جست‌وجوی استاد و پژوهشگر

• Management Science
• Operations Research
• Optimisation Algorithms
• +۷ مورد دیگر

Professor Celia Glass is a faculty member at Bayes Business School (formerly Cass), City, University of London, where she holds the title of Professor of Management Science within the Faculty of Actuarial Science and Insurance. She has been at the institution since 1999 and was awarded a personal Chair in 2003 for Operational Research and later in 2016 for Management Science. She earned her BSc and PhD from King's College London. She has held visiting appointments at Queen Mary University of London and has been actively involved in professional societies. Her research focuses on Management Science and Operations Research , particularly in scheduling, optimization, and logistics. Key areas include doctor and nurse rostering for the NHS, call centre tour scheduling, optimization in telecommunications (wireless mesh and optical networks), combinatorics of acyclic graphs, and production logistics in food safety laboratories. Her work is often supported by EPSRC grants and has practical industrial applications. Her recent publications demonstrate a consistent trend in combinatorial optimization and scheduling theory , with applications spanning healthcare , telecommunications , and manufacturing . Her work frequently involves developing and analyzing algorithms for complex scheduling problems, often with real-world constraints like perishability, employee preferences, and network limitations. Fellow, Operational Research Society Fellow, London Mathematical Society Fellow, Institute of Mathematics and its Applications Member, EPSRC College She has supervised numerous PhD students, including Robert Schumacher, Roger Knight, and Ian B. Davies, on topics related to scheduling and optimization. She has secured research funding and performed significant consultancy work for organizations such as the NHS (Whittington Hospital), OfCom, and the Registered Nursing Homes Association, translating her academic research into practical solutions. She has also co-organized workshops and served on editorial boards for journals like European Journal of Operational Research and Operations Research for Health Care . Professor Glass is a key member of research teams focused on applying operational research to healthcare planning and industrial optimization. Her spin-out company, Nightglass Medical Rostering Ltd., provides customized rostering software for the NHS, showcasing the direct impact of her research.

Ozgur Akgunمشاهده پروفایل

مدرس ارشد

• Constraint Programming
• Data Mining
• Cloud Computing
• +۸ مورد دیگر

Dr Ozgur Akgun is a Senior Lecturer and Director of Impact at the School of Computer Science, University of St Andrews. His research focuses on Constraint Programming (CP), including the development of the automated constraint modelling tool Conjure. He explores applications of CP in areas like data mining, cloud computing, network science, logistics, and economics. Akgun teaches core CS modules for undergraduates and advanced courses in constraint programming, discrete optimization, and video games. He supervises PhD students and leads the VIP project 'Artificial Intelligence for Decision Making'. His work also spans functional programming, data linkage, and the intersection of machine learning with CP. Research interests include automated constraint modelling, algorithm selection/configuration, and data linkage using graph databases. Current projects involve cost-efficient algorithm training and frugal algorithm selection. He collaborates on interdisciplinary projects such as the ADR UK Programme and Modelling and Optimisation with Graphs. His teaching includes supervising undergraduate, master's, and PhD students, contributing to both academic and applied computer science education.

Karen Cairnsمشاهده پروفایل

مدرس

• Multistate modelling
• longitudinal data analysis
• optimisation problems
• +۴ مورد دیگر

Karen Cairns is a Lecturer at the School of Mathematics and Physics at Queen's University Belfast, affiliated with the Mathematical Sciences Research Centre. Based in the United Kingdom, she is actively involved in statistical methodology development with applications in healthcare and education. Key Research Areas: Multistate modeling, longitudinal data analysis, discrete event simulation, and educational technology Recent Activities: Presented conference papers on survival forests and hybrid teaching methods Collaborations: Active in healthcare modeling with applications to patient re-admissions Scientific Awards: Royal Statistical Society Fellow (2006) IOP Computational Physics PhD Prize (2004) IOP Quantum Electronics PhD Commendation (2004) Undergraduate academic awards (1998-2000)

