جست‌وجوی استاد و پژوهشگر

Chris Stewartمشاهده پروفایل

مدرس ارشد

• Applied Econometrics
• Time Series Analysis
• Banking Efficiency
• +۵ مورد دیگر

Dr. Chris Stewart is a Senior Lecturer in Economics at Kingston University's Department of Economics within the School of Law, Social and Behavioural Sciences. He joined Kingston University in February 2013 after previously teaching at London Metropolitan University and London Guildhall University. His primary academic responsibilities include teaching econometrics and microeconomics courses across undergraduate programs including Business Economics BSc, Economics BSc, and Financial Economics BSc. Stewart's research focuses on applied econometrics with particular expertise in time series analysis, bank efficiency, purchasing power parity, and foreign direct investment determinants. His work demonstrates methodological innovation in unit root testing, cointegration analysis, and financial econometrics. Recent research projects include generalizing the ARDL bounds test for I(0) variables, analyzing bank profitability determinants, and re-evaluating purchasing power parity testing methodologies. His publication record includes over 40 refereed journal articles in high-impact economics journals such as Applied Economics, Economic Modelling, Empirica, Journal of International Money and Finance, and Socio-Economic Review. His research has been submitted to multiple Research Excellence Framework (REF) assessments at Kingston University and previous institutions. Stewart serves as an assessor for internal REF mock exercises at Kingston University. Fellow of the Higher Education Academy (FHEA), 2024 Stewart's administrative roles include serving as Assessment Officer and Level 6 tutor in the Economics Department, coordinating external examiner moderation and reviewing draft assessments. His research collaborations span international institutions with co-authors from universities worldwide. He maintains active profiles on academic networking platforms including Google Scholar, ResearchGate, and Academia.edu to disseminate his research findings.

• Econometrics
• Financial Economics
• Time Series Analysis
• +۲ مورد دیگر

Karsten Schweikert is a Professor at the Computational Science Hub (CSH) and an affiliate scientist at the Institute for Economics at Stuttgart University of Applied Sciences. He leads the Data and Statistical Consulting module and teaches the Financial Econometrics Seminar . Academic Rank: Professor Department: Institute for Economics Key Affiliation: Computational Science Hub (CSH) His research focuses on econometrics , financial economics , and time series analysis . He specializes in structural breaks, dynamic factor models, cointegration, and market microstructure. Recent work includes studies on integrated variance estimation, price discovery mechanisms, and urban crime forecasting. The 15 most recent publications highlight his expertise in econometric modeling for financial markets, macroeconomic nowcasting, and applied statistics. Key subfields include structural break detection, mixed-frequency data analysis, and market information share estimation. He leads statistical consulting initiatives and teaches advanced econometrics topics. No scientific awards or student advisement information were explicitly mentioned in the provided texts.

Christoph Clephasمشاهده پروفایل

استاد

• Sports Governance & Leadership
• Youth Competitive Sports Development
• Bio Banding Systems
• +۳ مورد دیگر

Christoph Clephas is a Professor of Sports Science at the DHGS German University of Health and Sport since 2021, serving as site manager for the university's Hamburg campus. His academic journey includes a sports science degree (focus: competitive sports) from DHGS (until 2014) and a PhD at Kiel University (2016-2019), researching 'Performance variability as a prognostic factor in elite sports' using swimming as a case study. Professional Background Former national youth performance coach and head coach of a state-level swimming facility (2012) Academic leader at spomedis Verlag publishing house Key role in the German Olympic Sports Confederation's performance sport reform (2019–2021), managing scientific networks and liaising with training scientists at Olympic training centers Research Focus Clephas specializes in leadership governance within sports organizations, bio banding systems for youth athletes, and performance variability analysis in elite sports. His work bridges applied sport science with organizational structures, emphasizing evidence-based approaches to talent development and competitive success. Publications His recent work explores Olympic success prediction models, swimming performance metrics in adolescents, and paralympic start dynamics. He serves as a peer reviewer for international journals. Grants & Teams Active in collaborative projects with the German Sports Science Institute (BISp) and International Association of Trainers (IAT). His expertise supports national sports federation strategies through interdisciplinary research partnerships.

