Alvaro Vazquez Alvarez is an Assistant Professor at the Higher Technical School of Engineering, University of Santiago de Compostela. He holds a PhD in Computer Engineering (2009) from the same institution, focusing on High-performance decimal floating point units under the supervision of Dr. Elisardo Antelo Suárez. His research interests span high-performance computing, decimal arithmetic hardware design, parallel programming models, and LiDAR data processing. His work emphasizes FPGA implementation of arithmetic units, numerical algorithms optimization, and compiler/runtime systems for parallel environments. He leads the CograDe Research Group (Computer Graphics and Data Engineering), exploring advanced topics in parallel computing and data engineering applications. Key contributions include universal MPI binding frameworks for new languages and efficient LiDAR point cloud processing algorithms. Notable technical expertise includes: digit-by-digit algorithms, redundant number systems, CORDIC-based computations, and multi-operand decimal arithmetic implementations. His publications bridge theoretical numerical methods with practical hardware/software co-design solutions. Current research focuses on extending parallel programming interfaces, improving decimal arithmetic precision in modern architectures, and applying advanced geometric processing techniques to 3D data challenges.
Filippo Maggioli is a Postdoctoral Researcher at the University of Milano-Bicocca and an Adjunct Professor at Pegaso University. He completed his Ph.D. in Computer Science at Sapienza University of Rome, where he also served as a teaching assistant and postdoctoral researcher. His research focuses on interdisciplinary topics in Computer Graphics, Geometry Processing, and Numerical Simulations, with a strong emphasis on scalable algorithms and real-world applications. He has contributed to projects involving plant wilting simulation, shape correspondence, and procedural texturing. Maggioli advocates for open-source software and has published extensively in top-tier conferences such as ECCV. His teaching roles include courses on Computer Architecture and Networking at Pegaso University. Education: Ph.D. in Computer Science, Sapienza University of Rome (2019–2023) M.Sc. in Computer Science, Sapienza University of Rome (2018–2019) B.Sc. in Computer Science, Sapienza University of Rome (2014–2017) Research Interests: Maggioli’s work bridges computational geometry, numerical methods, and interdisciplinary applications. He explores topics such as functional maps for shape correspondence, physically-based simulation of natural phenomena, and GPU-optimized algorithms. His projects often integrate machine learning and procedural content generation to address challenges in 3D modeling and real-time applications. Awards & Contributions: Maggioli is a proponent of open-source software and has contributed to projects like SBML2Modelica. His research has led to impactful publications in areas like scalable geometry processing and fluid dynamics simulation.
Tom Oomen is a full professor in the Department of Mechanical Engineering at Eindhoven University of Technology. He specializes in control systems, system identification, and mechatronics, with applications in precision engineering, semiconductor technology, and healthcare. His research focuses on data-driven control strategies, integrating machine learning and artificial intelligence to enhance system performance. He has held academic positions at KTH Royal Institute of Technology, The University of Newcastle, and Delft University of Technology. Recipient of the 7th Grand Nagamori Award and NWO Veni/Vidi grants. Editor roles: Senior Editor of IEEE Control Systems Letters and Co-Editor-in-Chief of IFAC Mechatronics. Research interests include advanced motion control, iterative learning control, and fault detection. He teaches courses like Advanced Motion Control and organizes post-academic courses through the Mechatronics Academy. Collaborates with industries in semiconductor equipment, printing, space technology, and healthcare. Recent articles explore topics such as random learning in ILC, nonlinear control for ventilators, and gravitational wave detection systems. Advises PhD students including Max van Meer, Max van Haren, and Koen Classens.
