جست‌وجوی استاد و پژوهشگر

Aurélie Germaمشاهده پروفایل

دانشیار

• Volcanology
• Geochronology
• Geochemistry
• +۳ مورد دیگر

Dr. Aurélie Germa is an Associate Professor at the School of Geosciences, University of South Florida , where she has been since 2014. She also serves as the Undergraduate Director for Geology and President of Sigma Xi USF Chapter . Her academic journey includes a PhD in Volcanology (2008) and MSc in Volcanology (2005) from Université Paris-Sud 11, and prior degrees from Université Blaise Pascal and Université Paul Sabatier, France. Research Focus: Spatio-temporal evolution of volcanic fields, volcano hazard assessments, magma petrogenesis, and computational modeling. Teaching: Undergraduate courses in Solid Earth, Physical Geology, Volcanology, and Geochemistry; graduate courses in Geochronology and Volcanic Petrology. Dr. Germa's research integrates radiometric dating , igneous petrology , and geochemical analysis to study magma evolution and volcanic hazards. Her work spans global volcanic regions, including the Lesser Antilles , Payun Matru (Argentina) , and San Rafael subvolcanic field (Utah) , with a strong emphasis on numerical modeling and statistical methods in hazard assessment. Her recent publications highlight advancements in lava geochemistry (2024), island arc element enrichment (2022), and pyroclastic current dynamics (2020). These works align with her expertise in volcanic field geochronology , magma storage conditions , and geomorphic evolution . Scientific Awards & Grants: USF Strategic Investment Pool (2024): $143,300 for ICP-MS laser system. NSF IF 2040066 (2021-2023): $126,390 for Keyence VHX-7000 digital microscope. USGS Cooperative Agreement (2020-2025): $25,000 for K/Ar dating modernization. NSF EAR 1347899 (2014-2018): $229,352 for statistical volcanic hazard models. Dr. Germa actively mentors PhD and Master’s students , including Erica Fancher (2024, USF), and her lab provides access to advanced equipment like the Keyence VHX-7000 digital microscope and atomic absorption spectrometer for potassium analysis.

Alexandra Psarrouمشاهده پروفایل

دانشیار

• Deep learning methods for Image Quality Performance
• Explainable learning models
• Reinforcement learning
• +۳ مورد دیگر

Alexandra Psarrou is a Reader in the School of Computer Science and Engineering at the University of Westminster, serving as Head of School from 2002-2019. She holds a BSc and MSc from Queen Mary University of London and a PhD from Queen Mary, London (1996). Her research focuses on dynamic vision, explainable AI, hyperspectral analysis, and image quality metrics. Education: BSc (1987), MSc (1988), PhD (1996) Roles: Reader, former Head of School, member of multiple University committees Research interests include deep learning, reinforcement learning, and applications in cultural heritage. She founded the Computational Vision Research Group (CVRG) in 1997, advancing techniques like recurrent neural networks and kernel PCA models. Awards include Best Paper Awards at BMVC (1999), IMECS (2008), and IS&T (2020). She led major grants like the NOESIS project (€709k) and diARTgnosis (€518k). Supervised 10+ PhD students in areas like gesture recognition and medical imaging. Labs/Teams: Computational Vision and Imaging Technologies Research Group Grants: NOESIS, diARTgnosis, HISTORIA, HEFCE NFF

