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Salvatore BarbaView profile

Professor

• Architectural Drawing
• Conservation and Restoration
• Remote Sensing
• +8 more

Salvatore Barba is a Full Professor in the Department of Civil Engineering at the University of Salerno, Italy, where he conducts research and teaching in architectural surveying, cultural heritage documentation, and digital reconstruction. He also serves as a Contract Professor at the Faculty of Architecture, University of Basilicata, Matera. His work bridges engineering, architecture, and heritage science, focusing on advanced digital methodologies. Research interests: His expertise lies in photogrammetry , remote sensing , 3D laser scanning , GIS-BIM integration , and augmented reality for cultural heritage. He investigates degradation diagnostics, low-cost survey tools, and micro-photogrammetry, emphasizing interdisciplinary and methodologically rigorous approaches to digital documentation. Publications and technology: His recent articles highlight the application of UAVs, wearable SLAM scanners, and handheld microscopes in heritage contexts. He validates instruments like the Shining 3D EinScan-Pro and develops protocols for high-accuracy documentation. His work consistently underscores the importance of expert interpretation over purely automated processes in surveying. Scientific Awards: No specific awards mentioned in the provided text. Advising and grants: While specific students and grants are not listed, he collaborates on international projects, participates in technology transfer agreements (e.g., with Dino-Lite), and contributes to methodological advancements in heritage science. His role as a faculty member implies advising graduate students in civil engineering and architectural documentation. Labs and teams: He is involved in research activities related to digital heritage documentation, likely within laboratory facilities at the University of Salerno. His collaborative work with researchers across Europe and on diverse heritage sites indicates active participation in interdisciplinary research teams focused on advanced surveying and conservation technologies.

Miguel F. AnjosView profile

Professor

• Mathematical Optimization
• Operational Research
• Conic Optimization
• +6 more

Miguel F. Anjos is a Professor in the School of Mathematics at the University of Edinburgh, where he serves as Deputy Head of School and Head of the research theme Data and Decisions. He also holds an Associate Professor position at Polytechnique Montréal in the Department of Mathematics and Industrial Engineering. Additionally, he chairs the Mathematical Optimization Society (2023-2025) and serves as Vice-President (International Activities) of INFORMS. His research focuses on mathematical optimization to solve large-scale discrete nonlinear optimization problems in engineering applications, particularly in conic optimization for facility layout problems and optimal power flows in electricity grids. His work addresses critical challenges in smart grid development, including efficient network utilization, renewable energy integration, energy storage, and customer participation in grid operations. He has extensive industry experience working with National Grid ESO, Hydro-Québec, Schneider Electric, Rio Tinto, and EDF. His publications reveal a strong focus on optimization methods applied to energy systems, with recent work spanning bilevel optimization, robust optimization with uncertainty quantification, electric vehicle infrastructure planning, and carbon credit markets. His research consistently bridges theoretical optimization with practical engineering applications. Canada Research Chair in Nonlinear Discrete Optimization in Engineering (2011-2016) NSERC-Hydro-Québec-Schneider Electric Industrial Research Chair in Smart Grid Optimization (2016-2019) INRIA International Chair for Optimizing Smart Grids (2016-2021) Winner of MOPTA 2020 and 2024 competitions Queen Elizabeth II Diamond Jubilee Medal (2012) Fellow of the Humboldt Foundation, EUROPT, and Canadian Academy of Engineering Professor Anjos has supervised numerous PhD students and postdoctoral fellows, with graduates securing positions at academic institutions including University of Tennessee, Western Ontario University, and industry roles at Amazon, CAISO, and major consultancies. He maintains three benchmark datasets (QAPLIB, FLPLIB, and the Jones Benchmark) as a service to the optimization community.