• Statistics
• Statistical Inference
• Time-Series Analysis
• +۳ مورد دیگر

Emanuele Taufer is a Full Professor of Statistics at the Department of Economics and Management of the University of Trento. His academic career includes roles as Vice Director of the Department of Computer Science and Business Studies and Faculty Delegate for International Relations. He holds a Ph.D. in Statistics from Cardiff University, an M.Sc. in Mathematical Statistics from George Washington University, and a Laurea in Economics from the University of Trento. His research focuses on statistical inference, stochastic processes, goodness-of-fit tests, and applications in ESG analysis. Notable contributions include work on exponentiality testing, graphical models, and financial dependence modeling. He has been recognized for his 2002 paper on mean residual life characterization at the SIS2002 conference. Recent research trends emphasize methodological advancements in ESG performance measurement, sparse network estimation for heavy-tailed data, and generalized precision matrices for financial risk modeling. His work spans theoretical statistics, applied econometrics, and interdisciplinary topics like environmental governance. Education: Ph.D. in Statistics, Cardiff University (UK) M.Sc. in Mathematical Statistics, George Washington University (USA) Laurea in Economics, University of Trento (Italy) Professional Roles: Full Professor of Statistics at University of Trento (2003–present) Associate Professor (2003–2003), Assistant Professor (1996–2002) Awards: 2002 SIS2002 Recognition for innovative statistical testing methodology Key Research Themes: Stochastic processes and estimation ESG methodology and financial reporting High-dimensional data analysis Goodness-of-fit tests and tail index estimation

François Bachocمشاهده پروفایل

استادیار

• Statistics
• Machine Learning
• Mathematical Statistics
• +۳ مورد دیگر

François Bachoc is an Assistant Professor at the Toulouse Mathematics Institute and the University Paul Sabatier, where he has held a tenured position since 2015. He is a Junior Member of the Institut universitaire de France (IUF) (2024–2029). His research focuses on Statistics, Machine Learning, and Gaussian Processes , with applications in industrial and interdisciplinary domains. He earned his Ph.D. in Statistics from the CEA and Université Paris VII (2013), followed by a postdoctoral position at the University of Vienna (2013–2015). He completed a Habilitation (HDR) at University Paul Sabatier in 2018. His work emphasizes uncertainty quantification, Bayesian methods, and optimal design of experiments. Bachoc leads several funded projects, including the ANR Project GAP (€205k) and the AI Chair UQPhysAI (€350k). He teaches courses on asymptotic statistics, machine learning, and Gaussian processes at the graduate level. His contributions span theoretical advancements and practical applications in coastal flood modeling, sensitivity analysis, and computational statistics.

• Probabilistic Forecasting
• Statistical Evaluation
• Climate Science
• +۳ مورد دیگر

Prof. Tilmann Gneiting is a Professor of Computational Statistics at Karlsruhe Institute of Technology (KIT) and Head of the Computational Statistics Working Group at the Heidelberg Institute for Theoretical Studies (HITS). His research focuses on probabilistic forecasting, statistical evaluation of predictive models, and applications in environmental science, epidemiology, and renewable energy. He has held academic roles in mathematics and statistics departments across multiple institutions. His work emphasizes rigorous statistical methodology for forecasting systems, including ensemble models, uncertainty quantification, and validation frameworks. Notable contributions include developing scoring rules for probabilistic predictions, Bayesian model averaging techniques, and geostatistical methods for weather and climate modeling. Courses taught include Forecasting: Theory and Practice and Time Series Analysis . Prof. Gneiting’s research spans diverse areas such as earthquake forecasting, solar energy prediction, and pandemic modeling. He has contributed to R packages like ensembleMOS and ProbForecastGOP , advancing computational tools for statistical analysis. His work bridges theoretical statistics with practical applications in environmental and public health domains. Professional awards and recognitions are not explicitly listed in the provided texts. His advising and grants involve collaborations in interdisciplinary projects, though specific student names or grant details are not mentioned. He leads computational statistics teams at HITS and actively publishes in top-tier statistical and applied journals.

