جست‌وجوی استاد و پژوهشگر

• Power Engineering
• Renewable Energy Systems
• Electrical Grids
• +۷ مورد دیگر

Bojan Erceg is a Senior Lecturer at the Department of Power Engineering, Faculty of Electrical Engineering, University of Banja Luka. Appointed on September 21, 2023, his research focuses on power systems, renewable energy integration, and fault detection in electrical grids. His work significantly contributes to improving the reliability and efficiency of modern power distribution networks through innovative technical solutions. Dr. Erceg's research spans critical areas in electrical engineering with emphasis on Power Engineering and Renewable Energy Systems . His expertise includes Photovoltaic Systems and Wind Energy applications, particularly for powering mobile telecommunication infrastructure. He has developed advanced methodologies for Distribution Networks including fault detection using magnetic field measurements, optimization of fault passage indicators, and minimization of power interruption times. His work on Hybrid Power Systems addresses the technical and economic challenges of integrating renewable energy sources with conventional generation and storage solutions. His publication record demonstrates a strong focus on practical applications and optimization techniques in power systems. Recent publications (2022-2025) highlight increasing sophistication in modeling approaches, with emphasis on probabilistic modeling, real-time monitoring systems, and digital twin applications for renewable energy integration. His work consistently bridges theoretical concepts with practical implementation challenges in modern power infrastructure. Dr. Erceg actively participates in collaborative research projects funded by the Ministry for Scientific-Technological Development and international organizations. His frequent co-authorship with colleagues like Predrag Mršić, Čedomir Zeljković, and Đorđe Lekić demonstrates his collaborative approach to addressing complex power engineering challenges. His research has practical applications in improving grid reliability, optimizing renewable energy integration, and developing cost-effective power solutions for telecommunications infrastructure.

Lukas Bruderمشاهده پروفایل

پژوهشگر

• Biomechanical Modeling
• Uncertainty Quantification
• Inverse Problems
• +۳ مورد دیگر

Lukas Bruder, Dr.-Ing., is a researcher affiliated with the Mechanics & High Performance Computing Group at the Technical University of Munich (TUM). His work focuses on biomechanical modeling, uncertainty quantification, and machine learning applications in computational medicine. He has presented research at international conferences including WCCM-ECCOMAS, UNCECOMP, and ECCOMAS Congress, with a particular emphasis on abdominal aortic aneurysm (AAA) rupture risk assessment and inverse problem solutions. Research Interests : Biomechanical Modeling, Uncertainty Quantification, Inverse Problems, Surrogate Modeling, Bayesian Methods, Machine Learning Teaching : Engineering Mechanics I/II Exercises, Research Internships Publications : 10+ peer-reviewed articles on AAA biomechanics, multi-fidelity methods, and AI in vascular medicine Bruder holds a PhD in Mechanical Engineering from TUM (2022) and has served as a research associate since 2017. Prior to this, he completed a Master's degree in Mechanical Engineering at TUM and worked as a visiting researcher at Sandia National Labs. His methodological expertise includes probabilistic frameworks, data-consistent inverse problem solutions, and predictive modeling using clinical data.

• Non-parametric Inference
• Regression Models
• Missing Data Analysis
• +۲ مورد دیگر

Ana Perez Gonzalez is a researcher at the Department of Statistics and Operational Research, Faculty of Business Sciences and Tourism, University of Vigo. She earned her PhD from the University of Santiago de Compostela in 2003, focusing on non-parametric inference for regression models with missing responses. Her research centers on robust statistical methods for missing data, functional regression, and non-parametric estimation. Key collaborations with Wenceslao Gonzalez Manteiga, Graciela Boente, and Ana Bianco Contributions to wild bootstrap calibration, bandwidth selection, and imputation techniques Her work addresses applications in biomedical statistics, climate modeling, and spectrometric studies. Notable methodologies include goodness-of-fit tests for missing data models, principal component-based estimators, and asymptotic variance analysis.

Tomás Del Barrio Castroمشاهده پروفایل

استاد

• Time Series Analysis
• Seasonality
• Applied Econometrics
• +۲ مورد دیگر

Dr. Tomás Del Barrio Castro is a Full Professor in the Department of Applied Economics at the Universitat de les Illes Balears (UIB) , where he has been affiliated since 2007. He holds a PhD in Economics (University of Barcelona, 1998) and a Degree in Economics (Autonomous University of Madrid, 1992). PhD in Economics, University of Barcelona (1998) Degree in Economic and Business Sciences, Autonomous University of Madrid (1992) His research focuses on Time Series Analysis , Seasonality , and Applied Econometrics , with methodological contributions to seasonal unit root testing and periodic integration. He has published in top journals including Econometric Theory , The Econometric Journal , and Journal of Time Series Analysis . A significant portion of his work examines temporal aggregation effects, persistent cycles, and mixed-frequency data challenges. Scientific Recognition : Econometric Theory Multa Scripsit Award (2014) As Principal Investigator since 2007, he leads competitive research projects in econometrics. His teaching spans undergraduate and graduate courses in Statistics and Econometrics, including Macroeconometrics and Time Series Analysis at UIB and University of Barcelona. Recent appointments include: 2025-26: Macroeconometrics (Double Degree in Economics & Tourism) 2025-26: Time Series Analysis (Master's in Big Data Analysis)

