جستجوی استاد راهنمای دانشگاهی

James B. Orlinمشاهده پروفایل

استاد

• Network Optimization
• Combinatorial Optimization
• Transportation
• +۷ مورد دیگر

James B. Orlin is the E. Pennell Brooks (1917) Professor in Management and a Professor of Operations Research at the MIT Sloan School of Management. He specializes in network and combinatorial optimization with applications spanning transportation, computer science, operations, and marketing. BA in Mathematics, University of Pennsylvania MA in Mathematics, California Institute of Technology MMath, University of Waterloo PhD in Operations Research, Stanford University His research focuses on designing efficient algorithms for network optimization problems, including shortest path, max flow, and min cost flow. He has contributed to algorithmic theory in logistics, telecommunications, and inventory management, with work on stochastic demand models and data-driven inventory policies. Recent publications include advancements in directed shortest path algorithms, robust submodular function maximization, and energy storage problem complexity. His seminal textbook Network Flows: Theory, Algorithms, and Applications (1993) remains a foundational reference. Leonard G. Abraham Prize Khachiyan Prize Test of Time Award As a mentor, he has advised numerous researchers through collaborative publications and teaching. His work addresses both theoretical algorithm development and practical implementation across diverse domains including airline scheduling, logistics, and network design.

Gruia Calinescuمشاهده پروفایل

دانشیار

• Algorithms
• Approximation Algorithms
• Theory of Computation
• +۳ مورد دیگر

Gruia Calinescu is an Associate Professor of Computer Science at Illinois Institute of Technology (IIT), affiliated with the College of Computing's Computer Science Department. He joined IIT in 2000 and has held visiting positions at the University of Bonn and the University of Wisconsin-Milwaukee. His research focuses on approximation algorithms, combinatorial optimization, and theoretical computer science, with contributions to graph theory, network design, and algorithmic problems in wireless networks. Education includes a PhD from Georgia Tech's Algorithms, Combinatorics, and Optimization program (1998) under Howard Karloff. He also holds a diploma from the University of Bucharest in scheduling theory. Key research interests include algorithms for Steiner trees, network connectivity, scheduling, and power optimization. He has published extensively on topics like minimum power covering, relay placement, and LP rounding techniques. His work often bridges theoretical foundations with practical applications in wireless networks and distributed systems. Recent work includes advancements in combination algorithms for Steiner tree variants (2022), energy-aware scheduling (2016), and improved approximation algorithms for relay placement (2014). He is also involved in teaching, such as CS 530 - Theory of Computation.

• Combinatorics
• Graph Theory
• Optimization on Graphs
• +۳ مورد دیگر

Martí Farré is a faculty member in the Department of Mathematics at the Faculty of Mathematics and Statistics, Universitat Politècnica de Catalunya (UPC). He is actively involved in the COMBGRAPH research group (Combinatorics, Graph Theory, and Applications) and the OMGRAPH subgroup focusing on optimization methods in graphs. His research spans theoretical and applied aspects of discrete mathematics. Research Interests: Martí's work centers on Combinatorics and Graph Theory , with particular emphasis on extremal problems, connectivity, matching theory, independence number, and spectral graph properties. His research often investigates structural and optimization properties of graphs under constraints such as fixed diameter, degree sequences, or matching numbers. The analysis of his recent publications reveals a consistent focus on extremal graph theory, where he derives bounds and characterizes graphs maximizing or minimizing certain parameters (e.g., edges, diameter, connectivity) under given constraints. Key themes include vulnerability and robustness of networks, spectral properties, and combinatorial optimization on graphs. Scientific Awards and Recognition: Premi o reconeixement (Recognition or Prize) Advising and Grants: Martí has contributed to competitive R&D+i projects and innovation initiatives in education. He has supervised doctoral theses, indicating an active role in mentoring graduate students, although specific student names are not listed in the provided data. Labs and Research Teams: He is a key member of the COMBGRAPH research group at UPC, a leading team in combinatorics and graph applications, and participates in the OMGRAPH subgroup dedicated to optimization on graphs, contributing to both theoretical advances and real-world applications.

