جست‌وجوی استاد و پژوهشگر

Maurizio Bocciaمشاهده پروفایل

دانشیار

• Operations Research
• Optimization
• Logistics
• +۵ مورد دیگر

Maurizio Boccia is an Associate Professor at the University of Naples Federico II, affiliated with the Department of Electrical Engineering and Information Technologies. His research focuses on optimization methodologies and their applications in logistics, transportation systems, and healthcare management. He holds a Ph.D. in Operations Research and has contributed extensively to the development of exact and heuristic algorithms for complex scheduling and routing problems. Key research interests include combinatorial optimization, vehicle routing problems (e.g., TSP variants with drones), automated guided vehicle (AGV) scheduling, and optimization in healthcare settings. He has also explored educational strategies, such as the OPS4Math project, which integrates optimization techniques into mathematics education. His work frequently involves mixed-integer linear programming (MILP), branch-and-cut algorithms, and matheuristics. Notable applications include railway dispatching for the Union Pacific Railroad, operating room planning in hospitals, and logistics network design. He has collaborated on real-world projects, such as improving exam scheduling in Norway and optimizing postal collection routes in rural areas. Boccia’s publications span over two decades, with contributions to both theoretical advancements (e.g., lifted cover inequalities, Fenchel cuts) and practical implementations (e.g., web cache location models, traffic sensor placement). His research often bridges theoretical optimization frameworks with real-world operational challenges.

Marcello Carioniمشاهده پروفایل

استادیار

• Optimal Transport
• Inverse Problems
• Machine Learning
• +۴ مورد دیگر

Marcello Carioni is an Assistant Professor in Mathematics at the University of Twente, affiliated with the Mathematics of Imaging & AI (MIA) group and the SACS group within the Faculty of Electrical Engineering, Mathematics and Computer Science. His research focuses on optimal transport theory, inverse problems, calculus of variations, and their applications in machine learning and imaging. He holds a PhD in Mathematics from the Max-Planck Institute for the Mathematics in the Sciences (Leipzig), preceded by a M.S. and B.S. in Mathematics from the University of Parma. Education: B.S. (2009, University of Parma), M.S. (2012, University of Parma), PhD (2017, Max-Planck Institute) Research interests include: Dynamic optimal transport and preferential path analysis Sparsity in variational regularization Applications to medical imaging and neural networks Generalized conditional gradient methods Recent work emphasizes sparsity optimization for dynamic inverse problems, Lipschitz spaces in neural networks, and end-to-end reconstruction techniques. His 2025 preprints explore Wasserstein curves and distributionally robust optimization. He has advised PhD students including Christian Amend (2024) and Floor van Maarschalkerwaart (2024).

• Scheduling
• Supply Chain Management
• Operational Research
• +۴ مورد دیگر

Ali Skaf is an Associate Professor at CESI LINEACT, affiliated with the Engineering and Numerical Tools Research team. His research focuses on scheduling problems, supply chain resilience, multi-objective optimization, and port logistics. He holds a PhD in Automatics from Belfort Montbéliard University of Technology (2020) and a Master's in Computer Science from the Lebanese-French University (2012). His educational activities span Computer Science, Operational Research, and Mathematics across engineering cycles and preparatory courses. Skaf's work emphasizes port terminal operations, including quay crane scheduling, yard truck coordination, and mixed-integer programming models for container handling. His recent publications (2018–2022) highlight contributions to NP-completeness analysis of scheduling problems and optimization algorithms for maritime logistics. His research integrates exact methods, heuristics, and computational complexity analysis to enhance supply chain efficiency. No scientific awards are explicitly mentioned, though his active publication record reflects ongoing scholarly engagement. His advising and grants details are not documented here, but his team's focus on numerical tools and engineering applications suggests collaborative projects in industrial automation and logistics systems. Labs/Teams: Member of the Engineering and Numerical Tools Research team at CESI LINEACT, focusing on applied operational research and port logistics solutions.

