جست‌وجوی استاد و پژوهشگر

Ram Mohapatraمشاهده پروفایل

استاد

• Mathematical Analysis
• Operator Theory
• Variational Inequalities
• +۳ مورد دیگر

Ram Mohapatra is a Professor in the Department of Mathematics at the University of Central Florida (UCF), part of the College of Sciences. His research interests span mathematical analysis, operator theory, variational inequalities, approximation theory, cybersecurity, and fluid dynamics. He has published extensively on topics including inverse scattering problems, generalized inverses, and optimal control theory. His work often intersects with applications in engineering and data science. Recent research focuses on operator theory applications, tensor decompositions, and mathematical modeling of physical systems. He has contributed to advancements in frame theory, numerical radius studies, and cybersecurity methodologies. His academic activities include teaching undergraduate and graduate courses in mathematics, such as MAC 1105C and MAC 2311C, and maintaining active collaborations in interdisciplinary research areas. Professional contributions include over 150 peer-reviewed articles and editorial roles in mathematics journals. While no explicit awards are listed in the provided texts, his prolific publication record underscores his scholarly impact in applied and theoretical mathematics.

• Mixed-Integer Optimization
• Combinatorial Optimization
• Discrete Mathematics
• +۳ مورد دیگر

Timm Oertel is a Professor in the Department of Data Science at Friedrich Alexander University Erlangen-Nuremberg (FAU), holding the Chair of Analytics & Mixed-Integer Optimization. His office is located in Room 03.344 at Cauerstraße 11, Erlangen, and he can be contacted via email at timm.oertel@fau.de or phone at +49 9131 85-67313. His research focuses on mixed-integer optimization, combinatorial optimization, and discrete mathematics, with significant contributions to sparse solutions in lattices and semigroups, integer Carathéodory rank, knapsack polyhedra, and parametric integer optimization. His work bridges theoretical computer science, operations research, and discrete geometry, emphasizing structural properties of integer solutions and algorithmic efficiency. Professor Oertel's publication record (2013-2025) reveals consistent trends in theoretical integer programming, with recent work (2020-2025) concentrating on sparsity patterns, approximation in algebraic structures, and complexity bounds. He frequently collaborates with leading researchers including Iskander Aliev, Robert Weismantel, and Joseph Paat, publishing in top venues like Mathematical Programming and SIAM Journal on Optimization. His research demonstrates deep connections between combinatorial geometry and optimization theory.

Madhur Tulsianiمشاهده پروفایل

استاد

• Theoretical Computer Science
• Complexity Theory
• Algorithm Design
• +۳ مورد دیگر

Madhur Tulsiani is a Professor at the University of Chicago's Department of Computer Science and a researcher at the Toyota Technological Institute at Chicago (TTIC). His research focuses on theoretical computer science, particularly complexity theory and algorithm design, with applications in coding theory and information theory. He has been supported by NSF grants 1254044, 1816372, and 2326685. Education: Bachelor’s in Computer Science, IIT Kanpur (2001-2005) Ph.D. in Computer Science, UC Berkeley (2005-2009), advised by Luca Trevisan Postdoctoral fellowships at the Institute for Advanced Study (IAS) and Princeton University Research Interests: Mathematical foundations of computation Complexity theory and algorithm design Coding theory and error-correcting codes Sum-of-Squares hierarchies and approximation algorithms Recent Contributions: Pioneering work on list decodable codes and expander-based constructions Advances in approximation algorithms for high-dimensional expanders Lower bounds for Sum-of-Squares algorithms using high-dimensional expanders Teaching: Information and Coding Theory Mathematical Toolkit (linear algebra/probability) Summer REU programs in theoretical computer science Students: Advised PhD students including Fernando Granha Jeronimo, Goutham Rajendran, and Shashank Srivastava Co-advised students with Sasha Razborov, Janos Simon, and others Labs/Groups: Member of the Theoretical Computer Science Group at TTIC and UChicago, contributing to cross-disciplinary research in algorithms and complexity.

