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Veronica Ruiz OrtizView profile

Assistant Professor

• Hydraulic Engineering
• Hydrology
• Groundwater Management
• +5 more

Veronica Ruiz Ortiz serves as Assistant Professor in the Department of Industrial Engineering and Civil Engineering at the University of Cádiz, specializing in hydraulic engineering with emphasis on Mediterranean water resource challenges. Her research integrates UAV photogrammetry, numerical modeling, and remote sensing to address critical issues in reservoir management, coastal aquifer dynamics, and surface-groundwater interactions across Andalusian basins. She completed her PhD at the University of Cádiz in 2019 with the thesis Contribution of photogrammetry and numerical modeling to the joint management of surface and groundwater. Application to basins in the province of Cádiz , supervised by Dr. Santiago García López. Her academic foundation enables advanced investigations into evaporation processes, salinity distribution, and rainfall variability in water-stressed regions. Ruiz Ortiz's research program focuses on innovative hydrological monitoring techniques, particularly UAV-based systems for coastal defense assessment, aquifer recharge quantification, and drought characterization. Her work bridges engineering practice with environmental sustainability, developing practical solutions for water management in Southern Spain's unique Mediterranean ecosystems. Key methodologies include SfM photogrammetry, LiDAR applications, and physiographic-based rainfall interpolation. Recent publications (2021-2025) reveal a clear trajectory toward remote sensing-driven hydrology, with 60% of her output centered on UAV applications for water resource assessment. Dominant themes include reservoir evaporation indexing, coastal aquifer vulnerability mapping, and SPI-based drought monitoring in the Strait of Gibraltar region, demonstrating consistent regional focus and methodological innovation. As faculty member, she supervises graduate research in hydraulic engineering while contributing to pedagogical development through project-based learning initiatives. Her educational publications highlight sequential project methodologies for engineering education, reflecting commitment to training next-generation water professionals. Ruiz Ortiz operates within the RNM373 Geosciences research group under the PAIDI Areas of Natural Resources and Environment at the University of Cádiz. This collaborative environment supports her fieldwork in coastal wetlands like the Bay of Cádiz Natural Park, where she utilizes integrated sensor networks and drone systems for real-time hydrological monitoring in natural laboratory settings.

Coşar GÖZÜKIRMIZIView profile

Assistant Professor

• Numerical Analysis
• Computational Mathematics
• Probabilistic Evolution Theory
• +3 more

Coşar GÖZÜKIRMIZI is an Assistant Professor in the Department of Computer Engineering at Beykent University's Faculty of Engineering and Architecture. He has been serving at Beykent University since 2016 as a Doctor Lecturer and previously worked as a Research Assistant at Istanbul Technical University's Institute of Information from 2014. His research focuses on probabilistic evolution theory, numerical methods for ordinary differential equations, and multivariate analysis. His work particularly emphasizes: Probabilistic evolution approaches to ODE systems Fluctuation expansion techniques Numerical integration methods Enhanced Multivariance Products Representation Telescope matrices and space extension techniques Dr. GÖZÜKIRMIZI's publication record shows consistent theoretical development from foundational numerical integration techniques (2008-2012) to sophisticated probabilistic evolution frameworks (2015-2025). His recent work has concentrated on probabilistic evolution theory for explicit autonomous ordinary differential equations, with particular attention to squarified telescope matrices and optimal space extension. His research demonstrates strong theoretical foundations combined with practical numerical implementations, primarily published in international computational mathematics conferences. Dr. GÖZÜKIRMIZI maintains an active research profile with publications spanning from 2008 to 2025. He frequently collaborates with Metin Demiralp (on most publications), Melike Ebru Kirkın, and Elif Tataroğlu. His work bridges theoretical mathematics with computational applications, particularly in solving complex differential equation systems through innovative probabilistic approaches.

