جست‌وجوی استاد و پژوهشگر

Jörg Jungwirthمشاهده پروفایل

استاد پژوهشی

• Concrete Construction
• Prestressed Concrete
• Bridge Engineering
• +۶ مورد دیگر

Prof. Dr.-Ing. Jörg Jungwirth serves as a Research Professor at Munich University of Applied Sciences within Faculty 02 (Civil Engineering). He founded and coordinates the Laboratory for Concrete Structures and Structural Engineering, while maintaining active membership in the Institute for Materials and Construction Research (IMB) and the BayWiss joint college 'Infrastructure, Construction and Urbanization'—a consortium including TU Munich, Bundeswehr University, and OTH Regensburg. Additionally, he represents the university at the Bavarian Chamber of Civil Engineers. His research centers on advanced concrete technologies, specializing in prestressed concrete systems, bridge engineering, and structural rehabilitation. Key focus areas include Ultra-High Performance Fiber Reinforced Concrete (UHPFRC) applications for repair/strengthening of aging infrastructure, sustainable precast construction methods, and digital planning integration through BIM. His experimental work emphasizes practical implementation of UHPFRC shotcrete layers for retrofitting reinforced concrete structures while analyzing crack distribution and durability performance. Recent publications (2016-2021) reveal consistent thematic progression: initial work on UHPFRC girders for bridges/parking structures evolved into specialized retrofitting techniques using thin UHPFRC layers, with growing emphasis on digital construction processes. His projects demonstrate strong industry collaboration, particularly with Bavarian construction associations and federal research institutes. Scientific Awards No awards explicitly mentioned in source text Prof. Jungwirth leads multiple federally funded research initiatives, including sustainable precast park deck systems for the Federal Institute for Research on Building (2018) and UHPFRC bridge girders for the Bavarian Construction Industry Association (2018). His Metro Doha planning consultancy (2014) and sustainability evaluation frameworks for road infrastructure (2013) highlight international project experience. Current grants focus on UHPFRC shotcrete durability and digital construction workflows. He directs the Laboratory for Concrete Structures and Structural Engineering, which serves as the operational hub for experimental UHPFRC research. Through IMB and BayWiss, he collaborates on cross-institutional infrastructure projects addressing urbanization challenges, with recent work integrating BIM methodologies into interdisciplinary master-level curricula.

Miguel Anjosمشاهده پروفایل

استاد

• Operational Research
• Optimization
• Power Systems
• +۴ مورد دیگر

Professor Miguel Anjos is Chair and Professor of Operational Research at the University of Edinburgh's School of Mathematics, with prior academic roles in Canadian engineering departments. His research focuses on optimization applications in power systems, smart grids, and predictive maintenance. He emphasizes bridging theoretical optimization with practical engineering challenges, advocating for operational research as a versatile tool for real-world problem-solving. As former President of the INFORMS Energy Section (2018–2020), he drives interdisciplinary collaboration in energy systems. His work spans mathematical abstraction and industrial applications, including electric vehicle charging infrastructure, hydropower scheduling, and facility layout optimization. Education: Holds a PhD in optimization, with foundational work in mathematical programming. His theoretical contributions, such as semi-definite optimization, have been applied to practical domains like power flow analysis. Collaborations with engineers highlight his cross-disciplinary approach. Research interests include optimization algorithms for energy networks, maintenance strategies under uncertainty, and decision-focused data acquisition. Recent projects address challenges in smart grid design, water supply systems, and transportation infrastructure. He advocates for the integration of machine learning and stochastic modeling in operational research. Leadership and Awards: Recognized for leadership in energy-related operational research through his INFORMS presidency. His work has influenced policy and industry practices in Canada and beyond. Grants and Labs: Engages in applied research funded by energy and infrastructure sectors. Collaborates with industry partners on projects like EV charging station optimization and grid reliability improvements.

