جستجوی استاد راهنمای دانشگاهی

Arnold Neumaierمشاهده پروفایل

استاد

• Global Optimization
• Heuristic Optimization
• Nonlinear Optimization
• +۶ مورد دیگر

Arnold Neumaier is a Full Professor (Chair for Computational Mathematics) at the Faculty of Mathematics, University of Vienna, Austria. His research focuses on global optimization, heuristic optimization, nonlinear optimization, and optimization under uncertainty. He leads a research group developing state-of-the-art software for optimization, numerical analysis, and statistics. Collaborations include work on global optimization with Immanuel Bomze and Vladimir Kolmogorov, heuristic optimization with Nysret Musliu and Günther Raidl, and optimization for data analysis with Dan Alistarh and Radu Boț. Neumaier holds a Ph.D. from the Free University of Berlin (1977) and has held academic positions at the University of Freiburg (1987–1993), AT&T Bell Laboratories (1993–1994), and visiting roles at Princeton University, the University of Nice, and RWTH Aachen. His work spans computational mathematics, quantum physics, and mathematical software development. He maintains extensive web resources on optimization and numerical analysis. Research interests include algorithmic software development, verified computing, mathematical modeling languages, uncertainty modeling, and quantum physics. His group comprises 1 full professor, 1 associate professor, 1 university assistant, 3 postdocs, and multiple PhD students. Key contributions include the 'Global Optimization' WWW site and foundational work in causal perturbation theory for quantum field theory.

Vardges Melkonianمشاهده پروفایل

دانشیار

• Combinatorial Optimization
• Network Design Problems
• Approximation Algorithms
• +۷ مورد دیگر

Vardges Melkonian is an Associate Professor in the Department of Mathematics at Ohio University, part of the College of Arts and Sciences. His academic work bridges theoretical and applied mathematics, with a strong emphasis on optimization and algorithmic problem-solving. Research Interests: Dr. Melkonian specializes in combinatorial optimization, network design, approximation algorithms, and applications of operations research. His research integrates mathematical programming and discrete modeling to solve complex real-world scheduling and allocation problems. These interests are reflected in his extensive publication record spanning sports leagues, hybrid work, exercise routines, and social partitioning. Publication Trends: Over the past decade, his scholarly output has consistently focused on developing integer programming and optimization models for diverse domains—from recreational puzzles like KenKen to large-scale logistical challenges in manufacturing and public policy. His work demonstrates a unifying theme: transforming practical problems into formal mathematical frameworks for efficient solution. Scientific Awards: No awards are mentioned in the provided text. Advising and Grants: The available information does not list any students or grant funding. However, his research contributions suggest active engagement in academic mentorship and potential involvement in funded projects, though specifics are not disclosed. Labs and Research Teams: There is no mention of specific labs, research groups, or collaborative teams associated with Dr. Melkonian in the provided content.

• Optimization
• Unit Commitment
• Generation Expansion Planning
• +۴ مورد دیگر

Dr. Diego Alejandro Tejada Arango serves as a Visiting Professor in the Energy Systems Modeling area at the Institute for Research in Technology (IIT) of Comillas Pontifical University's School of Engineering. His academic journey includes a BSc in Electrical Engineering from Universidad Nacional (Colombia, 2006), an MSc from Universidad de Antioquia (Colombia, 2013), and ongoing MSc studies in Research in Engineering Systems Modeling at Universidad Pontificia Comillas. BSc Electrical Engineering, Universidad Nacional, Medellin (2006) MSc Electrical Engineering, Universidad de Antioquia (2013) MSc Research in Engineering Systems Modeling, Universidad Pontificia Comillas (in progress) His research focuses on developing advanced modeling tools for energy sector analysis and planning, with particular expertise in optimization techniques for generation capacity expansion. Dr. Tejada's work bridges theoretical advancements with practical applications in power system operations and planning. His research interests span optimization methodologies, unit commitment formulations, generation and transmission expansion planning, and energy storage integration within modern power systems. Analysis of his publication record reveals a strong focus on improving computational efficiency in energy system modeling while maintaining accuracy, with recent work addressing temporal resolution flexibility, hydrogen infrastructure integration, and interpretable machine learning applications for power system operations. His research demonstrates consistent evolution from traditional power system planning toward addressing contemporary challenges of renewable integration and decarbonization. 4th EASE Student Award for PhD thesis on energy storage co-optimization Dr. Tejada has contributed extensively to collaborative research projects with industry partners including Endesa and Enel Iberoamerica, focusing on generation expansion, renewable integration, and strategic network planning. His technical reports for the Spanish Ministry of Economy demonstrate engagement with policy-relevant research. As a reviewer for leading power systems journals including IEEE Transactions on Power Systems and International Journal of Electrical Power & Energy Systems, he contributes to scholarly discourse in the field.