William Parcell is an Associate Professor in the Department of Geology at Wichita State University's Fairmount College of Liberal Arts and Sciences. He serves as Director of the Geology Field School and maintains an active research program spanning geological sciences, history of geology, and educational outreach. Ph.D. in Geological Sciences from University of Alabama (2000) M.S. in Geology from University of Delaware (1997) B.S. in Geology from Sewanee: The University of the South (1994) Dr. Parcell's research spans two primary domains: contemporary geological science and historical geology. His geological research focuses on stratigraphy, sedimentology, field geology, and sedimentary basin analysis, with particular expertise in microbialite systems, carbonate sedimentology, and sequence stratigraphy. His second major research thrust involves historical geology, particularly the translation and analysis of works by 17th-century Jesuit scholar Athanasius Kircher. His recent publications include complete translations of Kircher's Itinerarium Exstaticum (1656) and Iter Exstaticum II (1657), examining early conceptualizations of the cosmos and subterranean world. Dr. Parcell also maintains strong interests in decision-making methodologies in geology, incorporating expert systems, many-valued logic, and subjective probability. Analysis of Dr. Parcell's publication record reveals a distinctive dual trajectory in his scholarly work. On one hand, he produces contemporary geological research focused on Jurassic and Cretaceous stratigraphy in the Bighorn Basin of Wyoming, with particular attention to microbialites, sedimentary systems, and resource assessment. On the other hand, he has developed a significant body of work translating and analyzing historical geological texts, particularly those of Athanasius Kircher, bridging historical scholarship with modern geological understanding. This interdisciplinary approach connects 17th-century scientific thought with contemporary geological practice, creating a unique niche at the intersection of history, philosophy, and earth sciences. As Director of the Geology Field School, Dr. Parcell leads both traditional in-person field camps in the Bighorn Basin of Wyoming and Montana, as well as innovative virtual Minecraft gamified field experiences. His educational initiatives extend to K-12 outreach, as evidenced by his work on shrink-swell clays for middle school students. The Field School benefits from the Woolsey Family Fund, which provides substantial fee scholarships for participating students. Dr. Parcell maintains active research collaborations with colleagues at Wichita State University and beyond, particularly in the areas of sedimentary basin analysis, microbialite characterization, and historical geology. His work with the MUNDUS digital archive project demonstrates his commitment to preserving and making accessible historical earth science inquiry. His most recent projects include comprehensive translations of Kircher's major geological works and continued field-based research in the Bighorn Basin region, with a focus on Jurassic stratigraphy and sedimentary systems.
Vassalos Paraskevas is an Assistant Professor at the Department of Informatics , Athens University of Economics and Business . He holds a Degree in Mathematics from the University of Athens (1998), a Postgraduate Diploma in Computational Mathematics and Informatics , and a Doctorate in Computational Mathematics from the University of Ioannina (2003). His office is located at the Antoniadou Wing, 5th floor, and he can be reached at pvassal@aueb.gr or +30 210 8203 187. Education BSc in Mathematics, University of Athens MSc in Computational Mathematics and Informatics, University of Ioannina PhD in Computational Mathematics, University of Ioannina (2003) His research focuses on Numerical Linear Algebra , particularly on solving linear systems with structured matrices (e.g., Toeplitz and block Toeplitz systems), signal/image reconstruction , and numerical solutions of differential equations . He has developed preconditioning techniques for iterative solvers and studied stochastic matrices with Toeplitz-like structures. His publications (2002-2008) analyze preconditioning strategies for Toeplitz systems, covering spectral analysis of matrices from PDE discretization, superlinear convergence in PCG methods, and approximation techniques for multilevel Toeplitz matrices. These works span Numerical Analysis , Applied Mathematics , and Computational Science , with applications in linear systems , differential equations , and matrix theory .
Dr. Ian Jeffrey is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Manitoba's Price Faculty of Engineering. He holds a PhD in Electrical and Computer Engineering from the University of Manitoba and leads research in the Electromagnetic Imaging Lab (EIL), focusing on wavefield imaging algorithms for agricultural and biomedical applications. His work integrates computational electromagnetics, optimization, and machine learning. Research interests include time-domain imaging, generative adversarial networks for calibration, and Bayesian machine learning for uncertainty quantification. Dr. Jeffrey's publications emphasize machine learning-enhanced microwave imaging, computational methods, and industrial applications like grain storage safety. He is actively seeking MSc and PhD students with C++ experience. No scientific awards were listed. Graduate opportunities focus on microwave imaging, computational electromagnetics, and GPU computing.
Ioan Cleju is a Lecturer at the Technical University of Iasi , affiliated with the Faculty of Electronics and Telecommunications and the Department of Logic Circuits . His research spans coding theory, digital systems design, and error-correcting codes with computer-aided methods, while later works focus on AI-driven signal processing for ECG biometrics and smart traffic systems. Email: icleju@etc.tuiasi.ro Research Interests include: Computer-Aided Design of Error-Correcting Codes Multi-Distance and Cyclic Code Design PLD Circuit Applications in Coding Theory AI-Driven Sparse Signal Recovery ECG Biometric Authentication Smart Urban Mobility Systems Recent Publications highlight trends in: Machine learning for cybersecurity and traffic management ECG signal analysis using convolutional networks Dictionary learning for anomaly detection Historical document digitization with symbolic AI Multi-lane vehicle flocking algorithms
Vassilios Sevroglou is a Professor at the Department of Statistics and Insurance Science, University of Piraeus, School of Finance and Statistics. His academic career spans since 2002, with roles including Assistant Lecturer (2002-2006) and Lecturer (2007-2011) at the same university, and visiting positions at the University of Ioannina. He holds a PhD in Applied Mathematics and a bachelor's degree in Mathematics from the National and Kapodistrian University of Athens. His research focuses on Partial Differential Equations, Integral Equations (Riesz-Fredholm Theory), and inverse scattering problems in elasticity and acoustics. Key areas include 2D/3D elastic wave scattering, thermoelastic interactions, chiral media applications, and computational methods for boundary value problems. The 15 most recent articles highlight advancements in chiral electromagnetic scattering, stochastic boundary value problems, thermoelastic wave interactions, and numerical reconstruction techniques. These works emphasize inverse problems, Wiener chaos expansions, and regularization methods in acoustics and elasticity. Teaching activities encompass Calculus, Differential Equations, Stochastic Finance, and advanced applied mathematics courses. No specific scientific awards or lab affiliations are mentioned in the provided texts.
