جست‌وجوی استاد و پژوهشگر

Alexander Shestopaloffمشاهده پروفایل

مدرس ارشد

• Bayesian Statistics
• Network Science
• Quantitative Finance
• +۳ مورد دیگر

Dr. Alexander Shestopaloff is a Senior Lecturer in Statistics at the School of Mathematical Sciences, Queen Mary University of London, and a Fellow of the Alan Turing Institute. He holds a PhD in Statistics from the University of Toronto (2016). His research focuses on Bayesian statistics, network science, quantitative finance, and empirical market microstructure, with a particular emphasis on cryptocurrency exchanges like Binance and Bybit. He develops computational methods for Bayesian inference, including online learning algorithms, and explores statistical procedures to detect network structures. Education: PhD in Statistics, University of Toronto (2016) Research Interests: Bayesian computational methods and modeling Network science and statistical graph analysis Quantitative finance, market microstructure, and high-frequency trading Online learning and high-dimensional time series analysis Grants and Funding: Research Fellowship: Cross-sectional forecasting of high-dimensional time series (£112,358), Delphia Technologies Inc (2023–2026) Innovate UK KTP: Wise (£219,928), Innovate UK (2022–2025) Key Contributions: Advances in robust Kalman filtering and Bayesian online learning Statistical analysis of network structures and graph algorithms Analysis of cryptocurrency market dynamics using high-frequency data Affiliations: Centre for Probability, Statistics and Data Science at Queen Mary University of London.

• Macroeconometrics
• Time Series Econometrics
• Forecasting
• +۳ مورد دیگر

Domenico Giannone is a researcher affiliated with the International Monetary Fund (IMF), specializing in macroeconometrics, time series analysis, and forecasting. His work focuses on developing statistical methods for large datasets, including Bayesian vector auto-regressions, nowcasting frameworks, and dynamic factor models. He has collaborated with prominent institutions like the European Central Bank and Federal Reserve, contributing to policy-relevant research on monetary policy, financial stability, and global interest rates. His research interests span structural macroeconomic modeling, high-dimensional data analysis, and the integration of real-time data into forecasting systems. Key contributions include methodologies for prior selection in vector autoregressions and analyses of the effects of central bank policies such as Outright Monetary Transactions (OMT). Giannone's publications address topics like the natural rate of interest, vulnerabilities in economic growth, and the informational content of macroeconomic data. His work emphasizes practical applications of econometrics to inform policy decisions, particularly in the context of global financial systems and institutional frameworks.

Amirali Khodadadian Gostarمشاهده پروفایل

مدرس ارشد

• Machine Learning
• Data Analytics
• Multi-Object Tracking
• +۵ مورد دیگر

Dr. Amirali Khodadadian Gostar is a Senior Lecturer at the School of Engineering, RMIT University. His research focuses on machine learning, data analytics, multi-object tracking, sensor management, and data-driven manufacturing. He has contributed to advancements in autonomous systems, anomaly detection, and multi-agent coordination through projects like distributed information fusion for connected vehicles and geometrically-informed tracking algorithms. Research Interests : Image processing, sensor fusion, robotics, control systems, and industrial automation. Key Projects : Development of electronic pre-tension systems for seatbelts, AI-driven logistics optimization, and stereo vision systems for defect detection. Publications : Over 60 peer-reviewed articles in top journals/conferences such as IEEE Transactions on Intelligent Transportation Systems and ISA Transactions, focusing on tracking algorithms, anomaly detection, and multi-agent systems. His work bridges theoretical frameworks (e.g., random finite set theory) with practical applications in robotics, transportation, and manufacturing. He actively supervises PhD/Master's students on topics ranging from quantum AI in logistics to eye gaze tracking for visual attention modeling.

