Guillaume Puel is a Professor at Universite Paris-Saclay and a researcher at the Laboratory Paris-Saclay Mechanics (LMPS). His work focuses on inverse problems, parameter identification, and homogenization techniques in structural dynamics and mechanical engineering. His research bridges computational modeling with experimental validation, particularly in railway noise, vibro-acoustic coupling, and fatigue simulation of materials. He employs multi-scale methods, adaptive meshing, and regularization to solve transient nonlinear models with contact phenomena. Recent publications highlight trends in medium-frequency computing platforms (e.g., pyTVRC), time homogenization for fatigue analysis, and Trefftz methods for railway noise prediction. His collaborations include Denis Aubry, Andrea Barbarulo, and Nhat Quang Ta. Labs and teams: LMPS laboratory at Universite Paris-Saclay, where he contributes to advanced mechanical modeling and railway engineering applications.
Peter Brown serves as an Honorary Senior Lecturer at the School of Mathematics and Statistics, University of New South Wales (UNSW), Sydney. His academic role centers on undergraduate education as a casual tutor across first and second-year pure mathematics courses, while maintaining active research contributions in theoretical and historical mathematics. His research program bridges Number Theory and History of Mathematics, with significant work in Diophantine equations, arithmetic functions, and elliptic curves alongside cultural studies of Chinese mathematics and ancient Greek texts. This dual focus manifests in publications spanning rigorous theoretical proofs and contextual historical analysis, demonstrating consistent scholarly output from 1997-2015. Publication trends reveal evolving expertise: early work concentrated on classical number theory problems (Pell equations, Möbius functions), while later research incorporated historical dimensions (Chinese mathematics, cultural revolutions) and educational monitoring. The interdisciplinary nature connects abstract mathematical structures with socio-historical frameworks. Scientific Recognition Vice Chancellor's Teaching Award (2009) Science Faculty Lecturer of the Year (2016) Brown's teaching excellence has been formally acknowledged through university-wide awards, highlighting his effectiveness in foundational mathematics instruction. While specific grant funding and doctoral advising aren't documented, his sustained publication record and educational monitoring work indicate ongoing scholarly activity. The absence of laboratory facilities aligns with his theoretical research profile in pure mathematics.
Kinan Abbas is a researcher at the University of Strasbourg's College of Science and Engineering, Department of Computer Science, specializing in hyperspectral imaging and machine learning. His work focuses on spectral image processing techniques including unmixing, demosaicing, and low-rank matrix approximation. His research interests center on hyperspectral imaging and machine learning applications, particularly developing novel methods for snapshot spectral image processing. Key contributions include locally-rank-one-based joint unmixing frameworks, diffusion models for texture synthesis, and entropy-weighted spectral deconvolution techniques. His work bridges theoretical signal processing with practical applications in remote sensing and computational photography. Analysis of his publication trend (2021-2025) shows evolution from foundational spectral unmixing techniques toward advanced generative models, with increasing focus on diffusion-based synthesis and multifractal analysis. His research consistently addresses computational challenges in spectral image reconstruction. Abbas actively collaborates with researchers from ICube laboratory (Strasbourg), including Matthieu Puigt, Gilles Delmaire, and Gilles Roussel, evidenced by consistent co-authorship across 14 publications. His work appears in IEEE Transactions, ICASSP, and French GRETSI conferences, indicating strong institutional support for his research program.
Francesca Vipiana is a Full Professor of Electromagnetic Fields at the Department of Electronics and Telecommunications at the Polytechnic University of Turin (POLITO). She is also a member of the Interdepartmental Center PolitoBIOMed Lab - Biomedical Engineering Lab. With over 20 years of full-time research experience, Prof. Vipiana has established herself as a leading expert in computational electromagnetics and microwave imaging systems. Her research interests span antenna design , computational electromagnetics , microwave imaging for medical applications, and food safety/security monitoring . She has pioneered work in numerical techniques based on integral equations and method of moments, with particular focus on multiresolution and hierarchical schemes, domain decomposition, and advanced integration methods. Prof. Vipiana's recent publications show a strong trend toward medical diagnostics using microwave technology, particularly for Alzheimer's disease detection and brain stroke imaging. Her work increasingly integrates machine learning with electromagnetic sensing, and she has developed portable, low-cost microwave imaging systems using off-the-shelf components. Lot Shafai Mid-Career Distinguished Achievement Award (2017) URSI Young Scientist Award (2005) IEEE WiEM best poster award (2009) ISMB Best Paper Award (2011) Prof. Vipiana serves as Principal Investigator for multiple significant research projects including the Marie Sklodowska-Curie Action EMERALD, the Proof of Concept FastFood project, and the national PRIN project BEST-Food. She currently supervises 13 PhD students working on diverse topics from microwave brain imaging to glide-symmetric metamaterials. As an Associate Editor for IEEE Transactions on Antennas and Propagation and the IEEE Antennas and Propagation Magazine, she plays a key role in advancing the field. Her research group focuses on bridging theoretical electromagnetics with practical applications in healthcare and food safety.