PABLO VAZQUEZ REGUEIROمشاهده پروفایل

استاد

• Particle Physics
• High Energy Physics
• Atomic, Molecular and Nuclear Physics
• +۵ مورد دیگر

Pablo Vazquez Regueiro is a Professor at the Faculty of Physics, Department of Particle Physics, University of Santiago de Compostela. He is affiliated with the Galician Institute of High Energy Physics (IGFAE) and the GAES High Energy Physics Group. Education: Ph.D. from University of Santiago de Compostela (2000) - Thesis: Development and construction of electronics for microstrip detector readout Research Interests: His work focuses on experimental particle physics, including detector technology, microstrip electronics, and high-energy collision analysis. Key contributions involve CP violation studies, baryon spectroscopy, and trigger system development. Scientific Contributions: His publications highlight expertise in LHCb collaborations, detector simulation, and data analysis techniques. Research spans Higgs boson properties, dark matter searches, and hadronization mechanisms. Labs & Teams: Actively participates in IGFAE and GAES High Energy Physics Group activities, contributing to international collaborations like LHCb.

Marek Omelkaمشاهده پروفایل

دانشیار

• Asymptotic Statistics
• Nonparametric Statistics
• Copula Models
• +۱۰ مورد دیگر

Assoc. Prof. Marek Omelka is a faculty member at the Department of Probability and Mathematical Statistics , Faculty of Mathematics and Physics, Charles University in Prague. His research focuses on copula models, dependence modeling, and nonparametric statistical methods with applications in multivariate analysis, permutation tests, and conditional copula structures. Current teaching: NMST424 Mathematical Statistics 3 and JEM019 Statistical Methods for Data Analysis Research interests: Asymptotic statistics, copula estimation, permutation methods, multivariate association, and functional data depth His recent publications analyze copula parameter estimation, covariate effects in conditional models, and multivariate tail coefficients, with implementations in R. The PMSE program he guarantees connects theoretical statistics with real-world applications in data science, finance, and medical studies.

• Stochastic Geometry
• Spatial Statistics
• Neurophysiology
• +۶ مورد دیگر

Prof. Viktor Beneš is a distinguished academic at the Department of Probability and Mathematical Statistics , Faculty of Mathematics and Physics, Charles University, Prague. His work bridges stochastic geometry and point process theory with applications in neurophysiology , materials science , and public administration . Key research themes include space-time modeling , nonlinear filtering , and statistical inference for complex systems. His recent publications focus on spatio-temporal dynamics in biological and physical systems, unions of interacting discs for spatial modeling, and stochastic microstructure analysis in polycrystalline materials. Collaborative projects span Bayesian geostatistics , survival analysis , and neuronal signal processing . Key contributions to stochastic modeling of neurophysiological experiments Developed nonlinear filtering methods for doubly stochastic processes Led international research on Markov point processes with perfect simulation

• Astrostatistics
• Data Science
• Galactic Structure and Dynamics
• +۵ مورد دیگر

Josh Speagle is an Assistant Professor jointly appointed in the Department of Astronomy & Astrophysics and the Department of Statistical Sciences at the University of Toronto. He maintains strong affiliations with the Dunlap Institute for Astronomy & Astrophysics, which forms part of Canada's leading concentration of astronomers at the University of Toronto. Dr. Speagle received his Ph.D. from Harvard University in 2020, establishing a foundation for his interdisciplinary career at the intersection of astronomy and statistics. His educational background bridges these two critical fields for modern data-intensive astronomy. His research focuses on astrostatistics and data science with multi-wavelength, large-area surveys, with specific interests in Galactic structure and dynamics; stars and stellar populations; dust and the interstellar medium; galaxy formation and evolution; scalable inference; and Monte Carlo sampling. Dr. Speagle combines astronomy, statistics, and computer science to analyze billions of stars and galaxies from wide-field imaging and spectroscopic surveys to better understand how galaxies like the Milky Way form, behave, and evolve over time. His recent publications demonstrate a strong emphasis on developing and applying advanced statistical methods to astronomical problems, particularly simulation-based inference, Bayesian methodology, 3D dust mapping, and stellar population analysis. His work frequently leverages major astronomical datasets from Gaia, DESI Legacy Imaging Surveys, and the James Webb Space Telescope. Banting-Dunlap Postdoctoral Fellowship Dr. Speagle collaborates extensively with researchers across the University of Toronto's three campuses and internationally. His work has significant implications for understanding galactic evolution and developing statistical frameworks for astronomical data analysis. He contributes to the vibrant research environment at the Dunlap Institute, which includes over 50 faculty, postdocs, students, and staff dedicated to innovative technology, groundbreaking research, and world-class training in astronomy. As a member of the Dunlap Institute community, Dr. Speagle participates in research spanning optical, infrared and radio instrumentation; Dark Energy studies; large-scale structure; the Cosmic Microwave Background; the interstellar medium; galaxy evolution; cosmic magnetism; and time-domain science.

