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Ljupcho ArsovView profile

Professor

• Electrical Engineering
• Metrology
• Measurement Systems
• +5 more

Prof. Dr. Ljupcho Arsov is a full professor at the Faculty of Electrical Engineering and Information Technologies (FEIT), Ss. Cyril and Methodius University of Skopje. He leads the Laboratory for Electrical Measurements and Materials and the Metrology Laboratory for Electromagnetic Quantities. With over 35 years' experience, his expertise spans electrical engineering, measurement systems, and technological innovation. He served as Assistant Minister for Science (1987-1990), authored Macedonia's national technological development strategy, and chaired the Accreditation Institute of Macedonia (2003-2006). Education: Doctor of Technical Sciences (1988, University of Novi Sad) Master of Technical Sciences (1975, University of Belgrade) Electrical Engineering Diploma (1972, Belgrade University) Research focuses on advanced measurement techniques, control systems, and metrology. Key contributions include calibration systems for energy meters and industrial tanks, EU-funded quality infrastructure projects, and development of new energy technologies. He pioneered Macedonia's first accredited electromagnetic calibration laboratory in 1999. Professional roles include teaching over 20 university courses across FEIT, Technology and Metallurgy Faculty, and Military Academy. He has coordinated 15+ research projects including 'Management of Large Technological Systems' and 'Development of Electronic Measurement Methods'. Recipient of major innovation awards including MAKINOVA Gold Medal (2002) and Grand Prix (2004), and recognized by Macedonia's Ministry of Science and Chamber of Commerce for innovation leadership.

• Machine Learning
• Distributed Systems
• Sensor Networks
• +7 more

Miloš Stanković is a Professor at the Faculty of Informatics and Computing, Singidunum University. His research focuses on distributed systems, reinforcement learning, machine learning applications, and sensor networks. He has contributed significantly to areas like multi-agent systems, optimization algorithms, and cybersecurity. Recent work includes distributed sensor network calibration, adaptive consensus algorithms, and AI-driven solutions for healthcare and infrastructure monitoring. Education: Not explicitly listed in provided texts. Likely holds advanced degrees in computer science or electrical engineering based on his publications. Research Interests: His primary areas include distributed multi-agent reinforcement learning, metaheuristic optimization for machine learning models, and real-world applications of AI in healthcare, surveillance, and smart infrastructure. He explores topics like sensor fusion, fault detection in networks, and consensus-based algorithms for decentralized systems. Article Trends: Recent publications emphasize AI-driven solutions (e.g., Parkinson’s disease detection via LSTM tuning, student dropout analysis with explainable AI) and distributed algorithms for multi-agent systems. His work bridges theoretical advancements in reinforcement learning with practical applications in sensor networks and cybersecurity. Grants/Projects: Not explicitly detailed, but involvement in EU-funded projects like Sinteza and SITCON conferences suggests external funding. Labs/Teams: Likely part of Singidunum’s research groups in AI, distributed systems, and cybersecurity. Collaborates with institutions like KTH Royal Institute of Technology (via co-author K.H. Johansson) on sensor networks and control systems.

Xinyi XuView profile

Professor

• Bayesian Statistics
• Model Uncertainty and Selection
• Decision Theory
• +4 more

Xinyi Xu is a Professor of Statistics and Vice Chair for Graduate Studies at The Ohio State University's Department of Statistics. She is affiliated with the Institute for Population Research and serves as an Associate Editor for Bayesian Analysis . Her research focuses on Bayesian hierarchical modeling, decision theory, and high-dimensional data analysis with applications in biostatistics, sociology, and finance. Education Ph.D. in Statistics, University of Pennsylvania (2005) M.A. in Statistics, University of Pennsylvania (2003) B.S. in Mathematics, University of Science & Technology of China (2001) Research Interests Bayesian hierarchical modeling Statistical decision theory Model selection and averaging Applications in biostatistics, sociology, and finance Grants National Science Foundation grants (DMS-2015552, DMS-1613110, DMS-1209194) National Institute of Child Health grants National Center for Research Resources grants Labs/Teams Collaborates with the Translational Data Analytics center at Ohio State and interdisciplinary experts.

