جست‌وجوی استاد و پژوهشگر

• Logistics and Supply Chain Management
• Artificial Intelligence
• Machine Learning
• +۶ مورد دیگر

Haibo Wang is a Killam Distinguished Professor at Texas A&M International University, specializing in logistics, artificial intelligence, and quantum computing. With a Ph.D. from the University of Mississippi (2004), he brings over a decade of IT consulting experience to academia, focusing on bridging employer needs and student skills through data analytics education. He teaches in the MBA in Management program, emphasizing analytical skills, research proficiency, and business problem-solving using Excel, R, and Python. His research interests span decision support systems, supply chain resilience, and optimization algorithms. Key contributions include work on quantum-inspired optimization, econophysics frameworks for financial networks, and machine learning applications in supply chain security. He has secured grants related to infrastructure and border security as a principal investigator and expert consultant. Professor Wang has received prestigious awards including the International Faculty of the Year and University Scholar of the Year. His work integrates computational experiments with real-world challenges, such as predicting distressed firms in China and analyzing cross-border transportation data. He mentors students in NSF LSAMP grant programs and advocates for structured learning approaches to master business analytics tools. His academic output includes over 50 refereed journal papers across disciplines like operations research, financial engineering, and AI. Recent research highlights dynamic market relationships (e.g., BRICS stock markets and cryptocurrency), quantum machine learning algorithms, and network resilience in financial systems.

• Theoretical Physics
• Quantum Physics
• High Energy Physics
• +۳ مورد دیگر

Rafael Nepomechie is a Professor in the Department of Physics, College of Arts and Sciences, University of Miami. His research focuses on theoretical physics, quantum information science, and integrable systems, with key contributions to quantum spin chains, Bethe ansatz methods, and quantum entanglement analysis. University of Miami, College of Arts and Sciences, Department of Physics Research spans quantum computing, orthogonal polynomials, and boundary integrability Collaborates with Luc Vinet, David Raveh, and Nicolas Crampé Research Interests: His work addresses exact solutions of integrable models, quantum state preparation algorithms, and entanglement entropy in inhomogeneous systems. Recent projects include deterministic Dicke state generation, VQE-based Bethe root estimation, and symmetry analysis of spin chains. Publication Trends: Articles emphasize quantum information applications (e.g., Dicke states, quantum circuits), mathematical physics (Bethe ansatz, quantum groups), and entanglement in low-dimensional systems. Subfields include quantum computing, statistical mechanics, and algebraic combinatorics. Advising & Collaboration: No formal students listed, but collaborates closely with researchers like David Raveh on quantum computing implementations and Luc Vinet on orthogonal polynomial-based models. Labs & Teams: Engaged in the University of Miami's Physics Department research groups, particularly those focused on quantum field theory and quantum integrability.

• Operational Research
• Optimization
• Combinatorial Optimization
• +۳ مورد دیگر

Roberto Montemanni is a Full Professor in the Department of Engineering Sciences and Methods at the University of Modena and Reggio Emilia . He is affiliated with the Operations Research Group and specializes in the MATH-06/A Operational Research disciplinary sector. His role involves teaching and research, with office hours available by appointment via email. He focuses on advancing optimization methodologies for complex logistical, transportation, and healthcare challenges. His research interests include combinatorial optimization , algorithm design , and constraint programming . Key areas of exploration involve conflict-constrained problems (e.g., assignment, knapsack, and spanning tree), probabilistic routing scenarios, and drone-assisted delivery systems. He also investigates real-world applications in home healthcare logistics, warehouse management, and supply chain optimization, often blending heuristic methods with exact algorithms. Recent publications emphasize trends in exact algorithm development for hazardous routing, drone scheduling in heterogeneous vehicle routing, and model-based approaches for time-bomb knapsack and orienteering problems. These works highlight interdisciplinary solutions for industries and healthcare systems. While no scientific awards are listed, his contributions to optimization theory and practice are substantial. His advising and grant activities likely align with his research group’s focus on operational challenges, though specific student names or grant details are not provided. He collaborates actively through the Operations Research Group , addressing issues like network-based data interpretation and intelligent manufacturing systems.