Lassi Roininenمشاهده پروفایل

استاد

• Statistics and Probability
• Geophysics
• Applied Mathematics
• +۵ مورد دیگر

Lassi Roininen is a tenured Professor of Applied Mathematics at LUT University's School of Engineering Sciences, holding this position since September 2022 after serving as Assistant Professor there from 2018 to 2022. He maintains significant adjunct appointments as Associate Professor at University of Oulu, Assistant Professor at Bahir Dar University (Ethiopia), and faculty member at AIMS Rwanda, demonstrating strong international academic engagement. Education: Master of Science (Engineering), Tampere University of Technology Doctorate in Applied Mathematics, University of Oulu (2015) - conducted at Sodankylä Geophysical Observatory His research integrates Statistics, Geophysics, and Applied Mathematics with core expertise in Bayesian inference, uncertainty quantification, and inversion problems. He develops computational frameworks for geophysical imaging, climate modeling, and industrial applications, emphasizing robust statistical methodologies for real-world data challenges. Recent work shows increasing focus on African climate adaptation and medical/industrial tomography. Analysis of his 15 most recent publications reveals dominant trends in Bayesian approaches to climate science (particularly East African adaptation studies), medical/industrial imaging (tomography and fault detection), and geophysical modeling. His work consistently bridges mathematical innovation with practical applications across environmental science, healthcare, and manufacturing sectors. Research support includes Academy of Finland postdoctoral funding. Through AIMS Rwanda, he actively mentors African mathematicians and contributes to capacity building in computational sciences across the continent. His collaborative projects demonstrate commitment to solving region-specific challenges through advanced statistical methods. His work is closely tied to geophysical research networks including Sodankylä Geophysical Observatory, with recent expansions into East African climate resilience initiatives. Current projects integrate multi-instrument atmospheric data with Bayesian frameworks to address pressing environmental challenges in developing regions.

Heikki Haarioمشاهده پروفایل

استاد

• Bayesian Statistics
• Parameter Estimation
• Chaotic Dynamical Systems
• +۷ مورد دیگر

Heikki Haario is a Professor in Computational Engineering at the LUT School of Engineering Sciences, LUT University, Lappeenranta. His research focuses on robust Bayesian inference, parameter estimation, and uncertainty quantification in chaotic and stochastic systems. Broad research areas: Bayesian Statistics, Chaotic Dynamical Systems, Gaussian Processes, Machine Learning The 15 most recent publications (2025-2023) demonstrate expertise in computational modeling, data-driven methods, and interdisciplinary applications spanning finance, biology, and engineering. Articles emphasize Bayesian techniques, kernel flows, and neural network integration for solving inverse problems and optimizing predictions in uncertain environments.

Martin Eigelمشاهده پروفایل

پژوهشگر

• Numerical Mathematics
• Uncertainty Quantification
• Stochastic Partial Differential Equations
• +۵ مورد دیگر

Martin Eigel is a Researcher at the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) , specializing in numerical methods for stochastic partial differential equations, uncertainty quantification, and machine learning applications in computational mathematics. His work bridges tensor networks, Bayesian inversion, and quantum simulations. Research Interests : Adaptive stochastic Galerkin finite element methods Low-rank tensor approximations for high-dimensional problems Machine learning integration with PDE solvers Quantum circuit simulation techniques Bayesian inverse problems and error control Key Article Trends : His recent publications focus on merging deep learning architectures (e.g., ResNet, CNNs) with stochastic and tensor-based numerical methods for solving parametric PDEs, Bayesian inversion, and quantum systems. Topics include Hamilton-Jacobi-Bellman equations, Langevin dynamics, and risk-averse optimization under uncertainty.

