جست‌وجوی استاد و پژوهشگر

José E. Chacónمشاهده پروفایل

استاد

• Nonparametric Kernel Smoothing
• Cluster Analysis
• Mathematical Statistics
• +۳ مورد دیگر

José E. Chacón is a Professor of Statistics at the Department of Mathematics, University of Extremadura, Spain. He is also a member of the Institute of Mathematics at the same university. His research focuses on nonparametric kernel smoothing, cluster analysis, and mathematical statistics. He earned his PhD in Statistics from the University of Extremadura in 2004. Chacón’s work emphasizes methodological advancements in density estimation, clustering algorithms, and statistical theory. His recent publications address topics like geodesic distributions, Bayesian taut splines for mode estimation, and bump detection via density curvature. He has contributed to applied areas such as animal home range estimation and data science for pandemic analysis. His articles often explore cross-validation techniques, bandwidth selection, and mixture model clustering. He co-authored the textbook Multivariate Kernel Smoothing and Its Applications (2018), consolidating his expertise in kernel-based methods. Chacón’s research bridges theoretical statistics with practical applications, influencing both academic and applied domains.

• Data Analytics
• Statistical Learning
• Computational Science
• +۴ مورد دیگر

Giuseppe Brandi is an Assistant Professor in Data Science at Northeastern University London and an Honorary Research Fellow at Imperial College London's Centre for Climate Finance & Investment. His research focuses on DataMetrics, a discipline blending Data Analytics, Statistical Learning, and Computational Science to analyze complex systems, particularly in finance and climate contexts. He emphasizes adapting models to data rather than forcing data into pre-existing theoretical frameworks. Dr. Brandi's work spans Climate Data Science, Econometrics, and Quantitative Finance. He critiques rigid theoretical models and advocates for interdisciplinary methods to extract insights from multidimensional data. His recent publications explore multiscaling in financial systems, tensor-based decomposition for multidimensional data, and liquidity contagion in interbank markets. His articles highlight trends in financial risk assessment, systemic risk modeling, and the application of tensor methods to complex datasets. Notably, he develops tools like the DisaggregateTS package for time-series analysis and visual warning systems for financial anomalies. Despite these contributions, no scientific awards are explicitly mentioned in the provided texts. Dr. Brandi's advising and grant activities remain undocumented here, though his academic roles suggest involvement in student mentorship and collaborative research projects. His affiliation with Northeastern's global network positions him to engage with interdisciplinary teams across multiple campuses.

Christos Diouمشاهده پروفایل

دانشیار

• Machine Learning
• Artificial Intelligence
• Healthcare Applications
• +۳ مورد دیگر

Christos Diou is an Associate Professor of Artificial Intelligence and Machine Learning at the Department of Informatics and Telematics, Harokopio University of Athens, Greece. His academic career spans over 15 years of participation in national and international research projects, with a focus on machine learning algorithms, domain generalization, causal inference, and bias mitigation. He earned a BSc and Ph.D. in Electrical and Computer Engineering from Aristotle University of Thessaloniki. His research emphasizes the application of machine learning to healthcare, addressing challenges such as visual bias mitigation, causal effect estimation from observational data, and fairness-aware representation learning. Notable projects include REBECCA and RELEVIUM , both EU-funded, and MELIORA , targeting lifestyle interventions for breast cancer risk reduction. He has published extensively in top-tier venues like IEEE TPAMI, CVPR, and ICCV. Christos is a leading voice in AI ethics and healthcare innovation, with over 150 publications and best paper awards at IEEE Big Data Service 2023 and AIAI 2022. His work includes developing platforms like Effector for feature effects and Beam for behavior studies. He collaborates with institutions such as Karolinska Institutet and CERTH/ITI, and his students include PhD candidates Ioannis Sarridis and Aristotelis Ballas.

Emily Bergمشاهده پروفایل

دانشیار

• Statistics
• Small Area Estimation
• Agricultural Statistics
• +۳ مورد دیگر

Emily Berg is an Associate Professor in the Department of Statistics at Iowa State University. Her research focuses on small area estimation, Bayesian hierarchical models, and statistical applications in agriculture and environmental science. She holds a Ph.D. in Statistics from Iowa State University (2010), an M.S. in Statistics (2008), and a B.A. in Mathematics from Middlebury College (2005). Education: Ph.D. Statistics, Iowa State University, 2010 M.S. Statistics, Iowa State University, 2008 B.A. Mathematics, Middlebury College, 2005 Her research interests include developing statistical methodologies for small area estimation, analyzing agricultural and environmental data, and addressing challenges in informative sampling and missing data. She has contributed to applications in transportation safety, veterinary practices, and environmental monitoring. Dr. Berg’s work emphasizes practical solutions for data integration and estimation under complex scenarios, including the use of Bayesian models and quantile regression techniques. She collaborates on projects such as the Conservation Effects Assessment Project and has developed web applications for data visualization and quality assessment. No scientific awards have been explicitly mentioned in the provided materials. She has advised or collaborated on numerous statistical methodologies but no specific student names are listed. Her research extends to agricultural economics, environmental modeling, and public health surveillance.