Adriano Masoneمشاهده پروفایل

مدرس

• Operations Research
• Logistics Optimization
• Supply Chain Management
• +۴ مورد دیگر

Adriano Masone is a Lecturer in Operations Research at the University of Naples Federico II's Department of Electrical Engineering and Information Technology (DIETI-UNINA). His research focuses on optimization problems in logistics, healthcare operations, and smart infrastructure systems. He has developed innovative formulations and algorithms for drone routing, vehicle scheduling, and resource allocation in healthcare settings. His work integrates mathematical modeling (e.g., integer linear programming) with real-world applications like blood supply chain reorganization and operating room management. Notable research contributions include: Drone-team logistics for last-mile delivery Emergency room scheduling optimization Smart grid infrastructure optimization using ILP Feature-based approaches for customer classification in routing problems His articles frequently address combinatorial optimization challenges, with a strong emphasis on heuristic and exact algorithm development. Masone also engages in educational initiatives like the OPS4Math project, promoting problem-solving methodologies in mathematics education. He maintains a dedicated research laboratory focused on optimization and problem-solving applications, publishing extensively on topics spanning transportation, healthcare, and industrial engineering.

• Parallel and Distributed Programming
• Functional Programming
• High-Level Programming Models
• +۳ مورد دیگر

Professor Phil Trinder is a Professor of Computing Science at the University of Glasgow's School of Computing Science. He leads the Glasgow Parallelism Group (GPG) and is a member of the Glasgow Systems Section (GLASS). His research focuses on parallel and distributed programming models, functional programming, and scalable systems. Education: DPhil (Doctor of Philosophy) from the University of Oxford. Research Interests: Design and implementation of high-level distributed/parallel programming models, computational algebra applications, and collaboration with industry (Ericsson, Microsoft, etc.). Publications span over 78 works, emphasizing parallel algorithms, distributed systems, and functional programming frameworks like Erlang. Recent work explores tierless IoT architectures and reliability in distributed platforms. Grants/Projects: Principal Investigator on 12 major projects, including EU funding and EPSRC grants. Notable contributions to SD Erlang and SymGrid-Par frameworks. Labs/Teams: Glasgow Parallelism Group (GPG), GLASS section, and involvement in the Scottish Programming Languages Seminar (SPLS).

Natalya Zheltukhinaمشاهده پروفایل

مدرس

• Asymptotic Analysis
• Entire Functions
• Darboux Integrable Systems
• +۲ مورد دیگر

Natalya Zheltukhina is Lecturer in Mathematics at Bilkent University's Department of Mathematics. Her research examines asymptotic zero distributions of power series sections and tails, multiple positivity in sequences, and classification of Darboux integrable systems. Her work in complex analysis investigates properties of entire functions through coefficient patterns and zero distributions. Recent publications develop discretization methods for integrable systems while preserving key structural properties. Zheltukhina teaches undergraduate mathematics courses including calculus, discrete mathematics, and linear algebra. She provides extensive course materials including problem sets and lecture notes accessible through department websites.

Edward Lamمشاهده پروفایل

پژوهشگر ارشد

• Combinatorial Optimization
• Neuro-Symbolic AI
• Operations Research
• +۳ مورد دیگر

Edward Lam is a Research Fellow in the Department of Data Science and Artificial Intelligence at Monash University. His research focuses on the intersection of combinatorial optimization and neuro-symbolic AI, developing algorithms for routing, scheduling, control, and analytics, primarily applied to automated decision-making in transportation and logistics. He holds a PhD in Computer Science (2019) and a BSc(Hons) in Mathematics and Statistics (2010), both from the University of Melbourne. Lam is affiliated with CSIRO Data61 and actively supervises PhD students in optimization and machine learning. **Education**: PhD in Computer Science, University of Melbourne (2019) BSc(Hons) in Mathematics and Statistics, University of Melbourne (2010) **Research Interests**: Combinatorial optimization, neuro-symbolic AI, multi-agent systems, and applications in autonomous logistics. His work addresses challenges in routing, scheduling, and decision-making under constraints. **Article Trends**: Recent publications emphasize branch-and-cut-and-price algorithms for multi-agent pathfinding, electric vehicle routing, and hybrid optimization methods. His work bridges theoretical algorithm design with real-world applications in transportation and warehousing. **Awards**: Received the Doctoral Research Award (2019) for outstanding contributions to optimization algorithms. **Advising & Grants**: Currently accepting PhD students focused on combinatorial optimization and machine learning, with available scholarships. Collaborates on projects funded by industry and governmental entities like CSIRO. **Affiliations**: Maintains an external position at CSIRO Data61 and has contributed to patents in optimization algorithms (2018).