Natalia Karlsson is an Associate Professor and Lecturer at Södertörn University , specializing in Applied Mathematics and Mathematics Didactics . She serves as a Subject Coordinator and focuses on transforming mathematical concepts into accessible teaching frameworks. PhD in Mathematics and Physics (1997, Russian Academy of Sciences) Master in Applied Mathematics and Educational Sciences Licensed teacher for primary, secondary, and adult education Her research expertise spans two domains: (1) Applied Mathematics , involving mathematical modeling in nonlinear elasticity and fuzzy methods for formalizing strategy maps; and (2) Mathematics Didactics , focusing on how school-level mathematical concepts can evolve into rigorous ones without contradictions. She emphasizes task-oriented learning , diagnostic teaching , and transformative pedagogy to enhance educators and students. The 15 most recent articles highlight trends in pre-service teacher training , algebra instruction , and fractions education . Key areas include conceptual transitions from arithmetic to algebra, syntactic and substantive knowledge development, and inverse operations in teaching. Many works address proportional reasoning and multiplicative structures in classroom settings. As a supervisor , she has guided bachelor’s, master’s, and PhD students and served as an opponent for doctoral theses. She currently acts as a scientific reviewer for international journals but does not participate in active research projects.
Martina Chirilus-Bruckner is an Assistant Professor in the Mathematical Institute at Leiden University, Faculty of Science, specializing in the analysis of applied nonlinear partial differential equations. She is a member of the Analysis and Dynamical Systems group and actively contributes to the mathematical community through her research and service roles. Dr. Chirilus-Bruckner received her PhD in Mathematics in 2009 from the University of Karlsruhe (now Karlsruhe Institute of Technology), Germany, following her Diploma in "Technomathematik" in 2006. Her academic journey includes postdoctoral and lecturer positions at Centrum Wiskunde & Informatica in Amsterdam, Boston University, Brown University, and Sydney University, establishing her international research profile. Her primary research focuses on nonlinear partial differential equations, dynamical systems, and their applications. She investigates pattern formation, wave propagation, stability of solutions, and bifurcation phenomena in various contexts including reaction-diffusion systems, nonlinear wave equations, and ecological models. Her work combines rigorous mathematical analysis with applications to physical and biological systems, demonstrating the power of mathematical modeling in understanding complex phenomena. Analysis of her recent publications reveals a consistent focus on nonlinear phenomena in spatially extended systems. Her research spans from theoretical investigations of fundamental equations like the Swift-Hohenberg, Klein-Gordon, and reaction-diffusion systems to applications in ecological pattern formation. A notable trend is her work on validity of approximation methods, particularly modulation equations like the Nonlinear Schrödinger and Korteweg-de Vries equations for describing complex wave dynamics. Dr. Chirilus-Bruckner is actively involved in the mathematical community as a member of the Board of NDNS+ (Nonlinear Dynamics and Natural Systems Plus) NWO-Cluster of Mathematics and serves on the Education Committee of the Mathematical Institute at Leiden University. She is organizing academic events including an upcoming Autumn School on "From microscopic dynamics to continuum limits," demonstrating her commitment to advancing research and education in her field. Her research collaborations span multiple institutions and countries, reflecting the international nature of modern mathematical research. She frequently collaborates with researchers from the Netherlands, Germany, and the United States, contributing to a vibrant network of scholars working on nonlinear phenomena.
Alexander Mamonov is an Associate Professor in the Department of Mathematics at the University of Houston's College of Natural Sciences and Mathematics. He holds a Ph.D. in Computational and Applied Mathematics from Rice University and a Diploma with Distinction from Lomonosov Moscow State University. His research focuses on computational mathematics, inverse problems, wave propagation, and model order reduction. His research interests span computational mathematics, inverse scattering, wave propagation, model order reduction, and numerical linear algebra, with applications in geophysical imaging and scientific computing. Mamonov's publications demonstrate sustained focus on inverse problems and model reduction techniques, with recent work advancing tensor-based methods for parametric systems and data-driven approaches for waveform inversion. His articles consistently integrate mathematical theory with computational applications in wave propagation and imaging. Mamonov has received the ICERM Research Fellowship and maintains active research collaborations, including visiting positions at the University of Coimbra and ICERM. He coordinates the Seminar on Computational Mathematics in Oil & Gas Exploration and Imaging.