• Water Engineering
• Environmental Engineering
• Urban Hydrology
• +5 more

Dr. Prasanna Egodawatta is an Associate Professor in the School of Civil & Environmental Engineering at Queensland University of Technology (QUT). He specializes in Water/Environmental Engineering with over 15 years of industry and academic experience. His research focuses on urban hydrology, stormwater management, water quality modelling, and resilience in water systems. Education: PhD from QUT. Teaching areas include Engineering Hydraulics, Advanced Water Engineering, and Civil Engineering Thesis Projects. He has co-supervised over 15 doctoral/master’s students and contributed to teaching innovation projects funded by QUT and the Office for Learning and Teaching (OLT). Research interests span stormwater pollution, treatment technologies, water-sensitive urban design, and risk-based water management. Key projects include ARC Linkage grants on climate change adaptation of WSUD, and collaborations with Gold Coast City Council, Port of Brisbane Corporation, and industry partners like Holcim and SPEL Environmental. He has published over 75% of his work in Q1 journals (SCIMago) and secured >$600k in research funding. His work emphasizes statistical methods for environmental data analysis and multivariate techniques for pollutant source identification. Past roles include Higher Degree Research Director (2015–2017) and RDC committee member (2015–2017). He actively reviews for journals like Water Research and Environmental Modelling & Software . Notable contributions include advancing stormwater quality models, assessing health risks of PAHs, and developing methodologies for heavy metal prediction in urban catchments. His lab focuses on bridging environmental engineering with sustainable urban systems.

Julien ChiquetView profile

Part-Time Lecturer

• Statistics
• Machine Learning
• Sparse Methods
• +10 more

Julien Chiquet is a researcher and part-time lecturer at École Polytechnique (2020-2024), and the head of the UMR MIA Paris-Saclay research unit. He is affiliated with AgroParisTech on the Paris-Saclay campus and leads the SOLsTIS team. He serves as the chief editor of the journal Computo , focusing on computational statistics and data science. Education: Habilitation (2015) Research Interests: His work bridges statistical methodology and life sciences, with a focus on developing and applying advanced statistical methods such as sparse models, multivariate analysis, and optimization techniques. Key areas include dimension reduction in high-dimensional count data, probabilistic graphical models, and applications in ecology, genomics, and metagenomics. He emphasizes reproducible research practices, recently developing a Quarto template for HCERES reports integrating LaTeX and Pandoc. Recent Contributions: Recent work includes advancements in the Poisson lognormal model, stability selection via the sharp R package, and Python tools like pyPLNmodels for analyzing multivariate count data. His research often addresses challenges in ecological network inference, scRNA-seq analysis, and genomic prediction. Labs & Teams: Based at UMR MIA Paris-Saclay, he contributes to the SOLsTIS team, advancing computational methods for biological data analysis. The lab relocated to the new Palaiseau campus in 2022, fostering interdisciplinary collaborations.

Toyoko OrimotoView profile

Professor

• Experimental Particle Physics
• High Energy Physics
• Higgs Boson Research
• +4 more

Prof. Toyoko Orimoto is a Professor of Physics at Northeastern University's College of Science, specializing in experimental particle physics. She leads research at the CMS Experiment at CERN's Large Hadron Collider (LHC), focusing on Higgs boson physics, beyond the Standard Model (BSM) searches, and detector development. Her work includes studies of Higgs boson interactions, dark matter signatures, and the CMS electromagnetic calorimeter upgrade. Prof. Orimoto also advocates for diversity, equity, and inclusion in science. Education: PhD in Physics from UC Berkeley (2006), followed by postdoctoral roles at Caltech (2006-2009) and CERN (2009-2012). She has been at Northeastern since 2012. Research interests span experimental particle physics, including Higgs boson decay mechanisms, BSM physics (supersymmetry, extra dimensions), and future colliders like the muon collider. Her group contributes to CMS detector improvements, such as the MIP timing layer and electromagnetic calorimeter readout electronics. Notable achievements include co-winning the 2025 Breakthrough Prize in Fundamental Physics for LHC research and organizing the 2024 Large Hadron Collider Physics Conference at Northeastern. Her work bridges cutting-edge physics with detector technology and accelerator R&D.