Oktay GunlukView profile

Professor

• Discrete Optimization
• Integer Programming
• Mathematical Optimization
• +5 more

Oktay Gunluk is the Gary C. Butler Family Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology (Georgia Tech). Prior to joining Georgia Tech, he was a professor of practice in the School of Operations Research and Information Engineering at Cornell University and the manager of the Mathematical Optimization and Algorithms group at IBM Research. He also spent three years as a researcher in the Operations Research group at AT&T Labs, where he worked on various large-scale applied optimization projects. His educational background includes: Ph.D. in Operations Research (1995) from Columbia University M.Phil. in Operations Research (1993) from Columbia University M.S. in Industrial Engineering (1989) from Boğaziçi University B.S. in Industrial Engineering (1987) from Boğaziçi University Professor Gunluk's research focuses on theoretical and computational aspects of discrete optimization problems, primarily in integer programming. His seminal work centers on cutting planes for mixed-integer sets, and more recently he has developed integer programming approaches to classification and clustering problems in machine learning with considerations for fairness and interpretability. His work bridges theoretical optimization with practical applications in areas such as quantum computing, fair machine learning, and supply chain optimization. The interdisciplinary nature of his research is reflected in his publications spanning operations research journals, computer science conferences, and machine learning venues. His notable recognition includes: Gary C. Butler Family Professorship at Georgia Tech Professor Gunluk has advised several PhD students including Reka Agnes Kovacs (Oxford, 2023), Connor Lawless (Cornell, 2024), and Ishan Bansal (Cornell, 2024). His research has been supported by collaborations with industry partners including IBM Research where he led the Mathematical Optimization and Algorithms group. His work has addressed optimization challenges in telecommunications, quantum computing, and machine learning with practical implementations. At IBM Research, Gunluk managed the Mathematical Optimization and Algorithms group, leading a team focused on developing advanced optimization techniques for both theoretical advancement and practical application. His work has bridged the gap between academic research and industrial implementation, particularly in the areas of network design, quantum computing algorithms, and fair machine learning systems, demonstrating the real-world impact of his optimization expertise.

Nathaniel JohnstonView profile

Associate Professor

• Quantum Information Theory
• Linear Algebra
• Matrix Analysis
• +3 more

Nathaniel Johnston is an Associate Professor in the Department of Mathematics and Computer Science at Mount Allison University. His research focuses on quantum information theory, linear algebra, matrix analysis, and cellular automata. He is known for contributions to quantum entanglement, operator theory, and the development of the QETLAB toolbox for quantum entanglement research. Education details are not explicitly listed in the provided information. Johnston's research interests span quantum entanglement, matrix analysis, and the mathematical foundations of quantum computing. He has published extensively on topics such as quantum state characterization, tensor analysis, and cellular automata dynamics. His work often bridges abstract mathematical concepts with computational applications, as seen in his development of algorithms for quantum information processing. In his recent publications, he explores advanced techniques in quantum entanglement certification, spectral graph theory, and the application of linear algebra to quantum systems. His contributions include studies on matrix factorizations, operator norms, and the theoretical underpinnings of quantum coherence. No scientific awards are listed in the provided materials. Johnston has advised no students listed here, and no grants are mentioned. His work is primarily theoretical, with a focus on advancing mathematical frameworks in quantum information science. He is associated with the Department of Mathematics and Computer Science at Mount Allison University, contributing to both research and teaching in these fields.

Jun JiView profile

Professor

• Matrix Computation
• Optimization
• Boundary Value Problems
• +4 more

Jun Ji is a Professor of Mathematics at Kennesaw State University (KSU), affiliated with the Department of Mathematics in the College of Science and Mathematics. He earned his Ph.D. in Mathematics from the University of Iowa in 1993. Before joining KSU in 2002 as an Associate Professor, he worked at Valdosta State University from 1993 to 2002. He was promoted to Professor in 2007. His research focuses on matrix computation, optimization, boundary value problems of differential equations, and tensors. His academic career includes extensive contributions to linear algebra, numerical analysis, and differential equations. He has developed methods for computing matrix inverses (e.g., Drazin, Moore-Penrose, core-EP inverses) and explored their applications in tensor equations and boundary value problems. His work often combines theoretical insights with algorithmic innovation. Notably, he has contributed to frame theory, spectral analysis of discrete equations, and optimization techniques. He teaches undergraduate and graduate courses in mathematics, including linear algebra and specialized topics. His courses often utilize platforms like D2L for materials. He has organized educational programs, such as the 'China Revealed through its Math Education and Cultures' study abroad program, highlighting his engagement in international education. While no specific grants or awards are listed, his prolific publication record (over 60 articles from 1985 to 2024) reflects sustained research activity across matrix theory, numerical methods, and differential equations. His work bridges pure and applied mathematics, with implications for engineering and computational sciences.

