Oliver G. Ernst is a Professor of Numerical Analysis at Technische Universität Chemnitz . His research focuses on Numerical Analysis , Uncertainty Quantification , and Inverse Problems , with applications in Thermo-Hydro-Mechanical (THM) processes , Electromagnetics , and Stochastic Partial Differential Equations . He is associated with the Numerical Analysis group at TU Chemnitz. Key Research Areas : Efficient numerical methods for PDEs Krylov subspace techniques Stochastic finite element methods Multi-physics modeling Geoscientific applications Recent Publications (2025-2010): THM simulations under uncertainty Neural network PDE solvers Bayesian inversion frameworks Rational Krylov algorithms Deflated restarting strategies Collaborations : TU Bergakademie Freiberg University of Manchester Technical University of Munich University of Maryland University of Geneva Software Development : Contributor to OpenGeoSys platform Developer of FEMALY MATLAB library Academic Recognition : h-index 32, i10-index 66, with over 4423 citations since 2020.
John A G Roberts is a Professor and Deputy Head of the School of Mathematics and Statistics at the University of New South Wales (UNSW), Sydney. He serves as Chief Investigator of the ARC Centre of Excellence MASCOS and is Vice-President of the Australian Mathematical Society. His academic career spans several decades with significant contributions to nonlinear dynamical systems research. Roberts' research primarily focuses on nonlinear dynamical systems, with particular expertise in integrable dynamical systems, symmetry and time-reversal properties, and algebraic dynamics. His work bridges pure mathematics with applications in mathematical physics, exploring the deep connections between arithmetic properties and dynamical behavior. He has developed novel approaches to understanding integrability through algebraic and arithmetic methods, particularly in the context of discrete systems. Analysis of his recent publications reveals a strong emphasis on algebraic entropy, lattice equations, and the arithmetic structure of dynamical systems over finite fields. His work demonstrates consistent innovation in applying number-theoretic methods to dynamical problems, with a particular focus on symmetry properties and integrability criteria for discrete systems. The interdisciplinary nature of his research connects mathematical physics, algebraic geometry, and computational mathematics. Chief Investigator of the ARC Centre of Excellence MASCOS Vice-President of the Australian Mathematical Society Roberts actively supervises PhD students and has collaborated with numerous research associates including Dinh Tran, Alina Ostafe, Natascha Neumaerker, and Danesh Jogia. He has organized several major conferences including the Workshop on Algebraic, Number Theoretic and Graph Theoretic Aspects of Dynamical Systems (Sydney, 2015) and Dynamics Days Asia Pacific 6 (Sydney, 2010), demonstrating his leadership in the international dynamical systems community. His research program maintains strong connections with institutions worldwide, particularly with collaborators in Europe and North America.
Alberto A. Pinto serves as a full Professor in the Department of Mathematics at the Faculty of Sciences, University of Porto, Portugal. He concurrently holds a research position at the Laboratory of Artificial Intelligence and Decision Support within the Institute for Systems and Computer Engineering (LIAAD, INESC TEC), where he has been Research Coordinator since May 1, 2011. His academic journey began with a Master's thesis at the University of Warwick (1989) under David Rand, followed by a PhD (1991) on universality features of maps at the boundary between order and chaos. As a postdoctoral researcher with Dennis Sullivan at the City University of New York, he expanded his foundational work in dynamical systems. Pinto's research spans both theoretical and applied domains, with significant contributions to dynamical systems (particularly fine-scale structure of hyperbolic diffeomorphisms), game theory , mathematical economics , and financial mathematics . His work bridges pure mathematics with practical applications in immunology, epidemiology, climate science, and energy systems. He has published over one hundred scientific articles and authored the influential book 'Fine Structures of Hyperbolic Diffeomorphisms' (2010). His recent publications (2024-2025) demonstrate continued scholarly productivity across diverse fields including cooperative game theory, machine learning applications in pedestrian modeling, financial market prediction, and economic equilibrium theory. These works reflect his evolving research trajectory from pure dynamical systems toward increasingly interdisciplinary applications. President of International Center for Mathematics (CIM) 2011-2016 President of General Assembly of CIM since 2016 Founder and co-Editor-in-Chief of Journal of Dynamics and Games (2014-present) Executive Coordinator of Scientific Council of Exact Sciences and Engineering at FCT (2009-2010) Pinto has supervised numerous graduate students across mathematical economics, game theory, and financial mathematics. His editorial work includes founding the Springer Proceedings in Mathematics series through 'Dynamics and Games I and II' (2011), and contributing to multiple volumes in the CIM Series in Mathematical Sciences published by Springer-Verlag. At INESC TEC, he leads research in artificial intelligence and decision support systems, connecting theoretical mathematics with practical computational applications.