Hamza Alkhatibمشاهده پروفایل

پژوهشگر

• Bayesian Modelling
• Monte Carlo Methods
• Uncertainty Modelling
• +۵ مورد دیگر

Hamza Alkhatib is a Senior Researcher at the Geodetic Institute within the Faculty of Civil Engineering and Geodetic Science at Leibniz University Hannover. His research focuses on advanced computational methods in geodetic data analysis, including Bayesian modeling, Monte Carlo simulations, uncertainty quantification, and filtering techniques for multi-sensor systems. He leads projects funded by DFG, BfG, and BMVI, addressing topics like hydrographic monitoring, cooperative sensor networks, and machine learning applications in geodesy. Alkhatib holds a PhD from the University of Bonn (2007) and a Habilitation from Leibniz University Hannover (2020). Education & Career : PhD (2007): University of Bonn, Dissertation on Monte Carlo methods for satellite gravity missions Habilitation (2020): Leibniz University Hannover, Advanced methods in geodetic data analysis Senior Research Staff since 2019 Research Interests : Bayesian inference and robust statistical methods Filtering algorithms (Kalman, Particle Filters) for dynamic systems Machine learning for geodetic point cloud analysis and damage detection Sensor fusion and multi-sensor positioning systems Hydrographic monitoring and marine infrastructure assessment Key Projects : DFG-funded adaptive robust adjustment of geodetic measurement processes Research Training Group GRK 2159 on dynamic sensor networks WaMUT project for hydrographic water change zone monitoring 3D HydroMapper for port infrastructure inspection Awards : DAAD Award for Outstanding Achievements (2000) Syrian Ministry of Academic Education Award (1998) Teaching : Teaches courses on geodetic data analysis, recursive state estimation, and machine learning applications at both bachelor and master levels.

Elisabeth Ullmannمشاهده پروفایل

دانشیار

• Uncertainty Quantification
• Scientific Computing
• Numerical Analysis
• +۳ مورد دیگر

Prof. Elisabeth Ullmann is an Associate Professor for Scientific Computing and Uncertainty Quantification at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information, and Technology and the Department of Mathematics. She holds a PhD from TU Bergakademie Freiberg (2008) and has held academic roles including postdoctoral positions at the University of Bath, University of Hamburg, and University of Maryland. Her research focuses on developing efficient algorithms for uncertainty quantification in partial differential equations with random coefficients, Bayesian inverse problems, and rare event simulation. She is an Associate Editor for the SIAM Journal on Scientific Computing and SIAM/ASA Journal on Uncertainty Quantification , and teaches courses on numerical methods for uncertainty quantification and partial differential equations. Her work emphasizes multilevel Monte Carlo methods, stochastic Galerkin discretizations, and probabilistic numerical methods. Notable contributions include error analysis for rare event probabilities and multilevel estimators for high-dimensional problems. She collaborates internationally and maintains an active research group in Scientific Computing & Uncertainty Quantification at TUM.

R. Benjamin Davisمشاهده پروفایل

دانشیار

• Structural Vibration
• Acoustics
• Bayesian Inference
• +۵ مورد دیگر

R. Benjamin Davis, Ph.D. is an Associate Professor at the University of Georgia, affiliated with the School of Electrical & Computer Engineering. His research focuses on structural vibration and acoustics, with a strong emphasis on Bayesian methods, nonlinear dynamics, and experimental modal analysis. He has contributed to advancements in vibration isolation, energy harvesting, and structural health monitoring through innovative methodologies like system-equivalent singular vector basis and dynamic substructuring techniques. His academic career includes expertise in pavements backcalculation using Markov chain Monte Carlo methods and the development of open-source tools for frequency-based substructuring. Davis has also explored interdisciplinary applications, such as impact mechanics in agriculture (e.g., pecan cracking) and hydroelastic damping of marine structures. His work integrates computational modeling with experimental validation, exemplified by studies on piezoelectric actuators for modal testing and generative adversarial networks for vibration data generation. Notable contributions include optimizing sensor placement for structural load determination and validating impulse response filters for impact force reconstruction. His research spans academic partnerships with industry, addressing practical challenges like vibration mitigation in bladed systems and reducing cavity response in coupled acoustic-structure systems. Davis's publications reflect a balance between theoretical innovation and real-world engineering solutions.