Selin Ahipasaogluمشاهده پروفایل

استاد

• Operational Research
• Convex Optimization
• Robust Optimization
• +۵ مورد دیگر

Prof. Selin Damla Ahipasaoglu is a Professor in Operational Research at the University of Southampton's School of Mathematical Sciences . She serves on the management team of the UKRI CDT SustAI (Artificial Intelligence for Sustainability) as Senior Tutor and Co-Lead for the Transportation and Logistics Theme . Her work bridges mathematical optimization with practical applications in sustainability, finance, and transportation systems. Research Interests : Convex Optimization Robust Optimization Discrete Choice Theory Experimental Design Machine Learning Current Research : Focused on robust optimization and its applications in discrete choice modeling, portfolio optimization, and transportation systems. She explores theoretical frameworks alongside real-world implementations, particularly through interdisciplinary projects like the UKRI CDT SustAI. Teaching : In the 2025/2026 academic year, she teaches MATH3017: Mathematical Programming and MATH2013: Operational Research II . She supervises PhD students in Mathematical Sciences, including Kexin Lai, Samuel Jericho Ward, and others.

Vrinda Mehtaمشاهده پروفایل

پژوهشگر

• Quantum Computing
• Theoretical Physics
• Computational Physics
• +۴ مورد دیگر

Vrinda Mehta is a postdoctoral researcher at the Jülich Supercomputing Center (JSC) , part of the Research Center Jülich GmbH . Her research focuses on quantum computing, optimization algorithms, and theoretical physics.

• Combinatorial Optimization
• Applied Mathematics
• Integer Linear Programming
• +۳ مورد دیگر

Diego DELLE DONNE is an Assistant Professor (tenure-track) at ESSEC Business School in Paris, France, within the Information Systems, Data Analytics and Operations department. He holds a PhD in Computer Science from the University of Buenos Aires (2016), along with earlier degrees from the same institution. Prior to his current role, he held post-doctoral positions at ESSEC, École Polytechnique, and Sorbonne Paris Nord University. His research focuses on combinatorial optimization, applied mathematics, and their real-world applications in logistics, urban planning, and resource management. Education: PhD in Computer Science, University of Buenos Aires (2016) MSc in Computer Science, University of Buenos Aires (2009) BSc in Computer Science, University of Buenos Aires (2008) Research Interests: His work emphasizes optimization algorithms for sustainable urban logistics, such as integrating public transport for last-mile deliveries to reduce CO2 emissions, and integer linear programming applications in hydrocarbon field planning, waste management, and salmon farm operations. He also explores theoretical challenges in graph coloring, polyhedral studies, and sparse approximation techniques. Recent Article Trends: Recent publications highlight advancements in column generation methods for multi-echelon delivery systems, branch-and-price algorithms for clustering and coloring problems, and strategic planning for freight integration with public transit. His work bridges theoretical optimization with practical urban and industrial challenges, emphasizing sustainability and efficiency. Advising & Grants: He has supervised multiple master’s theses, including works on leaf collection optimization and logistics routing. While specific grant details are not detailed, his research projects reflect collaboration with industry partners like petroleum companies in Argentina and urban management initiatives. Labs/Teams: His current project focuses on leveraging public transport’s excess capacity for urban parcel delivery, part of broader efforts to enhance sustainable logistics through mixed-integer programming and operational research techniques.

Martin Skutellaمشاهده پروفایل

استاد

• Combinatorial Optimization
• Network Flows
• Discrete Mathematics
• +۶ مورد دیگر

Prof. Martin Skutella holds the Einstein Professorship in Mathematics and Computer Science at Technische Universität Berlin (TU Berlin), where he is part of Faculty II – Mathematics and Natural Sciences and the Department of Mathematics. His research focuses on combinatorial optimization, network flows, scheduling, and algorithmic game theory. He leads major initiatives such as the Berlin Mathematics Research Center MATH+ and previously served as Chair of MATHEON. Skutella is renowned for contributions to dynamic network flows, evacuation planning, and scheduling under uncertainty. His work bridges theoretical advancements with practical applications, including transportation systems, robotic welding, and gas network optimization. He has been recognized with awards like the Einstein Professorship (2015) and the Best Teaching Award (2004). Skutella advises numerous PhD students and collaborates internationally on projects funded by DFG, EU, and industry grants. Key roles include organizing the 2012 International Symposium on Mathematical Programming and editing the Notices of the German Mathematical Society. His lab focuses on discrete optimization and graph algorithms, with applications in logistics, transportation, and energy systems.