Sandra C. VazView profile

Assistant Professor

• Mathematical Modeling
• Dynamical Systems
• Epidemiology
• +3 more

Sandra C. Vaz serves as an Assistant Professor in the Department of Mathematics at the University of Beira Interior (UBI), Portugal, teaching core undergraduate courses including Probabilities and Statistics, Calculus II, and Quantitative Methods. Her academic profile remains active with 2025 course listings and recent scholarly contributions through 2023. Her research program bridges theoretical and applied mathematics, emphasizing Mathematical Modeling of infectious disease dynamics and Dynamical Systems theory. Key focus areas include discrete-time compartmental models for epidemiological forecasting, stability analysis of nonlinear systems, and number-theoretic applications in beta-transformations. This dual trajectory reflects both practical public health engagement and foundational mathematical inquiry. Analysis of her 2021-2023 publications reveals concentrated expertise in mathematical epidemiology, particularly discrete-time modeling frameworks for HIV immune response dynamics and Portugal-specific COVID-19 transmission patterns. Concurrently, she maintains theoretical work on Pisot numbers and symbolic dynamics, demonstrating sustained contribution to both applied and pure mathematics domains. No scientific awards are documented in the current profile. Dr. Vaz has participated in research projects including the completed grant 'Sistemas Dinâmicos Discretos de Baixa Dimensão e de Dimensão Infinita' (POCI/MAT/60380/2004), often collaborating with Delfim F. M. Torres. While her Ciência Vitae profile confirms publication output, no formal advisees or current grant funding are explicitly referenced in the provided materials.

Giacomo NanniciniView profile

Associate Professor

• Optimization
• Quantum Computing
• Algorithms
• +3 more

Giacomo Nannicini is an Associate Professor in the Daniel J. Epstein Department of Industrial & Systems Engineering at the University of Southern California's School of Advanced Computing, with a courtesy appointment in the Ming Hsieh Department of Electrical & Computer Engineering. His research focuses on optimization broadly defined, with particular interests in algorithms, software, models of computation, and quantum computing applications. His research interests span optimization theory , quantum algorithms , computational methods , and mixed-integer programming . Nannicini has made significant contributions to both classical optimization techniques and emerging quantum computing approaches, developing algorithms that bridge theoretical foundations with practical applications in various domains. Nannicini has received numerous prestigious awards including the 2021 Beale–Orchard-Hays prize, the best paper award at IEEE QCE 2021 (quantum algorithms track), the 2016 COIN-OR Cup, the 2015 Robert Faure prize, and the 2012 Glover-Klingman prize. His publication record demonstrates consistent high-impact contributions across top journals in operations research, computer science, and quantum computing. He has successfully advised multiple PhD students who have gone on to prominent positions including research scientists at Saint-Gobain and SINTEF, as well as tenure-track faculty positions. Nannicini actively recruits PhD students interested in computational optimization, quantum optimization, GPU-enabled computational optimization, and mixed-integer derivative-free optimization.

• Operations Research
• Discrete Mathematics
• Tropical Geometry
• +3 more

Georg Loho is an Assistant Professor in the department of Mathematics of Operations Research. His research focuses on the intersection of combinatorics, optimization, and geometry, with a particular emphasis on oriented matroids, tropical geometry, and algorithmic game theory. He explores applications in machine learning, neural network architecture, and stochastic games. Key research themes include the study of matroid theory, convex analysis, and the development of algorithms for optimization problems. His work bridges theoretical mathematics with practical applications in computational fields, such as analyzing the complexity of neural networks and solving problems in mean payoff games. Recent contributions include advancements in tropical geometry applied to neural networks and the reduction of stochastic games to semidefinite programming. He actively collaborates on interdisciplinary projects, as evidenced by his talks and publications in top-tier journals such as Selecta Mathematica and TheoretiCS . His research outputs span topics like polymatroids, lattice polytopes, and combinatorial optimization, reflecting a commitment to both foundational theory and computational methods.