Yifu Liمشاهده پروفایل

استادیار

• Data-driven modeling
• Graph AI
• Natural Language Processing
• +۶ مورد دیگر

Yifu Li is an Assistant Professor in the Department of Industrial and Systems Engineering at the University of Oklahoma's College of Engineering. His research focuses on integrating data-driven modeling techniques like spectral theory-based graph AI with multi-modality datasets in smart manufacturing, cybersecurity, and healthcare. His work emphasizes solutions for large-scale data analysis with enhanced performance and interpretability. Education: Ph.D., Industrial and Systems Engineering, Virginia Tech B.S., Industrial and Systems Engineering, Virginia Tech Research Interests: Li's domains include data-driven multi-modality modeling, spectral graph AI, and natural language processing applied to aerospace systems, healthcare systems, and industrial engineering challenges. His federally funded projects involve NSF, USDA, DARPA, and DoD grants. Key Grants: National Science Foundation (NSF) USDA Defense Advanced Research Projects Agency (DARPA) Department of Defense (DoD) Labs/Teams: Active in the Industrial & Systems Engineering department's research initiatives, with a lab webpage documenting ongoing projects.

• Bayesian Statistics
• Queueing Theory
• Stochastic Processes
• +۷ مورد دیگر

Maria Carmen Armero Cervera is a Full Professor in the Department of Statistics and Operations Research at the Faculty of Mathematics, University of Valencia. She is a leading researcher in the Valencia Bayesian Research Group (VABAR), with a sustained record of contributions to Bayesian statistics, queueing theory, and stochastic modeling. Her research interests lie at the intersection of Bayesian inference and applied probability, focusing on Bayesian modeling of queueing systems , hierarchical models , survival analysis , and industrial and medical applications . She has developed methodologies for bulk service queues, patient-ventilator asynchronies, and prostate cancer risk modeling, demonstrating the versatility of Bayesian approaches. Her recent work emphasizes flexible Cox models, joint modeling, and computational methods such as Sequential Monte Carlo. The trends in her publications show a consistent focus on Bayesian methodology applied to real-world stochastic systems , evolving from theoretical queueing models to complex biomedical and industrial applications. Her work frequently appears in top-tier journals like Journal of Statistical Planning and Inference , Queueing Systems , and SORT . She has not been explicitly mentioned as receiving scientific awards in the provided texts. Maria Carmen Armero has advised or collaborated with numerous researchers, including Danilo Alvares, Anabel Forte, and David Conesa. While specific student names are not listed, her editorial contribution and extensive co-authorship indicate a strong role in mentoring and collaborative research. There is no mention of specific grants, but her sustained output suggests external funding support. She is a core member of the Valencia Bayesian Research Group (VABAR), which focuses on advancing Bayesian theory and applications in operations research, medicine, and industry.

• Data Assimilation
• Remote Sensing
• Environmental Modeling
• +۶ مورد دیگر

Dr. Sanita Vetra-Carvalho is a researcher affiliated with the University of Reading, as evidenced by her publications in the CentAUR repository. Her work focuses on data assimilation, remote sensing, and environmental modeling, particularly in hydrology and meteorology. She collaborates with leading experts in geophysical data analysis and contributes to flood detection and monitoring systems using advanced imaging techniques. Her research interests span Data Assimilation , Remote Sensing , Environmental Modeling , Urban Flooding , and Stochastic Methods . These areas are central to modern environmental prediction systems, especially in the context of climate change and extreme weather events. The recent publications (2018–2021) demonstrate a consistent focus on merging satellite-derived data with predictive models. Key themes include floodwater detection using Synthetic Aperture Radar, data fusion for urban flood mapping, and advancing stochastic data assimilation techniques for non-Gaussian, high-dimensional systems in meteorology and oceanography. Scientific Contributions: Developed robust algorithms for urban flood detection using SAR imagery Contributed to state-of-the-art stochastic data assimilation methods Created methodologies for extracting water level data from digital river cameras Worked on integrating remote sensing data with hazard maps for improved flood risk assessment She has not been listed as supervising students in the provided data, and there is no mention of grants or awards. However, her collaborative work suggests strong integration within international research teams focused on environmental data science. Dr. Vetra-Carvalho is involved in research groups or projects related to environmental monitoring, data assimilation, and remote sensing, likely operating within interdisciplinary teams at the University of Reading or in collaboration with European research institutions.