Daniel Bienstock is the Liu Family Professor of Industrial Engineering and Operations Research and Professor of Applied Physics and Applied Mathematics at Columbia University's Fu Foundation School of Engineering and Applied Science. He has been at Columbia since 1989 and previously held roles at Bell Communications Research and Carnegie Mellon University. His research focuses on optimization, particularly discrete and nonconvex optimization, with applications to power grid vulnerability, epidemic modeling, and energy systems. Education: Ph.D. in Operations Research from MIT (1985). Research interests include optimization theory and algorithms, cascading failures in power grids, and the social impact of epidemics. His work integrates mathematical programming with real-world challenges in energy, infrastructure, and public health. Recent studies address risk-aware power system operations, robust optimization under uncertainty, and machine learning for energy markets. Major awards include the 1990 Presidential Young Investigator Award. He has contributed to significant conferences such as the SIAM Conference on Optimization (2005 plenary speaker) and the International Symposium on Mathematical Programming (2006 semi-plenary speaker). Affiliated with Columbia's Foundations of Data Science, Smart Cities, and Financial and Business Analytics centers. His work emphasizes translating theoretical advancements into practical solutions for critical infrastructure resilience and sustainable resource management.
Matthias Koeppe is a Professor of Mathematics at the University of California, Davis, with additional appointments as a Faculty Member in Applied Mathematics, Computer Science graduate programs, UC Davis TETRAPODS Institute of Data Science (UCD4IDS), and as a Faculty Affiliate at the UC Davis Center for Data Science and Artificial Intelligence Research (CeDAR) and UC Davis DataLab. His work uniquely bridges theoretical mathematics with practical software implementation, focusing on optimization and discrete mathematics. Dr. Koeppe completed his academic education at Otto-von-Guericke-Universität Magdeburg, earning his Ph.D. in Mathematics in 2002 with a dissertation titled "Exact Primal Algorithms for General Integer and Mixed-Integer Linear Programs," advised by Robert Weismantel. His academic journey progressed from Researcher at Otto-von-Guericke University (2003-2008) to Assistant Professor (2008-2010), Associate Professor (2010-2012), and Full Professor (2012-present) at UC Davis. Professor Koeppe's research centers on mathematical optimization (particularly integer programming), computational discrete mathematics, and mathematical software development. His seminal contributions include extensive work on the Gomory-Johnson infinite group problem, cut-generating functions, and rational generating functions for integer programming. He has developed several critical software packages including LattE integrale for lattice point enumeration, 4ti2 for algebraic computations, and cutgeneratingfunctionology for cut-generating function research. As the lead developer of SageMath since 2015 (where he is the #1 contributor by authored commits), and creator of passagemath (a pip-installable modularized fork) since October 2024, he has significantly advanced mathematical computation accessibility. His publication record shows a clear evolution from foundational integer programming algorithms to specialized areas like multi-row cuts and generating function methods, with recent emphasis on software implementation and accessibility. The work consistently demonstrates how theoretical advances can be translated into practical computational tools. Fakultätspreis 2000 for the best diploma thesis, Department of Mathematics, Otto-von-Guericke University of Magdeburg GOR Dissertation Award 2003 of the German Operations Research Society (GOR) Fakultätspreis 2003 for the best dissertation, Department of Mathematics, Otto-von-Guericke University of Magdeburg Professor Koeppe has mentored numerous doctoral students including Amitabh Basu (now at Johns Hopkins), Robert Hildebrand (Virginia Tech), Brandon Dutra (Google), Yuan Zhou (University of Kentucky), Jiawei Wang (Uber), and Chun Yu Hong (Google). His research has been supported by multiple NSF grants including "High-performance computations with rational generating functions" (2009-2013), "Infinite-dimensional relaxations of mixed-integer optimization problems" (2013-2017), and "Collaborative Research: Next-Generation Cutting Planes" (2020-2025). As the lead developer of SageMath and founder of passagemath, Professor Koeppe directs substantial software development efforts that have created essential infrastructure for mathematical research worldwide. His workgroup at UC Davis combines deep theoretical investigation with practical software engineering, fostering an environment where mathematical theory directly informs computational practice and vice versa, making significant contributions to both academic research and practical applications in optimization.
Simone Naldi is a Lecturer (Maître de conférences) in Applied Mathematics at the University of Limoges, France, since 2017. His research focuses on algebraic geometry, optimization, and symbolic computation, with applications to semidefinite programming and linear matrix inequalities. He holds a PhD from CNRS-LAAS in Toulouse and has held postdoctoral positions at the University of Dortmund and the Fields Institute in Toronto. His work includes developing algorithms for solving polynomial systems and creating the SPECTRA library for exact LMI solutions. He has collaborated on projects involving geometric configurations, hyperbolic curves, and infeasibility certificates for optimization problems. Key research areas include semidefinite programming, algebraic certificates for moment problems, and computational tools for polynomial equations. He actively contributes to conferences like Structured Matrix Days and has been supported by institutions like the Simons Foundation. His recent work addresses degenerate systems and geometric configurations of spheres in tetrahedrons, reflecting his interdisciplinary approach combining pure mathematics with algorithmic development.