Fred K. Boaduمشاهده پروفایل

دانشیار

• Geophysics
• Civil Engineering
• Environmental Engineering
• +۳ مورد دیگر

Fred K. Boadu is an Associate Professor at Duke University's Pratt School of Engineering , with additional appointments at the Duke Global Health Institute . His work bridges geophysics and civil/environmental engineering , focusing on non-invasive characterization of soil properties and sustainable infrastructure solutions in tropical environments. Education : PhD in Geophysics (Georgia Tech, 1994), M.S. in Geophysics (University of Calgary), Post-Graduate Diploma in Applied Geophysics (McGill University), B.S. in Geological Engineering (University of Science and Technology, Ghana) Research Interests center on petrophysical characterization of soils, geophysical methods for engineering applications, groundwater contamination studies in agricultural zones, and machine learning techniques for predicting geotechnical properties from electrical measurements. Selected Articles demonstrate trends in asphalt modification , fractal modeling of soil properties, and stress-dependent geophysical analysis for infrastructure applications in Ghana and tropical regions. Scientific Awards : Junior Faculty Enhancement Award - Applied Science Category (1997, University Oak Ridge Associated Universities) Seed Grant for 'Belonging While Black at Duke' (2021, Duke Office for Faculty Advancement) Grant History includes NASA and NSF funding for projects spanning electromagnetic earthquake signal analysis , groundwater sustainability in rural Ghana, and electrical-geotechnical correlation studies. As Director of Masters Studies (since 2024), he influences academic leadership and curriculum development in civil engineering education.

• Statistical Methodology
• Biostatistics
• Count Data Analysis
• +۴ مورد دیگر

Kimberly Sellers is a Full Professor of Mathematics and Statistics at Georgetown University. She holds a Ph.D. in Statistics from George Washington University, and M.A. and B.S. in Mathematics from the University of Maryland-College Park. Previously, she served as a Visiting Assistant Professor at Carnegie Mellon University and an Assistant Professor of Biostatistics at the University of Pennsylvania School of Medicine's Center for Clinical Epidemiology and Biostatistics (CCEB). Her research focuses on count data modeling (e.g., COM-Poisson distributions), image analysis techniques, and statistical methodologies addressing dispersion. She is an Elected Member of the International Statistical Institute and a Fellow of both the American Statistical Association and the Association for Women in Mathematics. Key research interests include generalized statistical methods for count data (regression, multivariate analysis, control charts), and low-level image processing (preprocessing, feature detection). Notable contributions include the development of flexible distributions for dispersed data and computational tools like COMPoissonReg. Her work bridges theoretical statistics and applied domains such as public health, epidemiology, and biomedical research. Awards: Elected ISI Member (202?), ASA Fellow (202?), AWM Fellow (202?) Education: Ph.D. (GWU), M.A. & B.S. (UMCP) Publications span statistical methodology, applications in healthcare analytics, and computational statistics. Active in editorial roles and promoting diversity in STEM through her leadership roles in statistical societies.

• Mathematics Education
• Numerical Reasoning Strategies
• Algebraic Reasoning
• +۳ مورد دیگر

Dr. Karen Zwanch is an Associate Professor of Mathematics Education at Oklahoma State University (OSU), within the School of Teaching, Learning and Educational Sciences. She holds a PhD in Curriculum & Instruction from Virginia Tech (2019), an M.Ed. in Mathematics from Millersville University (2011), and a B.S. in Secondary Education with a Mathematics focus from Penn State University (2004). Her research focuses on middle-grades students' numerical reasoning strategies in algebraic contexts and the efficacy of instructional routines. She has received grants such as the SecurAIty Nexus (2024–2027) and Building Mathematic Fluency Through Middle Grades (2023–2024). Dr. Zwanch teaches courses including Methods for Teaching Mathematics in the Primary Grades , Learning and Instructional Theory in Mathematics and Science Education , and Teaching Algebra and Mathematical Tasks . Her funded projects emphasize teacher training in STEM and mathematics fluency. Her work spans over 30 peer-reviewed articles, exploring topics like manipulative use, Notice and Wonder routines, and units coordination in spatial reasoning. Key awards or honors are not explicitly listed, but her contributions to mathematics education research are evident through her extensive publication record and grant activity. She leads initiatives to enhance teacher education and student engagement in mathematics, with a focus on equity and effective pedagogical strategies.