Saugata Basuمشاهده پروفایل

استاد

• Real Algebraic Geometry
• Computational Algebra and Geometry
• Theoretical Computer Science
• +۵ مورد دیگر

Saugata Basu is a Professor of Mathematics and Computer Science at Purdue University, with Mathematics as his home department. He holds a joint appointment and has a distinguished academic career, previously serving at the Georgia Institute of Technology, University of Michigan, and IBM T. J. Watson Research Center. His research spans real algebraic geometry, computational algebra and geometry, theoretical computer science, and applied topology. He has authored influential works such as the textbook Algorithms in Real Algebraic Geometry and contributed to foundational results in semi-algebraic geometry and computational complexity. Education: PhD from New York University's Courant Institute (1996) and BTech in Computer Science from IIT Kharagpur (1991). He has held visiting positions at institutions including Université de Rennes, Institute Henri Poincaré, and the Mathematical Sciences Research Institute in Berkeley. Research interests include O-minimal structures, computational topology, and algorithmic semi-algebraic geometry. His work bridges theoretical mathematics with applications in computer science, cryptography, and optimization. Notable awards include the Sloan Fellowship (2003–2005) and NSF Career Award (2002–2007). Grants and collaborations span topics like symmetry in algebraic geometry, quantum computations, and topological data analysis. He frequently presents at leading conferences such as FOCS, STOC, and SIAM Algebraic Geometry.

Carlo Cilibertoمشاهده پروفایل

دانشیار

• Machine Learning
• Statistical Learning Theory
• Optimal Transport
• +۷ مورد دیگر

Carlo Ciliberto is an Associate Professor in Machine Learning at University College London. His research focuses on theoretical and applied machine learning, with particular emphasis on structured prediction, meta-learning, optimal transport, and quantum computing. He has contributed to advancements in kernel methods, reinforcement learning, and robotics perception systems, notably through work with humanoid robots like the iCub. Key research interests include: Developing algorithms for distribution regression and Wasserstein-based learning. Exploring meta-learning frameworks for few-shot and incremental learning tasks. Designing robust systems for robotics applications, such as object recognition and tactile sensing. Investigating statistical foundations of quantum machine learning. Notable contributions to the field include the Manifold Structured Prediction framework, Sliced Wasserstein Kernels for distribution regression, and methodologies for conditional meta-learning. His work bridges theory and practice, with applications ranging from civil infrastructure analysis to humanoid robot perception.

Alexander Razborovمشاهده پروفایل

استاد

• Theoretical Computer Science
• Combinatorics
• Proof Complexity
• +۴ مورد دیگر

Alexander Razborov is the Andrew McLeish Distinguished Service Professor at the University of Chicago's Department of Mathematics. He holds a B.S. from Moscow State University (1985) and a PhD (1987) and Doctoral Degree (1991) from the Steklov Mathematical Institute. His research spans logic, theoretical computer science (TCS), and combinatorics, with major contributions to proof complexity, continuous combinatorics (flag algebras), and quantum computing. Notable achievements include the Nevanlinna Prize (1990), Gödel Prize (for foundational work on 'Natural Proofs'), and election to the American Academy of Arts and Sciences (2020). His work on flag algebras revolutionized extremal combinatorics, while his research on proof complexity established fundamental limits of propositional reasoning systems. Razborov's recent focus includes continuous combinatorics (studying infinite graph limits) and refining proof complexity trade-offs. His collaborative projects with Leonardo Coregliano and others explore topics like Sidorenko's conjecture and neural network convergence guarantees. He is affiliated with the university's computational theory group and actively publishes across top journals like the Annals of Mathematics and Journal of the ACM.

