جست‌وجوی استاد و پژوهشگر

Tristan Duboisمشاهده پروفایل

دانشیار

• Electromagnetic Compatibility (EMC)
• Electronic Circuit Immunity
• Reliability Engineering
• +۳ مورد دیگر

Tristan Dubois is an Associate Professor at Université de Bordeaux , affiliated with the Laboratoire de l'intégration, du matériau au système (IMS Bordeaux). He works within the RELIABILITY research group, focusing on electromagnetic compatibility (EMC), electronic component immunity, and aging effects in circuits. His expertise includes near-field scan immunity methods , RF interference analysis , and reliability engineering for embedded systems. PhD in Electronics, Université Montpellier II, 2009 HDR (Habilitation à Diriger des Recherches), Université de Bordeaux, 2022 His research spans broad domains such as electromagnetic compatibility , electronic circuit reliability , and nanocomposite materials for high-frequency applications. Key subfields include conducted and radiated immunity , phase-locked loop perturbation , and modeling of aging-induced failures in voltage references and operational amplifiers. Tristan has published extensively in IEEE Transactions and Microelectronics Reliability , covering topics from microwave oscillator systems to embedded capacitor design . Recent work includes a 2024 poster on proteomic classification of triple-negative breast cancer, indicating interdisciplinary collaborations. 2022: HDR thesis on electromagnetic security of connected devices 2014: PhD thesis on electromagnetic wave impact on electronic circuits

Olga Kloppمشاهده پروفایل

استاد

• Nonparametric Estimation
• High-Dimensional Inference
• Network Models
• +۴ مورد دیگر

Olga Klopp is a Professor of Statistics at ESSEC Business School and a permanent member of the CREST research center. Her work focuses on nonparametric estimation, high-dimensional inference, network models, and matrix completion. Research spans theoretical and applied statistics Specializes in sparse/high-dimensional data and network analysis Develops algorithms for matrix completion and graphon estimation Recent research includes tensor decomposition for economic networks, detection of change-points in dynamic networks, and handling missing data in epidemic modeling. She has received ERC and ANR grants for innovative projects. PhD Students: Solenne Gaucher, Mokhtar Alaya, Guillermo Martin Key methodological contributions in low-rank modeling and robust estimation

Frederic Mapsمشاهده پروفایل

استاد

• Marine Ecology
• Plankton Modeling
• Arctic Ecosystems
• +۴ مورد دیگر

Frederic Maps serves as a Full Professor in the Department of Biology at Université Laval, Québec, Canada, where he leads research in marine ecosystem modeling with emphasis on Arctic systems. His work is conducted through the Numerical Ecosystem and Oceanography (NEOLab) laboratory, and he holds leadership roles including Co-director of the FRQNT Québec Océan strategic network and membership in the Institut Nordique du Québec. Post-doctorate fellow, University of Maine (2009-2012) Ph.D. in Oceanography, Université du Québec à Rimouski (2004-2009) M.Sc. in Oceanography, Université du Québec à Rimouski (2000-2003) Maps' research centers on marine plankton responses to environmental variability across scales, using numerical modeling approaches including individual-based models, trophic network modeling, and coupled 3D bio-physical systems. He pioneers trait-based plankton ecology informed by in situ optical measurements of morphological properties, with specific focus on Arctic ecosystems. His methodology integrates machine learning for analyzing plankton imagery and lipid content estimation. Analysis of his 15 most recent publications (2021-2024) reveals consistent focus on Arctic marine systems, particularly Hudson Bay and Baffin Bay. Key themes include copepod life history strategies, phytoplankton bloom dynamics, zooplankton community seasonality, and trait-based ecological modeling. His work increasingly incorporates machine learning for image analysis while maintaining strong physical-biological coupling in numerical frameworks. Maps actively contributes to major collaborative projects including the BaySys initiative and Québec Océan network, though no individual scientific awards are documented in the provided materials. As Principal Investigator of NEOLab, Maps supervises research on marine ecosystem modeling with emphasis on Arctic plankton dynamics. His team develops innovative approaches connecting in situ observations with numerical simulations to address climate change impacts on polar marine systems.