Tasos Christofidesمشاهده پروفایل

استاد

• Probability Inequalities
• Demimartingales
• Stochastic Orders
• +۲ مورد دیگر

Tasos Christofides is a Professor in the Department of Mathematics and Statistics at the University of Cyprus, School of Natural and Applied Sciences. He has been serving in this position since 2004 after previously holding the rank of Associate Professor from 1991 to 2004. Prior to his appointment at the University of Cyprus, he was an Assistant Professor at the State University of New York at Binghamton from 1987 to 1991. His educational background includes a Ph.D. (1987) and MSE (1985) in Mathematical Sciences from The Johns Hopkins University, USA, and a Mathematics degree (1983) from the University of Athens, Greece. Professor Christofides specializes in Probability Inequalities, Demimartingales, Stochastic Orders, Survey Methodology, and Indirect Questioning Techniques. His research primarily focuses on theoretical statistics with applications to survey methodology, particularly in developing techniques for handling sensitive questions while preserving respondent privacy. His work bridges probability theory with practical survey design, making significant contributions to both theoretical foundations and applied methodologies. His publication record demonstrates consistent contributions to statistical theory, with a particular emphasis on U-statistics, demimartingales, and randomized response techniques. The trajectory of his research shows progression from foundational work on U-statistics and probability inequalities in the early career to more applied survey methodology techniques in later years, while maintaining strong theoretical underpinnings. Professor Christofides serves as Associate Editor for several prestigious statistical journals including Communications in Statistics-Theory and Methods, Communications in Statistics-Computation and Simulation, Journal of Statistical Theory and Practice, and Statistics and Probability Letters. His editorial service reflects his standing in the statistical community and his expertise across multiple domains of statistical theory and methodology. While specific grant information is not provided in the available text, his sustained research output and editorial positions indicate active engagement with the research community.

• Artificial Intelligence
• Bioinformatics
• Machine Learning
• +۷ مورد دیگر

Enrico Blanzieri is an Associate Professor at the University of Trento's Department of Information Engineering and Computer Science. His research focuses on artificial intelligence, bioinformatics, quantum computing, machine learning, and computational biology. He actively contributes to interdisciplinary projects such as Vitis OneGenE for gene network analysis and quantum machine learning pipelines. His teaching includes courses like Algorithms for Bioinformatics, Quantum Machine Learning, and Data Mining. Blanzieri's work bridges theoretical foundations and practical applications, with notable contributions in quantum annealing-based algorithms, gene network expansion using distributed computing, and socially-competent artificial agents. His research integrates computational methods with biological systems, addressing challenges in precision agriculture and systems biology. His recent publications emphasize quantum machine learning advancements, causal role attribution in gene networks, and ethical AI frameworks for language models. Collaborative projects include the GENE@HOME volunteer computing initiative for bioinformatics tasks. Blanzieri also engages in educational activities, supervising courses that blend theoretical computer science with real-world applications.

• Graph Theory
• Random Graph Theory
• Algorithms
• +۳ مورد دیگر

Stefanie Gerke is a Professor in the Department of Mathematics at Royal Holloway, University of London, where she joined in 2007 and is affiliated with the Centre for Algorithms and Applications. Her research spans discrete mathematics with emphasis on theoretical foundations and practical algorithmic applications. Her educational background includes a Diplom in Mathematics from Freie Universität Berlin followed by a PhD in Mathematics from the University of Oxford. She held prestigious postdoctoral positions at TU Munich (as a Bavarian Habilitationsfoerderpreis recipient) and ETH Zurich under Angelika Steger's group. Gerke's research focuses on graph theory, specializing in random graph structures, algorithmic solutions for combinatorial problems, and network analysis. Her work bridges theoretical computer science and discrete mathematics, with significant contributions to oriented graph theory, kernel conjectures, and economic network modeling. Recent publications demonstrate expertise in resolving longstanding graph theory conjectures through probabilistic methods and combinatorial optimization. Her publication trends (2023-2025) reveal concentrated work on oriented graph structures, anti-traceability properties, maximum cut problems, and economic network constraints. Key thematic threads include solving Seymour's and Sullivan's conjectures, developing bounds for directed cuts, and modeling public goods allocation in constrained networks, reflecting interdisciplinary applications from theoretical computer science to economic theory. Bavarian Habilitationsfoerderpreis Gerke has supervised 4 research students and secured significant funding including an EPSRC grant for "Successive Shortest Structures in Random Graphs" (2022-2023) and London Mathematical Society projects for "Probabilistic Combinatorics: Marking Colin McDiarmid's Retirement" (2016) and CMU-RHUL collaboration (2012). She actively participates in international combinatorics networks with recent collaborations across multiple countries. As a core member of Royal Holloway's Centre for Algorithms and Applications, she contributes to the university's discrete mathematics research ecosystem through collaborative projects, international workshops, and mentorship within the Department of Mathematics.