• Fracture Mechanics
• Solid Mechanics
• Finite Fracture Mechanics
• +۱۴ مورد دیگر

Pietro Cornetti is an Associate Professor in the Department of Structural, Building and Geotechnical Engineering (DISEG) at the Politecnico di Torino, where he also serves as Coordinator of the Doctoral School in Civil and Environmental Engineering. He is a member of the SISCON Interdepartmental Center for the Safety of Infrastructures and Constructions and the Doctoral School Council. His academic career has been deeply rooted at Politecnico di Torino, where he completed his education and has held continuous academic positions since 1999. His research interests span a broad spectrum of solid and fracture mechanics, with a focus on finite fracture mechanics , fractional calculus , fractal geometry , non-local elasticity , size effects in concrete and granular materials, and structural retrofitting using FRP/FRCM systems . His work bridges theoretical mechanics with practical applications in civil engineering and structural integrity. The 15 most recent publications highlight a strong trend toward integrating advanced mathematical frameworks—particularly fractional calculus and phase-field modeling—with classical and finite fracture mechanics. These works address brittle fracture, dynamic crack propagation, fatigue in composites, and debonding phenomena, demonstrating a consistent focus on improving predictive models for structural failure across scales. He actively mentors PhD students and leads major research initiatives, including the H2020-funded NEWFRAC project and the nationally funded REHARZE project on retrofitting historic architecture for zero emissions. His teaching portfolio includes Solid Mechanics, Structural Mechanics, Fracture and Plasticity, and advanced topics in computational mechanics across undergraduate, master's, and doctoral levels. He is affiliated with several professional organizations: Italian Group of Fracture (IGF) Italian Association of Applied and Theoretical Mechanics (AIMETA) European Structural Integrity Society (ESIS), Technical Committee TC16 He has served on the program committee of the International Conference on Fracture (ICF14) and participated in the organizing committee of AIMETA XXI. His research is supported by competitive grants from national (PRIN) and EU (H2020) funding programs. He supervises a research group focused on fracture mechanics and leads the Laboratorio di Meccanica della Frattura (DISEG) , which supports experimental and computational research in structural integrity. His team works on both theoretical developments and practical applications in civil and environmental engineering.

• Mechanism Design
• Voting Theory
• Game Theory
• +۱ مورد دیگر

Andreas Kleiner is a Professor in the Department of Economics at the University of Bonn. His research focuses on Mechanism Design , Voting Theory , Game Theory , and Political Economy . Research Interests Mechanism Design Voting Theory Game Theory Political Economy Recent Publications (2018-2025) Kleiner's work explores multidimensional delegation with stochastic mechanisms, convex choice properties in mechanism design, and extreme point analysis in fusions. He investigates sequential veto bargaining dynamics, interdependent voting models, and costly verification in resource allocation. His research connects linear programming to monopoly pricing duality and examines historical legislative agenda-setting strategies. Contact Email: andreas.kleiner@uni-bonn.de Email: andykleiner@gmail.com

Simon Lacoste-Julienمشاهده پروفایل

دانشیار

• Machine Learning
• Optimization
• Deep Learning
• +۵ مورد دیگر

Simon Lacoste-Julien is an Associate Professor at Université de Montréal, affiliated with the Department of Computer Science and Operations Research (DIRO). He also serves as the Associate Scientific Director of Mila – Quebec Institute of Artificial Intelligence and holds the position of Vice President Lab Director at Samsung SAIT AI Lab Montreal (SAIL). His research focuses on machine learning, optimization, and their applications in areas like deep learning, generative models, causality, and computer vision. Lacoste-Julien has held academic positions at INRIA in Paris and has a PhD from UC Berkeley, with postdoctoral work at the University of Cambridge. He teaches advanced graduate courses on probabilistic graphical models and structured prediction. His work includes contributions to optimization algorithms (e.g., Frank-Wolfe methods), causal discovery, and generative models. Lacoste-Julien has supervised numerous students and postdocs, and his awards include being a CIFAR Fellow and Canada CIFAR AI Chair. His research spans theoretical foundations and practical applications, with a strong emphasis on scalable and efficient machine learning techniques.