Nikolaos Karambetakis is a Professor at the Department of Computer Science and Numerical Analysis, Aristotle University of Thessaloniki (AUTh), specializing in the Mathematical Theory of Automatic Control Systems since 2014. He holds a PhD in Mathematics from AUTh (1993) and a degree in Mathematics (1989). His research focuses on algebraic-polynomial methods for control systems, computer algorithms for control problems, and feedback control systems. He has organized international conferences like the 4th International Workshop on Multidimensional Systems (2005) and CODIT'18 (2018), and served as an editor for journals such as Multidimensional Systems and Signal Processing . He has supervised 4 doctoral and over 50 postgraduate theses, authored/co-authored 60+ journal papers, and contributed to 11 research projects. Administrative roles include President of AUTh’s Mathematics Department (2013–2017) and Director of the Computer Lab (2011–2023). Research interests include Automatic Control Systems , Mathematical Systems Theory , and Symbolic Programming Languages . His work integrates algebraic methods with control theory, emphasizing system equivalence and numerical stability. He has developed algorithms for polynomial matrix manipulations and contributed to descriptor systems analysis. Education: BSc in Mathematics, AUTh (1989) PhD in Mathematics, AUTh (1993) Professional Roles: Professor, AUTh (2014–present) Associate Professor, AUTh (2009–2014) Editorial Board Member, Multiple Journals Awards & Recognition: None explicitly listed, but contributions to control systems and editorial work are notable. Advising: 4 PhD and >50 postgraduate theses supervised. Labs/Teams: Directed AUTh’s Computer Lab (2011–2023).
Matthias Köppe is a full Professor of Mathematics at the University of California, Davis, where he also chairs the Graduate Group in Applied Mathematics. He is a leading figure in mathematical optimization, integer programming, and computational discrete mathematics, with significant contributions to the development of open-source mathematical software, particularly SageMath and its modularized fork passagemath. Education: Ph.D. in Mathematics, University of Magdeburg, Germany (2002) Research Focus: Köppe's research spans mathematical optimization , integer programming , computational discrete mathematics , and mathematical software development . His work includes foundational studies on the Gomory–Johnson infinite group problem, cutting plane theory, Ehrhart quasi-polynomials, and polynomial optimization over polytopes. He also develops and maintains software tools for exact integration, lattice point enumeration, and algebraic combinatorics. Research Trends: Across his publications, a clear trend emerges toward integrating algebraic and geometric methods into discrete optimization. His recent work focuses on equivariant perturbation theory, automated theorem discovery, and software infrastructure for optimization research. He has also contributed to open-access publishing and open-source software ecosystems. Scientific Service & Awards: Köppe serves or has served on editorial boards including Mathematical Programming A , Asia-Pacific Journal of Operational Research , and SN Operations Research Forum . He has led or co-led major NSF-funded projects such as Next-Generation Cutting Planes: Compression, Automation, Diversity, and Computer-Assisted Mathematics (2020–2025). Advising & Collaborations: He has mentored a number of Ph.D. students who now hold academic or industry positions, including: Amitabh Basu (Johns Hopkins) Robert Hildebrand (Virginia Tech) Brandon Dutra (Google) Yuan Zhou (University of Kentucky) Jiawei Wang (Uber) Chun Yu Hong (Google) Labs & Teams: Köppe is a core contributor to the SageMath project and the lead developer of passagemath , a pip-installable modularized fork of SageMath. He is also affiliated with the UC Davis TETRAPODS Institute of Data Science (UCD4IDS), the Center for Data Science and Artificial Intelligence Research (CeDAR), and UC Davis DataLab.
Ambros Gleixner is a Professor at HTW Berlin since 2020 and an affiliated researcher at the Zuse Institute Berlin (ZIB) since 2008. His research focuses on computational aspects of mixed-integer linear and nonlinear programming, with emphasis on exact rational arithmetic and algorithm verification. PhD in Mathematics (2015), Technische Universität Berlin Diplom (MSc) in Mathematics (2008), Technische Universität Berlin Vordiplom (BSc) in Mathematics (2004), Universität Bayreuth His work spans mathematical optimization, operations research, and computational mathematics. At ZIB, he leads projects like developing the MINLP solver SCIP , the LP solver SoPlex , and verifying integer programming results through VIPR . Recent publications highlight advancements in exact rational MIP, GPU-parallel algorithms, and energy system optimization. Scientific Awards : MERIT Visiting Scholar at University of Melbourne (2013) Teaching : Offers bachelor's theses in optimization and computational mathematics. Requires students to have attended relevant seminars and possess programming skills. Office hours by email appointment through Ambros.Gleixner@HTW-Berlin.de . Labs & Teams : Principal investigator at ZIB's Mathematical Algorithmic Intelligence division, Research Campus MODAL , and Linear, Integer, and Constraint Programming project.