Mª Albina Puente Del CampoView profile

Senior Lecturer

• Game Theory
• Cooperative Games
• Simple Games
• +3 more

Mª Albina Puente Del Campo is a Senior Lecturer at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Applied Mathematics III. She is a core member of the ALBCOM research group (Algorithms, Bioinformatics, Complexity, and Formal Methods) and the GRTJ sub-group (Game Theory Research Group). Her expertise spans Game Theory, particularly in cooperative games, simple games, and bicooperative systems. She holds a Licenciatura in Mathematical Sciences and a Doctorate in Mathematics. Her research focuses on probabilistic values, axiomatic characterizations, and solution concepts in cooperative game theory. Recent work includes studies on bisemivalues, potential functions, and applications to political and social networks. She has contributed to over 130 publications, including articles in Discrete Applied Mathematics , Annals of Operations Research , and Optimization . Dr. Puente collaborates on projects funded by national and regional grants, exploring topics like voting systems, coalition structures, and algorithmic game theory. Her work bridges theoretical foundations with practical applications in policy analysis and network cooperation.

Zhening LiView profile

Senior Lecturer

• Operations Research
• Mathematics
• Optimization
• +3 more

Zhening Li is a Senior Lecturer in the School of Mathematics and Physics at the University of Portsmouth, affiliated with the Centre for Operational Research and Logistics. He holds a BSc from Peking University (1999), an MA from York University (2003), and a PhD from The Chinese University of Hong Kong (2011). His research focuses on the intersection of Operations Research and Mathematics, emphasizing theoretical and computational optimization, algorithm design, and tensor computation. He has previously served as faculty at Ningbo Institute of Technology and Shanghai University. His research interests include optimization algorithms, tensor norms, multilinear algebra, and their applications in feature extraction and data analysis. Notable contributions involve spectral and nuclear norms of tensors, approximation methods, and nonnegative Tucker/CP decompositions. Zhening Li's recent work (2023-2025) explores tensor norms and approximation techniques, with applications in robust feature selection and sphere covering. His articles often blend mathematical rigor with computational methods, addressing challenges in high-dimensional data analysis and optimization. He supervises PhD students and has published extensively in journals like SIAM Journal on Matrix Analysis and Applications, Mathematics of Operations Research, and IEEE Transactions. His work bridges theoretical foundations with practical computational tools in operations research and applied mathematics.

Kofidis EleftheriosView profile

Associate Professor

• Signal Processing
• Machine Learning
• Digital Communications
• +3 more

Kofidis Eleftherios is an Associate Professor at the Department of Statistics and Insurance Science, University of Piraeus, Greece. He holds a Ph.D. in Computer Engineering and Informatics from the University of Patras (1996) and a Dipl. Ing. from the same institution (1990). His research focuses on signal processing, machine learning, digital communications, and biomedical applications, with a particular emphasis on tensor decompositions, filter bank multicarrier (FBMC) systems, and MIMO communication. He has contributed to over 70 peer-reviewed publications, including books and technical reports. Teaching responsibilities include courses such as Numerical Analysis, Operations Research, and Topics in Data Science. His research interests span signal processing for communication systems, tensor-based algorithms for multidimensional data analysis, and applications in biomedical signal processing. He has collaborated extensively on projects such as the European FP7-ICT EMPhAtiC initiative, focusing on MIMO channel estimation and distributed systems. Key research trends in his publications include: (1) tensor decomposition techniques for channel estimation and data fusion, (2) development of semi-blind receivers for MIMO systems, (3) analysis of filter bank-based multicarrier modulation for 5G/6G applications, and (4) biomedical signal processing using higher-order tensor methods. His recent work addresses challenges in RIS-enabled wireless systems and federated learning frameworks. Education: Ph.D., Computer Engineering and Informatics, University of Patras, 1996 Dipl. Ing., Computer Engineering and Informatics, University of Patras, 1990 Key Research Grants/Projects: European FP7-ICT Project EMPhAtiC (MIMO channel estimation and distributed systems) National and EU-funded initiatives on tensor-based signal processing Awards: None explicitly listed in the provided texts. Labs/Teams: Active in University of Piraeus's research groups on statistical signal processing and communications. His work bridges theoretical advancements in tensor methods with practical applications in wireless communications and biomedical engineering, emphasizing solutions for next-generation communication systems and multidisciplinary data fusion challenges.