• Vibrational Spectroscopy
• Infrared Spectroscopy
• Medical Diagnostics
• +3 more

Eleanor Louisa Osborne is a Researcher at the University of Southampton specializing in vibrational spectroscopy and medical diagnostics. Her work focuses on improving analyte quantification techniques and developing rapid bedside diagnostic tools using infrared spectroscopy, with significant contributions to atmospheric interference correction and waveguide sensor design. Her core research areas include: Vibrational Spectroscopy Infrared Spectroscopy Medical Diagnostics Analyte Quantification Photonics Clinical Applications Analysis of her recent publications reveals a dominant trend toward medical applications of vibrational spectroscopy, particularly for point-of-care diagnostics. Her work bridges fundamental spectroscopic techniques with clinical translation, emphasizing atmospheric interference correction (water vapour/CO₂), exhaled air particle analysis (PExA), and germanium waveguide development. Key collaborations span analytical chemistry, photonics, and pulmonology. Ms. Osborne supervises PhD students in spectroscopy and medical diagnostics and actively collaborates with researchers including Ganapathy Senthil Murugan and Waseem Ahmed. Her team maintains strong industry and clinical partnerships while conducting public photonics outreach through local events to engage diverse demographics.

Ullrich KötheView profile

Associate Professor

• Machine Learning
• Neural Networks
• Explainable AI
• +6 more

Prof. Dr. Ullrich Köthe is an Associate Professor and group leader in the Visual Learning Lab Heidelberg at the University of Heidelberg . He focuses on Explainable Machine Learning , leveraging Invertible Neural Networks to enhance transparency and utility in image analysis and medical applications . He also maintains the widely used VIGRA image analysis library. Education: PhD in Informatics, University of Hamburg, 2000 Habilitation in Informatics, University of Hamburg, 2008 His research interests center around machine learning , image analysis , and scientific computing , particularly the development of robust algorithms for medical imaging , computer vision , and life sciences . His work on invertible neural networks and parameter-free segmentation has led to significant advancements in the field. Recent publications highlight his contributions to Bayesian inference , neural network interpretability , and stochastic modeling , with applications ranging from disease outbreak dynamics to connectomics . Key trends include generative models , parameter-free segmentation , and likelihood-free inference . Scientific Awards: DAGM 2003 Main Prize DAGM Best Paper Award 2008 He has supervised numerous Master and Bachelor theses in machine learning and image analysis , with teaching roles in Advanced Machine Learning and Explainable AI . His collaborative research grants include funding from HARMAN International (2024). He leads the Explainable Machine Learning subgroup and has contributed to open-source software projects like ilastik and VIGRA , which are critical tools in bioimage analysis .

• Environmental Science
• Climate Science
• Biostatistics
• +3 more

Jessica Matthews is an Adjunct Professor at North Carolina State University. Her research focuses on environmental science, climate modeling, public health, and remote sensing. She specializes in spatial analysis of greenspace impacts on mental health, climate data interpretation, and uncertainty quantification in Earth observation systems. Her work spans interdisciplinary topics including Arctic sea ice dynamics, meteorological influences on disease transmission (e.g., COVID-19), and applications of Bayesian statistics in satellite calibration. Notable contributions include studies on racial disparities in pregnancy complications linked to greenspace access and long-term analyses of atmospheric temperature profiles. Matthews has published extensively on topics such as intersatellite calibration, growing season determination using temperature metrics, and the sensitivity of climate models to observational data thresholds. Her research bridges statistical methodologies with real-world environmental and health challenges, emphasizing practical solutions through advanced data analysis techniques.

Tung KieuView profile

Assistant Professor

• Data Science
• Time Series Analysis
• Forecasting
• +4 more

Tung Kieu is a Tenure Track Assistant Professor in the Department of Computer Science at Aalborg University (Denmark), affiliated with The Technical Faculty of IT and Design and the Daisy Center for Data-intensive Systems. His research focuses on data engineering, time series analysis, anomaly detection, and machine learning applications in traffic forecasting and smart systems. Education: Ph.D. in Computer Science (Awarded May 2021). Research interests include time series forecasting, traffic modeling, robust autoencoder architectures for anomaly detection, and spatio-temporal data analysis. His work contributes to UN Sustainable Development Goals related to smart cities and infrastructure. Recent publications explore bias mitigation in text-video retrieval (BiMa), topology-aware traffic forecasting (TEAM), and stochastic routing in uncertain road networks. His frameworks emphasize lightweight algorithms (LightTS), causal relational learning, and continual calibration for quantized models (QCore). Collaborations involve international teams in data management and AI, with notable work on ensemble methods, explainable AI, and transfer learning in smart building systems.