• Statistical Physics
• Machine Learning
• Discrete Optimisation
• +۳ مورد دیگر

Vittorio Erba is a Lecturer at the Department of Physics within the School of Basic Sciences at École Polytechnique Fédérale de Lausanne (EPFL), with concurrent Scientist appointments across three laboratories: Statistical Physics of Computation Laboratory (SPOC1/SPOC2) and Information, Learning & Physics Laboratory (IDEPHICS2). His research integrates statistical physics methodologies with computational challenges, focusing on disordered systems applied to: Machine learning optimization Graph theory analysis Statistical inference frameworks Discrete computational problem-solving He teaches the graduate course 'Statistical physics of computation', training students in applying physics-derived tools to computational and statistical problems. Laboratory affiliations span both the School of Basic Sciences (IPHYS institute) and School of Computer and Communication Sciences (IINFCOM institute), reflecting interdisciplinary work at the physics-computation interface. Contact details remain active with office BSP 514 at Cubotron UNIL, Lausanne.

Arnau Padrolمشاهده پروفایل

دانشیار

• Discrete Geometry
• Combinatorial Geometry
• Convex Polytopes
• +۵ مورد دیگر

Arnau Padrol is an Associate Professor at the Department of Mathematics and Computer Science, Faculty of Mathematics, Universitat de Barcelona. He is a member of the Combinatorics Research Group, the Institut de Matemàtica de la Universitat de Barcelona (IMUB), and the Centre de Recerca Matemàtica (CRM). He is currently on leave from a similar position as Maitre de conférences HDR at Sorbonne Université’s Institut de Mathématiques de Jussieu - Paris Rive Gauche, where he was part of the Combinatoire et Optimisation team. His research lies at the intersection of discrete geometry, combinatorics, and algebra, with a focus on convex polytopes, oriented matroids, triangulations, and their applications. He explores deep structural properties of polytopes, including their realization spaces, deformation cones, and connections to cluster algebras and combinatorial optimization. His work often reveals surprising phenomena in higher dimensions, such as the existence of polytopes without rational realizations or with complete graphs. The recent publications reflect a strong trend in geometric combinatorics, particularly in the study of polytopal structures like associahedra, zonotopes, and hypersimplices, and their combinatorial and algebraic properties. Key themes include universality, realizability, and the interplay between geometry and combinatorics in high-dimensional spaces. Scientific Awards: No specific awards mentioned in the provided text. Advising and Grants: Arnau Padrol has supervised multiple PhD students and postdoctoral researchers, including Chiara Mantovani, Eva Philippe, Germain Poullot, and Leonardo Martínez-Sandoval. His supervision reflects an active research group in geometric combinatorics. He has been involved in research projects such as Geometry and Algebra in Combinatorics , Red Temática de Matemática Discreta y Algorítmica , Combinatorics and Complexity of Discrete Geometric Structures , and several ANR-funded projects including PAGCAP and CAPPS, indicating sustained grant support for his research. Labs and Teams: He is a core member of the Combinatorics Research Group at Universitat de Barcelona and is affiliated with IMUB and CRM. At Sorbonne Université, he was part of the Combinatoire et Optimisation team at IMJ-PRG. These affiliations place him within active research networks in discrete mathematics in Europe.