• Supply Chain Optimization
• Supply Chain Analytics
• Freight Transportation
• +۵ مورد دیگر

Michael Hewitt is a Professor of Supply Chain Management at the Quinlan School of Business, Loyola University Chicago, where he holds the Ralph Marotta Chair in Free Enterprise. He serves as Executive Director of the Quinlan Business Leadership Hub and Director of the Supply Chain and Sustainability Center, playing key leadership roles in academic and research initiatives. His research focuses on Supply Chain Optimization , Freight Transportation , and Network Design , leveraging advanced mathematical modeling and operations research techniques. His work bridges theoretical innovation with practical applications in logistics and transportation industries. The 15 most recent publications (limited here to 5) reflect a strong trend in continuous-time network modeling , dynamic discretization methods , and large-scale optimization for freight and service network design. His research spans disciplines including Operations Research, Transportation Science, and Supply Chain Analytics, with recurring subfields such as Benders decomposition, time-expanded networks, and resource-constrained pathfinding. Scientific Awards: CSoNet Best Paper Award 2023 INFORMS TSL Freight Transportation & Logistics SIG Best Paper Award 2021 Glover-Klingman Prize 2019 INFORMS TSL Best Paper Award 2018 Loyola Faculty Researcher of the Year 2015 Dr. Hewitt has secured research funding from the National Science Foundation , Material Handling Institute , and New York State Health Foundation . He actively mentors students through research collaboration and has advised projects with real-world impact at companies like Bayer Crop Science , Exxon Mobil , and Schneider . He serves on editorial boards and holds leadership roles in INFORMS , including past presidency of the Transportation Science and Logistics Society. He leads two major centers: the Supply Chain and Sustainability Center and the Quinlan Business Leadership Hub , fostering interdisciplinary research, industry partnerships, and student development in supply chain and leadership education.

• Finance
• Financial Engineering
• Corporate Finance
• +۳ مورد دیگر

Professor Yasunori Katsurayama at Waseda University's Faculty of Social Sciences is a leading scholar in finance and financial engineering with over 30 years of research experience. His work combines mathematical modeling with empirical analysis to address corporate finance challenges, particularly in Investment decision timing Credit risk valuation Real options frameworks . His research spans 1989-2025 with significant contributions to real options analysis , credit risk modeling , and portfolio optimization . Key trends in his 15 most recent papers show increasing focus on Stochastic investment timing Japanese market reforms Credit rating inconsistencies Technology-driven R&D valuation . As concurrent researcher at Waseda Research Institute for Science and Engineering (2024-2025) and director of the Institute for Motivation Science, he bridges finance with broader data science initiatives. His teaching portfolio includes Corporate Finance , Computational Finance , and Business Management courses at both undergraduate and graduate levels.

Roghayeh Hajizadehمشاهده پروفایل

مدرس

• Discrete Optimization
• Operations Research
• Mathematical Modeling
• +۴ مورد دیگر

Roghayeh Hajizadeh is a University Lecturer at Linköping University's Department of Mathematics (MAI) and Applied Mathematics (TIMA) research group. Her work focuses on discrete optimization techniques for real-world problems like urban snow removal and air traffic control, using mathematical modeling and algorithm development. PhD in Optimization of Snow Removal in Cities (2023) Member of Mathematics and Algorithms for Intelligent Decision Making research group Key projects: Optimization of Snow Removal in Cities (2017-present) and OPT@ATM (2023-present) Research keywords include: Discrete Optimization, Operations Research, Sustainable Urban Planning, Air Traffic Control Algorithms . Teaching includes Combinatorial Optimization for Computer Science/Software Engineering programs and Supply Chain Optimization for Industrial Economics/Mechanical Engineering students. Publications (2020-2024) demonstrate expertise in: Lagrangian relaxation techniques , vehicle coordination algorithms , and tree elimination methods for snow removal; Dantzig-Wolfe decomposition for aircraft scheduling.