• Quantum Computing
• Machine Learning
• Probabilistic Programming
• +۳ مورد دیگر

Timon Gehr is part of the Professorship for Computer Science at ETH Zurich's Department of Computer Science, affiliated with the Institute of Programming Languages and Systems. His research focuses on quantum computing, probabilistic programming, neural network robustness, and privacy enforcement. He has contributed to the development of quantum languages like Silq and frameworks for certifying adversarial robustness in machine learning systems. His research interests include formal methods for programming languages, scalable symbolic reasoning, and applying probabilistic techniques to security and privacy. Recent work emphasizes robustness certification of neural networks and symbolic integration in machine learning. Key projects involve exact inference for probabilistic programs and differential privacy violation detection. Notable publications include work on quantum uncomputation, adversarial examples, and integrating logic into neural networks. No specific advising or grant details are provided in the text, but his involvement with the Institute of Programming Languages and Systems indicates active participation in academic collaborations and research teams.

• Computer Science
• Algorithms
• Software Engineering
• +۱ مورد دیگر

Stefano Kron is part of the Professorship for Computer Science at ETH Zurich, serving in a research capacity within the Department of Computer Science. His affiliation with this prestigious institution reflects his involvement in computational and theoretical research areas. The exact nature of his academic role is tied to supporting or conducting research within the professorship framework. No specific educational background, research focus details, or publication information was explicitly provided in the source text. His work likely aligns with broader computer science disciplines such as algorithms and software engineering, inferred from his departmental affiliation. He currently holds no listed scientific awards, supervised students, or active grants. Contact can be made via his institutional email address: Stefano.Kron@pbl.ee.ethz.ch .

Enrico Biniمشاهده پروفایل

دانشیار

• Real-Time Systems
• Embedded Systems
• Control Theory
• +۳ مورد دیگر

Enrico Bini is an Associate Professor at the Department of Computer Science, Università degli Studi di Torino, Italy. His research focuses on real-time systems, control theory, and embedded systems. He has contributed extensively to scheduling algorithms, resource management, and system optimization in both academic and practical contexts. His work spans topics such as feedback-based resource allocation, multiprocessor scheduling, and the design of predictable interconnected systems. Recent publications emphasize real-time control systems, low-latency task scheduling, and efficient resource utilization in heterogeneous environments. Bini’s articles highlight advancements in optimizing task execution times, minimizing jitter, and achieving fairness in multi-core systems. His research also includes contributions to Linux kernel modules for temporal verification and novel approaches to handling communication delays in networked control systems. No scientific awards or grants are explicitly mentioned in the provided materials. His advising record is currently empty. He is affiliated with the University of Turin and collaborates with institutions in formal methods and embedded systems research.

Nikolay Gromovمشاهده پروفایل

استاد

• Theoretical Physics
• Quantum Field Theory
• String Theory
• +۴ مورد دیگر

Professor Nikolay Gromov is a faculty member at King's College London within the Department of Mathematics, part of the Faculty of Natural, Mathematical & Engineering Sciences. He holds the rank of Professor of Theoretical Physics and has been affiliated with King's since 2010, progressing from Lecturer to Reader and then to Professor in 2018. His research focuses on non-perturbative Yang-Mills theory, integrability, and the AdS/CFT correspondence, particularly exploring the quantum spectral curve (QSC) for exact spectrum calculations in supersymmetric gauge theories. He has contributed significantly to understanding string theory, conformal field theories, and integrable systems through collaborations with institutions like CERN, École Normale Supérieure, and DESY. Education: Bachelor's degree from Saint-Petersburg State University (2005) PhD from École Normale Supérieure (Paris) and Saint-Petersburg Nuclear Physics Institute (2007) Research Interests: His work bridges quantum field theory and string theory via integrability, emphasizing exact solutions and duality. Key areas include: Quantum spectral curve applications in gauge/gravity duality Defect conformal field theories and bootstrap methods Strongly γ-deformed SYM theory as integrable systems Non-perturbative methods for confinement and phase transitions Grants & Leadership: He secured major grants (e.g., for QSC tool development) and served as Deputy Head (Research) of the Department of Mathematics. His work has led to computational tools like the Fast QSC Solver. Labs/Teams: Active in collaborative theoretical physics groups at King's and international partners, focusing on integrability and holography.