Klaus NeymeyrView profile

Professor

• Numerical Analysis
• Linear Algebra
• Chemometrics
• +3 more

Prof. Dr. Klaus Neymeyr is a Professor of Numerical Mathematics at the University of Rostock, affiliated with the Faculty of Mathematics and Natural Sciences and the Institute of Mathematics. Since 2023, he serves as Prodekan of the faculty. Previously, he was Dean (2014–2023) and an associated member at the Leibniz Institute for Catalysis (LIKAT). He obtained his habilitation in mathematics at the University of Tübingen (2001) and has held academic roles since 2003. His research spans numerical linear algebra, chemometrics, and catalysis, with a focus on eigenvalue problems, multivariate curve resolution (MCR), and spectral data analysis. Notable contributions include advancements in preconditioned eigensolvers and the development of FAC-PACK for feasible solution computation. Neymeyr’s work integrates computational methods with chemical applications, such as catalyst characterization via FTIR and NMR spectroscopy. He has been recognized with awards including the Kowalski Prize (2016) and a highly cited paper designation by ISI (2003). His collaborations extend to interdisciplinary projects in catalysis and biopharmaceutical data analysis. Publications emphasize algorithm development, convergence analysis, and data-driven methodologies. Scientific achievements include patents on energy-optimized pump regulation and contributions to the theoretical foundations of MCR and eigensolver convergence. His research group actively publishes in top journals like SIAM Journal on Matrix Analysis , Analytica Chimica Acta , and Journal of Chemometrics .

Dray StéphaneView profile

Researcher

• Community Ecology
• Spatial Statistics
• Multivariate Analysis
• +3 more

Dray Stéphane is a Senior Research Scientist (CNRS DR2) at Université Claude Bernard Lyon 1, affiliated with the Laboratoire de Biométrie et Biologie Evolutive (LBBE, UMR 5558). His research focuses on statistical ecology, combining multivariate analysis, spatial statistics, and computational tools to address ecological questions. Key areas include community ecology, population dynamics, and the impact of biotic/abiotic factors on species distributions. Dr. Dray holds a PhD in Biometry (2003) and a Habilitation (2016) from Université Lyon 1. He has extensive postdoctoral experience, including a stint at Université de Montréal. His work bridges statistics and ecology, with notable contributions to software development (e.g., ade4 , adespatial ), which are widely used for ecological data analysis. His research emphasizes innovative statistical methods for analyzing ecological networks, spatial patterns, and species interactions. Recent work explores applications in camera trap data analysis, phylogenetic comparative methods, and the calibration of deep learning models for wildlife monitoring. Education: Habilitation, Université Lyon 1 (2016) PhD in Biometry, Université Lyon 1 (2003) MSc/BSc in Biometry, Université Lyon 1 (1997–1999) Labs/Teams: Laboratoire de Biométrie et Biologie Evolutive (LBBE), part of the Institut des Sciences de l'Évolution de Lyon (ISE-Lyon).

• Computer Algebra
• Sparse Interpolation
• Symbolic-Numeric Computation
• +4 more

Wen-shin Lee is a Lecturer at the University of Stirling's Division of Computing Science and Mathematics, specializing in computational mathematics and signal processing. Her research focuses on exponential analysis, sparse interpolation, and symbolic-numeric computation. She holds a PhD from North Carolina State University and has held positions at institutions like the University of Antwerp and INRIA. Current affiliations include the Computational Mathematics and Optimisation Research Group (COMMON). Education: Bachelor’s in Mathematics, National Taiwan University PhD in Computational Mathematics, North Carolina State University Research Interests: Her work bridges computer algebra and signal processing, emphasizing applications like antenna positioning, radar imaging, and texture decomposition. Recent trends include sub-sampled exponential analysis, validated algorithms, and high-resolution signal reconstruction from sparse data. Labs/Groups: Active in the COMMON group at the University of Stirling and collaborates on the EXPOWER project (Exponential Analysis Empowering Innovation).