Vera RoshchinaView profile

Associate Professor

• Convex Geometry
• Mathematical Optimization
• Nonsmooth Analysis
• +3 more

Associate Professor Vera Roshchina is a mathematician at the University of New South Wales (UNSW) Sydney , affiliated with the College of Science and School of Mathematics & Statistics . Her research focuses on convex geometry , mathematical optimization , and nonsmooth analysis , with significant contributions to projection methods, hyperbolicity cones, and Chebyshev approximation. Her recent work explores constructing spectrahedra with prescribed facial dimensions (2025) optimal camouflaging in billiards (2025) convergence of projection algorithms (2024) amenability of hyperbolicity cones (2024) topological properties of convex sets (2023) She is supported by multiple ARC Discovery grants and received the Christopher Heyde Medal (2021) . Her teaching includes courses in Mathematical Optimization and Calculus . She organized and participated in international research programs like MoCaO lectures (2024) and the WODCA2025 workshop , collaborating with researchers globally. Her publications span journals in optimization, nonlinear analysis, and dynamical systems.

Andreas WaechterView profile

Professor

• Large-scale nonlinear optimization
• Mixed-integer nonlinear optimization
• Software implementation of numerical algorithms
• +4 more

Andreas Waechter is a Professor in the Department of Industrial Engineering and Management Sciences at Northwestern University's McCormick School of Engineering. He holds a Ph.D. in Chemical Engineering from Carnegie Mellon University (2002) and an M.S. in Mathematics from the University of Cologne (1997). Before joining Northwestern in 2011, he was a Research Staff Member at IBM Research (Yorktown Heights, NY). His research focuses on developing numerical algorithms for nonlinear continuous and mixed-integer optimization, with applications in power systems, scientific computing, and sustainability. Key contributions include the open-source solver Ipopt (winner of the 2011 Wilkinson Prize and 2009 INFORMS Computing Society Prize), and the Bonmin and Couenne solvers for mixed-integer optimization. Waechter has received numerous awards, including the 2017 Charles Broyden Prize and the 2020 ARPA-E Grid Optimization Competition award. His work emphasizes both theoretical advancements and practical software implementation, with applications ranging from power grid optimization to machine learning. Education: Ph.D., Chemical Engineering, Carnegie Mellon University, 2002 M.S., Mathematics, University of Cologne, 1997 His research interests span optimization algorithms, algorithmic differentiation, and interdisciplinary applications. He has advised over a dozen graduate students and collaborates with industry and academic institutions globally. Waechter's software packages are widely used in academia and industry, reflecting his commitment to open-source computational tools.

David IsaacsonView profile

Professor

• Mathematical Physics
• Medical Imaging
• Electrical Impedance Tomography
• +3 more

David Isaacson holds the position of Professor in the Department of Mathematical Sciences at Rensselaer Polytechnic Institute (RPI), where he conducts cutting-edge research at the intersection of theoretical mathematics and clinical medicine. His primary institutional affiliation is with RPI, and he is also associated with the Center for Biotechnology and Interdisciplinary Studies, reflecting the multidisciplinary nature of his work. Professor Isaacson's long-term research goals center on improving diagnostic and therapeutic approaches for heart disease and cancer through innovative mathematical and engineering solutions. Professor Isaacson's research program is distinctly bifurcated into two major domains: mathematical physics and medical imaging. In mathematical physics, he develops advanced computational methods for approximating energy levels, particle masses, and critical exponents in quantum mechanics, quantum field theory, and statistical mechanics. His medical imaging research focuses on electrical impedance tomography (EIT), where he pioneers algorithms and hardware for non-invasively measuring and visualizing the electrical state of the body's interior from surface measurements. Specific applications include monitoring cardiac and pulmonary functions, diagnosing breast cancer through molecular impedance signatures, and optimizing chemotherapeutic interventions. His work in this area has significant clinical implications for conditions such as congenital heart defects, cystic fibrosis, and bronchopulmonary dysplasia. A review of Professor Isaacson's publication record from 2016 to 2025 demonstrates a sustained and evolving research trajectory in EIT technology. Early work concentrated on foundational reconstruction algorithms (e.g., D-bar and Calderón methods) and hardware development (ACT series systems), while recent publications emphasize clinical translation, particularly in pediatric cardiology and pulmonology. His team has produced innovative solutions for simultaneous EIT/ECG acquisition, 3D absolute imaging, and dynamic monitoring of ventilation-perfusion relationships. The consistent theme across these studies is the rigorous application of mathematical theory to solve practical biomedical imaging challenges, resulting in tools that are increasingly adopted in clinical settings for real-time physiological monitoring. No scientific awards or major honors were explicitly documented in the available sources. While detailed information about academic advisees, grant funding, or dedicated laboratory facilities was not provided in the current materials, Professor Isaacson's extensive collaborative network is evident from his numerous co-authored publications with clinicians and engineers. His work with the Center for Biotechnology and Interdisciplinary Studies suggests active participation in multidisciplinary research teams focused on translating engineering innovations into medical practice. Future research directions likely include further refinement of EIT for point-of-care diagnostics and expansion into new disease areas through continued cross-disciplinary partnerships.