Alexander Shapiroمشاهده پروفایل

استاد

• Stochastic Programming
• Risk Analysis
• Simulation-Based Optimization
• +۴ مورد دیگر

Alexander Shapiro is the A. Russell Chandler III Chair and Professor at the H. Milton Stewart School of Industrial and Systems Engineering , Georgia Institute of Technology. His work bridges optimization and statistics, focusing on stochastic programming, risk analysis, and simulation-based optimization. He has received numerous accolades, including the Khachiyan Prize (2013) , Dantzig Prize (2018) , and John von Neumann Theory Prize (2021) . Education: Ph.D. in Applied Mathematics-Statistics (Ben-Gurion University, 1981), M.Sc. in Mathematics (Moscow University, 1971) His research explores stochastic programming , risk-averse optimization , and multivariate statistical analysis , with recent work on distributionally robust control, Bayesian stochastic methods, and convex multistage optimization. Publications highlight theoretical advancements and computational frameworks for uncertainty modeling. Recent articles focus on asymptotics (2025), duality in MDPs (2023-2024), and statistical inference (2014-2024). These span stochastic control , robustness , and time consistency , reflecting his expertise in bridging probability theory with large-scale optimization. Scientific awards : Khachiyan Prize of INFORMS (2013) Dantzig Prize (2018) John von Neumann Theory Prize (2021) Election to National Academy of Engineering (2020) Dr. Shapiro has served as Area Editor (Optimization) for the Operations Research Journal and Editor-in-Chief of Mathematical Programming, Series A , demonstrating sustained leadership in his field.

Koenraad Schalmمشاهده پروفایل

استاد

• String Theory
• Theoretical Physics
• Holographic Duality (AdS/CFT)
• +۷ مورد دیگر

Koenraad Schalm is a Professor of Theoretical Physics at Leiden University, affiliated with the Leiden Institute of Physics (LION) within the Faculty of Science. He leads the Schalm Group, which focuses on string theory and its applications in condensed matter and cosmology through holographic duality (AdS/CFT). His research interests include string theory, quantum critical systems, holographic models of superconductivity, black hole physics, and early universe cosmology. He explores how string-theoretic models can explain experimental observations in quantum materials and primordial fluctuations. The recent publications highlight a strong focus on applying holography to condensed matter phenomena, including strange metals, superconductivity, and non-equilibrium transport. There is also significant work on cosmological perturbations and inflation, showing interdisciplinary reach between high-energy theory and observational cosmology. Vici Innovation Research Incentives Award, NWO (2013) Vidi Innovation Research Incentives Award, NWO (2004) FOM Program group grant 'Observing the big bang: the quantum universe and its imprint on the sky' (2014) NWO Graduate Program Award 'de Sitter Program in Cosmology' (2011) FOM Project grant with J. Zaanen (2009) FOM Program group grant 'A String Theoretic Approach to Cosmology and Quantum Matter' (2009) NWO van Gogh Grant with P.S. Corasaniti (2007) FOM Project grant with E. Verlinde and J.P. van der Schaar (2007) Schalm has advised numerous PhD students and collaborators, including Aravindh Swaminathan, Shankar Aleksandar Bukva, Ludwig Hoffmann, and Petter Säterskog. He has been principal investigator on multiple NWO and FOM grants. He co-organized workshops such as 'Black Hole Answers for Condensed Matter Questions' and 'Effective Field Theory in Inflation', and is actively involved in academic service. He leads the Schalm Group, which investigates the interface between string theory and experiment, particularly through the AdS/CMT correspondence. The group aims to uncover quantum signatures of string theory in condensed matter systems and cosmological data.

• Optimization Theory
• Variational Analysis
• Bilevel Programming
• +۶ مورد دیگر

Zhang Jin is a Professor at the Department of Mathematics in the College of Science at Southern University of Science and Technology (SUSTech) since December 2024. He also serves as the Associate Vice Director of the Shenzhen National Applied Mathematics Center since February 2023. Education: Ph.D. in Applied Mathematics (2014, University of Victoria); M.Sc. in Operational Research (2010, Dalian University of Technology); B.Art in Journalism (2007, Dalian University of Technology) Research Interests span optimization theory , variational analysis , bilevel programming , and their applications in machine learning , economics , and data science . His work includes convergence analysis of first-order methods , stochastic/robust optimization , and error bound conditions . Recent Publications focus on nonconvex bilevel optimization , gradient-based algorithms , and stochastic programming , with papers in IEEE TPAMI , SIAM Journal on Optimization , and conferences like ICML , NeurIPS , and ICLR . Scientific Awards include the Youth Science and Technology Innovation Award from Guangdong Province (2022) , Youth Science and Technology Award from the Operations Research Society of China (2020) , and Junior Research Award from SUSTech's Faculty of Science (2020) . Students: Supervises Ph.D. candidates like Yixia Song , Peixuan Yang , and Qichao Cao , along with Master's students Yixuan Zhang , Kaiqi Sun , and Feifan Wang . Grants: Leads projects such as the National Key R&D Program (3.2M RMB, 2024-2028) and National Science Fund for Distinguished Young Scholars (2M RMB, 2023-2025) .