Charles SOUSSENمشاهده پروفایل

استاد

• Inverse Problems
• Sparse Approximation
• Signal Processing
• +۳ مورد دیگر

Charles SOUSSEN is a Professor at CentraleSupélec and L2S (Laboratoire des Signaux et Systèmes). He holds a PhD from Université Paris-Sud (2000) and a Habilitation from Université de Lorraine (2013). His research focuses on inverse problems, sparse approximation, signal processing, and biomedical applications. He leads the Signals and Statistics Pole at L2S since 2020 and has coordinated the French ANR project BECOSE (2016–2020). His work spans areas like hyperspectral imaging, optical spectroscopy, and algorithm development for sparse representations. Education : Ecole Nationale Supérieure en Informatique et Mathématiques Appliquées (ENSI-MAG), Grenoble (1996) PhD in Signal and Image Processing, L2S, Université Paris-Sud (2000) Habilitation à Diriger des Recherches, Université de Lorraine (2013) Research Interests : His primary research areas include inverse problems, sparse approximation, signal processing algorithms, biomedical imaging, and hyperspectral data analysis. He develops methods for signal deconvolution, source separation, and modal analysis with applications in astronomy, geology, and medical diagnostics. Grants & Projects : ANR BECOSE (2016–2020): Sparse approximation algorithms for ill-conditioned inverse problems Labs & Teams : He is affiliated with L2S and leads the Signals and Statistics research group. His work involves collaborations in biomedical optics, hyperspectral analysis, and algorithm development for engineering and medical applications.

Benjamin Bériمشاهده پروفایل

استاد

• Topological Phases
• Condensed Matter Theory
• Quantum Information
• +۳ مورد دیگر

Professor Benjamin Béri is a faculty member in the Cavendish Laboratory (Department of Physics) at the University of Cambridge, holding the position of Professor of Theoretical Physics. His research group operates within the Theory of Condensed Matter (TCM) Group at the Ray Dolby Centre, JJ Thomson Avenue, Cambridge. Béri's research centers on topological phases of quantum matter, where collective behavior of particles produces nonlocal order characterized by topological invariants rather than conventional parameters. His work explores systems ranging from solid-state electron devices to ultracold atomic gases, with emphasis on phenomena like impurity-immune conducting boundaries and exotic quasiparticles for quantum information processing. Key areas include Majorana fermions, topological quantum computing, and non-equilibrium dynamics in many-body systems. Recent publications reveal a pronounced focus on topological quantum error correction (surface codes, Floquet codes), Sachdev-Ye-Kitaev model applications, and braiding protocols for Majorana zero modes. His group demonstrates consistent innovation in connecting abstract topological concepts to experimental quantum computing architectures, particularly through studies of delocalization effects and dynamical phase transitions. Professor Béri leads an active research team comprising postdoctoral fellows Dr. Jan Behrends and Dr. Jack Kemp, alongside research students Mircea Bejan, Vedant Motamarri, Cory Aitchison, and Campbell McLauchlan within the TCM Group's framework.

Christoph Koutschanمشاهده پروفایل

استاد پژوهشی

• Computer Algebra
• Algorithmic Combinatorics
• Symbolic Summation
• +۳ مورد دیگر

Christoph Koutschan is a Senior Fellow in the Symbolic Computation group at the Johann Radon Institute for Computational and Applied Mathematics (RICAM), Austrian Academy of Sciences. He holds a PhD in Mathematics from JKU Linz (2009) and a Habilitation in Mathematics (2017). His research focuses on computer algebra, algorithmic combinatorics, and symbolic summation/integration of holonomic functions. Koutschan has held positions at Tulane University (2009–2010), Tulane University (2009–2010), and INRIA (2011–2012) before joining RICAM in 2012. Education: PhD in Mathematics (2009), Research Institute for Symbolic Computation (RISC), JKU Linz Habilitation in Mathematics (2017), JKU Linz M.Sc. in Computer Science (2005), FAU Erlangen-Nürnberg Studies in Computer Science (1999–2005), FAU Erlangen-Nürnberg Research Interests: Koutschan's work revolves around symbolic computation with a focus on holonomic functions, combinatorial identities, and applications in mathematical physics. His tools include algorithmic methods for summation/integration, differential equations, and computational algebra. Publications & Awards: Over 40 journal articles, including work on determinant evaluations, Feynman amplitudes, and combinatorial enumeration. Notable awards include the David P. Robbins Prize (2016), ISSAC's Distinguished Software Presentation Award (2016), and the ACA Early Researcher Award (2021). Grants & Collaborations: Lead or co-PI in projects like 'Certificate-free Summation and Integration' (SFB F50, 2017–2021) and 'Security and Safety for Shared AI' (FFG COMET-K2, 2020–2023). Active in editorial roles at Journal of Symbolic Computation and Annals of Combinatorics . Labs/Teams: Core member of RICAM's Symbolic Computation group, collaborating internationally with institutions like INRIA, Tulane University, and TU Wien.