• Computational Chemistry
• Molecular Machine Learning
• Catalyst Design
• +۲ مورد دیگر

Clémence Corminboeuf is a Professor at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Laboratory of Computational Molecular Design (LCMD). Her research focuses on computational chemistry, machine learning for molecular design, and catalyst optimization using physics-inspired models. Email: clemence.corminboeuf@epfl.ch Scopus Author ID: 6603320500 Researcher ID: I-5973-2018 Current research involves: Developing integer linear programming algorithms for molecular machine learning training set selection Generative inverse design pipelines for singlet fission materials discovery Automated volcano plot analysis tools (SPOCK) for catalyst design Enantiospecific synthesis methods for planar chiral organometallic complexes Neural network potential frameworks for solute-solvent reaction dynamics Recent methodological advances include: Cost-informed Bayesian optimization (CIBO) for efficient chemical experimentation 3DReact geometric deep learning architecture for reaction property prediction MARC software for automated transition state ensemble analysis

Dirk Blömkerمشاهده پروفایل

استاد

• Stochastic partial differential equations
• Surface growth
• Cahn-Hilliard equation
• +۱۱ مورد دیگر

Dirk Blömker is a Professor in the Institute of Mathematics at the University of Augsburg (since 2006). His research focuses on Stochastic Partial Differential Equations (SPDEs) with applications to surface growth models , phase separation (Cahn-Hilliard, Allen-Cahn), stochastic bifurcation , and multiscale analysis (amplitude/modulation equations, fast diffusion). He also works on filter theory (Ensemble Kalman) and numerics of SPDEs . Habilitation : RWTH Aachen (2006) Doctorate : University of Augsburg (2000) Diploma : University of Münster (1997) Intermediate Diploma : University of Münster (1993) Abitur : Gymnasium Lengerich (1989) His work spans rigorous mathematical analysis of SPDEs (existence/uniqueness, blow-up, regularity) and numerical methods (convergence analysis, a-posteriori bounds). Recent articles examine fractional noise, Lévy processes, and ensemble Kalman inversion. Collaborators include Luigi Bianchi, Claudia Schillings, and Philipp Wacker. He has held academic positions at RWTH Aachen (Assistant/Acting Lecturer), University of Bonn (Acting Lecturer), and University of Warwick (DFG Research Fellowship).

Sumeetpal Singhمشاهده پروفایل

مدرس ارشد

• Computational Statistics
• Monte Carlo Methods
• Bayesian Inference
• +۷ مورد دیگر

Sumeetpal Singh serves as a Senior Lecturer in Engineering Statistics (Associate Professor) at the University of Cambridge within the Department of Pure Mathematics and Mathematical Statistics (DPMMS). He holds dual affiliations as an Affiliated Lecturer of the Statistical Laboratory and a Fellow of Churchill College. His office is located in BE3-25 with contact number +44 1223 764 139. His research centers on Computational Statistics, specializing in Monte Carlo methods including Particle Filters and Markov Chain Monte Carlo for Bayesian inference and Maximum Likelihood estimation. Application domains span multi-target tracking systems, sensor networks for industrial condition monitoring, online gaming analytics (cheat detection and player strategy inference), and signal processing for single molecule fluorescence microscopy. This interdisciplinary work bridges theoretical statistics with real-world engineering challenges. As an integral member of the Statistical Laboratory research group, Dr. Singh contributes to advancing statistical methodology while maintaining active industry collaborations. His professional resources include a personal homepage at https://www.dpmms.cam.ac.uk/~sss40 and primary contact via sss40@cam.ac.uk.