Robert Hildebrand is an Assistant Professor in the Grado Department of Industrial and Systems Engineering at Virginia Tech. He holds a Ph.D. in Applied Mathematics from the University of California, Davis (2013). His research focuses on Mixed-Integer Nonlinear Programming, Integer Programming, Complexity Theory, and Redistricting Analytics, with applications in Operations Research and Discrete Geometry. He has received grants from the Air Force Office of Scientific Research and the Office of Naval Research, and the 2019 Sporn Teaching Award. Education: Ph.D., Applied Mathematics (UC Davis, 2013); B.Sc., Mathematics (University of Puget Sound, 2008). Professional history includes postdoctoral roles at ETH Zurich (2013–2015), IBM Watson Research (2015–2017), and a Simons Institute Fellowship (2017). Current roles include Associate Editor for Discrete Optimization and service on INFORMS committees. Research interests emphasize theoretical and computational aspects of optimization, with recent work on gerrymandering analysis, robotic assembly optimization, and algorithmic complexity bounds. Notable publications include advancements in integer programming formulations and scheduling algorithms for autonomous systems. Grants include Virginia Tech's Whole Health Consortium (2024) for veterans' healthcare optimization and an ICTAS Seed Grant for autonomous fleet algorithms (2023). He advises students on topics like rectangle packing and robotic trajectory planning, with former students advancing to doctoral and industry roles. Labs/Teams: Active in Virginia Tech's FASER Lab (robotics optimization) and collaborates with interdisciplinary groups on redistricting analytics and space exploration technologies.
William Cook is a University Professor in the Department of Combinatorics and Optimization at the University of Waterloo. His research focuses on combinatorial optimization, computational discrete optimization, and the traveling salesman problem (TSP). He is renowned for co-developing the Concorde TSP Solver , a leading software for solving TSP instances. Cook has held editorial roles in top journals such as Mathematical Programming Computation and Mathematical Programming , and served as Chair of the Mathematical Optimization Society and Vice Chair of INFORMS Computing Society. His research interests include algorithm design, operations research, graph theory, and computational methods for NP-hard problems. Notable contributions include advancing cutting-plane methods, branch-and-bound algorithms, and hybrid optimization techniques. Cook has authored influential books like In Pursuit of the Traveling Salesman and The Traveling Salesman Problem: A Computational Study . Awards include membership in the National Academy of Engineering, SIAM Fellowship, INFORMS Fellowship, and the $100,000 Amazon Last Mile Routing Research Challenge prize (2021). He has advised numerous projects in computational optimization, including studies on deep learning applications and combinatorial algorithm development. His work bridges theoretical advancements with practical software tools like QSopt and QSopt_ex .
Waltraud Huyer is a faculty member at the Department of Mathematics, Faculty of Mathematics, University of Vienna. Her office is located in Room 04.120 at Oskar-Morgenstern-Platz 1, 1090 Wien, Austria. She actively teaches courses including Numerical Mathematics and Introduction to Mathematics for both undergraduate and secondary school teacher accreditation programs. Her research spans Global and Local Optimization , Numerical Analysis , Data Analysis , Protein Folding , and Population Dynamics with structured populations. She develops advanced optimization algorithms like SNOBFIT for noisy environments and MINQ8 for quadratic programming, while applying mathematical techniques to biological problems such as protein structure prediction and age-structured population modeling. Analysis of her 14 most recent publications (1994-2018) reveals three dominant research thrusts: (1) Algorithmic development in global optimization (7 papers), including multilevel coordinate search and exact penalty functions; (2) Mathematical biology applications (5 papers), particularly in protein folding and population dynamics; (3) Computational verification methods (2 papers) for linear systems and feasibility problems. Her work consistently bridges theoretical mathematics with practical computational implementations. Her scientific contributions include widely-used algorithms implemented in MATLAB, such as the MCS global optimizer and SNOBFIT for noisy optimization, available through the University of Vienna's software repository. Huyer supervises teaching activities for foundational mathematics courses, emphasizing practical computational skills using MATLAB for numerical analysis and regression problems. Her teaching materials include detailed exercise sets with structured assessment criteria requiring both theoretical understanding and programming implementation.