Jorge A. Sefairمشاهده پروفایل

دانشیار

• Network optimization
• Combinatorial optimization
• Integer programming
• +۵ مورد دیگر

Jorge A. Sefair is an Associate Professor and Associate Director at the Center for Applied Optimization in the Department of Industrial & Systems Engineering at the University of Florida. He also serves as Masters Program Co-Coordinator. His expertise spans operations research, mathematical programming, and optimization in environmental/urban planning, public policy, and service systems. Dr. Sefair holds a PhD in Industrial and Systems Engineering from the University of Florida (2015), with prior degrees including an MSc and BSc in Industrial Engineering, and a BA in Economics from Universidad de los Andes, Colombia. His research focuses on network optimization, multistage optimization, and integer programming, with interdisciplinary applications in civil engineering, public health, ecology, and economics. His work has been published in high-impact journals like INFORMS Journal on Computing and European Journal of Operational Research. Research funding comes from the National Science Foundation, Office of Naval Research, and Department of Homeland Security. He received an NSF CAREER award in 2021 for Operations Engineering. Key research trends include optimization models for hydropower planning, human trafficking interdiction, biosurveillance, and conservation planning. Collaborations span diverse fields such as ecology, biology, and economics. Current efforts emphasize scalable solutions for complex, real-world systems.

Oh Joon-Yeoulمشاهده پروفایل

دانشیار

• Network Optimization
• Wireless Telecommunication Network Expansion and Optimization
• Non-Linear Programming
• +۶ مورد دیگر

Oh Joon-Yeoul serves as Associate Professor in the Department of Mechanical and Industrial Engineering at Texas A&M University-Kingsville's College of Engineering, specializing in optimization methodologies for telecommunications and public infrastructure systems. His academic credentials include: Ph.D. in Industrial Engineering (Operations Research), New Mexico State University (2003) M.S. in Industrial Engineering (Engineering Management), New Mexico State University (2000) M.S. in Industrial Engineering (Operations Research), Chong-Ju University (1998) B.S. in Industrial Engineering, Chong-Ju University (1995) Dr. Oh's research integrates mathematical programming and algorithmic development to solve complex industrial problems, with core expertise in Network Optimization , Wireless Telecommunication Systems , and Queueing Theory . His work bridges theoretical operations research with practical applications in municipal services, robotics, and emergency management, emphasizing non-linear and heuristic integer programming approaches for large-scale systems. Analysis of his 15 publications (2015-2020) reveals consistent focus on vehicle routing optimization (including multi-vehicle scenarios), facility location for public safety infrastructure, and multi-criteria decision frameworks . His research demonstrates strong methodological continuity in applying combinatorial optimization to real-world challenges like garbage collection routing, fire station allocation, and telecommunication network expansion, primarily through conference presentations at INFORMS and industrial engineering forums. Dr. Oh actively mentors graduate researchers as evidenced by student co-authorship on projects involving blockchain applications, humanoid robotics, and municipal service optimization. While specific grant details aren't documented, his collaborative work with colleagues like Amir Hessami indicates engagement in funded research initiatives addressing regional infrastructure challenges in South Texas. His research team focuses on practical implementation of optimization models for Kingsville municipal operations, particularly in waste management and emergency response systems, though dedicated laboratory facilities aren't specified in available materials.

• Mathematical Optimization
• Statistical Modeling
• Machine Learning
• +۱۳ مورد دیگر

Vanesa Guerrero Lozano is an Associate Professor in the Department of Statistics at Universidad Carlos III de Madrid, affiliated with the Flores de Lemus Institute and the UC3M-Santander Big Data Institute. Her work bridges mathematical optimization, statistical modeling, and data science, with applications across disciplines including biomedicine, fluid mechanics, and sustainable development. Her research focuses on developing advanced statistical methodologies using mathematical optimization. Key interests include shape-constrained regression, P-splines smoothing, sparse modeling, clustering of categorical data, and interpretable machine learning. She applies these techniques to complex datasets in turbulence modeling, biological age imputation, and pandemic forecasting. The recent publications reveal a strong trend in integrating optimization techniques with statistical learning, particularly in nonparametric and semiparametric models. There is a consistent emphasis on interpretability, robustness, and scalability, especially for high-dimensional and dynamic datasets. Applications span from fluid dynamics to public health, demonstrating interdisciplinary impact. Scientific Awards and Recognition: Ayuda adicional within the Juan de la Cierva Incorporación Program (2020), awarded by the State Research Agency (AEI) Research Leadership and Advising: She has served as principal investigator on multiple competitive research projects funded by national and regional agencies, including the State Research Agency (AEI), the Jacques Hadamard Mathematical Foundation, and the Community of Madrid. Her projects cover topics such as constrained additive models, machine learning for sustainable fishing, turbulence control, and ADHD diagnosis using data science. She has supervised at least one doctoral thesis on constrained smoothing models, indicating active mentorship in methodological statistics and optimization. Laboratories and Research Groups: She is a member of the Energy Analytics research group and conducts her work within the UC3M-Santander Big Data Institute, which supports interdisciplinary data science research. Her affiliation with the Flores de Lemus Institute further underscores her engagement with advanced statistical and computational methodologies.

