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Stefano LucidiView profile

Professor

• Nonlinear Optimization
• Derivative-Free Optimization
• Mixed Integer Programming
• +3 more

Stefano Lucidi is a Full Professor of Operations Research at Sapienza University of Rome, where he is affiliated with the Department of Computer, Automatic and Management Engineering within the Faculty of Information Engineering, Computer Science and Statistics. He has held this position since November 1, 2000, after serving as Associate Professor from November 1, 1992 to October 31, 2000. He was coordinator of the PhD program in Operations Research from 2010 to 2012 and was a shareholder of the university spin-off ACTOR SRL until July 2024. His research interests span Nonlinear Optimization, Derivative-Free Optimization, Mixed Integer Programming, and Global Optimization. His methodological work focuses on unconstrained optimization methods, constrained optimization methods, non-differentiable optimization methods, derivative-free methods, and global optimization techniques. His applied research includes mathematical modeling of biological phenomena in cell kinetics, identification of astrophysical parameters, optimal management of bookable seats in rail transport, and optimal design of electromagnetic devices and industrial electric motors. His recent publications demonstrate a strong focus on derivative-free optimization methods, complexity analysis of algorithms, multi-objective optimization, and applications in diverse fields including healthcare, transportation, and neuroscience. His work bridges theoretical advances in optimization with practical applications across multiple domains. His significant contributions to the field include developing algorithms for simulation-driven design optimization, addressing nonsmooth optimization problems, and creating methods for multi-fidelity computations. His research has been published in top journals including Optimization Methods & Software, Journal of Optimization Theory and Applications, and Optimization Letters. Full Professor of Operations Research since 2000 PhD program coordinator (2010-2012) Shareholder of ACTOR SRL spin-off (2011-2024) Active contributor to optimization theory and applications Professor Lucidi has been instrumental in advancing optimization methodologies while maintaining strong connections to practical applications across various industries. His work continues to influence both theoretical developments and real-world implementations of optimization techniques.

• Industrial Engineering
• Operations Research
• Optimization
• +5 more

Shunichi Ohmori , a Professor at the Faculty of Science and Engineering , Waseda University , holds a Doctorate in Engineering from the same institution. His academic journey includes continuous affiliation with Waseda University since 2011, currently serving as a faculty member and concurrently as Director of Research Institute at the Institute of Global Production & Logistics (2022-2026). Education: Doctorate (2009-2012), Master's (2007-2009), and Bachelor's (2004-2007) from Waseda University His research focuses on industrial engineering and operations research , with specific interests in optimization , facility planning , logistics , and supply chain management . He has developed innovative frameworks for: Robust supply chain design under disruption risks Multi-echelon inventory optimization 3D facility layout planning with pipeline arrangements Collaborative vehicle routing cost allocation Green product portfolio strategies under uncertainty His publication record demonstrates expertise in stochastic optimization (15+ peer-reviewed papers) and heuristic algorithms (14+ conference proceedings). He has received multiple Best Paper Awards (2012-2016) and contributed to curriculum development through courses like Advanced Facilities Planning , Supply Chain Transformation , and Logistics at both undergraduate and graduate levels.

Dominique OrbanView profile

Professor

• Numerical Optimization
• Nonlinear Programming
• Numerical Linear Algebra
• +4 more

Dominique Orban is a Full Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal. He holds a Ph.D. from FUNDP Namur and INP Toulouse and has established himself as a leading researcher in numerical optimization. His academic affiliations include the Institute for Data Valorization (IVADO) and the Decision Analysis Study and Research Group (GERAD). Professor Orban's research focuses on numerical mathematics, particularly continuous nonlinear optimization, nonlinear systems of equations, and numerical linear algebra. His work involves designing specialized numerical algorithms for optimization problems, with particular interest in degeneracy and ill-posed problems. His research spans theoretical development of algorithms, their implementation in software, and applications to real-world problems such as image reconstruction, optimal structure design, and optimization under differential constraints. Analysis of his recent publications (2021-2025) reveals a strong focus on developing practical optimization algorithms with theoretical guarantees. His work spans multiple areas including nonsmooth optimization, iterative methods for linear systems, regularization techniques, and software implementation in Julia. A notable trend is his increasing focus on developing open-source software tools that make advanced optimization methods accessible to practitioners. Over 160 publications including journal articles, conference papers, and technical reports Multiple publications in top optimization journals each year through 2025 Strong emphasis on both theoretical foundations and practical implementation Increasing focus on Julia-based optimization software development Professor Orban has successfully supervised 9 doctoral students and 13 master's students to completion, demonstrating his commitment to mentoring the next generation of researchers. His supervision style appears to balance theoretical depth with practical implementation skills, preparing students for both academic and industry careers. His research has been supported through various institutional and collaborative grants, enabling him to maintain an active research program with multiple ongoing projects. His contributions to the field include significant software developments such as Krylov.jl, JSOSuite.jl, and DCISolver.jl, which have made advanced optimization techniques more accessible to the broader scientific community. These tools reflect his philosophy of bridging theoretical optimization with practical computational implementation.