Federico Bumbacaمشاهده پروفایل

استادیار

• Bayesian Hierarchical Models
• Target Marketing
• Loyalty Programs
• +۶ مورد دیگر

Federico Bumbaca serves as an Assistant Professor of Marketing at the Leeds School of Business, University of Colorado Boulder. His academic journey spans engineering, operations research, and business disciplines. Education Highlights: PhD in Marketing - Paul Merage School of Business, University of California Irvine MBA - Sloan School of Management, Massachusetts Institute of Technology MS in Operations Research - Virginia Tech MS & BS in Electrical Engineering - University of Toronto Bumbaca's research pioneers big data methodologies in marketing, specializing in Bayesian hierarchical models and distributed computing techniques for target marketing optimization. His work bridges complex statistical modeling with practical marketing applications, particularly in loyalty programs and customer segmentation. The evolution from his early computer vision research to contemporary marketing analytics demonstrates interdisciplinary expertise. His publication portfolio reveals a distinct transition from 1980s engineering research (laser rangefinders, color vision systems) to modern marketing analytics, with recent focus on scalable algorithms for Bayesian modeling. This trajectory reflects his unique ability to apply computational rigor to marketing challenges. Bumbaca teaches Marketing Research and Analytics in undergraduate programs and Data and Decisions in MBA courses, leveraging his industry experience from technology startups in the San Francisco Bay Area where he developed expertise in marketing, business development, and sales.

• Nanotechnology
• Materials Science
• Atomistic Simulations
• +۵ مورد دیگر

Namsoon Eom is a Senior Lecturer in the Department of Mechanics, Materials and Component Design at Lund University’s Faculty of Engineering (LTH). She is also a researcher at NanoLund: Centre for Nanoscience and a member of the LU Profile Area: Light and Materials. Her work focuses on computational nanomaterials research, with a strong emphasis on atomistic simulations and machine learning integration. Primary Affiliation: Department of Mechanics, Materials and Component Design, LTH, Lund University Secondary Affiliations: NanoLund: Centre for Nanoscience, LU Profile Area: Light and Materials Her research spans computational materials science, with a focus on metallic nanoparticles, nanowires, and their interfaces. Key projects include studies of diffusion in bimetallic systems nanowire growth mechanisms oxidation processes in nanoparticles machine learning for pattern recognition in simulation data Recent publications highlight her work in nanotechnology and materials synthesis, particularly in core-shell nanoparticle design and gas-phase synthesis methods. Trends include atomistic modeling of surface segregation, sintering, and oxidation phenomena. Scientific recognition includes the 2019 NanoLund Junior Scientist Ideas Award . She has supervised PhD students in projects related to heterogeneous nanoparticle synthesis and characterization. Her work aligns with UN Sustainable Development Goals, particularly in advancing nanotechnology for sustainable materials and energy-efficient synthesis methods.

Selpi Selpiمشاهده پروفایل

پژوهشگر

• Data-centric AI
• Multimodal Learning
• Unsupervised Learning
• +۶ مورد دیگر

Selpi Selpi is a Researcher at the Department of Data Science and AI , Chalmers University of Technology. They lead Work Package 3 (Methodologies for Assessment) in the EU-funded PANACEA project , focusing on fit-to-drive assessments for professional drivers through data fusion and impairment detection. Supervision: Main advisor to doctoral students Amer Mustajbasic (2023-present) and Mengyu Huang (2024-present). Research Interests: Data-centric AI, multimodal learning, unsupervised learning, and interpretable AI applied to autonomous driving and traffic safety. Key contributions include algorithms for driver behavior analysis in mixed traffic, BEV data fusion techniques, and scenario-based risk assessment frameworks. Publications from 2025–2017 span journals like IEEE Transactions on Intelligent Transportation Systems and conference proceedings at ECML PKDD.

Jonathan Essexمشاهده پروفایل

استاد

• Molecular simulations
• Computational chemistry
• Computer-aided drug discovery
• +۵ مورد دیگر

Professor Jonathan Essex holds the Chair in Computational Systems Chemistry at the University of Southampton, where he leads innovative research at the intersection of computational methods and biological systems. His work focuses on advancing molecular simulation techniques to enable more accurate predictions in drug discovery and medical diagnostics. Develops novel computational methodologies Applies simulations to antibody design and drug delivery Collaborates with academic and industrial partners globally His research spans fundamental challenges in simulation accuracy while addressing practical pharmaceutical needs. Key collaborations include industry partners like Johnson & Johnson and academic institutions through EPSRC and BBSRC grants. Scientific recognitions include: 2002 Marlow Medal from Royal Society of Chemistry Royal Society Wolfson Research Merit Award (2013) As an active PhD supervisor, he mentors students in computational chemistry and systems biology projects, maintaining editorial roles at Journal of Computer Aided Molecular Design and BMC Chemistry .