Anna Soós is an Associate Professor at the Hungarian Mathematics and Informatics Institute, Faculty of Mathematics and Informatics, Babes-Bolyai University (Cluj-Napoca, Romania). She holds a PhD in Mathematics (2002) and has been active in stochastic analysis, fractal theory, and approximation methods. Her academic career includes leadership roles such as Vice-Rector for Curriculum and Hungarian Studies (2012–present) and Vice-Dean of the Faculty of Mathematics and Informatics (2008–2012). Education: MSc in Mathematics (Babes-Bolyai, 1982), MSc in Computer Science (Eötvös Loránd University, 1996), PhD in Mathematics (Babes-Bolyai, 2002) Her research focuses on deterministic and random fractals, stochastic differential equations, and homogenization on fractal structures. She has contributed to stochastic analysis through: Developing contraction methods in probabilistic metric spaces Wavelet approximations for SDE solutions Fractal interpolation techniques Scientific awards include: Bolyai János Research Fellowship Erasmus and CEEPUS Fellowships Domus Hungarica Scientiarum et Artium Grants World Bank Project Participation Anna Soós has coordinated international research projects like ERASMUS IP DSL2013 and FP7/MASCIL, while organizing conferences including the International Symposium on Numerical Analysis and Approximation Theory and Central European Functional Programming Summer Schools.
Çağlar Arpalı is an Associate Professor at Çankaya University's Faculty of Engineering, Department of Mechatronics Engineering. He has held full-time academic roles since 2001, including Department Chair since 2021 and Associate Professor since 2010. His expertise spans optical communications, laser beam propagation, and biomedical imaging. He holds a Ph.D. in Electrical and Electronics Engineering from Gazi University and a postdoctoral fellowship at UCLA. Education: Ph.D. in Electrical and Electronics Engineering, Gazi University (2004–2009) M.Sc. in Computer Engineering, Çankaya University (2001–2004) B.Sc. in Computer Engineering, Çankaya University (1997–2001) Research Interests: Focuses on laser beam shaping, underwater optical communication, adaptive optics, and biomedical applications. His work includes developing novel optical systems for imaging and communication in challenging environments. Publications: Over 30 peer-reviewed articles in journals like Optics Communications and Journal of Modern Optics , with recent work on underwater turbulence effects and beam shaping algorithms. Awards: Received the Bronze Medal at the 3rd İstanbul International Buluş Fuarı (ISIF 2018). Grants/Projects: Led national/international projects such as 'New Generation Optical Sensor Microscope System' (2014–2015) and 'Underwater Optical Wireless Communication System Design' (2014–2016).
Lixin Shen is a Professor in the Department of Mathematics at Syracuse University, part of the College of Arts and Sciences. His research focuses on applied and computational harmonic analysis, optimization, imaging science, and information processing. Shen holds a Ph.D. in Mathematics from Sun Yat-Sen University (1996), an M.Sc. from Peking University (1990), and a B.Sc. from Peking University (1987). Education: Ph.D., Mathematics, Sun Yat-Sen University, 1996 M.Sc., Mathematics, Peking University, 1990 B.Sc., Mathematics, Peking University, 1987 Research Interests: Shen’s work emphasizes sparse optimization, image and signal processing, computational mathematics, and their applications. Notable areas include wavelet analysis, tensor decomposition, and robust algorithms for noise removal and high-resolution imaging. Grants & Awards: NSF Grant: Collaborative Research: Sparse Machine Learning and Sparse Optimization (2022–2025) Excellence in Graduate Education Faculty Recognition Award, Syracuse University (2024) Air Force Summer Faculty Fellowship (2024, 2023, 2021, 2020) Service & Leadership: Chair of Graduate Committee, Mathematics Department (2024–2025) Editor, Frontiers in Applied Mathematics and Statistics Organizer, SIAM Conferences on Optimization and Imaging (2023–2020) Teaching: Recent courses include Numerical Linear Algebra, Numerical Methods with Programming, Sparse Optimization, and Partial Differential Equations.