Carsten W. Schererمشاهده پروفایل

استاد

• Control Theory
• Robust Control
• Optimization
• +۷ مورد دیگر

Carsten W. Scherer is a Professor and Head of the Institute of Mathematical Methods in Engineering, Numerical Analysis and Geometric Modeling at the University of Stuttgart, Faculty of Engineering. He holds the Chair of Mathematical Systems Theory and serves as Erasmus Coordinator for the Department of Mathematics. His research focuses on robust control, multiobjective control, linear matrix inequalities (LMIs), and semi-definite programming, with applications in mechatronics and flight control. He has authored numerous publications and contributed to advanced control theory methodologies. His research interests include exploring LMIs in control systems analysis, robust optimization techniques, and nonlinear control strategies. He has developed frameworks for model predictive control (MPC) and gain-scheduled control, leveraging integral quadratic constraints (IQCs) for system analysis and synthesis. Dr. Scherer’s work bridges theoretical advancements with practical applications, emphasizing convex optimization and its role in solving complex control problems. His contributions span both foundational theory and real-world implementations, particularly in aerospace and mechatronic systems. His publications reflect a sustained focus on robustness, optimization, and control system design, with recent work addressing data-driven methods, trajectory generation, and algorithmic synthesis. He leads research initiatives in mathematical systems theory and collaborates on interdisciplinary projects integrating control engineering with optimization and machine learning.

Konstantinos Tyrosمشاهده پروفایل

دانشیار

• Ramsey Theory
• Density Combinatorics
• Banach Space Geometry
• +۲ مورد دیگر

Konstantinos Tyros is Associate Professor in Mathematics at the University of Athens, specializing in combinatorial analysis and Ramsey theory. His research connects density theorems in combinatorics with problems in Banach space geometry and probabilistic methods. Key contributions include density versions of combinatorial theorems (Carlson-Simpson, Hales-Jewett), structure theorems for stochastic processes on discrete cubes, and concentration inequalities for high-dimensional random arrays. His work on spreading models in Banach spaces reveals new structures in functional analysis. He has developed novel approaches to nonlinear spectral gaps and dual Ramsey theory for trees, while advancing the Moser-Tardos algorithmic framework. Honors include technical excellence awards from Greek academic institutions.

Jean-Bernard Lasserreمشاهده پروفایل

پژوهشگر

• Applied Mathematics
• Probability
• Optimization
• +۴ مورد دیگر

Jean-Bernard Lasserre is a Senior Researcher at LAAS-CNRS in Toulouse, France, and a member of the Institute of Mathematics at the University of Toulouse. He earned his PhD (1978) and Doctorat d'Etat (1984) from Paul Sabatier University in Toulouse, with prior academic training from ENSIMAG (Grenoble). He has held visiting positions at UC Berkeley’s Electrical Engineering Dept. (1978-79, 1985-86) via INRIA and NSF fellowships, and shorter visits at institutions like MIT, Stanford, MSRI, and Cinvestav-IPN (Mexico). Affiliations: LAAS-CNRS, Toulouse (since 1980) Institute of Mathematics (IMT), University of Toulouse (member) His research spans Applied Mathematics, Probability, and Optimization, with pioneering work in Markov Control Processes, Semi-Definite Programming, and the Moment-SOS approach for global optimization. He has developed computational methods for integrating functions on convex polytopes, invariant probability measures for Markov chains, and inverse problems in computational geometry. His work bridges continuous and discrete optimization, impacting robotics (e.g., NUROA algorithm), power systems (e.g., Optimal Power Flow Problem), and quantum computing. Key scientific awards include the John von Neumann Theory Prize (2015) , Khachiyan Prize (2015) , Lagrange Prize in Continuous Optimization (2009) , SIAM Fellowship (2014) , and an ERC Advanced Grant (2014) . He has served as Associate Editor for journals like IEEE Transactions on Automatic Control and SIAM Journal on Optimization , and co-developed software tools such as GloptiPoly and POCP for polynomial optimization and optimal control.

Shayan Oveis Gharanمشاهده پروفایل

استاد

• Algorithm Design
• Applied Probability
• Spectral Graph Theory
• +۷ مورد دیگر

Shayan Oveis Gharan is a Professor in the University of Washington 's Computer Science and Engineering department. His work bridges algorithm design, applied probability, and spectral graph theory, with a focus on leveraging Markov Chains and polynomial paradigms in approximation algorithms. Education: PhD in Computer Science from Stanford University (2013) Research interests span theoretical computer science, combinatorics, and optimization. He explores how algebraic techniques, particularly log-concave and hyperbolic polynomials, can enhance spectral graph theory and counting/sampling algorithms. His studies on the Traveling Salesman Problem and matroid theory have redefined approximation bounds. Recent publications highlight advancements in trickle-down theorems, polynomial paradigms for graph sparsification, and high-dimensional random walks. These works intersect with fields like machine learning and computational complexity. Scientific awards include the 2025 Michael and Sheila Held Prize, 2022 Simons Investigator Award, and 2016 NSF Career Award. His students, such as Nathan Klein and Kuikui Liu, have transitioned to academic and industry roles.