Abdellah SalhiView profile

Professor

• Optimization
• Metaheuristics
• Operations Research
• +3 more

Abdellah Salhi is a Professor at the School of Mathematics, Statistics and Actuarial Science (SMSAS) at the University of Essex. His academic qualifications include a PhD from Aston University and a BSc from the University of Constantine. He specializes in optimization, metaheuristics, and operations research, with applications in logistics, port operations, and mathematical modeling. His research addresses complex problems such as container port scheduling, vehicle routing, and numerical solutions for biological systems. Salhi's work spans optimization algorithms like the Plant Propagation Algorithm, Genetic Algorithms, and hybrid methods for solving real-world challenges. His articles focus on multi-objective optimization, logistics efficiency, and algorithmic advancements. He has supervised numerous PhD students, contributing to academic and industrial research. While grants and funding sections are listed, specific details are not provided in the text. No dedicated labs or teams are mentioned, though collaborations are implied through his extensive publication record.

• Quantum Computing Optimization
• Interior Point Methods
• Semidefinite Optimization
• +11 more

Professor Tamás Terlaky holds the Alcoa Endowed Chair at Lehigh University 's P.C. Rossin College of Engineering and Applied Science , directing the Quantum Computing and Optimization Laboratory (QCOL) . He maintains Adjunct Professor roles at McMaster University and University of Pannonia , and serves as Visiting Professor at Strathclyde University . Ph.D. & M.Sc. from Eötvös University, Budapest Doctor of Academy of Sciences (Hungary) Habilitation from University of Pannonia Research Interests span Quantum Computing Optimization , Interior Point Methods , High-Performance Computational Optimization , Mathematical Modeling , and Medical Engineering Applications . Specific foci include Proton Therapy Treatment Optimization , Nuclear Reactor Core Reloading , and Service System Optimization . Recent Publications demonstrate expertise in Quantum Interior Point Methods , Quantum Linear System Algorithms , and Combinatorial Optimization , with applications to Medical Physics , Structural Engineering , and Quantum Software Design . Scientific Awards : Fellow of Fields Institute , INFORMS , SIAM , IFORS , and Canadian Academy of Engineering INFORMS Wagner Prize recipient IISE Outstanding Innovation in Service Science award MITACS Mentorship Award Teaching includes courses on Conic Optimization , Interior Point Methods , and Optimization Methods and Software . Currently leading a $2.1M DARPA grant for Quantum Computing Optimization research.