Damek Shea Davis is an Assistant Professor in Cornell University's School of Operations Research and Information Engineering, College of Engineering. He received his Ph.D. in Mathematics from UCLA in 2015 and joined Cornell in 2016. Davis focuses on mathematical foundations of data science, with particular expertise in optimization, statistical learning, and numerical algorithms. His research develops theoretical frameworks and efficient algorithms for nonconvex, nonsmooth, and stochastic optimization problems. Key contributions include convergence guarantees for stochastic subgradient methods on nonconvex functions, analysis of optimization landscapes for phase retrieval, and development of methods for high-dimensional statistical estimation. His work bridges mathematical optimization with machine learning and signal processing applications. Davis has received numerous honors including the Sloan Research Fellowship (2020) and INFORMS Optimization Society Young Researchers Prize (2019). His publications advance understanding of optimization algorithm behavior in statistically challenging regimes, with recent work exploring geometric convergence properties and optimality conditions.
Örjan Smedby is a Professor at KTH Royal Institute of Technology, specializing in Medical Image Processing and Visualization. His research focuses on developing precise and efficient computational methods for medical decision-making, particularly in automated segmentation of medical images and quantitative analysis of bone structure. Key areas include tumor segmentation, bone graft analysis, and improving imaging techniques for clinical applications such as cancer treatment evaluation and osteoporosis assessment. His work integrates advanced machine learning techniques like deep learning and generative models to enhance image quality, reduce radiation exposure, and automate diagnostic processes. Collaborations involve interdisciplinary teams addressing challenges in oncology, neurology, and orthopaedics. Research Interests: - Medical Image Segmentation - Deep Learning Applications in Radiology - Quantitative Bone Microstructure Analysis - AI-Driven Diagnostic Tools - Iterative Reconstruction Algorithms His publications span topics such as optimizing PET/CT imaging protocols, Alzheimer’s disease neuroimaging, and breast cancer diagnostics. The research emphasizes translating computational methods into clinical workflows to improve healthcare efficiency and accuracy.
Christian Schroer is a Professor at the University of Hamburg and Leading Scientist at DESY, where he directs the scientific program of the PETRA III synchrotron radiation source. His research focuses on X-ray microscopy, X-ray optics, and their applications in materials science, nanotechnology, and condensed matter physics. He has contributed to the strategic development of PETRA IV, an ultra-low emittance synchrotron source, and co-founded the Helmholtz Imaging platform for data-driven imaging science. His career includes roles as a professor at TU Dresden (2006–2014), a scientist at DESY, and postdoctoral research at institutions like the University of Maryland and RWTH Aachen University. Schroer’s group develops advanced X-ray microscopy techniques and collaborates on projects ranging from solar cell analysis to catalyst dynamics. Education: PhD in Mathematical Physics, University of Cologne (1995) Studies in Physics at RWTH Aachen University (1986–1992) Habilitation in Physics, RWTH Aachen (2004) Research Interests: X-ray nanoscience and optics Aberration-corrected lenses and focusing techniques In situ and operando characterization of materials 3D tomography and multimodal imaging Synchrotron and free-electron laser applications Grants & Collaboration: Lead scientist for PETRA III and PETRA IV projects Member of Helmholtz Imaging initiative Active in international collaborations on X-ray microscopy and photon science Labs & Teams: X-ray microscopy group at DESY PtyNAMi (Ptychographic Nano-Analytical Microscope) facility
Yair Censor is a Professor of Mathematics at the University of Haifa, affiliated with the Department of Mathematics within the Faculty of Natural Sciences. His research focuses on optimization, feasibility problems, and their applications in medical imaging and radiation therapy, particularly in proton computed tomography (pCT) and intensity-modulated radiation therapy (IMRT). He pioneered the superiorization methodology , a framework for enhancing iterative algorithms to reduce objective function values while maintaining feasibility. His work spans theoretical contributions to applied mathematics, including projection methods, convex optimization, and perturbation resilience. Censor has collaborated extensively on interdisciplinary projects, such as developing algorithms for proton CT reconstruction and radiation treatment planning. He is associated with the Center for Mathematics and Scientific Computation (CMSC) at the University of Haifa and has contributed to special issues of journals like Inverse Problems and Applied Analysis and Optimization . Notable recent research includes advancements in superiorization for inverse planning in proton therapy, immunity to condition numbers in linear optimization, and floorplanning algorithms using feasibility-seeking methods. His work emphasizes bridging mathematical theory with practical medical and engineering applications.