• Stochastic Programming
• Mathematical Modeling
• Financial Mathematics
• +۷ مورد دیگر

Prof. habil. dr. Leonidas Sakalauskas is an Affiliated Scientist at the Smart Technologies Research Group within the Institute of Data Science and Digital Technologies at Vilnius University. His extensive research career spans mathematical modeling, stochastic programming, and statistical analysis with significant contributions across multiple disciplines. He maintains an active research profile with recent publications extending into 2025. Professor Sakalauskas' research focuses on stochastic programming, mathematical modeling, financial mathematics, statistical analysis, optimization, and queueing theory. His work demonstrates a consistent pattern of interdisciplinary research, bridging theoretical mathematics with practical applications in finance, social sciences, and computer science. He has developed innovative approaches to agent-based modeling, fractal analysis, and Monte Carlo methods, contributing significantly to both theoretical frameworks and practical implementations. His recent publications reveal a continuing evolution of research interests, with increasing emphasis on data science applications, computational social science, and advanced statistical modeling techniques. The articles demonstrate sophisticated methodological approaches to complex problems across diverse domains including financial modeling, social cohesion analysis, and network performance optimization. Throughout his career, Professor Sakalauskas has maintained a strong publication record in high-impact journals and conference proceedings, reflecting his ongoing engagement with the international research community. His work shows consistent methodological rigor combined with practical relevance across multiple application domains. Professor Sakalauskas has collaborated extensively with researchers across Lithuania and internationally, demonstrating strong teamwork and interdisciplinary engagement. His research has practical applications in financial modeling, social science analysis, and computational systems optimization, contributing to both theoretical advances and real-world implementations.

• Robotics
• Prosthetics Engineering
• Control Systems
• +۴ مورد دیگر

Donald Ebeigbe is an Assistant Professor in the Department of Electrical Engineering at Cleveland State University. His research spans interdisciplinary domains including robotics, prosthetics, infectious disease dynamics, and control systems. He has made significant contributions to nonlinear filtering, robust control, and mathematical modeling of epidemics with behavioral feedback. Academic Rank: Assistant Professor Department: Electrical Engineering University: Cleveland State University His work integrates robotics with biomedical applications , focusing on adaptive control techniques for prosthetic limbs and drones. In epidemiology, he investigates parameter identifiability and equilibrium stability in disease models. Recent publications highlight his expertise in Kalman filtering and non-Gaussian forecasting . Research trends include: Generalized unscented transformation for non-Gaussian processes (2025) Behavioral feedback in infectious disease dynamics (2025) Regressor-free control algorithms (2024) Manifold-based identifiability analysis in epidemics (2022) Key collaborations include research with scholars at institutions like IEEE and SIAM journals, with applications in both biomedical engineering and public health.

Caio Graco Rodrigues Leandro Rozaمشاهده پروفایل

پژوهشگر

• Ecology
• Environmental Sciences
• Functional Diversity
• +۵ مورد دیگر

Caio Graco Rodrigues Leandro Roza is a Postdoctoral Researcher and University Researcher at the University of Helsinki , affiliated with the Faculty of Biological and Environmental Sciences and Lammi Biological Station . His work focuses on metacommunity dynamics , functional diversity , and phytoplankton community assembly across aquatic systems. Doctor of Philosophy (Plant Biology, State University of Rio de Janeiro, 2021) MSc in Plant Biology (State University of Rio de Janeiro, 2014) Research themes include ecological synthesis , biodiversity loss , and trait-based approaches , with projects like METAFOR (global metacommunity analysis) and TRACE (trait responses in ecosystems). His collaboration network spans over 90 researchers globally. Recent publications address microplastic impacts on microbial flocs, phytoplankton responses to environmental variables, and methodological frameworks for functional diversity analysis. Awards include the 2012 RAIC Best Oral Presentation in Brazil. He teaches R programming and functional diversity courses, and coordinates thematic reports for the Brazilian Platform of Biodiversity and Ecosystem Services . Current funding includes support from the Finnish Cultural Foundation and the Academy of Finland .

• Statistical Science
• Data Science
• Machine Learning
• +۷ مورد دیگر

Nick Whiteley is a Professor in the School of Mathematics at the University of Bristol and serves as Director of the Institute for Statistical Science. He also held a Heilbronn Chair in Data Science and previously directed the EPSRC Centre for Doctoral Training in Computational Statistics and Data Science. His research spans statistical science and data science with interdisciplinary applications. Education : Ph.D. (University of Cambridge, 2009), M.Eng. (University of Oxford, 2003) Whiteley's research focuses on sequential Monte Carlo methods , Bayesian inference , hidden Markov models , and nonlinear filtering . His work connects statistical theory with practical applications in epidemic modeling, graph representation, and temporal data analysis. His recent publications emphasize scalable algorithms for high-dimensional problems and theoretical foundations of approximation methods. Key article trends include epidemic modeling , dynamic network analysis , stochastic processes , and machine learning theory . Sub-fields span from computational statistics to geometric methods in AI. Whiteley has received prestigious recognition through the Heilbronn Chair and Brunel Research Fellowship.

