جست‌وجوی استاد و پژوهشگر

• Optimization Algorithms
• Network Flows
• Large Sparse Systems
• +۵ مورد دیگر

Donald Goldfarb is the Alexander and Hermine Avanessians Professor in the Department of Industrial Engineering and Operations Research at Columbia University. His research spans optimization algorithms, network flows, and large-scale computational methods, with applications in finance, image processing, and machine learning. Research Focus: Goldfarb specializes in developing efficient algorithms for convex and nonconvex optimization problems, including linear, quadratic, semidefinite, and second-order cone programming. His work extends to robust optimization for financial modeling and innovative methods for image restoration using total variation techniques. Publication Trends: His recent research emphasizes optimization in computational imaging (e.g., image decomposition and restoration) and finance (e.g., robust portfolio management). Algorithmic contributions include interior-point methods, Bregman iterations, and large-scale convex optimization techniques.

Roghayeh Hajizadehمشاهده پروفایل

مدرس

• Discrete Optimization
• Operations Research
• Mathematical Modeling
• +۴ مورد دیگر

Roghayeh Hajizadeh is a University Lecturer at Linköping University's Department of Mathematics (MAI) and Applied Mathematics (TIMA) research group. Her work focuses on discrete optimization techniques for real-world problems like urban snow removal and air traffic control, using mathematical modeling and algorithm development. PhD in Optimization of Snow Removal in Cities (2023) Member of Mathematics and Algorithms for Intelligent Decision Making research group Key projects: Optimization of Snow Removal in Cities (2017-present) and OPT@ATM (2023-present) Research keywords include: Discrete Optimization, Operations Research, Sustainable Urban Planning, Air Traffic Control Algorithms . Teaching includes Combinatorial Optimization for Computer Science/Software Engineering programs and Supply Chain Optimization for Industrial Economics/Mechanical Engineering students. Publications (2020-2024) demonstrate expertise in: Lagrangian relaxation techniques , vehicle coordination algorithms , and tree elimination methods for snow removal; Dantzig-Wolfe decomposition for aircraft scheduling.

Stephan Mescherمشاهده پروفایل

پژوهشگر

• Algebraic Topology
• Differential Geometry
• Robot Motion Planning
• +۵ مورد دیگر

Stephan Mescher is a Researcher at the Institute of Mathematics within the Faculty of Natural Sciences II at Martin Luther University Halle-Wittenberg. Based at the Georg Cantor House on Theodor-Lieser-Strasse in Halle (Saale), he conducts advanced research at the intersection of algebraic topology and differential geometry with applications in robotics. His research focuses on: Topological complexity and spherical complexities in motion planning Geodesic analysis in Riemannian and Finsler manifolds Morse theory and A-infinity algebra structures Closed geodesics and critical point theory Geometric properties of configuration spaces for robots Recent publications reveal a cohesive trajectory toward sequential topological complexity and fibered decompositions of cut loci, demonstrating how abstract topological invariants solve concrete problems in robot navigation. His work bridges pure mathematics with engineering applications through rigorous analysis of manifold structures. No scientific awards were documented in available sources. Information regarding student supervision, research grants, or collaborative teams was not provided in the source materials.

Tobias Glasmachersمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Optimization Algorithms
• +۶ مورد دیگر

Prof. Dr. Tobias Glasmachers is a Full Professor at the Institut für Neuroinformatik , Ruhr-Universität Bochum, Germany, specializing in the Theory of Machine Learning . He leads the Optimization of Adaptive Systems group and holds appointments in both Computer Science and Interdisciplinary AI research. Key Research Areas : Optimization algorithms, evolutionary computation, reinforcement learning, supervised learning, and neural networks Technical Focus : Gradient-based methods, support vector machines, and adaptive coordinate descent Applications : Robotics, waste sorting facilities, 3D game environments (e.g., Doom/Minecraft), and human-centered AI design Notable Contributions : Development of LM-MA-ES evolution strategy, Hessian Estimation Evolution Strategy, and tachAId tool for ethical AI design. His work bridges theoretical analysis with practical implementations across diverse domains. Teaching : Offers courses in Informatik 1 - Programmieren, Machine Learning: Supervised Methods, and Evolutionary Algorithms. Supervises numerous Bachelor's and Master's theses on AI/ML applications.