• Game Theory
• Reinforcement Learning
• Multiagent Systems
• +۳ مورد دیگر

Bolin Gao is an Adjunct Assistant Professor in the School of Electrical and Computer Engineering at Georgia Institute of Technology. His research focuses on game theory, reinforcement learning, and optimization algorithms with applications in multiagent systems and control theory. He explores topics such as equilibrium analysis, convergence dynamics, and stability in complex systems through continuous-time models and second-order methods. Key research interests include: Game dynamics and equilibrium computation in noncooperative games Reinforcement learning in multiagent environments Optimization algorithms for concave games and potential games Passivity theory and stability analysis in learning systems His recent work emphasizes second-order mirror descent methods and their convergence properties beyond traditional equilibrium assumptions. Articles from 2020-2023 demonstrate advancements in discounted dynamics, higher-order learning, and exact convergence analysis. No scientific awards were explicitly mentioned in the provided text. His academic contributions focus on theoretical foundations of game-theoretic learning and optimization frameworks.

• Artificial Intelligence
• Optimization
• Scheduling
• +۴ مورد دیگر

Marie-Louise Lackner is a Research Fellow (PostDoc Researcher) in the Department of Databases and Artificial Intelligence at Technische Universität Wien. She holds Diplom-Ingenieur and Dr. techn. degrees and actively contributes to the CD Laboratory for Artificial Intelligence and Optimization for Planning and Scheduling (2017–2025). Her research focuses on Artificial Intelligence and Optimization , with emphasis on industrial scheduling, constraint programming, and algorithm design. Key domains include: Industrial oven scheduling and production leveling Metaheuristics (simulated annealing, large neighborhood search) Multi-objective optimization in manufacturing systems Combinatorial mathematics and discrete structures Her publications demonstrate a strong trajectory in applied optimization , with recent work concentrating on energy-efficient scheduling algorithms for electronic manufacturing, while earlier research explored permutation patterns and social choice theory. Methodologies consistently integrate theoretical computer science with industrial problem-solving. She supervises graduate researchers, including P. Malik's work on memetic algorithms for production leveling. Grant involvement includes the CD Laboratory project focused on AI-driven planning systems. Lackner is affiliated with the CD Laboratory for Artificial Intelligence and Optimization, conducting applied research in industrial scheduling systems within TU Wien's Databases and AI group.

• Medical Image Analysis
• Segmentation Metrics
• Hausdorff Distance
• +۲ مورد دیگر

Abdel Aziz Taha is a Lecturer in Information Systems Engineering at Vienna University of Technology specializing in medical image analysis validation. His research focuses on segmentation metrics, Hausdorff distance optimization, and validation frameworks for biomedical imaging. He developed efficient algorithms for exact Hausdorff distance computation and authored the influential 'Metrics Reloaded' framework. He teaches Self-Organizing Systems and contributes to EU projects on social network analysis in Industry 4.0. His work addresses metric selection bias and validation challenges in image analysis competitions.

Nysret Musliuمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Optimization
• +۶ مورد دیگر

Nysret Musliu is an Associate Professor and Head of the Christian Doppler Laboratory for Artificial Intelligence and Optimization for Planning and Scheduling at TU Wien (Vienna University of Technology). He holds a PhD in Computer Science from TU Wien (2001) and a Habilitation (2007). His research focuses on AI techniques, optimization, scheduling, and timetabling, with applications in workforce scheduling, combinatorial optimization, and constraint satisfaction. Education: PhD in Computer Science, TU Wien, 2001 Habilitation in Applied Computer Sciences, TU Wien, 2007 MSc (Dipl.-Ing) in Computer Science, University of Prishtina, 1996 Research Interests: His work addresses AI-driven solutions for scheduling problems, including staff scheduling, timetabling (e.g., high school timetabling), and combinatorial optimization. He has developed algorithms for tree/hypertree decompositions and hybrid methods combining constraint programming with metaheuristics. Grants & Leadership: Project leader of the Christian Doppler Laboratory (2017–2024) Funded projects: ARTE (employee scheduling), Softnet Austria, Hypertree Decompositions Professional Activities: Conference chairs for CPAIOR 2021/2020 and PATAT 2018 Editorial roles in Constraints Journal and Journal of Mathematical Modelling and Algorithms Program committee member for IJCAI, AAAI, and GECCO Teaching: He teaches courses on artificial intelligence, problem-solving techniques, and machine learning at TU Wien. Lab & Teams: Leads the Christian Doppler Laboratory and collaborates with industry partners like Bosch and Ximes Corp.