• Probability
• Statistical Science
• Bayesian Statistics
• +۵ مورد دیگر

Vladislav Tadic serves as a Senior Lecturer in Statistics at the University of Bristol's School of Mathematics Statistics, where he advances theoretical frameworks in statistical science with emphasis on stochastic modeling and inference. His academic foundation includes: B.Eng M.Sc. Ph.D. from the University of Belgrade Dr. Tadic's research spans Probability Theory , Bayesian Statistics , and Stochastic Processes , with significant contributions to hidden Markov models , state-space systems , and recursive estimation algorithms . His work integrates rigorous mathematical analysis with applications in machine learning, focusing on stability properties, entropy rates, and asymptotic behavior of statistical estimators in nonlinear dynamic environments. Analysis of his publication record reveals a cohesive research trajectory centered on theoretical challenges in statistical inference for complex systems. Key themes include the analyticity of entropy rates, stability of optimal filters, and asymptotic properties of maximum likelihood estimation in non-linear state-space models, demonstrating deep connections between probability theory, information theory, and computational statistics. No scientific awards or fellowships are documented in his current profile. Dr. Tadic has supervised 3 research students, reflecting his commitment to academic mentorship, though specific grant funding details remain undisclosed in available records.

Patrick Rebeschiniمشاهده پروفایل

مدرس

• High-Dimensional Probability
• Statistics
• Optimization
• +۴ مورد دیگر

Patrick Rebeschini is a Lecturer in the Department of Statistics at the University of Oxford. Before joining Oxford, he held positions at Yale University, including Lecturer in Computer Science and Postdoctoral Associate at the Yale Institute for Network Science. He earned his Ph.D. in Operations Research and Financial Engineering from Princeton University, supervised by Ramon van Handel. His research spans high-dimensional probability, statistics, and optimization, focusing on designing computationally efficient and statistically optimal algorithms for machine learning and artificial intelligence. His recent activities include organizing workshops on Online Learning and Game-Theoretic Statistics (2025), serving as Senior Area Chair for ICML 2025, and chairing sessions at the IMS Annual Meeting (2022). He co-organizes the joint Maths-Stats colloquium series at Oxford and contributes to doctoral training as a Co-Investigator for the Imperial-Oxford StatML Centre for Doctoral Training (CDT) and the Fundamentals of AI Erlangen Hub. Scientific Awards: 2019 Oxford MPLS Teaching Award Excellence in Teaching Award from Princeton Engineering Council (2013) Grants & Collaborations: Co-Investigator for StatML CDT and Fundamentals of AI Erlangen Hub Member of Bernoulli Society, Institute of Mathematical Statistics, and ELLIS Teaching: Organized reading groups on learning theory (2017–2025) Module Leader for doctoral courses on Online Learning and Reinforcement Learning (2023) Supervisor for UNIQ+ DeepMind summer interns (2022–present)

Seth Flaxmanمشاهده پروفایل

دانشیار

• Machine Learning
• Bayesian Statistics
• Spatiotemporal Statistics
• +۵ مورد دیگر

Seth Flaxman is an Associate Professor of Computer Science at the University of Oxford and a Tutorial Fellow at Jesus College. His research focuses on scalable Bayesian machine learning and spatiotemporal statistics, with applications in public policy, global health, and social science, particularly in areas like public health, orphanhood, and computational epidemiology. BA in Mathematics and Computer Science, Harvard University (2004-2008) PhD in Machine Learning and Public Policy, Carnegie Mellon University (2011-2015) His work bridges computational statistics and machine learning with real-world challenges, including disease modeling for COVID-19, analysis of social media filter bubbles, and statistical methods for big data paradoxes. He actively supervises DPhil, Master’s, and undergraduate research projects. Key trends in his recent publications include applications of Gaussian processes and Bayesian hierarchical models to global health challenges such as HIV risk mapping, SARS-CoV-2 surveillance, and spatiotemporal forecasting. His research integrates multimodal data (e.g., satellite imagery, census microdata) and emphasizes scalable inference algorithms. Scientific Awards : Samsung AI Researcher of the Year Award (2020) Best Student Paper Award, KDD 2015 Suresh Konda Award for Heinz first paper His EPSCRC Fellowship Award (£1.18 million) supports research on Spatiotemporal Statistical Machine Learning. He collaborates with institutions including Imperial College London, University of Copenhagen, and the World Health Organization.