Stepan Paulمشاهده پروفایل

عضو هیئت علمی

• Mathematics Education
• Scholarship of Teaching and Learning
• 3D Modeling in Education
• +۵ مورد دیگر

Stepan Paul is an Associate Teaching Professor in the Department of Mathematics at North Carolina State University (NC State), where he has been since 2020. He holds a BS in Mathematics from Duke University (2007) and a PhD in Mathematics from the University of California, Santa Barbara (2013). His primary research focuses on the scholarship of teaching and learning (SoTL) in mathematics, particularly how spatial and visualization skills influence student success in calculus and the impact of 3D manipulatives in active learning environments. He is also deeply involved in mathematical illustration, designing physical and digital tools to enhance the understanding of advanced mathematical concepts, such as non-Euclidean geometry and differential geometry. Paul’s work includes collaborations on projects like Manipulative Calculus , which developed 3D models for multivariable calculus education, and the Flat Klein Bottle sculpture, a curved-crease origami piece displayed at the National Museum of Mathematics. He has published extensively on calculus pedagogy, resource utilization in teaching, and the application of 3D printing in education. His contributions to the Illustrating Mathematics program at ICERM (2019) further highlight his commitment to bridging mathematical research and educational outreach. Education: BS Mathematics, Duke University, 2007 PhD Mathematics, UC Santa Barbara, 2013 Key Projects: Non-Euclidean paper thermoforming for geometry education Desmos applets for calculus visualization Thingiverse 3D print designs (e.g., Sudanese Möbius Band, Siefert Surface) Awards/Grants: Funded by NSF Grant DMS-1439786 and Sloan Foundation (2019) His teaching and research emphasize innovative methods to make abstract mathematical concepts accessible, leveraging technology and hands-on tools to improve student engagement and achievement.

• Probability Theory
• Stochastic Processes
• Optimization Theory
• +۴ مورد دیگر

Petr Lachout is an Associate Professor at the Department of Probability and Mathematical Statistics, Faculty of Mathematics and Physics, Charles University. His teaching and research focus on advanced mathematical topics including stochastic processes, optimization theory, and invariance principles in probability. He has led lectures on subjects such as Random Processes I , Optimization Theory , and Principles of Invariance , with syllabi covering convex sets, duality in linear programming, weak convergence of probability measures, and Skorokhod spaces. Contact: Petr.Lachout@mff.cuni.cz . Office located in Karlín building, Sokolovská 49/83, Prague 8.

• Mechanism Design
• Game Theory
• Information Economics
• +۲ مورد دیگر

Prof. Dr. Andreas Kleiner is a Professor at the Department of Economics, University of Bonn, with affiliations to the Institute for Microeconomics, Hausdorff Center for Mathematics, ECONtribute, and CRC TR 224. His research bridges economic theory, game theory, and political science, focusing on mechanism design, information economics, and institutional decision-making. Kleiner's work centers on mechanism design , game theory , and political economy , with emphasis on voting systems, delegation models, and bargaining under asymmetric information. His research addresses fundamental questions about how institutions aggregate preferences, design incentives, and verify information in collective decision-making. His publications predominantly explore theoretical frameworks in microeconomics, featuring recurring themes: delegation mechanisms in multidimensional environments (2024-2025), voting theory robustness under incomplete information (2021-2023), and bargaining efficiency with veto players (2020-2021). Methodologically, he employs mathematical optimization, equilibrium analysis, and combinatorial geometry to solve economic design problems. Kleiner collaborates with interdisciplinary teams across the Hausdorff Center for Mathematics and CRC TR 224, contributing to advanced mathematical frameworks for economic applications. No awards or supervised students are documented in the provided text.

Kurt Anstreicherمشاهده پروفایل

استاد

• Mathematical Programming
• Optimization Expertise
• Interior-Point Algorithms
• +۳ مورد دیگر

Kurt Anstreicher is a Professor of Business Analytics at the University of Iowa's Tippie College of Business, with complementary appointments in the Department of Computer Science and Department of Industrial Engineering. He holds the Gary C. Fethke Chair in Leadership. His research focuses on mathematical programming, optimization, interior-point algorithms, and nonlinear programming. Anstreicher earned his Ph.D. from Stanford University in 1983 and a B.A. from Dartmouth College in 1978. Research interests include optimization theory and methods, particularly in nonconvex programming, convex relaxations, and algorithm design. His work addresses challenges in quadratic programming, semidefinite optimization, and global optimization techniques. Recent contributions explore applications in trust-region subproblems, copositive cones, and maximum-entropy sampling. Publications span topics like convex hull representations, Kronecker product constraints, and spherical codes. His work bridges theoretical advancements and practical computational methods, with implications for operations research, data analysis, and engineering systems.

