جست‌وجوی استاد و پژوهشگر

• Discrete Optimization
• Non-Convex Optimization
• Operational Research
• +۳ مورد دیگر

Akshay Gupte is a Professor in the School of Mathematics at the University of Edinburgh, specializing in Optimization and Operational Research. His research focuses on mathematical and algorithmic aspects of discrete and non-convex optimization, with applications in engineering, finance, and decision-making systems. He holds a PhD in Operational Research from Georgia Tech and teaches advanced courses such as Optimization Methods in Finance for MSc students. Education: BEng in Industrial Engineering from the University of Bombay MSc in Operational Research from the University of Arizona PhD in Operational Research from Georgia Tech Research Interests: Optimization problems involving discrete choices and non-convexity, with a focus on algorithm design and computational strategies. His work bridges applied mathematics, computer science, and engineering, addressing challenges in logistics, finance, and large-scale decision systems. Articles Trends: Recent publications emphasize algorithmic advancements in non-convex optimization (e.g., spatial branch-and-bound, semidefinite programming), multi-objective decision-making (e.g., biobjective programming), and stochastic frameworks for real-world problems like facility location and home service scheduling. Awards & Grants: No specific awards listed, but his research has been supported by collaborative grants in mixed-integer programming and optimization algorithms. He advises students through his teaching and research collaborations within the Optimization Group at the University of Edinburgh.

Purushothaman Damodaranمشاهده پروفایل

استاد

• Industrial Engineering
• Systems Engineering
• Operations Research
• +۷ مورد دیگر

Purushothaman Damodaran is a Presidential Teaching Professor at the Department of Industrial and Systems Engineering at Northern Illinois University . With expertise in large-scale optimization , logistics , simulation , scheduling , Lean Manufacturing , and Six Sigma , he develops mixed-integer linear formulations and novel algorithms for real-world applications in electronics manufacturing, healthcare, and logistics. Ph.D. , Industrial Engineering, Texas A&M University (2002) M.S. , Industrial Engineering, Northern Illinois University (1997) B.E. , Mechanical Engineering, University of Madras (1994) His research focuses on optimization models for batch processing machines , parallel machine scheduling , and simulation-based decision support systems . He has collaborated with industries like 3M , Caterpillar , Royal Caribbean Cruise Lines , and IBM , addressing challenges in manufacturing systems , warehouse design , and service operations . Recent publications highlight heuristic approaches (e.g., GRASP , Simulated Annealing , Particle Swarm Optimization ) for flow shop scheduling , job shop optimization , and material slitting problems . His work often integrates Lean Six Sigma principles and simulation-optimization for real-time manufacturing improvements . Scientific Awards : Best Paper Award, IISE Annual Conference (2021) National Science Foundation Grant (2008-2009) for server manufacturing research As a faculty teaching mentor at NIU, he teaches undergraduate and graduate courses in Six Sigma , Warehousing , and Advanced Lean Manufacturing , emphasizing real-life projects and student engagement .

• Operations Research
• Robust Optimization
• Queueing Theory
• +۷ مورد دیگر

Chaithanya Bandi is an Associate Professor at the National University of Singapore (NUS) in the Analytics and Operations Department of the NUS Business School, with a joint appointment in the Department of Mathematics. His research focuses on decision-making under uncertainty, robust optimization, and their applications in operations management, healthcare, e-commerce, and energy systems. He develops robust optimization models for queueing control, risk optimization, and mechanism design. Key research areas include robust queue inference, two-stage distributionally robust optimization, and multi-item auction mechanisms. He has contributed to operational challenges in healthcare (e.g., patient re-entry scheduling), energy systems (electricity generation optimization), and e-commerce (price optimization for fashion products). His work integrates theoretical advancements with practical implementations in large-scale systems. Recent publications emphasize adversarial evaluation of large language models, dynamic scheduling algorithms, and robust policies for uncertain environments. His methodologies often involve novel optimization frameworks and scalable computational approaches. Dr. Bandi holds a PhD in Operations Research and has collaborated with industry leaders like Flipkart and healthcare providers to apply his models in real-world settings. His contributions bridge theoretical rigor and practical applicability in complex operational systems.