• Bayesian Methods
• Biostatistics
• Data Science
• +۴ مورد دیگر

Saman Muthukumarana is a Professor and Head of the Department of Statistics at the University of Manitoba. He joined the department in 2010 as an Assistant Professor, was promoted to Associate Professor in 2016, and became a full Professor in 2022. He holds a BSc (Honours Special) in Statistics from the University of Sri Jayewardenepura, an MSc from Simon Fraser University, and a PhD from Simon Fraser University under Dr. Tim Swartz, focusing on Bayesian methods and applications. His research emphasizes Bayesian methodologies for complex models, with applications in social networks, health studies, sports analytics, environmental science, and machine learning. He has secured over $8.4M in research funding from NSERC, Mitacs, CIHR, and other organizations. His work has been published in journals such as the Canadian Journal of Statistics, Machine Learning with Applications, and IEEE Open Journal of Instrumentation & Measurement. Dr. Muthukumarana’s research spans Bayesian computation, biostatistics, data science, and environmental statistics. He has contributed to anomaly detection in buildings, predictive modeling for public health (e.g., Long COVID), and ecological studies like salmon stock recruitment. His collaborative projects include developing statistical tools for microbiome analysis and improving machine learning approaches for imbalanced datasets. He also leads the Data Science Nexus, fostering interdisciplinary research. His grants and collaborations highlight his role in advancing statistical methodologies for real-world challenges, including health, energy efficiency, and ecological conservation. While no specific awards are listed, his extensive funding and publication record reflect his scholarly impact. He currently supervises graduate students and actively participates in academic leadership roles.

Tobias Glasmachersمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Optimization Algorithms
• +۶ مورد دیگر

Prof. Dr. Tobias Glasmachers is a Full Professor at the Institut für Neuroinformatik , Ruhr-Universität Bochum, Germany, specializing in the Theory of Machine Learning . He leads the Optimization of Adaptive Systems group and holds appointments in both Computer Science and Interdisciplinary AI research. Key Research Areas : Optimization algorithms, evolutionary computation, reinforcement learning, supervised learning, and neural networks Technical Focus : Gradient-based methods, support vector machines, and adaptive coordinate descent Applications : Robotics, waste sorting facilities, 3D game environments (e.g., Doom/Minecraft), and human-centered AI design Notable Contributions : Development of LM-MA-ES evolution strategy, Hessian Estimation Evolution Strategy, and tachAId tool for ethical AI design. His work bridges theoretical analysis with practical implementations across diverse domains. Teaching : Offers courses in Informatik 1 - Programmieren, Machine Learning: Supervised Methods, and Evolutionary Algorithms. Supervises numerous Bachelor's and Master's theses on AI/ML applications.

Sergio Alvarezمشاهده پروفایل

دانشیار

• Data Mining
• Machine Learning
• Clinical Medical Data Analysis
• +۱ مورد دیگر

Sergio Alvarez is Associate Professor in the Computer Science Department at Boston College. His research focuses on data mining and machine learning applications for clinical medical data analysis, with particular expertise in performance analysis of learning systems. Alvarez investigates foundational aspects of machine learning metrics and develops deep learning techniques for physiological data interpretation. His recent work includes sleep stage classification models and autism diagnostic tools using behavioral data analysis. Publications demonstrate consistent methodological innovation in neural network applications for healthcare, including convolutional networks for sleep pattern recognition and kernel alignment techniques for model evaluation. His research advances diagnostic tools for neurological conditions through computational approaches.

Constance van Eedenمشاهده پروفایل

استاد

• Estimation in restricted parameter spaces
• Decision theory
• Nonparametrics
• +۳ مورد دیگر

Constance van Eeden (1927–2021) was an Honorary Professor at the University of British Columbia (UBC) and held academic positions at institutions such as the University of Minnesota, Université de Montréal, and Université du Québec à Montréal. She contributed significantly to statistical theory, particularly in estimation in restricted parameter spaces, decision theory, and nonparametric methods. Her career spanned over five decades, during which she supervised numerous PhD and MSc students. Van Eeden was recognized with prestigious awards, including the 1990 Gold Medal from the Statistical Society of Canada and Fellowships from the Institute of Mathematical Statistics and American Statistical Association. Her research interests included foundational statistical problems such as parameter estimation under constraints, selection procedures, and robust estimation. She played a pivotal role in developing statistical programs in Canada and contributed to the establishment of the Constance van Eeden Endowment Fund at UBC, supporting statistical education and research initiatives. Van Eeden's work also involved editorial roles in journals like the Annals of Statistics and Canadian Journal of Statistics . Her legacy includes over 70 publications, including influential books and articles on estimation theory. The van Eeden Fund continues to support distinguished lectures, summer schools, and student awards, reflecting her commitment to advancing statistical science.