• Applied Mathematics
• Graph Theory
• Combinatorics
• +۳ مورد دیگر

Prof. Angelika Wiegele is a full Professor at the Department of Mathematics at Alpen-Adria-Universität Klagenfurt. She serves as a member of the university senate and head of the Institut für Mathematik. Her research focuses on semidefinite optimization, combinatorial optimization, and their applications in graph theory and operations research. She holds a Dipl.-Ing. (2001) and Dr. techn. (2006) from Alpen-Adria-Universität Klagenfurt, with thesis supervision by Franz Rendl. Her academic career includes positions at TU Graz, IASI-CNR Rome, and Universität zu Köln, as well as a visiting professorship at Università di Roma Tor Vergata. Her educational background includes studies at City University London (Erasmus) and TU Eindhoven (Leonardo project). Recent work emphasizes SDP-based methods for graph partitioning, edge expansion, and combinatorial problems, with a focus on high-performance computing solutions. Her research bridges theoretical optimization with practical applications in network design and algorithmic development. Publications from 2021–2024 highlight advancements in semidefinite programming for graph partitioning, SDP-based bounds for cutwidth, and exact solvers for clustering problems. She contributed to the BiqBin and SOS-SDP solvers for quadratic optimization and co-edited special journal issues on mixed-integer nonlinear optimization. Her work often appears in top operations research journals and conference proceedings. Prof. Wiegele has held global faculty positions at the University of Cologne (2022–2024) and is active in international research collaborations through PRACE high-performance computing initiatives. She maintains an ORCID profile and a university webpage with teaching and research materials.

Ely Poratمشاهده پروفایل

دانشیار

• Algorithms
• Data Structures
• Streaming Algorithms
• +۵ مورد دیگر

Ely Porat is an Associate Professor at the Department of Computer Science, Bar-Ilan University, where he has been since 2000. He holds visiting professorships at the University of Michigan and Tel Aviv University, and has worked at Google (Mountain View in 2007 and Tel Aviv in 2011). His academic contributions include redefining the BSc degree in Computer Science at Bar-Ilan University and significant involvement in teaching committees. Research Interests: Algorithms and Data Structures Streaming Algorithms Pattern Matching Coding Theory Compressed Sensing His work focuses on advancing efficient algorithms for data processing, with applications in information retrieval, signal processing, and bioinformatics. He has organized multiple conferences including Stringology (2009–2011), ICALP2011GT, and UM Coding. He has served on program committees for CPM, SPIRE, and ESA. Advising & Collaboration: Current advisees include Ariel Shiftan, Guy Feigenblat, and others. Former students include Ohad Lipsky and Klim Efremenko. He hosts short-term researchers from abroad and collaborates with institutions like Google and Weizmann Institute. Publications span FOCS , STOC , ICALP , and other top venues, emphasizing theoretical foundations and practical algorithmic solutions.

Amihood Amirمشاهده پروفایل

استاد

• Algorithms Design and Analysis
• Multidimensional Pattern Matching
• Knowledge Discovery Algorithms
• +۳ مورد دیگر

Amihood Amir is a Professor of Computer Science at the Department of Computer Science within the Faculty of Exact Sciences at Bar-Ilan University, Israel. His research focuses on algorithm design, pattern matching, knowledge discovery, real-time systems, and computational biology. He has served on program committees for conferences including STOC, CPM, WABI, and SPIRE, among others. Education: No explicit educational details provided in the text. His research interests emphasize advanced algorithmic techniques with applications in multidimensional pattern matching and computational biology. He has contributed to knowledge discovery algorithms and real-time system optimizations. Teaching responsibilities include courses such as Pattern Matching Algorithms (CS 663), Algorithms II (89-332), Data Structures (89-120), and Analysis of Algorithms (89-974-01). He has also presented invited addresses at conferences and maintained active participation in academic committees. No specific grants or lab affiliations are detailed in the provided texts.