Anna-Liisa Laineمشاهده پروفایل

استاد

• Ecology
• Evolutionary Biology
• Host-Pathogen Coevolution
• +۵ مورد دیگر

Professor Anna-Liisa Laine is affiliated with the University of Helsinki as a leading researcher in the Faculty of Biological and Environmental Sciences and the Organismal and Evolutionary Biology Research Programme . Her work bridges ecological epidemiology, evolutionary biology, and climate change impacts on plant-pathogen systems. Research Focus : Host-pathogen coevolution, evolutionary epidemiology, trophic interactions Grants : Funded by ERC (2024-2028), Academy of Finland (2024-2028), and Carbon Action/Hiilipilotti (2017-) Recent publications analyze biodiversity-stability relationships (2025), climate-driven community homogenization (2025), and microbial responses to warming (2024). Her team explores how plant diversity mediates disease resistance and carbon cycling under global change. Key Scientific Awards Academy of Finland Award for Scientific Courage (2012) L’Oreal-UNESCO For Women in Science Award (2012) Adjunct Professor of the Year at University of Helsinki (2015) As a supervisor for the Doctoral Programme in Plant Sciences and Wildlife Biology, she mentors emerging researchers in eco-evolutionary dynamics and community-level responses to environmental change.

• Scientific AI/ML
• Optimal Experimental Design
• Objective-Based Uncertainty Quantification
• +۴ مورد دیگر

Byung-Jun Yoon is a Professor at the Department of Electrical and Computer Engineering , Texas A&M University , with a joint appointment as a Scientist in the Computational Science Initiative at Brookhaven National Laboratory . His academic journey includes a B.S. in Electrical Engineering from Seoul National University (1998), and M.S. & Ph.D. in Electrical Engineering from Caltech (2002 & 2007). Key Research Areas: Scientific AI/ML, Optimal Experimental Design (OED), Objective-Based Uncertainty Quantification (UQ), Computational Network Biology & Bioinformatics. Notable Contributions: Development of uncertainty-aware machine learning frameworks for materials discovery, computational biology applications, and optimization of virtual screening pipelines. Scientific Awards & Recognitions: NSF CAREER Award (2012) Best Paper Award at APBC (2011) Best Paper Award at MCBIOS (2015) SLATE Teaching Excellence Award (2009) Microsoft Research Ph.D. Fellowship (2004) Killgore Fellowship (2001) KFAS Doctoral Scholarship (2001) Current Appointments: Professor at Texas A&M University (2023–present), Scientist at Brookhaven National Laboratory (2018–present). Former roles include Associate Professor (2014–2023) and Assistant Professor (2008–2014) at Texas A&M.

• Machine Learning
• Uncertainty Quantification
• High-Dimensional Approximation
• +۲ مورد دیگر

Dr. Guannan Zhang is a Distinguished Staff Scientist at Oak Ridge National Laboratory (ORNL) and holds joint faculty appointments with the Department of Mathematics and Statistics at Auburn University (since 2014) and the Department of Mathematics at the University of Tennessee (since 2022). His research spans high-dimensional approximation, uncertainty quantification, machine learning for scientific computing, stochastic optimization, and nonlinear filtering. Ph.D. in Applied Mathematics, Florida State University (2012) M.S. and B.S. in Mathematics, Shandong University (2009, 2007) Dr. Zhang’s work focuses on integrating machine learning with scientific computing. His recent research includes training-free diffusion models for uncertainty quantification, score-based filters for nonlinear data assimilation, and scalable generative AI frameworks for high-dimensional systems. He has pioneered methods that improve sampling efficiency in generative models and address degeneracy in high-dimensional filtering. His publications from 2024 highlight advancements in diffusion models for scientific applications, uncertainty quantification in climate modeling, and ensemble score-based filtering techniques. These works emphasize computational efficiency, stability, and applicability to complex systems. U.S. Department of Energy Early Career Award (2022) Best Paper Award at SC 24 (2024) Dr. Zhang has mentored several researchers, including Dr. Zezhong Zhang and Dr. Yanfang Liu, who have transitioned to tenure-track faculty positions. He leads the DyGenAI grant (2024) to develop dynamic generative AI for scientific prediction and control. His collaborations span institutions like Johns Hopkins University and Ohio State University, and he serves on the SIAM Committee on Programs and Conferences and the editorial board of SIAM Journal on Scientific Computing .