Petra Mutzelمشاهده پروفایل

استاد

• Graph Algorithmics
• Graph Learning
• Combinatorial Optimization
• +۳ مورد دیگر

Petra Mutzel is a Professor of Computer Science at the University of Bonn. Her research focuses on graph algorithmics, temporal networks, and combinatorial optimization with applications in data science and bioinformatics. She holds a PhD in Computer Science from the University of Cologne (1994) and a Diplom in Mathematics from the University of Augsburg (1990). Her work emphasizes algorithmic solutions for complex graph problems, including temporal graph analysis, graph learning, and optimization frameworks for real-world networks. She has contributed to open-source libraries like Tglib for temporal graph processing and frameworks like Scaffold Hunter for medicinal chemistry. Her research spans theoretical foundations and practical implementations, with notable contributions to graph drawing, vehicle routing optimization, and protein complex analysis. Mutzel’s recent publications (2022-2025) explore temporal network dynamics, robust combinatorial optimization, and scalable graph kernel methods. She actively engages in academic leadership, organizing conferences such as WALCOM 2022, and serves on editorial boards for journals in algorithms and computational geometry.

Dimitris N. Politisمشاهده پروفایل

استاد

• Time Series Analysis
• Nonparametric Statistics
• Bootstrap Methods
• +۶ مورد دیگر

Dimitris N. Politis is a Distinguished Professor in the Department of Mathematics and the Halicioglu Data Science Institute at the University of California, San Diego. He holds the prestigious Halicioglu Data Science Institute Chancellor's Endowed Chair II position and has been affiliated with UCSD since 1997, progressing from Associate Professor to his current distinguished position. His educational background includes a Ph.D. in Statistics from Stanford University (1990), along with multiple master's degrees in Statistics, Mathematics, and Computer and Systems Engineering from Stanford and Rensselaer Polytechnic Institute. Professor Politis's research focuses on advanced statistical methodologies, with particular expertise in: Time Series and Random Fields analysis Computer-Intensive Methods in Statistics Resampling and Subsampling for Dependent Observations Spatial Statistics and Point Processes Nonparametric Spectral and Probability Density Estimation Model-free Prediction and Regression Information Theory and Signal Processing Econometric Analysis of Financial Time Series His scholarly output includes over 100 journal papers and several influential books, most notably "SUBSAMPLING" (1999), "MODEL-FREE PREDICTION AND REGRESSION" (2015), and "TIME SERIES: A FIRST COURSE WITH BOOTSTRAP STARTER" (2020), which has become a key educational resource in the field. Professor Politis has received numerous prestigious awards and honors: Guggenheim Fellowship (2011) Fellow of the American Statistical Association (2011) Fellow of the Institute of Mathematical Statistics (2004) Distinguished Author Award from the Journal of Time Series Analysis (2020) Econometric Theory Multa Scripsit Award (2013) Tjalling C. Koopmans Econometric Theory Prize (2012) He has been principal investigator on numerous NSF and NIH grants, including current funding for "Computer-intensive methods for dependent and complex data" (NSF DMS 24-13718, 2024). Professor Politis has held significant leadership roles, including serving as Chair of the Faculty Council of the Halicioglu Data Science Institute (2019-2023) and Associate Director (Founding) of the Institute (2018-2024). As a co-founder of the International Society for NonParametric Statistics, Professor Politis has made substantial contributions to the organization of major conferences and workshops in his field, including the First Conference of the International Society for NonParametric Statistics in 2012. His editorial work includes serving as Co-Editor of the Journal of Time Series Analysis since 2013 and Senior Editor for the ACM/IMS Journal of Data Science since 2022.