Alexander Artikisمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Distributed Systems
• Multi-Agent Systems
• +۳ مورد دیگر

Alexander Artikis is an Associate Professor at the Department of Maritime Studies, University of Piraeus. His academic work bridges artificial intelligence, distributed systems, and maritime informatics, with a focus on complex event recognition and temporal reasoning. PhD in Multi-Agent Systems from Imperial College London Research areas include: Artificial Intelligence and Machine Learning Distributed and Real-Time Systems Event Calculus and Temporal Reasoning Maritime Data Analytics Recent publications highlight advancements in: Knowledge Graph Inconsistency Detection and Resolution Tensor-Based and Symbolic Register Transducer Formalisms Human-in-the-Loop Visual Analytics Temporal Specification Optimization Large Language Model Applications in Event Recognition Scalable Event Processing for Maritime Mobility He contributes to the academic community through program committee roles at premier conferences like AAMAS, IJCAI, and AAAI.

• Natural Language Processing
• Data Mining
• Data Management
• +۵ مورد دیگر

Kevin Chen-Chuan Chang is a Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign. He received a BS from National Taiwan University and a PhD in Electrical Engineering from Stanford University (2001). Research Focus: Large-scale information access, knowledge acquisition, Web search/mining, social media analytics, and integration of structured/unstructured data. His research bridges structured and unstructured data through systems in natural language processing , data mining , and machine learning , with applications in semantic modeling and AI. Recent work includes knowledge graphs , language models , and graph analytics . Awards: ICDE 10-Year Test of Time Award (2022), NSF CAREER Award (2002), IBM Faculty Awards (2004, 2005), and multiple excellent teaching honors. Entrepreneurship: Co-founder of Cazoodle and developer of GrantForward.com, a vertical search engine for funding discovery.

• Telecommunications
• Compressive Sensing
• Cognitive Radio
• +۴ مورد دیگر

Ricardo Blasco-Serrano is a researcher affiliated with KTH Royal Institute of Technology , specifically within the School of Electrical Engineering (EES) and the Communication Theory department. His work focuses on telecommunications , with emphasis on compressive sensing , cognitive radio , and network coordination . He has contributed to theoretical frameworks for interference management and sparse signal recovery. Education : Doctorate in Telecommunications from KTH Royal Institute of Technology (2013). His research explores the tradeoff between measurement rates and estimation accuracy in compressive sensing, interference coordination in cognitive radio channels, and polar codes for network compression. Key collaborations include work with Ragnar Thobaben, Mikael Skoglund, and Eduard Jorswieck, supported by EU FP7 grant 257626. Notable publications include work on vehicle-to-everything (V2X) communication , partial support recovery , and MISO cognitive channel strategies . His methods have implications for machine-to-machine communication and network densification .

• Combinatorial rigidity
• Graphs and matroids
• Discrete and computational geometry
• +۷ مورد دیگر