• Operations Research
• Mathematical Optimization
• Column Generation Algorithms
• +3 more

Hatem Ben Amor is an Associate Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal. With over two decades of academic experience since completing his Master's degree in 1997, he has established himself as a researcher in operations research and mathematical optimization. His research primarily focuses on: Operations Research and Mathematical Optimization Column Generation Algorithms and Stabilization Techniques Shortest Path Problems with Resource Constraints Vehicle Routing and Scheduling Linear and Integer Programming Professor Ben Amor's publication record demonstrates consistent scholarly output in top-tier operations research journals including Operations Research, Transportation Science, and Computers & Operations Research. His work shows a progression from foundational research on column generation stabilization to more applied work in transportation optimization, reflecting both theoretical depth and practical relevance in the field. He has supervised graduate students, including at least one doctoral candidate whose research extended his work on shortest path algorithms. His research has practical implications for transportation logistics, manufacturing optimization, and resource allocation problems. Contact information: Email: hatem.ben-amor@polymtl.ca Phone: (514) 340-4711 ext. 6042 Office: AA-4431

Stefano MarkidisView profile

Professor

• High-Performance Computing
• Heterogeneous Systems
• Quantum Computing
• +3 more

Stefano Markidis is a Professor of Computer Science specializing in high-performance computing systems at KTH Royal Institute of Technology in Sweden. He works in the Division of Computational Science and Technology, focusing on supercomputers, quantum computers, and computational methods for scientific simulations. His research spans multiple domains including plasma physics, computational fluid dynamics, and quantum computing. Markidis holds an MS degree from Politecnico di Torino and a PhD in Nuclear Engineering from the University of Illinois at Urbana-Champaign. Prior to joining KTH, he was a graduate research assistant at Los Alamos National Laboratory and Lawrence Berkeley National Laboratory, followed by a postdoc at KU Leuven. His academic journey reflects a strong foundation in both engineering and computational science. His primary research interests include High-Performance Computing , Heterogeneous Systems , and Quantum Computing . Markidis develops computational methods for plasma physics, particle-in-cell simulations, and fluid dynamics. His work bridges theoretical physics and practical computing, with applications in space physics, fusion energy, and materials science. He is particularly known for contributions to parallel computing, GPU acceleration, and the development of scalable simulation frameworks like Neko for computational fluid dynamics. His research increasingly integrates machine learning techniques with traditional numerical methods. Analysis of Markidis' recent publications reveals a strong focus on quantum-classical hybrid computing, advanced particle-in-cell methods, and high-fidelity computational fluid dynamics. His work demonstrates expertise in programming models for heterogeneous architectures including GPUs and quantum processors, with growing emphasis on AI-enhanced scientific computing. R&D100 award (2005) for the CartaBlanca project R&D100 award (2017) for the SHIELDS project Markidis teaches multiple courses at KTH including Applied GPU Programming, Quantum Computing for Computer Scientists, and High-performance Computing for Computational Scientists. He has supervised numerous degree projects across various specializations in computer science and electrical engineering. His research has been supported by various grants related to high-performance computing and quantum technologies, with applications spanning from space physics to medical treatments. Markidis leads research in computational science with a focus on developing frameworks like Neko for extreme-scale computational fluid dynamics. His team works on integrating traditional HPC methods with emerging technologies including quantum computing and AI, contributing to advancements in scientific simulation across multiple disciplines.

• Mathematical Optimization
• Conic Optimization
• Smart Grids
• +7 more

Miguel Anjos is Professor and Chair of Operational Research at the School of Mathematics, University of Edinburgh. He serves as Deputy Head of School and leads the Data and Decisions research theme. His professional roles extend to Chair of the Mathematical Optimization Society (2023–2025) and Vice-President (International Activities) of INFORMS. He holds Fellowships from the Humboldt Foundation, EUROPT, and the Canadian Academy of Engineering. Research Focus: Mathematical optimization for large-scale nonlinear problems in engineering applications, particularly conic optimization in facility layout and power grid optimization. Collaborations: National Grid ESO, Hydro-Québec, Schneider Electric, Rio Tinto, EDF, and Yeji Data Lab. Datasets: Maintains QAPLIB, FLPLIB, and Jones Benchmark for optimization research. His work addresses smart grid challenges: maximizing grid efficiency, integrating renewables, optimizing energy storage, and enabling customer participation. Former students have secured positions at universities (University of Tennessee, Western Ontario) and tech companies (Amazon, Shopify). Scientific Awards: Fellow of the Humboldt Foundation Fellow of EUROPT Fellow of the Canadian Academy of Engineering He has supervised PhD researchers including Elspeth Adams (2014 MOPTA runner-up), Christian Bingane (2019 CORS Student Paper Competition winner), and Mathieu Tanneau (2018–2021 FRQNT Merit Scholar). His postdocs have contributed to bilevel attacks in AI, energy storage scheduling, and electric vehicle infrastructure planning.