James Renegarمشاهده پروفایل

استاد

• Applied Mathematics
• Optimization
• Algorithms
• +۳ مورد دیگر

James Renegar is the Class of 1912 Professor of Engineering in the Department of Mathematics at Cornell University, affiliated with the College of Engineering. His research focuses on optimization algorithms, particularly linear programming, hyperbolic programming, and convex optimization. He explores foundational questions in computational complexity, such as the existence of polynomial-time algorithms for solving systems of linear inequalities. His work bridges applied mathematics and theoretical computer science, emphasizing algorithm design and analysis. Notable contributions include advancements in first-order methods, subgradient techniques, and the generalization of central paths in optimization. His research also delves into hyperbolic polynomials and their role in modern optimization frameworks. Renegar has authored influential works on interior-point methods and has contributed to understanding the geometry of optimization problems. He has been funded through grants like the NSF CCF AF: EAGER project assessing first-order methods for conic optimization.

Andreas Heinrich Hamelمشاهده پروفایل

استاد

• Convex and variational analysis
• Optimization theory with set-valued objectives
• Mathematical finance (market models with transaction costs)
• +۲ مورد دیگر

Andreas Heinrich Hamel is a Tenured Full Professor at the Faculty of Economics and Management , Free University of Bozen-Bolzano, Italy. His research focuses on advanced mathematical fields including convex and variational analysis, set-valued optimization, mathematical finance (market models with transaction costs), mathematical economics (incomplete preferences and game theory), and multivariate statistics (quantiles). Academic Rank: Professor Institution: Free University of Bozen-Bolzano School: Faculty of Economics and Management Professor Hamel co-developed the groundbreaking area of Set Optimization , with applications in mathematical finance, economics, statistics, game theory, and multi-criteria decision making. His work includes inventing set optimization approaches to risk measures for multivariate positions and developing optimization methods for economics and business applications. Recent publications highlight his expertise in set-valued risk measures, variational principles, and duality theories. His research spans topics such as cone distribution functions, multivariate shortfall risk measures, and utility maximization under transaction costs, reflecting interdisciplinary contributions to financial mathematics and optimization.

Giuseppe Savaréمشاهده پروفایل

استاد

• Mathematical Analysis
• Optimal Transport
• Gradient Flows
• +۴ مورد دیگر

Giuseppe Savaré is a Full Professor of Mathematical Analysis at the Department of Mathematics, University of Pavia, and a Hans Fischer Senior Fellow at the Technical University of Munich's Institute for Advanced Study (TUM-IAS) since 2019. His research focuses on variational methods for evolution problems, including gradient flows, rate-independent models, nonlinear PDEs, and optimal transport theory in metric spaces. Education: PhD in Mathematics (1988) from University of Pavia. His work bridges optimal transport, metric measure spaces, and analysis in non-Euclidean geometries, with applications to PDEs, curvature bounds, and generalized gradient flows. Key contributions include foundational studies on Wasserstein gradient flows, entropy-transport problems, and abstract action spaces in dynamical systems. Scientific awards include the prestigious Ennio De Giorgi Prize (2011) and the Gioachino Japichino Prize (1994), alongside an invitation as a speaker at the 7th European Congress of Mathematics (2016). Collaborations with researchers like Massimo Fornasier and Alexander Mielke have advanced consensus-based optimization, multi-agent control, and quantum drift-diffusion models.

Andrea Wagnerمشاهده پروفایل

پژوهشگر

• Convex Optimization
• Vector Optimization
• DC Programming
• +۳ مورد دیگر

Dr. Andrea Wagner is a faculty member at the School of Statistics and Mathematics (Vienna University of Economics and Business, WU). Her research focuses on convex optimization , vector optimization , and DC programming , with applications in locational analysis and mathematical economics . Education: Dr.rer.nat. (PhD equivalent) in Statistics and Mathematics Research Interests: Andrea specializes in solving complex optimization problems, particularly unbounded convex vector optimization and semi-obnoxious facility location using Manhattan distances. Her work bridges theoretical mathematics with practical applications in economics and urban planning. Publication Trends: Recent articles emphasize computational methods for multi-objective optimization, duality theory applications, and algorithmic solutions for non-convex problems, reflecting her interdisciplinary approach. Scientific Awards: GOR Young Researchers Award (2020) Scholarship from Society of Friends and Patrons of Martin Luther University Halle-Wittenberg (2014) Ebert Beratung und Innovation GmbH Advancement Award (2010) Traveling Scholarship from General Endowment Fund of Theoretical Physics and Mathematics (2010)