Urs Schreiberمشاهده پروفایل

استاد

• Mathematical Physics
• Quantum Physics
• Algebraic Topology
• +۷ مورد دیگر

Urs Schreiber is a Professor at New York University in Abu Dhabi (NYU AD), working in the Mathematics Division of Science. He leads the Research Center for Quantum and Topological Systems (CQTS) under the group of Prof. Hisham Sati. His work bridges mathematical physics, quantum theory, and topology, with significant contributions to understanding strongly-coupled quantum systems and their applications to topological quantum technology. His research focuses on applying tools from algebraic topology and geometric homotopy theory to fundamental problems in quantum physics. His major projects include Hypothesis H on flux quantization in M-theory, geometric engineering of anyons on M5-branes, and developing quantum language via linear homotopy types. His work represents a unique synthesis of advanced mathematical techniques with cutting-edge theoretical physics, particularly in the areas of M-theory, topological quantum computing, and differential cohomology. Schreiber has established himself as a leading figure in the application of higher categorical and homotopical methods to physics. His research demonstrates a clear trajectory toward developing mathematical frameworks for next-generation quantum technologies, with particular emphasis on topological approaches that could provide fault-tolerant quantum computing platforms. His work shows strong connections between abstract mathematical structures and practical quantum computing applications, particularly through homotopy type theory implementations. Monograph: The Character Map in Non-Abelian Cohomology (with D. Fiorenza & H. Sati, 2023) Monograph: Equivariant Principal ∞-Bundles (with H. Sati, 2025) Contributor: Mathematical Foundations of Quantum Field and Perturbative String Theory (2011) Schreiber has advised numerous graduate students in areas spanning homotopy theory, quantum field theory, and mathematical physics. His teaching portfolio includes courses on Mathematical Quantum Field Theory, Topological K-Theory, and Stable Homotopy Theory. He is also the founder of the nLab research wiki project (started in November 2008), which has become an essential resource for researchers working at the intersection of mathematics and physics. His research group actively develops mathematical foundations for quantum and topological systems with practical implications for quantum technology development.

Laurent Pfeifferمشاهده پروفایل

پژوهشگر

• Optimal Control
• Mean Field Games
• Stochastic Optimization
• +۳ مورد دیگر

Laurent Pfeiffer is a researcher at the Signals and Systems Laboratory , focusing on Optimization and Control Theory . His work bridges theoretical advancements in mean field games, stochastic optimization, and numerical methods with practical applications in energy systems and fluid dynamics. Research Interests: Optimal Control, Mean Field Games, Stochastic Optimization, Nonlinear Programming Recent Publications: Explore mean field games, nonconvex optimization, and control theory applications in nuclear energy, gas portfolios, and fluid dynamics. Labs: Signals and Systems Laboratory, specializing in control systems and mathematical modeling.

Garth Isaakمشاهده پروفایل

استاد

• Discrete Mathematics
• Graph Theory
• Combinatorics
• +۵ مورد دیگر

Garth Isaak is a Professor in the Department of Mathematics and Statistics at Lehigh University , part of the College of Arts and Sciences. He has held roles such as Department Chair (2012–2022) and Associate Dean for Graduate Studies (2012–2016). Isaak earned his Ph.D. in 1990 from Rutgers University’s RUTCOR interdisciplinary program. His research focuses on discrete mathematics, particularly graph theory, with contributions to interval orders, pebbling games, Ramsey numbers, and tolerance graphs. He has also explored combinatorial duality and alternative calculus pedagogy. Isaak teaches courses like Graph Theory and Introduction to Mathematical Reasoning . His work emphasizes algorithmic solutions for graph problems and structural characterizations. Over 150 publications and a strong academic mentorship record highlight his scholarly impact. Education: Ph.D. (Rutgers University, 1990); Postdoctoral research at Dartmouth College (1990–1992). Research Interests: Discrete Mathematics, Graph Theory, Interval Orders, Combinatorial Optimization, Ramsey Theory, and Algorithmic Graph Analysis. His work bridges theoretical foundations and practical applications, such as efficient graph recognition algorithms and game-theoretic graph dynamics.