Iro ArmeniView profile

Assistant Professor

• Computer Vision
• 3D Scene Understanding
• Built Environment
• +5 more

Iro Armeni is an Assistant Professor in the Civil and Environmental Engineering Department at Stanford University's School of Engineering. She leads the Gradient Spaces research group, focusing on the intersection of civil engineering, architecture, and machine perception to design and construct data-driven environments across physical and digital space. Her educational background is highly interdisciplinary: PhD in Civil and Environmental Engineering with Minor in Computer Science from Stanford University (2020), Postdoctoral Researcher at ETH Zurich (2023), MSc in Computer Science from Ionian University (2013), MEng in Architectural Engineering from University of Tokyo (2011), and Diploma in Architectural Engineering from National Technical University of Athens (2009). Before academia, she worked as an architect and consultant for both private and public sectors. Dr. Armeni's research focuses on developing quantitative and data-driven methods that learn from real-world visual data to generate, predict, and simulate new or renewed built environments with humans at the center. She is particularly interested in creating gradient spaces that blend 100% physical (real reality) to 100% digital (virtual reality) using Mixed Reality. Her work spans computer vision, 3D scene understanding, semantic mapping, and their applications in the built environment. Her recent publications demonstrate significant contributions across multiple venues including CVPR, ECCV, SIGGRAPH, and ISPRS Journal, with research themes centered around 3D scene understanding, appearance transfer, scene synthesis, SLAM in dynamic environments, and semantic mapping. Her work shows a consistent trajectory toward creating sustainable, inclusive, and adaptive built environments that support current and future physical and digital needs. She has received prestigious awards including the ETH Zurich Postdoctoral Fellowship, Google PhD Fellowship, and MEXT Scholarship. Her teaching includes graduate courses such as Designing for Gradient Spaces (CEE342), Computer Vision for the Built Environment (CEE247C), and AI Applications in AEC (CEE329), reflecting her interdisciplinary approach to integrating machine perception with civil engineering applications.

Margaret H. WrightView profile

Professor

• Optimization
• Numerical Linear Algebra
• Scientific Computing
• +3 more

Margaret H. Wright is Professor of Computer Science at the Courant Institute of Mathematical Sciences, New York University, where her work spans optimization, numerical linear algebra, and scientific computing. Education: Specific degrees and institutions are not listed in the provided text. Research Interests: Her scholarship focuses on the theory, algorithms, and practical implementation of optimization methods, especially interior-point techniques for nonlinear programming and the analysis of direct-search algorithms such as the Nelder–Mead simplex method. She pursues both fundamental convergence questions and numerical stability issues arising in large-scale scientific computation. Publication Trends: Across four decades Wright has co-authored seminal books and journal articles that shaped modern optimization. Her 1981 and 1991 monographs integrated numerical linear algebra with optimization practice, while later papers illuminated the interior-point revolution and provided rigorous convergence analyses of the Nelder–Mead method in low dimensions. Scientific Awards & Service: The text does not enumerate specific awards, but records major professional service: Chair of the 2010 International Review of Mathematical Sciences Research (United Kingdom), Scientific Advisory Board memberships for Matheon (Berlin) and the Stony Brook Institute for Advanced Computational Science, and Senior Fellow of the Simons Society of Fellows. Teaching & Grants: During 2018 she taught “Introduction to Numerical Optimization” (fall) and “Numerical Computing” (spring) to undergraduates. No grant information or lists of supervised students are provided. Labs & Teams: No dedicated laboratory or research group names are mentioned.