Andrea Gasparriمشاهده پروفایل

استاد

• Robotics
• Sensor Networks
• Networked Multi-Agent Systems
• +۴ مورد دیگر

Andrea Gasparri is a Full Professor at the Department of Civil, Computer Science, and Aeronautical Technologies Engineering, Roma Tre University, Rome, Italy. His research spans robotics, sensor networks, networked multi-agent systems, and precision agriculture. Coordinated Robotics and Networked Multiagent Systems Laboratory. Active in H2020 projects like CANOPIES and PANTHEON, focusing on precision farming. Recipient of the Young Author Award (Agricontrol Conference 2019) and ICRA2017 Best Paper Finalist. Research Interests: Robotics and autonomous systems Distributed control and estimation Multi-agent coordination in precision agriculture Swarm intelligence Networked cyber-physical systems Federated learning for agricultural robots Recent Publications show expertise in finite-time consensus, distributed optimization, and precision agriculture applications, with a focus on robustness and scalability in multi-robot systems. Scientific Awards: Young Author Award, Agricontrol Conference 2019 Best Multi-Robot Systems Paper Finalist, ICRA2017 He has contributed to projects involving UAV path planning, robotic spraying systems, and demand-side management in agricultural cyber-physical systems.

Mark de Bergمشاهده پروفایل

استاد

• Algorithms
• Computational Geometry
• Geometric Networks
• +۳ مورد دیگر

Mark de Berg is a Full Professor at TU/e and the chair of the TU/e Algorithms Group, focusing on algorithms and spatial data structures. His research spans computational geometry, FPT algorithms, and geometric networks, emphasizing practical applications in complex networks and design. He holds a PhD from Utrecht University (1992) and is co-author of seminal textbooks on computational geometry. Department: Mathematics and Computer Science Institute: EAISI Research Groups: Algorithms, EAISI Foundational His work addresses algorithmic challenges in spatial data efficiency, with over 225 publications and notable grants like the VICI grant and leadership in the Gravitation Program Networks. He serves on editorial boards and conference committees, advancing algorithmic research globally. Recent work explores approximation algorithms for geometric coverage, truthful budget aggregation mechanisms, and geometric network optimization. His contributions bridge theory and application, influencing both academic and industrial domains. Awards: VICI grant from NWO Teaching includes courses on algorithms, geometric algorithms, and discrete mathematics. His research extends to motion planning for robots and diverse combinatorial solutions, showcasing interdisciplinary impact.

Eddie Wadbroمشاهده پروفایل

استاد

• Topology Optimization
• Inverse Problems
• Large-Scale Optimization
• +۷ مورد دیگر

Eddie Wadbro is a Professor of Mathematics at Karlstad University , Sweden. His research focuses on design optimization , inverse problems , and large-scale computational methods , with applications in acoustic device design , fluid dynamics , and mechanical engineering . He leads projects in topology optimization of antennas, loudspeakers, and acoustic systems, often involving multi-physics simulations. Key contributions include: Development of minimum-size control techniques for topology optimization Investigation of waveguide acoustic black holes for enhanced wave focusing Optimization of superhydrophobic surfaces to delay turbulent transition in boundary layers Integration of deep reinforcement learning for real-world control systems His work bridges applied mathematics and engineering , with publications in high-impact journals and collaborations across disciplines like computational fluid dynamics and machine learning.

Dag Hauglandمشاهده پروفایل

استاد

• Combinatorial Optimization
• Global Optimization
• Network Flow Models
• +۷ مورد دیگر

Dag Haugland is a Professor in the Department of Informatics at the University of Bergen, Norway, where he conducts research and teaches in optimization, particularly combinatorial and global optimization with applications in network flows, energy systems, and logistics. He is affiliated with the Optimization research group and maintains an active research profile with recent publications in top-tier journals. Research Interests: His work focuses on Combinatorial Optimization , Global Optimization , and Network Flow Models , applying mathematical programming techniques to real-world problems in offshore wind energy, gas pipeline transportation, wireless networks, and vehicle routing. His research emphasizes integer programming, polyhedral analysis, and algorithmic design. Recent Research Trends: His latest publications (2023–2024) address tighter bounds in broadcast time problems and hydropower scheduling, reflecting a continued focus on theoretical and applied optimization in communication and energy networks. Earlier works (2016–2020) explore pooling problems, offshore wind farm cable layouts, and portfolio optimization, demonstrating interdisciplinary reach. Scientific Contributions: Extensive publication record in optimization and operations research. Supervision of multiple PhD and master’s students. Active involvement in conference proceedings and technical reports. Advising and Grants: Dag Haugland has supervised numerous students, including Marika Ivanova and Arne Klein, on topics such as offshore wind farm optimization and multicast tree problems. His work has been supported by Norwegian research funding, including the Research Council of Norway (project reference 249994). He contributes to academic service through conference organization (e.g., Norsk Informatikkonferanse) and collaborative research. Labs and Teams: He is a key member of the Optimization research group at the Department of Informatics, University of Bergen, which focuses on algorithmic and mathematical approaches to complex decision problems in engineering and industry.