Eleni Vasilakiمشاهده پروفایل

استاد

• Bioinspired Machine Learning
• Neuromorphic Computing
• Computational Neuroscience
• +۳ مورد دیگر

Professor Eleni Vasilaki holds the Chair in Bioinspired Machine Learning at the University of Sheffield's School of Computer Science, where she serves as Head of the Machine Learning research group and member of the Complex Systems Modelling research group. She joined as Lecturer in 2009 and became Professor in 2016. Education includes: Bachelor's in Informatics and Telecommunications from University of Athens Master's in Microelectronics from University of Athens DPhil in Computer Science and Artificial Intelligence from University of Sussex Research focuses on developing novel machine learning techniques inspired by biological principles, particularly in reinforcement learning and reservoir computing methods. Her team collaborates with material scientists and engineers to design neuromorphic computing hardware. Publications demonstrate strong emphasis on neuromorphic computing, machine learning applications in neuroscience, reservoir computing systems, and computational modeling of biological processes. Recent work addresses device-agnostic modeling, physical neural networks, and stochastic computing platforms. Significant grants include: MARCH: Magnetic Architectures for Reservoir Computing Hardware (£936,815, Co-PI) ActiveAI - active learning and selective attention for robust AI (£953,584, Co-PI) Modeling probabilistic reinforcement learning in Drosophila (Google, £50,769, PI) CausalXRL: Causal explanations in Reinforcement Learning (£309,915, PI) Brains on Board: Neuromorphic Control of Flying Robots (£2,128,934, Co-PI) Leads the Machine Learning research group and collaborates with the Complex Systems Modelling group, focusing on brain-inspired computing architectures and neuromorphic hardware design.

• Fluid Dynamics
• Multiphase Flow
• Reactive Flow
• +۳ مورد دیگر

Henrik Ström is an Assistant Professor in Fluid Dynamics at Chalmers University of Technology, specializing in multiphase flow modeling, reactive flow analysis, and rarefied flow simulation. His research spans molecular to continuum levels, emphasizing interdisciplinary collaboration. He has contributed to over 160 publications, focusing on automotive catalysis, bubble dynamics, biomass conversion, and CFD-DEM modeling. Research Interests: Multiphase flow dynamics, computational fluid dynamics (CFD), reactive flow modeling, automotive exhaust systems, and biomass thermochemical processes. He explores phenomena such as lift forces on deformable bubbles, soot generation in gasifiers, and flow distribution optimization in catalytic converters. Projects include advanced modeling of catalytic converters, particle-fluid coupling in biomass conversion, and carbon capture technologies. He collaborates on automotive pollution control, heat exchanger design, and nanoscale catalytic systems. His work integrates experimental validation with high-resolution numerical simulations.

Yuejiao Cindy Fuمشاهده پروفایل

استاد

• Statistical Modeling
• Mixture Models
• Empirical Likelihood
• +۲ مورد دیگر

Yuejiao Cindy Fu is a Full Professor in the Department of Mathematics and Statistics at York University's Faculty of Science. Her office is located in the Ross Building. Research specializes in mixture models, empirical likelihood, density ratio models, and statistical analysis of high-dimensional spatial, genetic, and DNA methylation data. Methodologies include homogeneity testing, dimension reduction techniques, and robust inference for genomic applications. Education: Ph.D. in Statistics from University of Waterloo (2004). Contact: (647) 831 1208.

J. Michael Mooreمشاهده پروفایل

عضو هیئت علمی

• Human-Computer Interaction
• Software Design
• Information Retrieval
• +۶ مورد دیگر

J. Michael Moore is an Instructional Associate Professor in the Department of Computer Science & Engineering at Texas A&M University, part of the College of Engineering. He holds a PhD in Computer Science from Texas A&M (2007) and has extensive teaching experience in courses like Human-Computer Interaction and Accessible Computing. His research focuses on HCI, software design, accessibility, and digital libraries, with notable work on design exploration and document triage systems. Education includes a B.S. in Biochemistry from Texas A&M (1991) and an M.S. in Computer Science from Texas State University (1997). He has authored over 20 peer-reviewed publications and has been recognized with awards such as the 2014 Mentoring Award and the Guseman Award (2002). Moore also contributes to professional service roles, including advising student organizations like DeAF and OSTEM. Teaching: Over 20 years of instruction in programming, HCI, and accessible computing. Research: Specializes in HCI, accessibility, and collaborative design tools. Service: Serves on committees for diversity initiatives and professional networks. His work integrates technical innovation with user-centered design principles, emphasizing accessibility and inclusive computing practices.