• Graph Theory
• Combinatorics
• Interconnection Networks
• +۶ مورد دیگر

Jose Luis Andres Yebra is a faculty member at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Applied Mathematics IV within the College of Engineering. He has been a central figure in research groups such as COMBGRAPH (Combinatorics, Graph Theory, and Applications) and OMGRAPH (Optimisation Methods on Graphs), contributing extensively to theoretical and applied mathematics. His research interests lie at the intersection of discrete mathematics and computer science, with a primary focus on Graph Theory , Combinatorics , and the design and analysis of Interconnection Networks . His work encompasses Spectral Graph Theory , Network Optimization , and the mathematical modeling of Dynamic Memory Networks , with applications in computer architecture and communication systems. The analysis of his 15 most recent publications reveals a consistent trajectory in extremal graph theory and network design. His research frequently addresses problems related to graph diameter, connectivity, and vulnerability, using spectral methods (eigenvalues of adjacency and Laplacian matrices) to derive bounds and construct optimal network topologies. A significant portion of his work is dedicated to solving the degree-diameter problem and related extremal problems in graph theory, demonstrating a deep commitment to foundational mathematical inquiry with practical implications for high-performance computing. Distinció Jaume Vicens Vives a la Qualitat de la Docència Universitària en la modalitat col·lectiva 7è premi a la Qualitat de la Docència Universitària Andres Yebra has played a significant role in academic service, serving on the editorial boards of prestigious journals such as Discrete Mathematics , IEEE Transactions on Computers , and Journal of Graph Theory . He has been a key participant in numerous competitive R&D+I projects, including national and regional grants focused on combinatorics and graph theory, which have supported his research and collaborations. His academic advising, while not explicitly detailed, is inferred from his long-standing mentorship within research groups and co-supervision of doctoral theses by colleagues. He has been an integral member of the COMBGRAPH and OMGRAPH research groups at UPC, fostering a collaborative environment for the study of combinatorial structures and their applications in network design and optimization. His work has often involved large collaborative efforts, particularly in national research projects, highlighting his role as a team leader and coordinator in the field of discrete mathematics.

• Machine Learning
• Deep Learning
• Recurrent Neural Networks
• +۷ مورد دیگر

Sepp Hochreiter is University Professor ( Univ.-Prof. ) at the Institute for Machine Learning , Johannes Kepler University Linz, and heads the LIT Artificial Intelligence Lab . He is Principal Investigator of several major Austrian and EU projects, including the FWF Cluster of Excellence “Bilateral Artificial Intelligence” and industry collaborations on certified deep learning, generative CFD simulation, and quantum-AI integration. Research interests revolve around the foundations and applications of deep learning: Recurrent neural networks: Inventor of LSTM; recent work on the xLSTM family that scales to billion-parameter language models while outperforming Transformers in efficiency. Uncertainty & reliability: New uncertainty estimation techniques for language generation, conformal prediction, and out-of-distribution detection using modern Hopfield networks. Generative models: Discrete diffusion samplers, generative drug-design pipelines (Bio-xLSTM), masked-image-model refiners, and physics-informed neural samplers. Applied AI: Few-shot learning in robotics, traffic prediction, medical risk modeling, antibody design, and climate downscaling. His 2022-2025 publications reveal a clear trend toward scaling recurrent architectures (xLSTM, FlashRNN, pLSTM) with hardware-aware optimisation, bridging theory and practice through energy-based models, and deploying trustworthy AI in safety-critical domains such as healthcare and autonomous systems. Scientific recognition & service: Keynote & invited speaker at NeurIPS, ICLR, ICML, and leading industry venues (60+ invited talks since 2022). Principal Investigator on 61 funded projects totalling >€50 M, spanning FWF, FFG, EU Horizon, and direct industry contracts. Editorial board and senior programme committee roles for top-tier ML conferences and journals. Supervision & team leadership: Leads a vibrant lab of 30+ PhD students and post-docs across machine learning theory, generative modelling, and AI for science. Active in joint doctoral training programmes with IST Austria and international partner labs.

Peter Keevashمشاهده پروفایل

استاد

• Extremal Combinatorics
• Graph Theory
• Hypergraphs / Set Systems
• +۵ مورد دیگر

Prof. Peter Keevash is a Professor of Mathematics at the University of Oxford and a leading expert in Combinatorics . His affiliation with the Mathematical Institute underscores his focus on theoretical and applied combinatorial research. Research interests include: Extremal Combinatorics Graph Theory Hypergraphs / Set Systems Algebraic and Probabilistic Methods in Combinatorics Random Structures Combinatorial Optimisation Combinatorial Number Theory Recent publications address problems in hypergraph theory, subspace designs, quasirandom graph decompositions, and probabilistic structures. These works span extremal combinatorics, probabilistic methods, and algebraic approaches to discrete mathematics. Scientific awards : European Prize in Combinatorics (2009)

• Theoretical Computer Science
• Discrete Mathematics
• Reachability Problems
• +۳ مورد دیگر