Mahbub Khanمشاهده پروفایل

استادیار

• Corporate Governance
• Capital Market Efficiency
• Asset Pricing
• +۳ مورد دیگر

Dr. Mahbub Khan is Assistant Professor in Finance at Coventry University's School of Economics, Finance and Accounting. With doctoral training from the University of Newcastle Australia and an MSc from the University of Leeds, his research examines corporate governance mechanisms, market efficiency phenomena, and asset pricing models. Professional memberships include the European Finance Association and UK Higher Education Academy fellowship. Research foci include: Equity return predictability Corporate social responsibility-performance relationships Financial market efficiency Trading strategy development Publications demonstrate methodological expertise in regression analysis and forecasting techniques applied to firm performance and market behavior.

• Data Science
• Econometrics
• Economics
• +۵ مورد دیگر

Univ.-Prof. Martin Wagner is a Professor at the Department of Economics, Alpen-Adria-Universität Klagenfurt, where he serves as Vice-Dean of the Faculty of Management, Economics and Law and Head of the Quantitative Economics Division. His research bridges theoretical and applied econometrics with environmental and macroeconomic challenges. Affiliation: Department of Economics, Faculty of Management, Economics and Law, Alpen-Adria-Universität Klagenfurt Wagner’s work focuses on econometric theory and its applications, particularly in modeling nonlinear cointegrating relationships, environmental economics, and macroeconomic forecasting. His expertise includes: Cointegrating polynomial regressions Unit root processes and stationarity High-dimensional time series analysis Environmental Kuznets curves Panel data econometrics Macroeconomic modeling Recent publications highlight his contributions to robust estimation techniques and the intersection of climate science with economic growth dynamics. Contact: martin.wagner@aau.at

• Statistical Signal Processing
• Pattern Recognition
• Machine Learning
• +۳ مورد دیگر

Dr. Miroslaw Pawlak is a Professor in the Department of Electrical and Computer Engineering at the University of Manitoba’s Price Faculty of Engineering. He holds a Ph.D. and D.Sc. in Computer Engineering from Wrocław University of Technology, Poland. As a Professional Engineer registered in Manitoba, he has held visiting positions at multiple institutions worldwide, including the University of Ulm and University of Göttingen as an Alexander von Humboldt Foundation Fellow. His research focuses on statistical signal processing, machine learning, nonparametric modeling, and system identification. He authored/co-authored influential books like Nonparametric System Identification (Cambridge University Press, 2010) and over 100 peer-reviewed publications. Dr. Pawlak has served as an Associate Editor for journals such as Pattern Recognition and IEEE Transactions on Information Theory , and was recognized in Stanford University’s World’s Top 2% Scientists list. Education: 1985: Ph.D. in Computer Engineering, Wrocław University of Technology 1999: D.Sc. in Computer Engineering, Wrocław University of Technology Research Interests: Dr. Pawlak specializes in advanced signal processing techniques, including nonparametric system identification, statistical analysis of stochastic signals, and machine learning applications in engineering systems. His work bridges theoretical foundations (e.g., operator theory with Hermite polynomials) and practical implementations (e.g., wind farm modeling and power system stability prediction). Publications: His recent work emphasizes nonparametric methods for system testing (e.g., Hammerstein systems) and interdisciplinary applications like power grid oscillation prediction using LASSO algorithms. He frequently collaborates with international researchers to advance signal processing theory and energy systems modeling. Awards: World’s Top 2% Scientists (Stanford University, 2021/2022) Professional Contributions: Dr. Pawlak’s editorial roles and visiting fellowships demonstrate his global influence. He has advised numerous graduate students and contributed to key textbooks shaping the field of system identification.