David B. Brownمشاهده پروفایل

استاد

• Stochastic models
• Mathematical optimization
• Energy transition
• +۳ مورد دیگر

David B. Brown is the Snow Family Business Distinguished Professor in Decision Sciences and Faculty Director of the Center for Energy, Development and Global Environment (EDGE) at Duke University's Fuqua School of Business. He holds a Ph.D. from MIT (2006). His research focuses on algorithm design for decision problems under uncertainty, with applications in energy systems, sustainability, and stochastic modeling. Key contributions include dynamic resource allocation frameworks, stochastic dynamic programming relaxations, and robust optimization techniques. Professor Brown leads the GRACE project (funded by the Department of Energy, 2020–2025), exploring risk-aware grid management for clean energy systems. His work bridges theoretical advancements with practical applications in energy transition, climate mitigation, and mathematical optimization. He has received recognition including the Snow Family Distinguished Professorship (2024). Recent publications emphasize dynamic frameworks for integrated energy systems, fluid policies in resource allocation, and sequential decision-making under uncertainty. His research also addresses pricing strategies in shared resource networks and asymptotic optimization techniques. Education: Ph.D., Massachusetts Institute of Technology (2006) Grants: DOE-funded GRACE Project, Duke EDGE Faculty Seed Grant (2024) Affiliations: Center for Energy, Development and Global Environment (EDGE)

Feiran Zhaoمشاهده پروفایل

پژوهشگر

• Data-Driven Control
• Adaptive Control
• Reinforcement Learning
• +۲ مورد دیگر

Feiran Zhao is a Researcher at the Institute of Automatic Control, part of the Department of Mechanical and Process Engineering at ETH Zürich. He holds a B.S. in Control Science and Engineering from Harbin Institute of Technology (2018) and a Ph.D. from Tsinghua University (2024). His research focuses on data-driven control, adaptive control, reinforcement learning, and their applications in engineering systems. Zhao is currently a postdoc under Prof. Florian Dorfler at ETH's Automatic Control Lab. Research interests span topics like policy optimization for LQR systems, quantized feedback control, and model predictive control acceleration. His work bridges machine learning and classical control theory, with applications in robotics, power systems, and aerospace engineering. His publications (2019–2025) explore theoretical foundations of policy gradient methods, convergence analysis, and practical implementations in autonomous systems. Though no awards are explicitly listed, his active research in high-impact areas suggests potential recognition. As part of the Automatic Control Lab, Zhao collaborates on projects involving data-enabled control strategies and real-world system applications. No student advisees are currently listed.

David Huckleberry Gutmanمشاهده پروفایل

استادیار

• Continuous Optimization Theory
• Large-Scale Optimization
• Optimization over Manifolds
• +۳ مورد دیگر

David Huckleberry Gutman is an Assistant Professor in the Department of Industrial & Systems Engineering (ISEN) at Texas A&M University. He holds a Ph.D. in Mathematical Sciences from Carnegie Mellon University (2019), an M.S. in Mathematics from Tulane University (2010), and a B.S.B.A. in Finance & Accounting from Georgetown University (2008). Education: Ph.D. in Mathematical Sciences, Carnegie Mellon University, 2019 M.S. in Mathematics, Tulane University, 2010 B.S.B.A. in Finance & Accounting, Georgetown University, 2008 His research focuses on continuous optimization theory , emphasizing large-scale optimization methods and optimization over manifolds . He has developed innovative algorithms such as tangent subspace descent for Riemannian manifolds and advanced proximal gradient methods with convergence guarantees. Key awards include the 2023 NSF CAREER Award and the 2022 INFORMS Optimization Society Young Researcher Prize (jointly with Nam Ho-Nguyen). His work bridges theoretical optimization with practical machine learning and engineering applications. Advising: Currently mentoring PhD students Allan Gurwicz, George Lobo, and Leandro Maia. Previously held positions at Texas Tech University (2019-2023) and conducted research at Bosch Research and IBM. Labs/Teams: Leads an active research group at Texas A&M exploring optimization algorithms for modern computational challenges, though no open PhD spots are available at this time.