Florian Pommereningمشاهده پروفایل

استاد

• Classical Planning
• Heuristic Search
• Linear Programming
• +۳ مورد دیگر

Dr. Florian Pommerening is a Professor at the Department of Mathematics and Computer Science within the Faculty of Science at the University of Basel, Switzerland. He holds an email address of florian.pommerening@unibas.ch and can be reached via +41 61 207 05 37. His academic career is deeply rooted in classical domain-independent planning, with a focus on optimal planning through heuristic search and the application of linear and mixed integer programming in planning domains. University of Basel, Switzerland Faculty of Science Department of Mathematics and Computer Science Professor Rank Florian completed his PhD at the University of Basel in 2017 under the supervision of Malte Helmert. He also holds a Master's degree in Computer Science from the University of Freiburg (2012) and a Master's in Information Technology from Monash University (2010). His research spans topics such as classical planning, heuristic search, linear programming, mixed integer programming, and automated planning systems. His publication record from 2012 to 2025 demonstrates sustained contributions to planning algorithms, including cost partitioning heuristics, Lagrangian decomposition, and transition landmark generation. His work has been recognized with multiple awards, including the ICAPS 2024 Influential Paper Award, ECAI 2023 Quality Champion PC award, and the Dux of the Master of Information Technology at Monash University. ICAPS 2024 Influential Paper Award Quality Champion PC award (ECAI 2023) Runner Up, Best Student Paper Award (ICAPS 2023) CoRe Challenge 2022 Awards Best Student Paper Runner-Up (ICAPS 2022) Best System Demonstration (ICAPS 2022) Outstanding Senior Program Committee Award (AAAI 2021) ICAPS 2019 Best Paper Award Faculty Award 2018 (University of Basel) EurAI Artificial Intelligence Dissertation Award 2017 ICAPS 2018 Best Dissertation Award Outstanding PC Member Award (SoCS 2016) Winner, Unsolvability IPC 2016 AAAI 2015 Outstanding Paper Award ICAPS 2014 Outstanding Paper Award Dux of the Master of Information Technology 2010 (Monash University) Florian actively contributes to the planning community through tutorials at AAAI 2019 and ICAPS 2015, and through software development such as the Fast Downward planner and Planutils framework. His notable side project includes creating a quine in the Shakespeare Programming Language (SPL) with Thomas Mayer, showcasing technical creativity beyond core research.

Konstantin Pavlikovمشاهده پروفایل

دانشیار

• Operations Research
• Stochastic Programming
• Integer Programming
• +۳ مورد دیگر

Konstantin Pavlikov is an Associate Professor at the Department of Business & Management (DBM) under Strategic Organization Design (SOD) at the University of Southern Denmark. His research focuses on Operations Research, Integer Programming, and Stochastic Programming , with particular emphasis on vehicle routing optimization and network flow modeling. Education : PhD in Operations Research (University of Florida, 2014), MSc in Applied Mathematics (Moscow State University, 2007) His work spans combinatorial optimization and network interdiction problems , developing exact and approximate solution algorithms for complex logistics challenges. Recent publications analyze heterogeneous vehicle routing capacity inequalities and two-commodity flow formulations for routing problems. Scientific contributions have been recognized with the Best Reviewer Award (2019). He actively reviews for journals like Computational Management Science and supervises academic works through examination roles.

Kurt Anstreicherمشاهده پروفایل

استاد

• Mathematical Programming
• Optimization Expertise
• Interior-Point Algorithms
• +۳ مورد دیگر

Kurt Anstreicher is a Professor of Business Analytics at the University of Iowa's Tippie College of Business, with complementary appointments in the Department of Computer Science and Department of Industrial Engineering. He holds the Gary C. Fethke Chair in Leadership. His research focuses on mathematical programming, optimization, interior-point algorithms, and nonlinear programming. Anstreicher earned his Ph.D. from Stanford University in 1983 and a B.A. from Dartmouth College in 1978. Research interests include optimization theory and methods, particularly in nonconvex programming, convex relaxations, and algorithm design. His work addresses challenges in quadratic programming, semidefinite optimization, and global optimization techniques. Recent contributions explore applications in trust-region subproblems, copositive cones, and maximum-entropy sampling. Publications span topics like convex hull representations, Kronecker product constraints, and spherical codes. His work bridges theoretical advancements and practical computational methods, with implications for operations research, data analysis, and engineering systems.