Professor Igor Potapov serves as a Professor of Computer Science at the University of Liverpool, leading the Algorithms, Complexity Theory and Optimisation research group and acting as Council Member for Networks Sciences & Technologies. He holds key administrative roles including Director of MSc Studies in CS with Year in Industry and module coordination for Efficient Sequential Algorithms (COMP309), MSc Industrial Project (COMP599), and MSc Placement Experience (COMP598). His research centers on theoretical computer science with emphasis on reachability problems in infinite state systems , distributed computing and pattern formation , combinatorial optimisation , and decidability questions for mathematical structures. Current interdisciplinary work includes Algorithmic Crystal Structure Prediction for Material Design (Royal Society APEX Award 2024-2026) and foundational studies in automata-matrix theory connections. His methodological approach integrates abstract algebra, topology, and computation theory to analyze computational boundaries. Recent publications (2024-2025) demonstrate strong convergence between theoretical frameworks and practical applications, particularly in robotics scheduling (addressing collision avoidance and safety verification) and mathematical decidability (matrix semigroups, linear recurrence systems). These works bridge computational geometry with distributed algorithm design, revealing novel complexity boundaries in reachability analysis. His scientific recognition includes: Royal Society Apex Award (2024-2026) for Algorithmic Crystal Structure Prediction Royal Society Leverhulme Trust Senior Research Fellowship (2020-2021) for "Cornerstones of Reachability" As an active grant recipient, he manages multiple projects including Algorithmic Intelligence for Life, Society and Science (Royal Society 2024-2026) and UoL-SumDU Collaboration for Digitalisation of Ukraine (Research England 2023-2024). He supervises thesis work on crystal structure prediction and distributed shape formation while serving on examination committees for Oxford, Leicester, and Gran Sasso institutions. He co-leads the Science for Ukraine initiative's UK branch, developing academic mentoring programs and research twinning partnerships between UK and Ukrainian universities. His editorial work spans Fundamenta Informaticae (2020-present) and Lecture Notes in Computer Science (2009-2013), alongside conference organization for the Reachability Problems series.

Daniel Karapetyanمشاهده پروفایل

استادیار

• Combinatorial Optimization
• Artificial Intelligence
• Data Science
• +۱۷ مورد دیگر

Daniel Karapetyan is an Assistant Professor at the School of Computer Science , University of Nottingham, and a member of the Computational Optimisation and Learning Lab . His research spans Artificial Intelligence , Data Science , Operational Research , and Optimization , with applications in transportation , logistics , satellite mission planning , and access control . Education: PhD in Computer Science, Royal Holloway, University of London (2007–2010) BSc/MSc in Computer Science, Bauman Moscow State Technical University (2001–2007) His research interests focus on the automated design of heuristic algorithms , multi-objective optimization , and parameter tuning for machine learning and combinatorial problems. He integrates Large Language Models (LLMs) and hyperparameter optimization into practical systems, addressing real-world challenges in airport sequencing , TV ad scheduling , and electric vehicle range prediction . Notable scientific contributions include the CORS Practice Prize (2014) for ferry scheduling work and pioneering fixed-parameter algorithms in security-related optimization problems. His publications emphasize combinatorial optimization , Markov Chain methods , and automated algorithm configuration . Teaching roles include module convener for COMP3008: Knowledge Representation and Reasoning , personal tutoring for COMP1002 , and supervision of COMP2002: Software Engineering Group Project . He also offers PhD supervision in areas like AI for optimization , interpretability in ML , and emergency department coordination .

• Algorithms
• Combinatorial Optimization
• Discrete Metric Spaces
• +۷ مورد دیگر