Adam Hugh ClarkView profile

Researcher

• Heterogeneous Catalysis
• X-ray Absorption Spectroscopy
• Electrochemistry
• +9 more

Dr. Adam Hugh Clark is a tenure track scientist at the Paul Scherrer Institute (PSI) in Switzerland, specializing in advanced spectroscopic techniques and heterogeneous catalysis. He holds a Bachelor's degree in Physics from the University of Nottingham , and a Master's and PhD in Molecular Modelling and Materials Science from University College London . At PSI, he focuses on uncovering structure-activity relationships in catalytic materials using time-resolved X-ray absorption spectroscopy (XAS). Education B.Sc. in Physics, University of Nottingham M.Sc. and PhD in Molecular Modelling and Materials Science, University College London Research Interests Clark’s research centers on heterogeneous catalysis , particularly ceria-based materials for semi-hydrogenation and perovskite oxides for oxygen evolution reactions (OER). He develops and applies highly time-resolved XAS and modulation-excitation spectroscopy to study dynamic material properties during reactions. His work bridges materials science and operando XAS , with applications in electrochemistry and sustainable chemical processes. Recent projects include platinum single-atom catalysts for vinyl chloride production and LaFe0.8Ni0.8O3 perovskites for redox studies. Publications & Software Development His 2025 publications span quantum simulation , MOF synthesis , and high-energy particle physics collaborations. Earlier works (2019–2020) introduced ProQEXAFS , a Python-based software for rapid QEXAFS data processing. He also advanced fluorescence-detected XAS for low-concentration samples and contributed to understanding Fe-doped Co perovskites in OER. Institutional Roles Clark manages the SuperXAS beamline at the Swiss Light Source and leads software development for quick-scanning XAS analysis . Since 2025, he has served on the ChemCatChem early career advisory board , promoting interdisciplinary catalysis research. Labs & Collaborations He works within the Operando Spectroscopy group at PSI, collaborating with institutions like CERN and ETH Zürich on projects involving density functional theory (DFT) , synchrotron techniques , and advanced detector systems (e.g., CMS). His affiliations include the Particle Physics LTP and Advanced Spectroscopy and X-ray Sources LSX laboratories at PSI.

David Jaime Cocovi SolbergView profile

Researcher

• Environmental Chemistry
• Analytical Chemistry
• Chromatographic Analysis
• +7 more

David Jaime Cocovi Solberg is a postdoctoral researcher at the University of Natural Resources and Life Sciences, Vienna (BOKU), focusing on instrumentation design for exposome assessment. He is also a visiting professor at Gdańsk University of Technology (GUT) and maintains academic ties with the University of the Balearic Islands (UIB), where he completed his education and contributed to the FITRACE research group. His interdisciplinary work bridges environmental chemistry, analytical instrumentation, and data automation. Education: Bachelor in Chemistry, University of the Balearic Islands (UIB), 2010 Master in Chemical Science and Technology, UIB, 2011 PhD in Chemical Science and Technology, UIB, 2017 (cum laude) Predoctoral stays at DTU (Denmark), BOKU (Austria), and University of Valencia (Spain) His research focuses on environmental chemistry, chromatographic analysis, automation, and instrumentation technology. He specializes in the orthogonalization of separations, data processing using Python, and the development of miniaturized, field-deployable analytical systems. His work supports exposome research through innovative instrumentation and software tools. The 15 most recent publications highlight a strong trend in developing automated, miniaturized analytical systems for environmental monitoring, particularly for emerging contaminants, PFAS, and endocrine disruptors. His research integrates separation science, microfluidics, sensor networks, and machine learning, with a consistent emphasis on open-source software (e.g., CocoSoft) and real-time, on-site detection. The keywords span environmental chemistry, analytical instrumentation, and data science, reflecting his interdisciplinary approach. Scientific Awards: Recipient of 3 research awards (specific names not provided) Holds intellectual property for the software 'CocoSoft' He has delivered over 300 hours of teaching in graduate and undergraduate programs, particularly in Instrumental Methods in Environmental Chemistry and Soil Chemistry and Control at UIB. He has served as a reviewer for leading journals including Science of the Total Environment , Talanta , Micromachines , and Sensors . He has not disclosed specific grant funding, but his research output and software development suggest active project involvement. His professional service includes membership in SEQA, SECyTA, QUIMIBAL, SETAC, and the judicial expert panel of the Balearic Islands High Court. David Cocovi Solberg is actively involved in the Flow Injection and Trace Analysis (FI-TRACE) Consolidated R+D+I Group at UIB as a member, contributing to trace analysis and flow-based systems research. His work combines laboratory innovation with practical applications in environmental monitoring, supported by a strong publication record and software development.