• Integer Programming
• Stochastic Programming
• High-Performance Computing
• +۳ مورد دیگر

Jeffrey Linderoth is a Professor in the Department of Industrial & Systems Engineering at the University of Wisconsin-Madison, within the College of Engineering. His research focuses on large-scale optimization problems, including integer programming and stochastic programming, with an emphasis on high-performance algorithms and software development. He holds a PhD from Georgia Institute of Technology (1998) and has been recognized with numerous awards, including INFORMS Fellow (2016) and multiple best paper awards in optimization journals. Linderoth is actively involved in teaching optimization courses such as COMP SCI 524 and I SY E 524, and mentors pre-dissertation and thesis research. Education: PhD (1998), MS (1994) – Georgia Tech; BS (1992) – University of Illinois Research Interests: Linderoth specializes in high-performance computing for optimization, numerical methods, and distributed algorithms. His work addresses challenges in computational grids, combinatorial optimization, and applications in fields like energy systems and computational biology. He has developed influential software tools like Minotaur for mixed-integer nonlinear programming. Recent Achievements: His 2024 work on stochastic integer programming and dual decomposition methods, as well as contributions to autocatalytic networks in origin-of-life studies, highlight his interdisciplinary impact. Linderoth has also been honored multiple times for teaching excellence, including the University Housing Honored Instructor Award (2019, 2016, 2008). Awards: Over 20 awards spanning best papers, teaching excellence, and early career recognition Grants/Projects: Includes Department of Energy funding and collaborations on optimizing power systems and minimizing cascading failures Linderoth leads research teams and contributes to academic organizations, advancing both theoretical and applied aspects of optimization science.

• Computer Science
• Artificial Intelligence
• Control Systems
• +۳ مورد دیگر

Sisi Li is an Associate Professor of Computer Science at Mercy College's School of Liberal Arts. Her research focuses on interdisciplinary areas spanning machine learning, control systems, energy efficiency, and optimization algorithms. She has contributed to advancements in domain adaptation, cyber-physical systems, and smart grid technologies. Her work emphasizes practical applications in energy management, vehicle systems, and building automation. Research interests include developing novel algorithms for imbalanced classification, multi-objective optimization, and swarm intelligence-based clustering. She explores the intersection of computational methods with real-world challenges in manufacturing, HVAC systems, and eco-driving strategies. Her recent studies address thermal model reduction, distributed load management, and social incentive policies for demand response in commercial buildings. Publications highlight a strong focus on model predictive control, energy storage optimization, and sensitivity analysis techniques. Her work frequently integrates advanced mathematical frameworks like pseudospectral methods and dynamic programming to solve complex engineering problems. While no awards are explicitly listed, her prolific publication record reflects significant contributions to computational and energy-related research domains.

• Optimization
• Convex Optimization
• Nonconvex Optimization
• +۷ مورد دیگر

Wotao Yin is a Professor of Mathematics at the University of California, Los Angeles, with a distinguished research career spanning over two decades in optimization theory and its applications. His work bridges theoretical mathematics with practical applications in machine learning, image processing, and signal analysis. As a leading researcher in optimization algorithms, he has made significant contributions to the development of methods like ADMM (Alternating Direction Method of Multipliers), proximal algorithms, and decentralized optimization techniques. Department: Department of Mathematics School: College of Letters and Science University: University of California, Los Angeles Yin's research focuses on developing efficient algorithms for large-scale optimization problems, with particular expertise in convex and nonconvex optimization, distributed and decentralized optimization, and mathematical foundations of machine learning. His work has profound implications for image reconstruction, signal processing, and modern machine learning systems. He has pioneered methods for handling sparse data, non-smooth objectives, and constrained optimization problems that arise in real-world applications. An analysis of his recent publications reveals a strong trend toward addressing optimization challenges in machine learning, particularly in federated learning, attention mechanisms, and nonconvex problem structures. His work demonstrates a consistent pattern of bridging theoretical optimization with practical machine learning applications, developing algorithms that balance computational efficiency with theoretical guarantees. Recent papers show increasing focus on heterogeneous data settings, large language model optimization, and fundamental limitations of optimization methods in complex learning scenarios. Throughout his career, Professor Yin has mentored numerous PhD students and postdoctoral researchers who have gone on to successful careers in academia and industry. His collaborative network spans multiple institutions worldwide, with particularly strong connections to researchers in China and across the United States. His work has been supported by various funding agencies recognizing the fundamental importance of optimization theory for advancing computational science. Professor Yin leads a vibrant research group focused on mathematical optimization and its applications, where students and collaborators work on cutting-edge problems at the intersection of mathematics, computer science, and engineering. The group maintains strong connections with both theoretical and applied research communities, participating in major conferences across optimization, machine learning, and computational mathematics.