Moritz Diehlمشاهده پروفایل

استاد

• Optimization
• Control Systems
• Embedded Systems
• +۳ مورد دیگر

Prof. Moritz Diehl is a Professor at the University of Freiburg, leading the Systems Control and Optimization Laboratory within the Department of Microsystems Engineering (IMTEK) and affiliated with the Department of Mathematics. Born in Hamburg, Germany, he holds a Ph.D. from Heidelberg University (2001) and previously served as a professor at KU Leuven (2006–2013), where he directed the Optimization in Engineering Center (OPTEC). His research focuses on optimization and control, emphasizing numerical methods for engineering applications, particularly embedded systems and renewable energy. Key areas include model predictive control (MPC), nonlinear optimization, and real-time control systems. Education: He studied physics and mathematics at Heidelberg University and the University of Cambridge (1993–1999), culminating in a Ph.D. in Scientific Computing. His academic journey includes roles at KU Leuven and Freiburg, where he has developed influential tools like the AWEbox framework for airborne wind energy systems and the acados optimization library. Research Interests: His work spans numerical optimal control, MPC algorithms, and their applications in robotics, energy systems, and automotive engineering. Recent advancements include collision-free motion planning, real-time NMPC with convex-concave constraints, and stochastic control methods for mobile robots. He also explores optimization for hybrid systems, leveraging finite elements and switch detection for nonsmooth dynamics. Publications: His 2023–2025 work highlights contributions to MPC stability, energy-efficient control systems, and software tools like LCQPow for quadratic programming. His research bridges theory and practice, addressing challenges in industrial processes, renewable energy integration, and autonomous systems. Labs & Teams: He leads the Systems Control and Optimization Lab, fostering interdisciplinary projects in optimal control, robotics, and sustainable energy. His group collaborates on tools like acados, emphasizing real-time feasibility and scalability for complex systems.

Alexander Barvinokمشاهده پروفایل

استاد

• Computational Complexity
• Algorithms
• Algebra
• +۶ مورد دیگر

Alexander Barvinok is a Professor in the Department of Mathematics at the University of Michigan, Ann Arbor. His office is located in East Hall (4066 East Hall), where he has been conducting research and teaching advanced courses in computational mathematics since receiving his Ph.D. from Leningrad State University in 1988. Professor Barvinok's research focuses on computational complexity and algorithms in algebra, geometry and combinatorics. He is particularly interested in connections between various notions of phase transition in statistical physics, analytical properties of partition functions and computational complexity. His work bridges theoretical mathematics with practical computational approaches, exploring how physical phenomena can inform algorithmic design and analysis. His research spans convex geometry, combinatorial optimization, and the computational aspects of polynomial systems. His recent publications (2016-2024) demonstrate a consistent focus on partition functions, computational aspects of convex bodies, and approximation algorithms for counting problems. He has made significant contributions to understanding the zeros of partition functions in statistical physics models, developing efficient volume estimation algorithms for polyhedra, and creating polynomial-time approximation schemes for problems previously thought to be computationally intractable. His work frequently connects algebraic properties of polynomials with computational feasibility. Professor Barvinok has authored several influential textbooks including "A Course in Convexity" (AMS Graduate Studies in Mathematics, 2002), "Integer Points in Polyhedra" (Zurich Lectures in Advanced Mathematics, 2008), and "Combinatorics and Complexity of Partition Functions" (Springer, 2016). He regularly teaches advanced graduate courses such as Math 669 on specialized topics including "Combinatorics, Geometry and Complexity of Integer Points" and "Topics in Convexity," with his lecture notes often evolving into significant research contributions.

• Electrical engineering
• Electronics, sensors and digital hardware
• Distributed computing and systems software
• +۶ مورد دیگر

Masood Parvania is the Roger P. Webb Endowed Professor at the University of Utah in Electrical and Computer Engineering. He serves as Director of the Utah Smart Energy Laboratory (U-Smart) and Co-Director of the NSF WIRED Global Center . His research focuses on mathematical optimization , control theory , and machine learning applications in power system operation, resilience, and interdependent infrastructure modeling. His work addresses critical challenges including: Equity-aware grid restoration Climate-resilient energy systems Cyber-physical security analysis Hybrid energy storage coordination Extreme heat event mitigation strategies Key research trends across his 15 most recent publications reveal deep integration of: Renewable energy with storage systems Machine learning in real-time grid operation Cybersecurity frameworks for critical infrastructure Equity metrics in energy distribution Honors include: IEEE Outstanding Associate Editor Award (2022) University of Utah Presidential Scholar (2020) IEEE Utah Section Outstanding Educator Award (2017) Multiple Best Reviewer and Distinguished Service Awards As Associate Editor for IEEE Transactions on Power Systems , he actively shapes energy research discourse while leading NSF-funded initiatives like: U.S.-Canada Climate-Resilient Grid Center ($90M+ Western EV Infrastructure Scale-Up Wasatch Multi-Modal Corridor Electrification