• Stochastic Processes
• Mathematical Statistics
• Financial Mathematics
• +۳ مورد دیگر

Marko Voutilainen is a University Lecturer in the Department of Accounting and Finance at the University of Turku. His research focuses on stochastic processes, time series analysis, and statistical modeling with applications in finance and mathematical statistics. He has published extensively on topics such as stationary processes, AR models, and generalized Ornstein-Uhlenbeck processes. His work emphasizes theoretical developments in stochastic modeling, including Lamperti transformations, spatial sign autocovariance matrices, and parameter estimation techniques. Recent contributions explore discrete and continuous-time stationary processes, as well as applications of ARCH models in financial contexts. Voutilainen frequently collaborates with researchers like Pauliina Ilmonen, Lauri Viitasaari, and Ciprian Tudor, contributing to peer-reviewed journals like Stochastic Models , Theory of Probability and Mathematical Statistics , and Scandinavian Journal of Statistics . Despite his prolific output, no scientific awards or grants are explicitly mentioned in the provided materials. No doctoral students are listed, though his research likely engages graduate-level collaboration.

Ben Duanمشاهده پروفایل

عضو هیئت علمی

• Reinforcement Learning
• Graph Neural Networks
• Intelligent Transportation Systems (ITS)
• +۴ مورد دیگر

Ben Duan is an Adjunct Lecturer at the Department of Data Science & AI, Faculty of Information Technology, Monash University. He holds a PhD in Big Data and Electronics from the University of Technology Sydney. Previously, he was a Research Fellow at the Hong Kong University of Science and Technology. His research focuses on reinforcement learning, graph neural networks, intelligent transportation systems (ITS), and fintech. He teaches courses such as FIT5221: Intelligent Image and Video Analysis, FIT5215: Deep Learning, and FIT5217: Statistical Data Modeling at Monash’s Suzhou campus. Dr. Duan’s work aligns with UN Sustainable Development Goals related to sustainable cities and communities (SDG 11) and industry innovation (SDG 9). His recent research explores AI-driven solutions for traffic congestion, stock trend prediction using graph neural networks, and spiking neural networks for neural activation control. He has contributed to over 28 publications and a patent with the Monash Suzhou Research Institute. He supervises PhD students interested in reinforcement learning, graph neural networks, or ITS, offering full scholarships at Monash Suzhou. Key collaborations span transportation systems, fintech, and data-driven industrial processes.

Richard A. Davisمشاهده پروفایل

استاد

• Applied Probability
• Time Series Analysis
• Stochastic Processes
• +۴ مورد دیگر

Richard A. Davis is a Professor of Statistics at Columbia University and a Hans Fischer Senior Fellow at the Technical University of Munich's Institute for Advanced Study (TUM-IAS). He holds the Howard Levene Professorship in Statistics. His research focuses on applied probability, time series analysis, stochastic processes, and extreme value theory. Davis has contributed to nonlinear time series models, spatial modeling, and financial econometrics. He has held academic positions at Colorado State University and MIT, and has been recognized with awards including the Koopmans Prize, IBM Faculty Award, and fellowships from the Institute of Mathematical Statistics and American Statistical Association. His education includes a B.A. and Ph.D. in Mathematics from the University of California, San Diego. He co-authored influential textbooks like Time Series: Theory and Methods and has advised numerous papers on topics ranging from count time series to spatial extremes. Current research explores machine learning applications in time series and environmental risk modeling through collaborations like the STARMAP program.