• Operations Research
• Transportation Logistics
• Mathematical Modelling
• +۶ مورد دیگر

Dr. Dmytro Krushynskyi is an Associate Professor in the Department of Operations Research and Logistics at Wageningen University & Research. He holds a M.Sc. in Applied Mathematics from the National Technical University of Ukraine 'KPI' and a Ph.D. in Economics and Business from the University of Groningen. After his Ph.D., he conducted postdoctoral research at Eindhoven University of Technology. His research focuses on applied operations research, mathematical modeling of complex systems, and optimization problems in transportation and logistics, including routing, network design, and cell formation in manufacturing systems. Education: M.Sc. in Applied Mathematics, National Technical University of Ukraine 'KPI' Ph.D. in Economics and Business, University of Groningen Postdoctoral Researcher at Eindhoven University of Technology (3 years) Research Interests: Optimization algorithms and heuristics Transportation logistics and ridesharing Cell formation in group technology Network design and arc routing Complex systems modeling (e.g., pedestrian crowd dynamics) Publications: His work emphasizes heuristic methods for routing problems, cell formation optimization, and computational complexity analysis. Recent trends include transportation logistics and ridesharing systems, reflecting a shift toward applied real-world challenges in operations research. Grants & Awards: None explicitly mentioned in the provided materials. Teaching & Supervision: He teaches courses in decision science, operations research, and logistics, including MSc internships and thesis supervision. No listed advisees or students are provided. Labs & Teams: Active within the Operations Research and Logistics (ORL) group at Wageningen University, contributing to interdisciplinary projects in logistics optimization and complex systems analysis.

• Algorithms
• Graph Theory
• Matroid algorithms
• +۸ مورد دیگر

Fedor Fomin is a Professor in Algorithms at the Department of Informatics, University of Bergen, Norway, since 2002. His research focuses on fundamental problems in computer science and mathematics, particularly in algorithm design and graph theory. Research Interests: His work spans advanced algorithmic techniques such as Matroid algorithms Algorithmic graph minors Treewidth and its applications Exact and exponential time algorithms Pursuit-evasion games and graph searching Parameterized algorithms and kernelization Graph coloring Publications: He has authored over 150 peer-reviewed journal articles in venues like J. ACM, SIAM J. Computing, and Combinatorica, alongside 160 conference papers in top-tier events including FOCS, STOC, and AAAI. His research demonstrates expertise in bridging theoretical computer science and discrete mathematics. Scientific Awards: EATCS Fellow (2019) ERC Advanced Investigator Grant (2010) EATCS-IPEC Nerode Prize 2017 (with F. Grandoni and D. Kratsch) EATCS-IPEC Nerode Prize 2015 (with E. D. Demaine, M. T. Hajiaghayi, and D. M. Thilikos) Norway's Outstanding Young Investigator Award (2005) Grants: He has secured major grants from The Research Council of Norway (NFR), the Russian Ministry of Education and Science (mega-grant), and the European Research Council (ERC) as Principal Investigator.

• Real Algebraic Geometry
• Optimization
• Computational Complexity
• +۵ مورد دیگر

Cordian Riener is a Professor of Mathematics at UiT The Arctic University of Norway, serving as Pro-Dean for PhD education within the Faculty of Science and Technology. He leads the Algebra group and co-directs the Lie-Størmer Center for Fundamental Structures in Computational and Pure Mathematics. His academic roles include chairing the Norwegian Mathematics Council and membership on the Abel Prize board. Riener holds a Habilitation in Mathematics from Universität Konstanz (2018) and a PhD from Goethe University Frankfurt (2011), with prior roles including Acting Professor at Universität Konstanz and Aalto Science Institute Fellow in Helsinki. His research focuses on real algebraic geometry, optimization, computational complexity, and symmetry exploitation in algorithms. Notable contributions include work on semidefinite programming, polynomial optimization under symmetry, and cohomology of semi-algebraic sets. Recent publications address connectivity in symmetric sets, orbit space descriptions, and applications of symmetry in trigonometric polynomials. Riener actively organizes conferences like ISSAC and MEGA, and contributes to educational initiatives such as the Norwegian Mathematics Council.