Professor Anthony Nixon is a faculty member at the School of Mathematical Sciences, Lancaster University , where he serves as Professor in Mathematical Sciences since August 2024. He leads Lancaster's Combinatorics research theme and contributes to the Geometric Rigidity research theme . His research focuses on combinatorial rigidity theory , specifically analyzing systems of equations from geometric constraint systems to determine rigidity/flexibility of structures like bar-joint frameworks. This work intersects structural graph theory , matroid theory , projective geometry , and semi-definite programming . Notable applications include biophysical materials and robotic formation control . The 10 submitted papers cover topics like hypergraph rigidity, nearly planar graph rigidity, angular constraints, tropical rigidity approaches, and k-fold matroid circuits. His recent EPSRC grants include £428,712 for abstract rigidity research and £45,952 for graph rigidity in arbitrary dimensions. Scientific awards include: EPSRC grant (2023-2026): Abstract rigidity for natural stability problems (£428,712) EPSRC grant (2022-2023): The graph rigidity problem in arbitrary dimension (£45,952) Heilbronn Institute fellowship (2020-2021): £42,135 He supervises PhD students Rebecca Monks , Daniel Hodgson , Jack Trainer , and Andrew Sainsbury (part-time). Collaborators include Sean Dewar , Bill Jackson , and Brigitte Servatius . He has organized major events like the Mathematics, AI and Data Science for Materials Innovation workshop (Lancaster, 2025) and ICERM semester program on Geometry of materials (Brown, 2025).

Uzay Kaymakمشاهده پروفایل

استاد

• Intelligent Decision Support Systems
• Computational Intelligence
• Fuzzy Set Theory
• +۶ مورد دیگر

Uzay Kaymak is a Full Professor and Chair of Information Systems in Health Care at Eindhoven University of Technology (TU/e), with concurrent appointments at JADS Den Bosch, EAISI Foundational, and EAISI Health. His work bridges computational intelligence and healthcare, developing adaptive decision support systems through linguistic-numerical data fusion. His educational background includes: MSc in Electrical Engineering (1992, Delft University of Technology) Chartered Designer Degree in Information Technology (1995) PhD in Control Engineering (1998, Delft University of Technology) Research Interests: Professor Kaymak pioneers computational intelligence frameworks integrating fuzzy set theory with machine learning for intelligent decision support . His work spans data/process mining , reinforcement learning , and clinical decision modeling , with applications in financial systems, economic analysis, and healthcare. Recent focus includes serious games for diabetes management and AI-driven water infrastructure monitoring , emphasizing model interpretability and real-world implementation. Publication Trends: His 2023-2025 output reveals intensified focus on environmental AI applications (water systems, desalination) and healthcare informatics . Publications demonstrate consistent integration of fuzzy logic with deep learning for interpretable models, particularly in leak detection, river monitoring, and clinical alert systems. The work shows strong industry-academia collaboration with practical implementations in Dutch hospitals and water utilities. Scientific Awards: Best Industrial Paper Award (2020) Best Student Paper Award (2019) Advising and Grants: With 91 supervised students, Professor Kaymak leads major EU-funded projects including SmartDATA (2020-2025) for AI in connected systems, DiaGame (2019-2025) for diabetes self-management games, and GOAL (2018-2021) for gamified active lifestyles. His Eurostars project PADS pioneered reinforcement learning for programmatic advertising, securing significant industry partnerships with Shell and healthcare institutions. Labs and Teams: He co-leads the EAISI Health initiative and TU/e's Clinical Informatics program, collaborating with Jheronimus Academy of Data Science (JADS) and Zhejiang University. His interdisciplinary team combines AI researchers, clinical informaticians, and environmental engineers to develop deployable solutions for UN Sustainable Development Goals in health and clean water.

• Operations Research
• Combinatorial Optimization
• Algorithmic Graph Theory
• +۳ مورد دیگر

Prof. Dr. Marzena Fügenschuh is a Professor at the Berlin School of Economics and Law (BHT Berlin), affiliated with the Department of Mathematics - Physics - Chemistry. She has been actively involved in teaching since the summer semester of 2009, offering courses in applied mathematics, computer science, and media informatics. Her research focuses on operations research, combinatorial optimization, algorithmic graph theory, and network theory, particularly analyzing multiplex networks and their layered structures. Her work emphasizes synthetic model development for complex networks, including air transportation systems and layered network topologies. She has presented her research at major conferences such as Complex Networks and the European Conference on Operational Research. Supervising theses, she guides students in topics like multiplex network generation and optimization algorithms. Prof. Fügenschuh’s contributions span theoretical advancements in graph bisection and practical applications in magnetic measuring systems and industrial design. Her interdisciplinary approach bridges algorithmic theory with real-world engineering challenges.