Radul ShishkovView profile

Assistant Professor

• Parametric Design
• Algorithmic Modeling
• Computational Design
• +3 more

Radul Shishkov serves as Chief Assistant at the University of Architecture, Civil Engineering and Geodesy (UACEG), specifically within the Faculty of Architecture's Department of Interior and Design for Architecture. He also holds the position of Managing Partner and Lead Architect at CAST Studio while lecturing in the master's program "Contemporary and Computational Design" at UACEG. His educational background includes a Doctorate in "Strategies for the application of algorithmic modeling in architectural design" (2020), a Master's degree in Architecture (2008-2013) from UACEG, and earlier education at the Professional High School of Construction, Architecture and Geodesy in Plovdiv. Shishkov's research focuses on computational approaches to architectural design, with particular emphasis on parametric and algorithmic modeling techniques. His work bridges theoretical exploration with practical application in both interior and architectural contexts. He has developed specialized courses in parametric design, computational design, and digital fabrication, contributing significantly to the integration of advanced computational methods in architectural education. His publication history reveals a consistent focus on applying computational techniques to architectural challenges, spanning from interior design to urban environment analysis. The publications demonstrate progression from practical design applications toward more sophisticated algorithmic approaches for building information modeling and urban analysis. Shishkov actively participates in international design workshops and research projects, including "Parametric Planning" with SOFIAPLAN and various EU-funded initiatives. His professional practice includes significant architectural projects such as residential buildings, restaurant designs across multiple countries, and technical expertise for historical building restoration. He leads international workshops on parametric design and digital fabrication across locations including Athens, Vienna, Sofia, and Amman, demonstrating his commitment to advancing computational design methodologies globally. His CAST Studio serves as both a professional practice and research platform for exploring the frontiers of computational architecture.

Simon ApersView profile

Researcher

• Quantum Algorithms
• Theoretical Computer Science
• Random Walks
• +4 more

Simon Apers is a CNRS researcher at IRIF (Institut de Recherche en Informatique Fondamentale) at Université Paris Cité. He focuses on quantum algorithms with broader interests in theoretical computer science, including random walks, graph theory, and combinatorial optimization. His primary research interests span quantum computing, theoretical computer science, and algorithms. Apers has made significant contributions to quantum walks, quantum property testing, graph algorithms, and computational complexity. His work often bridges quantum information with classical theoretical computer science, exploring how quantum techniques can improve classical algorithms and solve problems more efficiently. He has developed quantum speedups for various computational tasks including sampling, optimization, and graph problems. Simon Apers serves as a program committee member for prestigious conferences including TQC'25, QIP'25, TQC'23, SODA'23, and ESA '21. He also serves as an editor for the journal Quantum. His teaching activities include courses at Sorbonne University (2022-present) on Advanced Quantum Algorithms, MPRI (2021-present) on Quantum Algorithms and Complexity, and Bad Honnef (2022). His publication record shows a strong trend toward quantum algorithms for graph problems, quantum walks, and connections between quantum computing and classical theoretical computer science. He frequently collaborates with researchers from various institutions across Europe and has published in top venues including FOCS, STACS, ESA, PRL, and JMLR. Simon Apers actively mentors students and has open positions for PhD students and postdocs in quantum algorithms at IRIF. His research group is involved in cutting-edge work at the intersection of quantum computing and theoretical computer science, with significant contributions to quantum walks, property testing, and computational complexity.

• Operations Research
• Probability and Stochastic Modeling
• Optimization
• +5 more

Michael Veatch is a Professor of Mathematics at Gordon College in the School of Science, Technology and Health. Holding a Ph.D. from MIT with prior industry experience in defense logistics, he bridges theoretical operations research with practical humanitarian applications. His educational background includes: B.A. from Whitman College M.S. from Rensselaer Polytechnic Institute Ph.D. from Massachusetts Institute of Technology Dr. Veatch specializes in applying probability models and optimization techniques to humanitarian logistics and queueing networks. His research spans pandemic vaccine allocation strategies, gift-in-kind donation systems for organizations like World Vision, and airport congestion management during disaster relief operations. He investigates how faith-based values influence operational decisions in Christian relief organizations through collaborations with Wheaton College and MIT. His work uniquely integrates mathematical rigor with real-world humanitarian challenges, particularly in crisis response scenarios. Analysis of his publication record reveals a strategic evolution from theoretical queueing network research toward increasingly applied humanitarian logistics work. His recent publications demonstrate sophisticated optimization frameworks addressing urgent global health challenges like pandemic response, while maintaining strong theoretical foundations in stochastic modeling and dynamic programming. The interdisciplinary nature of his work connects mathematics, operations research, public health, and ethical decision-making. Dr. Veatch has made significant contributions through his textbook Linear and Convex Optimization: A Mathematical Approach (Wiley, 2021) designed for mathematics majors. He developed an industry-focused course through the Preparation for Industrial Careers in Mathematical Sciences program and contributes to vocational guidance for mathematics students. His active research collaborations include: International Vaccine Allocation with MIT researchers Gift-in-Kind Acceptance Strategies for World Vision Informed Compassion project on faith-based operational decisions Disaster airport scheduling using Haiti earthquake data