Hubert Tsz-Hong Chan is an Associate Professor in the Department of Computer Science, School of Computing and Data Science at The University of Hong Kong. He earned his PhD in 2007 from Carnegie Mellon University under the supervision of Anupam Gupta, followed by post-doctoral research at the Max-Planck-Institut für Informatik (2007-2009). Education PhD in Computer Science, Carnegie Mellon University, 2007 Research Interests Dr Chan's research lies at the intersection of algorithms , combinatorial optimisation , discrete metric spaces , and security & privacy . A recurring theme is the design of provably efficient approximation algorithms for geometric and graph-theoretic problems under realistic or adversarial settings. Representative contributions include polynomial-time approximation schemes (PTAS) for TSP and Steiner Forest in doubling metrics, spectral analysis of hypergraph Laplacians, and foundational work on differential obliviousness and oblivious RAM. Publications & Trends With more than 80 peer-reviewed papers in premier venues such as JACM , SIAM Journal on Computing , Algorithmica , FOCS , SODA , EUROCRYPT , ASIACRYPT , CCS , and WWW , his recent output (2018-2021) demonstrates a shift toward privacy-preserving algorithms, differential obliviousness, and socially-aware optimisation models, often combining rigorous theory with practical datasets like Netflix and Twitter. Scientific Awards IPDPS 2019 Best Paper Award WWW 2018 Honorable Mention Students & Mentoring Dr Chan has successfully graduated 17 PhD and MPhil students and currently mentors 7 PhD candidates and 1 MPhil student. His graduates have secured academic and industry positions worldwide, and their theses frequently build on his funded projects. Research Funding & Labs Since 2012 he has been Principal Investigator on 12 competitive grants from the Hong Kong Research Grants Council (RGC), totalling more than HK$8 million, spanning topics from privacy-preserving aggregation to Byzantine-resilient federated learning. While no dedicated laboratory name is advertised, his group operates within the Security & Privacy and Algorithms Labs in the Department of Computer Science.

Marcel Mongeauمشاهده پروفایل

استاد

• Operations Research
• Global Optimization
• Numerical Optimization
• +۵ مورد دیگر

Marcel Mongeau is a Professor in Operations Research at the École Nationale de l'Aviation Civile (ENAC) in Toulouse, France, affiliated with the OPTIM team under the MORO axis . His research focuses on operational research, global optimization, numerical optimization, and mixed-integer programming, with applications in aeronautics, chemical engineering, life sciences, and other industries. Education PhD in Combinatorics & Optimization (1991), University of Waterloo, Canada MSc in Mathematics (1987), University of Montreal, Canada BSc in Mathematics (1985), University of Montreal, Canada Habilitation to Direct Research (2003), Paul Sabatier University, France Research Interests : Mongeau's work addresses complex optimization problems through mathematical modeling and algorithm development. Key areas include aircraft trajectory planning, contrail avoidance, stochastic programming, and multi-modal transportation optimization. His methodologies integrate exact optimization, heuristic approaches, and computational modeling. Publications Trends : Recent publications emphasize air traffic flow management , contrail-safe trajectories , stochastic aircraft scheduling , and multi-modal transportation synchronization . Applications span environmental impact minimization, machining efficiency, and climate-aware aviation planning. Scientific Recognition : He is a Member of the Academy of Technologies , reflecting his contributions to applied mathematical sciences. Advising & Grants : Mongeau has supervised PhD students and postdoctoral fellows, particularly in optimization and operational research. His research has been supported through collaborations with institutions like IBM, INRIA, and CNRS. Labs & Teams : He is associated with the OPTIM team at ENAC and previously worked with the MOGISA group at LAAS-CNRS and the Optimisation & Interactions group at Paul Sabatier University.

Archontis Giannakidisمشاهده پروفایل

مدرس ارشد

• Machine Learning
• Deep Learning
• Biomedical Image Analysis
• +۸ مورد دیگر

Archontis Giannakidis is a Senior Lecturer in Data Science at the School of Science and Technology , Nottingham Trent University (NTU). He leads modules in Discrete Mathematics & Computational Complexity (Year 2) and Convex Optimisation (Year 3), while supervising undergraduate and Masters dissertations. Education: PhD in Electronic Engineering (Inverse Problems), University of Surrey (2010) Research Interests focus on applying Deep Learning and Machine Learning to biomedical data processing, particularly in Biomedical Image Analysis , Convolutional Networks , and Diffusion MRI . His work emphasizes automating intellectual tasks, efficient data representation, hidden pattern discovery, and decision optimization in healthcare and environmental contexts. Recent Article Trends highlight his expertise in 2D echocardiography-based cardiac quantification , MRI-driven ACL tear diagnosis , and landslide-tsunami prediction via geometry-invariant machine learning. These studies often integrate uncertainty modeling, attention mechanisms, and lightweight architectures for clinical and environmental applications. Scientific Awards : Fellow of the Higher Education Academy (FHEA) Advisory Roles include mentoring PhD students Tuan Aqeel Bohoran (Marie Skłodowska-Curie-funded) and David Gwillym Jenkins (internal funding), alongside visiting/external PhD candidates Michael Lystbaek (Aarhus University) and Athanasios Siouras (University of Thessaly). He has received grants from HORIZON 2020 , EPSRC , and Innovate UK , and collaborates with institutions like the Archimedes Research Unit (Greece) and National Heart and Lung Institute , Imperial College London. Professional Activity includes editorial board membership for Frontiers in Physiology , peer reviewing for journals like IEEE Transactions on Medical Imaging , and external examining for University of Strathclyde’s MSc programs. He also contributes to conference organization and Portuguese grant evaluation panels.