Edgar SolomonikView profile

Associate Professor

• Tensor computations
• Numerical optimization
• Parallel and high performance computing
• +5 more

Edgar Solomonik is an Associate Professor at the University of Illinois at Urbana-Champaign, affiliated with the Siebel School of Computing and Data Science. His research focuses on high performance computing, parallel algorithms, and tensor computations. He earned a PhD from UC Berkeley (2014) and a BS from UIUC (2010). Notable contributions include the Cyclops Tensor Framework and work on communication-avoiding algorithms. Awards include the NSF CAREER Award (2020) and the Jack Dongarra Early Career Award (2024). He teaches courses on parallel numerical algorithms, tensor computations, and numerical analysis. Current research explores quantum computing, graph algorithms, and efficient tensor operations for scientific computing. Education: PhD, UC Berkeley (2014); BS, University of Illinois (2010) Research interests span numerical linear algebra, parallel programming systems, and quantum algorithms. Key projects include optimizing tensor contractions for quantum chemistry simulations and developing scalable algorithms for distributed-memory systems. His work has been published in top venues like SISC, SIAM Review, and SC conferences.

Aydin BuluçView profile

Adjunct Associate Professor

• Parallel Computing
• High Performance Graph Analysis
• Machine Learning
• +4 more

Aydin Buluç is a Senior Scientist at Lawrence Berkeley National Laboratory (LBNL) and an Adjunct Associate Professor of EECS at UC Berkeley. His work focuses on parallel computing, high-performance graph analysis, machine learning, sparse computations, and computational biology. He leads the Sparsitute DOE center and directs the PASSION Lab, emphasizing scalable algorithms and their applications in genomics and data science. Education: PhD in Computer Science from UC Santa Barbara (2010); BS in Computer Science and Engineering from Sabanci University, Turkey (2005). Research: Develops parallel algorithms for graph analysis, sparse linear algebra, and communication-avoiding techniques. Key projects include HipMCL for metagenome analysis and GraphBLAS for combinatorial computing. Awards: DOE Early Career Award (2013), IEEE TCSC Early Career Award (2015), ACM Gordon Bell Prize Finalist (2022). Teaching: Co-teaches CS267 (Applications of Parallel Computers) at UC Berkeley, emphasizing practical high-performance computing. Professional Roles: Editor of ACM Transactions on Parallel Computing, PC member for major conferences (SPAA, IPDPS), and leader in ExaGraph and DOE initiatives.

• Algebra
• Discrete Mathematics
• Linear Algebra
• +4 more

Göran Bergqvist is a Professor at Linköping University, affiliated with the Department of Mathematics and the division of Algebra, Geometry and Discrete Mathematics (ALGD). His work contributes to theoretical and applied aspects of algebra and discrete mathematics. His research interests include: Linear Algebra Tensor Analysis Matrix Theory Computational Mathematics Discrete Mathematics The recent publications indicate a strong focus on tensor decompositions, matrix spectra, and probabilistic aspects of tensor ranks. His work bridges pure mathematical theory with applications in signal processing and numerical analysis. There are no scientific awards listed in the provided information. He has advised no students listed in the available data and no information is available about grants or funding. No specific research team or laboratory is mentioned.

Francisco Villarroya AlvarezView profile

Assistant Professor

• Harmonic Analysis
• Partial Differential Equations
• Numerical Analysis
• +9 more

Francisco Villarroya Alvarez is an Assistant Professor in the Department of Applied Mathematics at Santa Clara University's School of Engineering. He holds a Ph.D. in Mathematics from the University of Valencia, Spain, and has held academic positions at prestigious institutions including the University of Edinburgh, Lund University, and the University of California Los Angeles. Research Interests His research spans three main areas: Harmonic Analysis : Singular Integration, T1 Theory, Time-Frequency Analysis, Wavelet Theory, and compact Calderón-Zygmund operators. Partial Differential Equations : Dispersive and elliptic equations, Schrodinger and Wave maximal operators, Cauchy Integral operators, and Double Layer Potential operators. Numerical Analysis : Development of algorithms for rapid evaluation of dense matrices associated with compact operators. His work bridges theoretical mathematics with practical applications in signal processing and computational methods.

Andrea Bruno CarbonaroView profile

Associate Professor

• Mathematical Analysis
• Harmonic Analysis
• Partial Differential Equations
• +3 more

Andrea Bruno Carbonaro is an Associate Professor in the Department of Mathematics (DIMA) at the University of Genova. His research focuses on mathematical analysis, particularly divergence-form operators with complex coefficients, harmonic analysis, and partial differential equations. His recent publications address topics such as semigroup maximal operators, bilinear embeddings, and Lp estimates for singular integrals. These works highlight his contributions to understanding operator theory in complex and irregular domains. He teaches courses including Fourier Analysis, Functional Analysis, and Mathematical Analysis for undergraduate and graduate mathematics programs.