Joseph Romanoمشاهده پروفایل

استاد

• Nonparametric Statistics
• Bootstrap Methods
• Multiple Testing
• +۷ مورد دیگر

Joseph Romano is a Professor of Statistics and Economics at Stanford University, where he has served since 1986. He holds dual appointments in both the Department of Statistics and the Department of Economics. His research focuses on theoretical statistics, nonparametric methods, bootstrap techniques, multiple testing, and their applications in econometrics, climate science, genetics, and clinical trials. He has received numerous awards, including the LGBTQ+ Scientist of the Year (2021) and the Presidential Young Investigator Award (1989–1994). Education: Ph.D. in Statistics, University of California, Berkeley (1986) M.S. in Statistics, University of California, Berkeley (1983) B.A. in Statistics, Princeton University (1982), Summa Cum Laude Research interests include developing robust statistical methodologies for high-dimensional data, controlling errors in multiple testing, and applying resampling techniques to complex datasets. He has authored over 100 papers, focusing on topics like permutation tests, bootstrap methods, and econometric modeling. Notable works include advancements in fixed sequence testing and randomized inference for modern data challenges. Awards and Recognition: Fellow, Institute of Mathematical Statistics (2000) Fellow, International Association of Applied Econometrics (2020) Canadian Journal of Statistics Award (1989) Romano has advised numerous students, including doctoral candidates in statistics and economics. He has held administrative roles at Stanford, such as Associate Chair and member of faculty committees. His grants include NSF-funded projects on resampling methods, multiple testing, and econometric theory. He is a vocal advocate for LGBTQ+ visibility in academia and has contributed to initiatives like 500 Queer Scientists. Beyond academia, he enjoys music, tennis, cooking, and architecture.

Rishikesh Yadavمشاهده پروفایل

پژوهشگر

• Approximation Theory
• Functional Analysis
• Dynamical Systems
• +۳ مورد دیگر

Rishikesh Yadav is a postdoctoral researcher at the Department of Mathematics and Mathematical Statistics, Umeå University, Sweden , and previously held a postdoctoral position at the Namur Institute for Complex Systems (naXys), University of Namur, Belgium . He earned his Ph.D. in Approximation Theory from Sardar Vallabhbhai National Institute of Technology Surat, India. His research focuses on Approximation Theory, Functional Analysis, Dynamical Systems, Operator Theory, and Optimization Theory. Notable contributions include work on Koopman operators, control theory, and compressed sensing. He has received awards such as the V. M. Shah Prize (2020) and Best Oral Presentation Award (2019). Recent research emphasizes developing algorithms for convex optimization on measure spaces, supported by the Kempe Foundation (2024–2026). His work bridges approximation theory with dynamical systems, including publications on Szász-Mirakjan operators, statistical convergence, and Koopman operator approximations via Bernstein polynomials. Grants/Fellowships: Kempe Foundation (2024–2026) Labs/Teams: naXys Institute (Belgium), Umeå University's Department of Mathematics

• Information Systems
• Econometrics
• Business Economics
• +۴ مورد دیگر

Prof. Dr. Kosta Sotiroski is a Full Professor at the Faculty of Economics - Prilep, University 'St. Kliment Ohridski' - Bitola. He holds a PhD in Economics from the same institution (1998), with research focused on statistical modeling and production optimization. His academic roles include mentoring doctoral students in Marketing, Management, and Business Informatics, and serving as Secretary of the Doctoral Studies Committee. Education: Bachelor's in Mathematical-Statistical-Cybernetics (1986) Master's in Monte Carlo Simulation in Economic Management (1993) PhD in 'Modeling Optimal Organization of Production Processes' (1998) Research Interests: Business Informatics, ERP Systems, Big Data Technologies, Econometrics, E-Banking Services, and Statistical Process Control. His work emphasizes data-driven decision-making in economics and healthcare. Publications: Over 112 publications in areas like arterial stiffness in dialysis patients, e-learning adoption, and SME innovation. Recent trends focus on healthcare analytics and cloud computing in education. Awards & Grants: Involved in EU-funded projects such as TEMPUS and CARDS initiatives. Recognized as a top-ranked scientist in Macedonia for contributions to economics and management science. Labs/Teams: Leads research in Business Informatics and Data Analytics. Active in editorial roles for journals like Strategic Management and International Journal of Economics, Politics, Humanities & Social Sciences.

Michael Kutnerمشاهده پروفایل

استاد

• Statistical Modeling
• Clinical Trials
• Epidemiology

Michael Kutner is the Rollins Professor of Biostatistics at the Emory School of Public Health, Department of Biostatistics and Bioinformatics. He holds an Endowed Chair and has served in leadership roles, including Chair of the Department of Biostatistics and Bioinformatics (2002-2009) and Chair of the Department of Biostatistics and Epidemiology at the Cleveland Clinic (1994-1999). He has also held roles such as Executive Associate Dean for Faculty Affairs at Emory's School of Public Health (1990-1993). Education: PhD from Texas A&M University, M.S. from Virginia Polytechnic Institute and State University (VPI & SU). His research focuses on Statistical Modeling , Clinical Trials , and Epidemiology . He has contributed extensively to statistical methodology, including works on linear models, regression analysis, and experimental design, with a strong emphasis on applied biostatistics. His publications span foundational textbooks like Applied Linear Statistical Models and Applied Linear Regression Models , which remain influential in statistical education. Recent work includes multi-omic cancer genetics studies and collaborative statistical practices. Awards: ASA Mentoring Award (2018) Lifetime Achievement Awards (ASA Georgia Chapter, 2018) Fellow of the American Statistical Association (1984) H.O. Hartley Award (1984) His career highlights include leadership in academic departments, prolific textbook authorship, and recognition for mentoring and service to the statistical community.