• Computer Science
• Software Engineering
• Systems Research
• +۲ مورد دیگر

Abir De is a Postdoctoral Researcher affiliated with the Max Planck Institute for Software Systems (MPI-SWS). Their research focuses on core areas of computer science and software systems engineering, though specific projects are not detailed in the provided text. No educational background or prior career history is explicitly mentioned. Affiliation: Max Planck Institute for Software Systems Research interests appear centered around foundational aspects of software systems, though exact subfields remain unspecified. No recent publications or grant details are provided in the available content. No scientific awards or academic recognitions have been documented here. Abir has not listed any advisees or students associated with their work.

Pradeesha Ashokمشاهده پروفایل

دانشیار

• Theoretical Computer Science
• Algorithms
• Graph Theory
• +۳ مورد دیگر

Dr. Pradeesha Ashok is an Associate Professor and Controller of Examinations at IIIT Bangalore. She holds a Ph.D. from the Indian Institute of Science, Bangalore, and previously worked as a Postdoctoral Fellow at the Institute of Mathematical Sciences, Chennai. Her research focuses on Theoretical Computer Science, with specializations in Algorithms, Graph Theory, Combinatorics, and Parameterized Complexity. Her research interests include geometric problems such as polygon guarding, covering, and packing, as well as conflict-free coloring in graphs and hypergraphs. She has contributed to developing exact and parameterized algorithms for these problems. Dr. Ashok has also taught courses like Exact and Parameterized Algorithms, Graph Theory, and Design and Analysis of Algorithms. Her publications span conferences like IWOCA, CSR, and COCOON, as well as journals such as Discrete Applied Mathematics and SIAM Journal on Discrete Mathematics. Her work emphasizes algorithmic efficiency and combinatorial optimization in geometric and graph-theoretic contexts. Dr. Ashok has advised several students, including PhD candidates and MTech thesis students, focusing on topics like the chromatic art gallery problem and parameterized complexity of coloring problems. She is affiliated with the Department of Computer Science & Engineering at IIIT Bangalore and contributes to the institute's academic governance through her role as Controller of Examinations. Her research also extends to geometric separability, bichromatic covering problems, and experimental studies of the Steiner Tree problem. She collaborates on projects involving theoretical and applied aspects of computational geometry and algorithms.

• Operations Research
• Logistics Optimization
• Heuristic Algorithms
• +۳ مورد دیگر

Associate Professor Dario Pacino is affiliated with the Technical University of Denmark (DTU), specifically within the Department of Technology, Management and Economics under the Operations Research Section. His research focuses on optimization challenges in logistics and transportation systems, including container stowage planning, berth allocation, and the application of AI techniques like reinforcement learning. He holds a position at DTU's Management Science Division and is based in Lyngby, Denmark. His work emphasizes heuristic and metaheuristic algorithms for solving complex logistics problems. Notable areas include Roll-on/Roll-off (RoRo) ship stowage planning, dynamic berth allocation, and discrete event simulation for healthcare logistics. He has contributed to mathematical models addressing draft limits in shipping and stochastic fleet optimization for urban mobility. Publications from 2022 onward highlight advancements in reinforcement learning integration with traditional operations research methods, demonstrating a trend toward AI-driven logistics solutions. His work bridges theoretical optimization with practical applications in maritime and healthcare sectors. No scientific awards are explicitly listed in the provided text. His advising and grants sections remain unspecified, though his research collaborations are evident through multi-institutional projects. He is part of the Operations Research Section's research team, focusing on decision support systems and optimization under uncertainty.