Swarnendu Biswasمشاهده پروفایل

استادیار

• Programming Languages
• Compilers
• Runtime Systems
• +۷ مورد دیگر

Swarnendu Biswas is an Assistant Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur. He teaches courses including Programming for Performance (CS 610), Analysis of Concurrent Programs (CS 636), and Compiler Design (CS 335), demonstrating his expertise across multiple areas of computer systems. His research interests center on Programming Languages, Compilers, Runtime Systems, and Parallel Software Systems. He leads the PROSPAR (Programming Languages and PARallel Systems) research group, which focuses on developing techniques to build efficient and correct parallel software through program analysis, compiler optimizations, and runtime systems. His recent publications reveal a strong trend in addressing fundamental challenges in parallel computing, with work spanning cache coherence, false sharing detection, data race analysis for GPUs, verification of neural networks, and thermal-aware management of heterogeneous systems. His research bridges theory and practice with significant contributions to both hardware and software aspects of parallel systems. His scientific achievements have been recognized through multiple prestigious awards: Google India Research Award 2021 Google Explore CSR 2022 Research Grant from Intel Corporation SERB Start-up Research Grant 2019 Google Cloud Platform Research Credits (2019, 2020) IITK Initiation Grant 2019 As an advisor, he mentors several PhD and MTech students working on cutting-edge research in parallel systems. His PROSPAR group has secured significant funding from industry and government sources, supporting innovative research in programming languages and parallel systems. The group actively collaborates with industry partners including Google and Intel, addressing real-world challenges in parallel computing. He leads the PROSPAR research group at IIT Kanpur, which brings together faculty, PhD students, and MTech researchers to tackle challenging problems at the intersection of programming languages, compilers, and parallel systems. The group maintains strong industry connections and focuses on practical solutions that can be deployed in real systems.

• Integer Programming
• Operations Research
• Cutting and Packing
• +۵ مورد دیگر

José Manuel Vasconcelos Valério Carvalho is a Full Professor at the School of Engineering, University of Minho, and Senior Researcher at Algoritmi Research Center's SEOR R&D Group. His academic career spans decades with significant contributions to combinatorial optimization and operations research. His educational background includes: PhD in Production Engineering (Operational Research major) from University of Minho MSc in Industrial Engineering and Operations Research from Virginia Polytechnic Institute (Fulbright scholar) Research focuses on large-scale integer programming applications in cutting/packing, network design, and operations planning. He develops exact and heuristic algorithms for complex optimization problems, frequently integrating domains like bin packing with vehicle routing using arc-flow formulations and column generation. Recent publications (2016-2025) reveal strong trends toward integrated logistics solutions, emphasizing temporal aspects, multi-trip scenarios, and combined routing/packing challenges. His methodologies consistently yield robust formulations for real-world supply chain applications. He has supervised 7 postdoctoral and 12 PhD students, many achieving award-winning work. Research leadership includes coordination of FCT-funded national projects and European project workpackages. As core member of Algoritmi's SEOR Group and former coordinator of the SEOOR Research Line (consistently rated 'Excellent' by international panels), he contributes to Portugal's leading operations research team.

• Artificial Intelligence
• Optimization
• Logistics
• +۹ مورد دیگر

Eduardo Anibal Lalla is an Associate Professor at the Digital Society Institute and Industrial Engineering & Business Information Systems. His research focuses on applying artificial intelligence and optimization techniques to logistics and transportation challenges, particularly in maritime and urban logistics. He has published extensively on topics such as vehicle routing, metaheuristics, and mathematical programming, contributing to advancements in intelligent transport systems and container logistics. Research Trends : Over the past decade, his work has emphasized optimization algorithms for multi-depot vehicle routing collaborative berth allocation models matheuristics for dynamic scheduling AI-driven decoupling strategies in inter-terminal transport robot stations integration in logistics networks Scientific Awards : Best algorithm in Generalized Continuous Optimization Contest (2016) EURO Excellence in Practice Finalist/Runner-up (2021) Extraordinary doctoral distinction (2017)

• Operations Research
• Optimization Models
• Linear Programming
• +۶ مورد دیگر

Juan Carlos Espinoza-García is an Adjunct Professor in the Department of Administration and Business Management at Tecnológico de Monterrey (Campus Querétaro). His part-time role includes teaching optimization-focused courses and conducting applied research in industrial systems. He holds affiliations with the Challenge Based Research Fund and contributes to Sustainable Development Goals (SDG 9: Industry/Innovation, SDG 11: Sustainable Cities). Education: PhD Business Administration - ESSEC Business School MS Industrial Engineering - University of Michigan MS Manufacturing Systems - Tecnológico de Monterrey BS Industrial Engineering - Tecnológico de Monterrey His research specializes in operations research and optimization methodologies , particularly Linear/Mixed-Integer Programming applied to logistics, scheduling, and robust decision-making under uncertainty. This work addresses real-world industrial challenges through mathematical modeling, emphasizing efficiency in supply chains and production systems. Recent publications highlight interdisciplinary applications: A 2024 study integrates project management frameworks into engineering education, while 2019 work optimizes quality inspection processes in manufacturing. This reflects consistent focus on bridging theoretical optimization with practical implementation. Teaching responsibilities include: Heuristics/Metaheuristics for Process Optimization Value Chain Modeling Intelligent Logistics Networks Organizational Process Optimization