Amanda Tuckerمشاهده پروفایل

دانشیار

• Number Theory
• Algebraic Number Theory
• Diophantine Equations
• +۳ مورد دیگر

Amanda Tucker is an Associate Professor (non-tenure) and Assistant Director of Undergraduate Studies in the Department of Mathematics at the University of Rochester. Her research focuses on algebraic number theory, modular forms, and Diophantine equations. She actively promotes inclusive mathematics education through initiatives like GGM@Rochester, a summer program for gender minority high school students. Her work emphasizes Federico Ardila’s axioms regarding equitable mathematical potential and joyful learning experiences. Dr. Tucker’s educational background includes a PhD in Mathematics, as evidenced by her thesis on 'Special Groups of Units.' She collaborates extensively with researchers like Kevin J. McGown, Frank Thorne, and Marcus Elia, producing peer-reviewed publications in journals such as the Bulletin of the London Mathematical Society and Annales de l’Institute Fourier. Her teaching responsibilities include courses on Multivariable Calculus and Honors Calculus II. She also provides resources for GRE preparation, focusing on calculus, algebra, and advanced topics.

• Probability Theory
• Financial Mathematics
• Stochastic Processes

Paul Eisenberg is an Assistant Professor at the Institute for Statistics and Mathematics at Vienna University of Economics and Business. He holds a doctorate in Mathematics and has held positions at the University of Liverpool, Vienna University of Technology, and Technical University of Dortmund. His research focuses on probability theory and financial mathematics, particularly stochastic processes, term structure modeling, and applications to financial markets. He teaches courses in Probability, Quantitative Methods, and Business Mathematics. Research outputs concentrate on stochastic processes in finance, including term structure modeling, stochastic optimization, market microstructure, and applications to cryptocurrency markets. Recent work shows emphasis on regime-switching models and high-dimensional stochastic systems.

Ilyas Masihمشاهده پروفایل

دانشیار

• Hydrology
• Water Resources Planning
• Climate Change Impact Assessment
• +۴ مورد دیگر

Dr. Ilyas Masih is an Associate Professor of Water Resources Planning at the Department of Water Resources and Ecosystems, IHE Delft Institute for Water Education. His roles include coordinating the MSc Programme in Water Management and Governance (2015–2018) and teaching courses on water resources planning, environmental impact assessment, and drought management. He has extensive experience in hydrological modeling, integrated water resources management, and capacity building across Asia and Africa, including Ethiopia, Pakistan, Iran, and Vietnam. Dr. Masih holds a PhD from UNESCO-IHE, focused on hydrological variability in semi-arid regions. His research spans climate change impacts, drought assessment, agricultural water management, and water treatment technologies. He has supervised numerous MSc and PhD students and contributed to global datasets for hydrological modeling, such as remote sensing and reanalysis data. His publications emphasize drought management strategies, climate adaptation, and bioremediation technologies. Key themes in his work include the interplay between climate extremes and crop productivity, sustainable water use in agriculture, and policy alignment with disaster risk reduction frameworks. Leveraging interdisciplinary approaches, Dr. Masih collaborates with international organizations like IWMI and engages in training programs to enhance water resource management capacities globally.

• Computer Graphics
• Visualization
• Data Visualization
• +۵ مورد دیگر

Helwig Hauser is a Professor in the Department of Computer Graphics within the Faculty of Informatics at Vienna University of Technology (TU Wien). His research focuses on advancing visualization techniques across multiple scientific domains. With a career spanning over two decades, he has established himself as a leading expert in visualization research. His primary research interests encompass Computer Graphics, Visualization, Data Visualization, Visual Analytics, Scientific Visualization, Information Visualization, Set Visualization, and Molecular Visualization. Dr. Hauser's work bridges theoretical foundations with practical applications, developing innovative techniques for visual data exploration and analysis across diverse fields from medical imaging to molecular biology. Analysis of his publication record reveals a consistent research trajectory focused on developing novel visualization methodologies. His work demonstrates strong emphasis on interactive visual analytics, set visualization techniques, and domain-specific applications in medical and molecular visualization. Notably, he has made significant contributions to set visualization (Radial Sets), molecular visualization (Watergate), and medical visualization (Aortic Dissection Maps). Best paper award (one out of three) at EuroVis 2007 Heinz Zemanek Preis (2006) Best paper award at SimVis 2005 Dr. Hauser has supervised numerous doctoral and master's students, with a particular focus on visual analytics of complex data types. His research has been supported by various projects including the Punkt-basierte Volumen-Graphik project (2006-2009). He has contributed significantly to the visualization community through his editorial work and service as a journal reviewer, though specific details on current grants were not provided in the source text.