• Machine Learning
• Mathematical Image Analysis
• Scientific Computing
• +6 more

Rihuan Ke serves as a Lecturer within the School of Mathematics at the University of Bristol, United Kingdom. With expertise spanning mathematical theory and computational applications, Dr. Ke actively contributes to interdisciplinary research that bridges abstract mathematics with real-world imaging challenges across scientific domains. Education: BSc MSc PhD Research Interests: Dr. Ke's work centers on developing mathematical frameworks integrated with data-driven methodologies for large-scale imaging analysis. Their research pioneers hybrid approaches combining variational methods, tensor algebra, and deep learning architectures to solve complex inverse problems. Key application areas include medical diagnostics (MRI/CT enhancement), materials science (microscopy segmentation), environmental monitoring (satellite imagery), and astronomical observations (telescope data processing). The research emphasizes creating interpretable models that maintain mathematical rigor while leveraging modern machine learning techniques. Publication Trends: Analysis of 15 recent publications (2016-2024) reveals a strong trajectory from foundational tensor mathematics toward applied deep learning for imaging. The 2020-2024 period shows concentrated innovation in semi-supervised segmentation frameworks, invertible neural architectures for inverse problems, and unsupervised denoising methods. A consistent theme involves developing mathematically grounded solutions for data-scarce scenarios, with growing emphasis on real-world deployment in traffic monitoring, medical imaging, and astronomical systems. Scientific Awards: No scientific awards or honors were documented in the provided materials. Advising and Grants: The available information does not specify any doctoral students, postdoctoral researchers, or research funding sources. Dr. Ke's collaborative network includes prominent figures in mathematical imaging such as Carola-Bibiane Schönlieb, indicating active participation in interdisciplinary research consortia. Laboratory Affiliations: While specific lab assignments aren't detailed, Dr. Ke's work aligns with computational imaging groups at Bristol, particularly those focused on inverse problems and machine learning applications in scientific imaging domains.

Jonathan GryakView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Applied Algebra
• +5 more

Jonathan Gryak is an Assistant Professor of Computer Science and Data Science at Queens College and the CUNY Graduate Center, where he also serves as Deputy Executive Officer. He leads the Interdisciplinary Data Science Lab (IDSL), focusing on developing novel AI/ML methods for biomedical applications that leverage underlying problem morphology and operational constraints. Education: B.S. in Mathematics and Computer Science (Dual), Rensselaer Polytechnic Institute M.S. in Mathematics, Fairfield University M.Phil. in Computer Science, CUNY Graduate Center Ph.D. in Computer Science, CUNY Graduate Center (2017) Gryak's research centers on Nonlinear Algebraic Data Analysis, combining tensor analysis, numerical algebraic geometry, deep neural networks, and invariant theory to create more accurate models of complex real-world data. His Sequential Algebrogeometric Analysis (SAGA) approach captures disease progression through heterogeneous, longitudinal medical data. His work bridges the gap between theoretical mathematics and practical clinical applications, particularly in cardiac care, traumatic brain injury, and food allergy diagnostics. His recent publications demonstrate a strong emphasis on interpretable machine learning methods, especially those employing tropical geometry, for critical healthcare applications. These include predicting heart failure therapy needs, detecting acute respiratory distress syndrome, and improving oral food challenge outcome prediction. His research consistently focuses on methods that incorporate domain knowledge, handle asynchronous data, and leverage intrinsic data structures while maintaining clinical interpretability. Scientific Awards: CUNY Faculty Fellowship Publication Program (2024) NSF grant for improving treatments for heart failure patients through machine learning Gryak actively mentors PhD and undergraduate students in the IDSL, guiding research that spans from theoretical mathematical developments to clinical implementations. His lab collaborates with medical professionals to translate AI techniques into commercial applications that impact patient care. Current projects include multimodal data integration for clinical decision support across various conditions including cancer, traumatic brain injury, and inflammatory bowel disease. The Interdisciplinary Data Science Lab under Gryak's leadership develops end-to-end solutions, from novel mathematical techniques to industry and clinical partnerships for commercialization. Recent work has produced automated systems for endoscopic disease assessment, hematoma segmentation, and cardiac event prediction, with multiple patents stemming from this research.