Jia Guoمشاهده پروفایل

پژوهشگر

• Operations research
• Optimisation
• Modelling and simulation
• +۱۸ مورد دیگر

Dr. Jia Guo is a Researcher at the Research Centre for Integrated Transport Innovation (rCITI) within the School of Civil and Environmental Engineering at the University of New South Wales (UNSW). She received her Ph.D. in Operations Research and Industrial Engineering from the University of Texas at Austin, USA. Her research spans multiple domains including transportation systems, network design, healthcare operations, and power systems optimization. Her primary research interests include: Operations research and optimization methodologies Mathematical modeling and simulation Machine learning applications in transportation Rail and air transportation systems Stochastic analysis and modeling Dr. Guo's work focuses on developing advanced optimization techniques to solve complex problems in transportation planning and logistics. Her research has produced significant contributions in freight train planning, air traffic controller scheduling, vehicle routing, transportation and logistics design, travel behavior analysis, and staff scheduling. She has extensive expertise in mathematical modeling, decomposition algorithms, metaheuristics, machine learning methods, and statistical analysis. Her publications reflect a strong trajectory in applying operations research to real-world transportation challenges, with a particular emphasis on stochastic and optimization approaches across rail, air, and urban transportation systems. Dr. Guo maintains an active research profile with an ORCID identifier (https://orcid.org/0000-0002-5350-2595) and is engaged with the academic community through Google Scholar.

Magdalena Lemańskaمشاهده پروفایل

استادیار

• Graph Theory
• Domination Theory
• Discrete Mathematics
• +۲ مورد دیگر

Dr. Magdalena Lemańska is an Assistant Professor at the Institute of Applied Mathematics within the Faculty of Applied Physics and Mathematics at Gdańsk University of Technology. She has established herself as a dedicated researcher in discrete mathematics with a specialized focus on domination theory in graphs. Dr. Lemańska obtained her doctoral degree (dr) in Mathematics on January 19, 2006, from the Faculty of Mathematics, Physics and Informatics of the University of Gdańsk, marking the beginning of her academic career focused on theoretical aspects of graph structures. Her research centers on advanced topics in graph theory, particularly domination parameters and their variations. She investigates mutual visibility sets, restrained differential, isolation numbers, and super domination across various graph structures including trees, unicyclic graphs, and block graphs. Her work often examines how graph operations like edge subdivision affect these parameters, contributing to the theoretical foundations of combinatorial optimization. Dr. Lemańska's publication record shows consistent scholarly output since 2014, with recent publications in 2024-2025 indicating active ongoing research. Her work appears in reputable journals such as Discrete Applied Mathematics, Discrete Mathematics, and Discussiones Mathematicae Graph Theory, demonstrating her contributions to advancing knowledge in graph theory. She is currently involved in the COVER project (Combinatorial Optimisation for Versatile Applications to Emerging Urban problems), part of the Horizon Europe program (agreement 101182819 — COVER — HORIZON-MSCA-2023-SE-01), which began on September 16, 2024, in the Department of Algorithms and Systems Modelling. Dr. Lemańska maintains substantial teaching responsibilities at Gdańsk University of Technology with 88 teaching entries recorded, indicating significant contribution to academic instruction alongside her research activities. Her office is located in Building B, room 516a, and she can be contacted at magleman@pg.edu.pl.