Seth Pettieمشاهده پروفایل

استاد

• Algorithms
• Graph Theory
• Distributed Computing
• +۳ مورد دیگر

Seth Pettie is a faculty member in the Department of Electrical Engineering and Computer Science (EECS) at the University of Michigan. His research focuses on algorithms, graph theory, distributed computing, and data structures, with significant contributions to problems like minimum spanning trees, Davenport-Schinzel sequences, and energy complexity in radio networks. Research Interests : Algorithms, graph theory, distributed systems, combinatorics, and computational complexity. Students : Mentored numerous PhD students and postdocs, including Dingyu Wang, Shang-En Huang, and Yi-Jun Chang, some of whom have won prestigious awards like the Principles of Distributed Computing Doctoral Dissertation Award. Scientific Contributions : Authored over 15 recent articles (2021–2025) on topics spanning connectivity labeling, fraud detection, extremal combinatorics, and energy-efficient distributed algorithms. His work often bridges theoretical insights with practical applications in databases and network optimization. Awards : Recipient of the Outstanding Dissertation Award (2004) and Best Student Paper Award at ICALP 2002. His students have also received recognition for their work. Professional Service : Organized workshops (e.g., Dagstuhl, Bertinoro) and served on steering/editorial/program committees for major conferences like SODA, STOC, and PODC.

Marco Veneroniمشاهده پروفایل

دانشیار

• Partial differential equations
• Calculus of variations
• Liquid crystals
• +۱۸ مورد دیگر

Marco Veneroni is Associate Professor at the Department of Mathematics , University of Pavia . His research spans multiple fields including Partial Differential Equations , Calculus of Variations , and Optimal Transportation , with applications to materials science, biology, and engineering. Specializes in gradient flows and Wasserstein metrics for imaging and chemical processes Contributes to stochastic homogenization and elasto-plasticity models Research includes nematic liquid crystals , diblock copolymers , and cardiac electric field modeling Veneroni teaches courses like Mathematical Analysis and Advanced Mathematical Methods . He has supervised numerous students through tutoring programs and pre-course initiatives . His work involves collaborations across McGill University , TU Dortmund , and Università degli Studi di Pavia .

Joanna Tomasikمشاهده پروفایل

پژوهشگر

• Discrete optimization
• Algorithms
• Complexity theory
• +۱ مورد دیگر

Joanna Tomasik is a researcher affiliated with the Interdisciplinary Laboratory of Digital Sciences. Her work focuses on discrete optimization, algorithms, complexity theory, and performance evaluation. Research interests include network optimization, quantum computing applications, cloud energy efficiency, and combinatorial problem-solving. She has contributed to areas like simulated annealing, Latin hypercube designs, and optical network performance. Her publications reveal a trend toward algorithmic innovation, particularly in wireless and ad hoc networks, cloud computing, and quantum annealing. Topics span resource allocation, congestion control, and energy-aware scheduling.

Brian Van Kotenمشاهده پروفایل

استادیار

• Applied Probability
• Numerical Analysis
• Computer Simulation of Molecular Systems
• +۴ مورد دیگر

Brian Van Koten is an Assistant Professor in the Department of Mathematics and Statistics at the University of Massachusetts Amherst. His research focuses on applied probability and numerical analysis, with applications to molecular systems simulation, variance reduction in statistical physics, and numerical methods for saddle point identification. He teaches courses such as Math 652 (Numerical Analysis II), emphasizing computational methods for partial differential equations and optimization in machine learning. Key research interests include computer simulations of biological and materials systems, rare event sampling techniques, and atomistic/continuum coupling methods. He has presented work at institutions like the Institute for Computational and Experimental Research in Mathematics (ICERM) and the Institute for Pure and Applied Mathematics (IPAM), focusing on topics like stratification for Markov chain Monte Carlo and the string method for minimum energy paths. Van Koten's work bridges theoretical numerical analysis with practical computational challenges, addressing issues in multiscale modeling and error analysis. His teaching philosophy integrates advanced numerical methods with real-world applications, supported by detailed course materials on topics like finite difference methods and optimization algorithms for large-scale data science problems.