Jean-François Côtéمشاهده پروفایل

استاد

• Combinatorial optimization
• Stochastic programming
• Mathematical programming
• +۵ مورد دیگر

Jean-François Côté is a Full Professor at the Department of Operations and Decision Systems within the Faculty of Business Administration at Laval University. His research focuses on combinatorial optimization, stochastic programming, and mathematical programming with applications in vehicle routing, cutting and loading problems, and logistics optimization. Dr. Côté holds a Doctorate in Computer Science, Operations Research from the University of Montreal, along with Master's and Bachelor's degrees in the same field from the same institution. His academic credentials provide a strong foundation for his interdisciplinary research at the intersection of computer science and operations research. His research spans several key areas in operations research including: Combinatorial optimization techniques for complex routing problems Stochastic programming approaches for handling uncertainty in logistics Mathematical programming models for integrated decision making Vehicle routing and delivery optimization in various contexts Cutting and loading problems in transportation and manufacturing Professor Côté's recent publication record demonstrates a strong focus on solving complex logistics and transportation problems with mathematical rigor. His work addresses challenging real-world problems including same-day delivery optimization, bike sharing systems, home healthcare routing, and automotive manufacturing sequencing. His methodological contributions include novel algorithmic approaches such as logic-based Benders decomposition, disaggregated integer L-shaped methods, and branch-and-regret algorithms. Dr. Côté has published extensively in top-tier operations research journals including: INFORMS Journal on Computing European Journal of Operational Research Transportation Science Computers & Operations Research Operations Research He actively collaborates with researchers across North America and Europe through the CIRRELT research center. His work addresses practical problems faced by industries including transportation, healthcare, manufacturing, and e-commerce. Professor Côté supervises graduate students working on cutting-edge optimization problems and contributes to advancing both theoretical and applied aspects of operations research. His laboratory, part of the broader research ecosystem at Laval University, focuses on developing innovative mathematical models and algorithms to solve complex decision-making problems in logistics and supply chain management.

• Real-Time Scheduling
• Model-Based Systems Engineering
• Unmanned Aerial Vehicles
• +۳ مورد دیگر

Emmanuel Grolleau is a Full Professor at ISAE-ENSMA (Institut Supérieur de l'Aéronautique et de l'Espace - École Nationale Supérieure de Mécanique et d'Aérotechique) specializing in real-time systems. He is affiliated with the LIAS laboratory (Laboratoire d'Ingénierie des Applications de la Sensorique) where he leads the Real Time Team. His work bridges theoretical scheduling principles with practical embedded system implementations across multiple domains. Professor Grolleau's primary research interests include: Real-Time Scheduling: uniprocessor, multiprocessor, and distributed scheduling with practical considerations like transactions and precedence constraints Model-Based Systems Engineering (MBSE): developing bridges between UML-MARTE and AADL to real-time scheduling tools Unmanned Aerial Vehicles (UAVs): autopilot architecture design and optimization Energy Systems: co-heading the LabCom ANR Laboratoire d'Insertion des Énergies Nouvelles et d'Optimisation des Réseaux (LIENOR) His publication record demonstrates a clear progression from fundamental scheduling theory to applied work spanning avionics, drone technology, and power systems. Recent work shows strong focus on UAV autopilot architectures, model-based frameworks for real-time systems, and energy management in power distribution networks. Professor Grolleau serves on multiple prestigious program committees including Real-Time Networks & Systems (RTNS) since 2012, ACM/SIGAPP Symposium On Applied Computing (SAC) since 2015, DETECT since 2018, and DroneSE in 2023. He has led significant research projects such as PIA CORAC Panda and FUI WARUNA, which developed the Time4Sys pivot meta-model to connect theoretical scheduling with practical implementation. His collaborative work extends across multiple institutions and industries, with publications spanning real-time scheduling theory, UAV systems, energy management, and avionic architectures. The consistent thread through his work is the practical application of real-time scheduling principles to solve complex engineering problems in safety-critical systems.