Jeff Calderمشاهده پروفایل

دانشیار

• Partial Differential Equ
• Numerical Analysis
• Applied Probability
• +۵ مورد دیگر

Jeff Calder is an Associate Professor in the School of Mathematics at the University of Minnesota. He specializes in interactions between partial differential equations (PDE), numerical analysis, applied probability, and computer science, with applications to machine learning and data analysis. His research has been supported by the National Science Foundation, Alfred P. Sloan Foundation, and McKnight Foundation. Ph.D. in Applied and Interdisciplinary Mathematics (2014, University of Michigan) Morrey Assistant Professor (2014-2016, UC Berkeley) His research interests include: Rigorous analysis of PDEs Algorithm development for machine learning Variational methods in data science Graph-based learning techniques Computational biomedical imaging Interdisciplinary applications in anthropology Recent publications focus on t-SNE improvements, medical imaging segmentation, and variational PDEs for inversion problems. His work spans computer science, applied mathematics, and biomedical engineering. Scientific awards include: NSF Career Award (2020) Sloan Research Fellowship (2020) McKnight Presidential Fellowship (2021) Guillermo E. Borja Award (2021) Albert and Dorothy Marden Professorship (2023-2025) He supervises graduate and undergraduate research projects and co-founded the AMAAZE consortium for mathematics-anthropology collaboration. His GraphLearning Python package provides open-source tools for graph-based machine learning.

Mohammed Hasanمشاهده پروفایل

دانشیار

• Signal Processing
• Biomedical Research
• Adaptive Filtering
• +۳ مورد دیگر

Mohammed Hasan is an Associate Professor in the Department of Electrical and Computer Engineering at the Swenson College of Science and Engineering, University of Minnesota Duluth. He joined the department in September 1997 and focuses on research areas including modeling and estimation of random processes, adaptive filtering, sinusoidal estimation, and their applications to signal processing and biomedical research. B.S. in Mathematics, University of Baghdad, Iraq (1979) M.S. in Electrical Engineering, Colorado State University (1992) Ph.D. in Applied Mathematics, Colorado State University (1991) Ph.D. in Electrical Engineering, Colorado State University (1997) His research bridges theoretical and applied domains, with a emphasis on signal processing techniques for biomedical applications and stochastic systems. Dr. Hasan's work includes adaptive algorithms for filtering and harmonic signal analysis.

• Life and Health Informatics
• Computer Systems
• Control and System Engineering
• +۵ مورد دیگر

Masao Yanagisawa is a Professor at Waseda University's School of Fundamental Science and Engineering, with over 25 years of academic experience since 1998. An IEEE and ACM member, he holds a Doctor of Engineering degree from Waseda University.

Céline Helbertمشاهده پروفایل

مدرس ارشد

• Statistics
• Numerical Experimental Designs
• Functional Data Analysis
• +۶ مورد دیگر

Céline Helbert is a Senior Lecturer at the École Centrale de Lyon, affiliated with the Department of Mathematics and Computer Science and the Camille Jordan Institute (UMR CNRS 5208). Her research focuses on statistics, numerical experimental design, and functional data analysis. Education: DEA in Financial and Actuarial Sciences (ISFA, Lyon), ICM at École des Mines de Saint-Étienne. Research: Expertise in kriging (Bayesian, high-dimensional), Gaussian process optimization, metamodelling for complex systems, and sensitivity analysis for functional inputs. Projects: Led the CIROQUO consortium (2021-2024) and contributed to initiatives like Chaire OQUAIDO (2016-2020) and ANR Pepito (2014-2019). Students: Supervised PhD candidates including Martin Buisson (automotive design), Reda El Amri (functional uncertainty analysis), and Simon Nanty (stochastic sensitivity analysis). Software: Maintainer of R packages DiceDesign and DiceEval for computer experiment design and validation. Conferences: Active in organizing events like JDS2022 and GDR Mascot Num workshops.

• Neuromorphic Information Processing
• Artificial Neural Networks
• Machine Learning
• +۶ مورد دیگر

Prof. Dr. Martin Bogdan is a faculty member at the University of Leipzig since 2008, currently holding the Professorship for Neuromorphic Information Processing in the Faculty of Mathematics and Computer Science . His academic career spans roles as a research assistant, assistant professor, and department head at institutions including the University of Tübingen and University of Leipzig. Education : Studied technical computer science at Fachhochschule Offenburg (1987–1993) and industrial informatics at Université Grenoble I (1991–1993); earned PhD in 1998 from University of Tübingen. Research Interests focus on: Neuromorphic Information Processing Spiking Neural Networks Brain-Computer Interfaces (BCI) Embedded Systems for Bio-Analogous Processing Real-Time Signal Processing in Medicine Machine Learning Applications in Neurology Mainframe Computing Techniques Article Trends show expertise in: spiking neural networks for real-time applications; BCI systems for locked-in syndrome patients; hyperspectral imaging for agricultural analysis; FPGA-based evolving hardware; and machine learning applications in medical diagnostics. His work bridges neuroscience, computer science, and biomedical engineering. Academic Roles include leadership of the NeuroTeam (2000–2015), editorial positions, and extensive teaching experience in technical computer science and neuromorphic systems. Labs & Teams : Leads the Neuromorphic Information Processing division; collaborates with researchers including Dr. Sophie Adama, Dr. Jörn Hoffmann, and engineers like Max Braungardt.