at events like the Museum Exposition on Food & AI. His lab affiliations include the ABIZ Research Lab and the Digital Dermatology group, where he explores ethical AI, clinical decision support systems, and data-centric healthcare solutions.
Jan S Hesthaven is a Professor of Mathematics and Dean of the School of Basic Sciences at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. He holds adjunct professorships at Brown University and the Technical University of Denmark. His research focuses on computational methods for partial differential equations, model order reduction, and high-order numerical methods like discontinuous Galerkin schemes. He leads the Chair of Computational Mathematics and Simulation Science (MCSS) and founded SCITAS, EPFL's Scientific IT and Application Support unit. Education: M.Sc. (1991) and Ph.D. (1995) in Numerical Analysis from Technical University of Denmark, with postdoctoral training at Brown University and NASA Langley. He earned a Dr.Techn. (2009) for contributions to nodal discontinuous Galerkin methods. Research interests include computational fluid dynamics, seismic monitoring of porous media, and machine learning integration with PDE solvers. Notable contributions include physics-informed neural networks, reduced basis methods for acoustics, and surrogate models for gravitational waves. He has received prestigious awards like the SIAM Fellowship (2014) and the Philip J. Bray Teaching Award (2004). Leadership roles: Dean of SB (2017–present), Director of CCV at Brown (2006–2013), and Deputy Director of ICERM (2010–2013). His work bridges fundamental mathematics with applications in engineering, physics, and environmental science. Grants and collaborations include NSF funding for model reduction and NASA partnerships. He supervises interdisciplinary projects on seismic data analysis and acoustic simulations. SCITAS, under his leadership, provides high-performance computing support across EPFL.
Dr. Luca Galimberti is a Lecturer in Quantitative Finance at King's College London's Department of Mathematics, part of the Faculty of Natural, Mathematical & Engineering Sciences. He holds a PhD in Mathematics from ETH Zurich and previously served as a Postdoctoral Researcher at the Norwegian University of Science and Technology and the University of Oslo. His research focuses on fusing theoretical deep learning with infinite-dimensional financial models, particularly addressing problems in mathematical finance through operator learning and stochastic analysis. Research interests include partial differential equations (PDEs), stochastic PDEs, and their applications to financial mathematics. He explores geometric PDEs, functional analysis, and the mathematical foundations of AI in finance. Current projects involve limit order book modeling via operator learning, long-term behavior of generative models, and graph neural networks for socio-economic phenomena like fake news mitigation. Galimimberti's work bridges pure mathematics (functional analysis, Riemannian geometry) with applied domains such as econometrics and computer science. He actively seeks collaborations with industry partners in AI and finance. No scientific awards are explicitly listed. His academic career includes postdoctoral training at top institutions and a strong focus on interdisciplinary research. No formal advisees or grants are mentioned in the provided texts.
Professor Yalin Wang is a faculty member in the School of Computing and Augmented Intelligence (SCAI) at Arizona State University (ASU), where he has held roles since 2010. He was promoted to full Professor in 2023, following his tenure as Associate Professor starting in 2017. His research focuses on medical imaging, computer vision, machine learning, and neuroimaging, with an emphasis on Alzheimer’s disease biomarker discovery and geometric modeling. He leads the Geometric & Scientific Computing Lab (GSL), hosting a research website at http://gsl.lab.asu.edu . Education: PhD in Electrical Engineering (University of Washington, 2002), MS and BS in Computer Science (Tsinghua University, 1996 and 1994). Research interests include surface-based morphometry, optimal transport applications in neuroimaging, and deep learning for medical image analysis. He has published over 200 peer-reviewed papers and received the 2016 ASU CIDSE Best Junior Faculty Researcher Award, among other accolades. His grants include NIH-funded projects on Alzheimer’s biomarkers, retinotopic mapping, and pediatric brain development. Collaborations span institutions like Mayo Clinic and ADNI. Notable contributions include developing neurodegeneration biomarkers using low-rank/sparse subspace decomposition and predictive models for cognitive decline via hyperbolic stochastic coding.
Hailiang Liu is a Professor of Mathematics at Iowa State University, with a courtesy appointment in Computer Science and membership in the Translational AI Center (TrAC). He also serves as the Data Science/AI Coordinator in the Department of Mathematics. Currently, he is on leave as a Program Director in the Applied Mathematics Program at the National Science Foundation (NSF). Dr. Liu holds a Ph.D. in Applied Mathematics from the Chinese Academy of Sciences (1995), an M.Sc. from Tsinghua University (1988), and a B.Sc. in Math Education from Henan Normal University (1984). His research focuses on computational and applied mathematics, including mathematical modeling via PDEs, numerical methods, scientific computing, and their intersections with deep learning and data-driven approaches. Key areas include: Deep Learning: Neural ODEs, selection dynamics, and optimal transport-based algorithms Mathematical Biology: Population dynamics, ecological dispersal, and aggregation modeling Computational Methods: Discontinuous Galerkin (DG) schemes, energy-stable numerical methods, and Gaussian beam methods Kinetic Theory: Photon scattering, Bose-Einstein condensation, and hyperbolic balance laws His work has been supported by the NSF and DOE. Notable contributions include structure-preserving numerical schemes, critical threshold analysis in hyperbolic systems, and data-driven optimal control frameworks.