Lewis Ntaimoمشاهده پروفایل

استاد

• Stochastic Programming
• Optimization Methods
• Healthcare Operations
• +۶ مورد دیگر

Dr. Lewis Ntaimo is a Professor and Department Head of Industrial & Systems Engineering at Texas A&M University. He holds a Ph.D. in Systems & Industrial Engineering (with a minor in Electrical & Computer Engineering) from the University of Arizona, alongside M.S. and B.S. degrees in Mining Engineering. His research focuses on stochastic programming, optimization methods, and their applications in healthcare, wildfire management, energy systems, and operations research. Notable awards include the Distinguished Educator Award (2022), INFORMS Computing Society Prize (2015), and the Marilyn and L. David Black Faculty Fellowship (2016). He is a Fellow of the Institute of Industrial and Systems Engineers (IISE). Key contributions include developing stochastic models for wildfire risk mitigation, healthcare scheduling under uncertainty, and carbon capture systems. His work bridges simulation and optimization, exemplified by the DEVS-FIRE wildfire modeling framework and the SIPLIB test problem library. Education: Ph.D., Systems & Industrial Engineering, University of Arizona (2004) M.S., Mining & Geological Engineering, University of Arizona (2000) B.S., Mining Engineering, University of Arizona (1998) Grants & Projects: Includes NSF-funded research on wildfire fuel treatment planning, space surveillance scheduling, and wind energy reliability. Labs/Teams: Leads research in stochastic optimization and discrete event simulation, collaborating on interdisciplinary projects with IISE and INFORMS.

Vaughan Prattمشاهده پروفایل

استاد

• Global Environmental Change
• Foundations of Geometry
• Autonomous Vehicles
• +۷ مورد دیگر

Vaughan Pratt is a Professor in the Department of Computer Science at Stanford University, with affiliations in Electrical Engineering (by courtesy), the AI Lab, Theory group, and the Center for the Study of Language and Information (CSLI). His research spans computer science, mathematics, and climate science. Education: M.Sc. in Computer Science from Sydney University (1969), Ph.D. in Computer Science from Stanford University (1971) Research Interests include global environmental change, foundations of geometry, autonomous vehicles, concurrency theory, and speech recognition. His work on climate change integrates data analysis and modeling, while his mathematical research explores category theory and geometric frameworks. Recent Publications highlight his interdisciplinary contributions, from concurrency models using Chu spaces to climate sensitivity analysis and geometric algebra. Key trends involve linear logic, coalgebraic structures, and climate feedback mechanisms. Scientific Awards include ACM Fellow IEEE Life Senior Member American Geophysical Union (AGU) Life Member University of Sydney 2017 Alumni Award for Innovation and Entrepreneurship Advising : Directed Ph.D. theses of notable students like David Harel, Bob Streett, and Keyvan Mohajer. His work bridges formal methods, programming language design, and applications in robotics and climate science.

Robert Gary Blandمشاهده پروفایل

استاد

• Linear Programming
• Combinatorial Optimization Algorithms
• Optimization
• +۳ مورد دیگر

Robert G. Bland is a Professor at Cornell University's School of Operations Research and Information Engineering (ORIE). He earned his B.S., M.S., and Ph.D. in Operations Research from Cornell University (1969–1974). His career includes roles as Assistant Professor at SUNY Binghamton (1974–1978) and has been at Cornell since 1978, advancing to Full Professor. His research focuses on linear programming, combinatorial optimization, and algorithmic design, with notable contributions to the simplex method and matroid theory. He is affiliated with the Center for Applied Mathematics and holds memberships in the Mathematical Optimization Society and the American Society for Engineering Education. Bland’s teaching spans optimization, game theory, and legislative mathematics. He has received prestigious awards, including the Sloan Foundation Fellowship (1978–1982), multiple Cornell Outstanding Educator Awards, and the McCormick Advising Award (2017). His service includes contributions to engineering education and final exam scheduling. He has authored influential papers in Mathematics of Operations Research , Journal of Combinatorial Theory , and Scientific American . His research emphasizes duality theory, network flow algorithms, and applications in scheduling and resource allocation. Bland’s work bridges theoretical rigor with practical computational methods, shaping modern optimization practices.