Dorsa Ghoreishi is an Assistant Professor in the Department of Mathematics and Statistics at Saint Louis University, part of the College of Arts and Sciences. She holds a Ph.D. in Mathematics from the University of Missouri-Columbia and a B.S. in Applied Mathematics from Khajeh Nasir Toosi University of Technology. Her research focuses on frame theory, phase retrieval, and their applications in signal processing and environmental science. Education: Ph.D. Mathematics, University of Missouri-Columbia M.A. Mathematics, University of Missouri-Columbia B.S. Applied Mathematics, Khajeh Nasir Toosi University of Technology Research interests include applied harmonic analysis, frame theory, phase retrieval, and compressive sensing. Her work bridges theoretical mathematics with practical applications, such as soil carbon estimation using hyperspectral imagery and wavelet decomposition. Recent research emphasizes stability and locality in phase retrieval algorithms, as well as recovery of signals from saturated measurements. Her scientific contributions include a National Science Foundation grant (DMS-2154931) supporting frame theory and phase retrieval research. She actively participates in professional organizations like the American Mathematical Society and the Association for Women in Mathematics. Community engagement includes co-organizing the Sonia Kovalevsky Math Day, encouraging girls in middle and high school to explore mathematics. Her research trends reflect interdisciplinary collaboration, blending pure mathematical theory with environmental and engineering challenges.
Ken Nakayama is the Edgar Pierce Professor of Psychology at Harvard University, where he has been since 1990. Previously, he spent 20 years at the Smith Kettlewell Eye Research Institute in San Francisco. He holds a B.A. from Haverford College and a Ph.D. from UCLA. University: Harvard University Department: Department of Psychology Lab: Vision Sciences Laboratory Research Interests Nakayama’s work focuses on understanding the mechanisms of visual perception, including motion perception, attention, face recognition, and the neural basis of visual processing. He explores how the brain constructs coherent visual scenes from dynamic sensory inputs, emphasizing the interplay between low-level visual processing and higher-order cognitive functions. His research employs methods such as visual psychophysics, computational modeling, and neuroimaging to study topics like the perception of surfaces, motion, and attentional deployment. Recent work includes investigations into developmental prosopagnosia (face blindness) and the role of visual perception in social cognition. Key Contributions Nakayama has contributed fundamentally to understanding the aperture problem in motion perception, the role of attention in visual search, and the neural correlates of face recognition. He co-developed the Cambridge Face Memory Test, a gold-standard tool for assessing face recognition abilities. His lab has also studied the impact of perceptual learning, the effects of brain lesions on visual processing, and the application of visual psychophysics to neurological disorders. Collaborations with neuroscientists and clinicians highlight his interdisciplinary approach to understanding vision and cognition.
Sina Akhbari is a Research Associate in the field of Digital Manufacturing for Robotic 3D Concrete Printing & Milling at Loughborough University. He holds a PhD in Mechanical Engineering from the University of Tabriz, Iran (2021), where his doctoral research focused on kinematics and trajectory generation of multi-DOF parallel robots. He previously served as an Adjunct Lecturer at the Department of Mechanical Engineering, University of Tabriz (2018–2024), and co-supervised PhD students there. Sina also worked as a part-time CAD/CAM engineer at ModelSazi Tabriz Ltd. (2009–2021), blending academic research with industry experience. Education: PhD in Mechanical Engineering, University of Tabriz, Iran (2021) Ranked 4th in Iran’s National PhD Entrance Exam (National Organization of Educational Testing) Research Interests: He specializes in robotic systems, parallel kinematics, additive manufacturing, and sensor-based control. His work emphasizes trajectory optimization, real-time control systems, and energy efficiency in industrial applications. Recent projects include developing low-cost motion firmware for parallel robots and enhancing tactile sensor integration in robotic grippers. Awards: Outstanding academic achievement: 4th rank in Iran’s national PhD entrance examination Advising & Labs: Current role: Co-supervisor of PhD students at University of Tabriz Laboratory affiliation: DAV 0.042 Intelligent Automation Researchers Industry Link: His industry experience at ModelSazi Tabriz Ltd. informed his research on CAD/CAM integration and practical manufacturing challenges.