Jing Zhouمشاهده پروفایل

مدرس

• Statistics
• High-Dimensional Data Analysis
• Machine Learning
• +۴ مورد دیگر

Jing Zhou is a Lecturer in Statistics at the University of East Anglia, affiliated with the School of Engineering, Mathematics and Physics. Their research focuses on high-dimensional statistical methodologies, including variable selection, false discovery rate control, and robust estimation techniques. Dr. Zhou actively contributes to peer review for journals like Statistics and Computing and Statistics , and has presented at conferences such as the 2024 IMS International Conference on Statistics and Data Science. Research interests emphasize advancing statistical methods for social science and big data applications, particularly leveraging model-X knockoffs and black-box model assessments. Recent work explores trade-offs in false discovery vs. true positive rates in logistic regression and nonparametric quantile regression via vine copulas. Collaborative efforts include interdisciplinary projects addressing replication crises through methodological improvements. No scientific awards are explicitly listed. Advising activity and grants information is not provided in the text. Zhou is engaged in academic activities including invited talks and editorial work, contributing to both theoretical and applied statistical research.

Kaushallya Adhikariمشاهده پروفایل

دانشیار

• Array Signal Processing
• Statistical Signal Processing
• Estimation and Detection Theory
• +۵ مورد دیگر

Kaushallya Adhikari serves as an Associate Professor in the Department of Electrical, Computer and Biomedical Engineering at the University of Rhode Island's College of Engineering. Her research bridges theoretical signal processing with practical applications in sonar, radar, and biomedical systems through innovative array design and algorithm development. Her academic credentials include: Ph.D. in Electrical Engineering, University of Massachusetts Dartmouth (2016) M.S. in Electrical Engineering, University of Massachusetts Dartmouth (2012) B.E. in Electronics and Communication Engineering, Tribhuwan University, Nepal (2008) Dr. Adhikari's research centers on array signal processing with emphases on sparse array configurations, direction of arrival estimation, and information-theoretic approaches to detection. She leads the ASPIRE lab (Array Signal Processing for Information REtrieval), which explores statistical signal processing, machine learning integration, and sensor array optimization. Her work demonstrates consistent innovation in transforming circular arrays to linear configurations, developing subspace estimation techniques, and applying neural networks to traditional signal processing challenges. Analysis of her 15 most recent publications reveals a decisive shift toward deep learning integration with classical array processing methods, particularly for direction of arrival estimation in sonar systems. While maintaining strong foundations in estimation theory and information metrics, her recent work increasingly focuses on convolutional neural networks for signal classification, sparse sampling optimization, and biomedical applications like tEEG-based cortical mapping. This evolution reflects her strategic expansion from theoretical frameworks to real-world implementation challenges. Her scientific recognition includes: Office of Naval Research Young Investigator Program (YIP) Award (2020) Dr. Adhikari has secured competitive funding including multiple Office of Naval Research grants for sonar array optimization (2020, 2023) and a NE Big Data Innovation Hub grant for neural network-based cortical mapping (2021). She actively mentors graduate researchers in the ASPIRE lab, where current projects span sonar signal processing, EEG/tEEG analysis, and renewable energy system modeling through autoregressive techniques. The ASPIRE lab maintains a cohesive research program focused on sensor array design, statistical signal processing, and machine learning applications across defense, biomedical, and energy domains, with particular expertise in coprime/nested array configurations and direction of arrival estimation algorithms.

George Yanevمشاهده پروفایل

استاد

• Branching Stochastic Processes
• Probability Distribution Characterizations
• Applied Statistics in Environmental, Biological, and Medical Fields
• +۲ مورد دیگر

George Yanev is a Professor in the School of Mathematical and Statistical Sciences at the University of Texas Rio Grande Valley (UTRGV), affiliated with the College of Sciences. His research focuses on branching stochastic processes, characterizations of probability distributions using ordered variables, and applied statistics in environmental, medical, and biological contexts. Professional Interests: His work emphasizes limit theorems, tree structures in branching processes, and applications of statistical methods to real-world problems. He has authored the book Controlled Branching Processes and contributed to journals such as Journal of Statistical Theory and Practice and Mathematics . Publications Trends : His recent work explores exponential distribution characterizations (2023), continuous-time branching processes (2021), and applied statistical studies in agriculture and medicine (2015–2016). He frequently collaborates on interdisciplinary projects, including coral reef ecology and vitamin D metabolism in chronic kidney disease patients. Presentations & Engagement : Yanev has presented at major conferences like the International Conference on Stochastic Processes (Bulgaria, 2014–2015) and the World Congress of Nonlinear Analysis (Athens, 1996). He actively participates in workshops on branching processes and their applications, contributing to both theoretical and applied research communities.