• Continuous Optimization
• Numerical Methods
• Convex Optimization
• +12 more

Mirai Tanaka is an Associate Professor at The Institute of Statistical Mathematics (ISM) in Tachikawa, Tokyo, where they lead research in optimization theory and its applications. Currently affiliated with the Statistical Decision-Making Group in the Department of Fundamental Statistical Mathematics, Tanaka also holds concurrent appointments at the Risk Analysis Research Center at ISM and the Statistical Science Program at The Graduate University for Advanced Studies. Previously, Tanaka served as an Assistant Professor at ISM (2017-2020) and at Tokyo University of Science (2014-2017). Tanaka's research focuses on continuous optimization, numerical methods, and their applications across diverse fields. Their work spans convex and non-convex optimization, proximal algorithms, DC programming, and mathematical modeling for real-world problems. Specific interests include gradient methods, subgradient methods, Bregman proximal algorithms, Newton methods, and interior point methods. Tanaka has applied these techniques to problems in machine learning, data analysis, signal and image processing, control engineering, maritime affairs, supply chain management, forest science, and polymer chemistry. Their recent publications reveal a strong emphasis on developing efficient algorithms for challenging optimization problems, with particular attention to convergence properties and practical implementations. Tanaka's work bridges theoretical developments with real-world applications, as evidenced by publications spanning pure optimization theory to applied problems in various scientific domains. 12th Research Encouragement Award, Operations Research Society of Japan (2022) Best Paper Award, 15th Congress of the Asian Network for Quality (2017) Best Paper Award, 14th Congress of the Asian Network for Quality (2016) Teshima Seiichi Memorial Research Award (Doctoral Dissertation Award), Tokyo Institute of Technology (2015) Tanaka actively supervises graduate students at The Graduate University for Advanced Studies and has received multiple research grants from the Japan Society for the Promotion of Science (JSPS), including current funding for projects on forest pest control and practical aerial image processing. They have organized numerous workshops including the ZIB-IMI-ISM-NUS-RIKEN-MODAL-NHR Workshop series and the Continuous Optimization and Related Fields Summer School.

• Mathematical Optimization
• Semidefinite Programming
• Quantitative Finance
• +4 more

Farid Alizadeh is a Professor at Rutgers Business School, Rutgers University, and serves as the Director of the Master of Information Technology and Analytics (MITA) program since 2018. His expertise lies in mathematical optimization, particularly semidefinite programming, with applications spanning quantitative finance, statistical learning theory, computer science, and engineering. Education: Ph.D. in Computer and Information Sciences from the University of Minnesota. Professor Alizadeh's research focuses on advancing semidefinite programming techniques for shape-constrained approximation and regression. His work has been supported by the National Science Foundation (NSF) and Office of Naval Research (ONR), and he has pioneered significant methodologies like the Q method for symmetric and second-order cone programming. He has received prestigious accolades, including the NSF CAREER award and the 2014 Farkas Prize from the INFORMS Optimization Society. His academic supervision includes notable advisees such as Yu Xia, Stefan Schmieta, and Reuben Settergren.

• Computational Optimization
• Mathematical Optimization
• Numerical Analysis
• +4 more

Alberto De Marchi is a Research Associate at the Institute of Applied Mathematics and Scientific Computing at Universität der Bundeswehr München (UniBw M) in Germany. He holds a doctoral degree (Dr.rer.nat.) in Applied Mathematics from UniBw M (2021), an M.Sc. in Mechatronics Engineering (2016), and a B.Sc. in Industrial Engineering (2014) from the University of Trento (UniTn) in Italy. In Fall 2022, he was a Visiting Research Associate with Ryan Loxton at Curtin University, Australia. Education Dr.rer.nat., Applied Mathematics, UniBw M (2021) M.Sc., Mechatronics Engineering, UniTn (2016) B.Sc., Industrial Engineering, UniTn (2014) His research spans computational optimization, mathematical modeling, numerical analysis, and control systems, with a focus on developing robust numerical optimization tools. Recent work includes applications in IoT digital twins, blockchain-based anonymity frameworks, and hybrid optimal control methods. His 2025–2023 publications emphasize nonlinear and mixed-integer optimization techniques, constrained composite optimization, and advanced algorithms for control systems. These works explore topics like augmented Lagrangian methods, proximal gradient approaches, collision avoidance, and blockchain ethics. Scientific Awards COAP 2022 Best Paper Prize Alberto collaborates internationally and is intellectually curious about interdisciplinary topics, including the philosophy of mind and literature.