Raluca Felea is a Professor in the School of Mathematics and Statistics at the Rochester Institute of Technology (RIT), part of the College of Science. She holds a BS from the University of Iasi (Romania) and a Ph.D. from the University of Rochester. Her research focuses on Microlocal Analysis, FIOs with Singularities, Inverse Problems in SAR and Seismology, and Mathematical Modeling. She has published notable works in journals like Journal of Pseudo-Differential Operators and Applications and Inverse Problems and Imaging , addressing topics such as cusp singularities, Doppler SAR analysis, and SAR imaging dynamics. Felea has presented her research at venues including Michigan State University and conferences on seismic data analysis. She is actively involved in education, teaching courses like Linear Algebra, Boundary Value Problems, and Complex Variables. Her work also extends to organizing the Summer Math Workshop ( SMS ). Though no formal awards are listed, her contributions to inverse problems and applied mathematics highlight her scholarly impact.
Rahul Mourya is a Lecturer in Computer Science at the Faculty of Science and Engineering , University of Wolverhampton, UK. He joined in October 2023 and is affiliated with the Digital Innovations and Solution Centre (DISC) , which focuses on fundamental and applied research with societal and economic impact. PhD & MSc in Computer Science (Signal/Image/Vision) from Université Jean Monnet Saint-Etienne, France BEng in Electronics & Telecommunications from University of Pune, India His research develops foundational tools for machine learning, computer vision, inverse problems , and signal/image processing , with applications in autonomous systems, robotics, and sensor networks . Teaching includes computational mathematics, robotics engineering, and deep learning modules. Collaborations span institutions like Heriot-Watt University, Telecom ParisTech, and interdisciplinary projects in underwater acoustics and astronomical imaging. Current research explores measurement-consistent neural networks and optimization algorithms for inverse problems.
Dr. Chandrasekhar Venkataraman is an Associate Professor in Mathematics at the University of Sussex's School of Mathematical and Physical Sciences. His research focuses on applied mathematics, numerical analysis, and mathematical biology, with a particular emphasis on modeling cell migration, reaction-diffusion systems, and biological pattern formation. He has contributed to understanding mechanisms in cancer biology, immunology, and plant pathology through computational frameworks. Key research interests include multiscale modeling, free boundary problems, and parameter identification in biological systems. He has secured grants from institutions like the Leverhulme Trust and Innovate UK for projects addressing drug discovery, cell manufacturing, and 3D cell migration dynamics. His work integrates mathematical theory with experimental data, as seen in collaborations analyzing tumor-immune interactions and cellular force estimation. Teaching responsibilities include courses on numerical analysis, discrete mathematics, and partial differential equations. Professional activities include editorial roles at Royal Society Open Science and participation in international workshops on multiscale modeling and oncology. His research has been published in high-impact journals such as Nature, Royal Society Open Science, and the Journal of Theoretical Biology.
Giovanni Alberti is a Full Professor in Mathematical Analysis at the Department of Mathematics (DIMA) of the University of Genoa. He earned his D.Phil. at the University of Oxford and completed postdoctoral positions at École Normale Supérieure (Paris) and ETH Zurich. His research focuses on partial differential equations, applied harmonic analysis, inverse problems, and machine learning. University of Genoa MaLGa Center (Machine Learning Genoa) Mathematical Institute, Oxford Maths Department, ETH Zurich His work bridges mathematical analysis with computational methods, particularly in inverse problems , compressed sensing , and machine learning . He has developed algorithms for real-time geotechnical predictions, sparse optimization for scatterer localization, and continuous generative models. Recent publications emphasize physics-data-driven integration and low-dimensional manifolds. He received the Gioacchino Iapichino Prize (2017), Eurasian Association on Inverse Problems Young Scientist Award (2018), and an ERC Starting Grant (2021). He serves on editorial boards for journals including Inverse Problems and SIAM Journal on Imaging Sciences.
Krishna Naishadham serves as Adjunct Professor in Electrical and Computer Engineering at Georgia Tech. His research bridges microwave engineering and nanotechnology, with focus areas including gas sensors, carbon nanotube applications, and electromagnetic theory. Key research domains: Development of microwave transducers for environmental sensing Functionalized carbon nanotubes for ozone/nitrogen dioxide detection State-space modeling of electromagnetic systems RF sensor integration for vital signs monitoring His recent publications demonstrate advancements in sensor selectivity, metamaterial structures, and non-invasive health monitoring techniques. Dr. Naishadham has developed novel antenna-integrated sensors and characterization methods for graphene thick films. Educational contributions include courses in RF engineering and radar applications. He collaborates with industry partners on nanotechnology-based sensing solutions.
