جست‌وجوی استاد و پژوهشگر

Nizam Uddinمشاهده پروفایل

استاد

• Design and Analysis of Experiments
• Linear/Nonlinear Models
• ANOVA/Covariance Analysis
• +۵ مورد دیگر

Dr. Nizam Uddin is a Professor and Undergraduate Coordinator in the Department of Statistics and Data Science at the University of Central Florida (UCF), part of the College of Sciences. He has held this position since joining UCF and oversees undergraduate academic programs while maintaining an active research agenda. Education: Dr. Uddin holds a Ph.D. in Statistics from Old Dominion University (1989), an M.S. in Mathematics from the University of Saskatchewan (1985), and dual M.S. and B.S. degrees in Statistics from the University of Dhaka, Bangladesh (1979 and 1977). Research Interests: His work focuses on statistical methodology including experimental design (linear/nonlinear models, ANOVA/covariance analysis), regression techniques, and categorical data analysis. He has applied these methods to diverse domains such as transportation safety engineering, biostatistics, environmental science, and healthcare analytics. Recent research emphasizes predictive modeling for traffic safety interventions, analysis of driver behavior patterns, and statistical methods for environmental contamination studies. Professional Contributions: Dr. Uddin has authored over 100 peer-reviewed articles spanning experimental design theory, transportation safety analytics, and interdisciplinary applications of statistical methods. His work bridges theoretical statistical advancements with real-world problem-solving in areas like infrastructure safety optimization and public health policy analysis. Office & Contact: Located in TC2 Room 208, Dr. Uddin can be reached at 407-823-2692 or via email at Nizam.Uddin@ucf.edu.

Kerem Tuzcuogluمشاهده پروفایل

پژوهشگر ارشد

• Econometrics
• Macroeconomics
• Monetary Economics
• +۳ مورد دیگر

Kerem Tuzcuoglu is a Senior Economist at the Bank of Canada and a Research Fellow at the Centre for Monetary and Financial Economics at Carleton University's Faculty of Public and Global Affairs. He holds a Ph.D. in Economics from Columbia University. His research focuses on econometrics, macroeconomics, and financial economics, with applications to monetary policy, systemic risk, and international financial dynamics. He leads the Systemic Risk Analytics Division at the Bank of Canada, specializing in modeling macroeconomic and financial stability. His work bridges theoretical econometric advancements with practical policy analysis, leveraging tools such as hierarchical volatility models, Bayesian methods, and nonlinear transmission frameworks. Recent studies address high-frequency policy impacts, international spillovers of quantitative easing, and supply-side inflation drivers post-pandemic. He has contributed to the development of the Risk Amplification Macro Model (RAMM) and pioneered composite likelihood estimation techniques for panel data models. His research underscores the interplay between macroeconomic fluctuations and financial system resilience.

Tariq Hasanمشاهده پروفایل

استاد

• Longitudinal Data Analysis
• Zero-Inflated Models
• Spatiotemporal Statistics
• +۲ مورد دیگر

Tariq Hasan is Professor and Director of Graduate Studies in Mathematics and Statistics at the University of New Brunswick. His research develops statistical methods for complex data structures including longitudinal, zero-inflated, and spatiotemporal datasets. Applications span environmental science, public health, and ecology. Methodological innovations include: joint modeling of clustered multinomial outcomes, Tweedie GLMs for skewed longitudinal data, and zero-inflated models accounting for autocorrelation. Work addresses real-world challenges like amphibian roadkill mortality analysis, air pollution health impacts, and osteoporotic fracture risk prediction. Publications demonstrate consistent focus on handling data complexities: simultaneous modeling of correlated processes, preservation of experimental designs in paired analyses, and integration of random effects for cluster heterogeneity. Recent advances address state-space frameworks for binomial series with random cluster sizes.

Qiang Chengمشاهده پروفایل

دانشیار

• Data Mining
• Machine Learning
• Artificial Intelligence
• +۲ مورد دیگر

Qiang Cheng is an Associate Professor at the University of Kentucky with joint appointments in the Division of Biomedical Informatics and the Stanley and Karen Pigman College of Engineering. His research bridges computational methods with biomedical applications, focusing on machine learning, data mining, and AI-driven solutions for precision medicine. Research interests center on developing novel algorithms for biomedical data analysis, including tabular data processing, gene expression modeling, drug response prediction, and temporal pattern recognition in healthcare contexts. His work frequently integrates deep learning architectures with domain-specific challenges in omics and clinical informatics. Recent publications demonstrate strong focus on generative models (diffusion networks, autoregressive architectures), efficient learning methods (sparse attention, lightweight networks), and biological applications (circadian rhythm prediction, molecular generation). Methodological innovations consistently target high-dimensional biomedical data challenges through multi-view learning, tensor decomposition, and causal inference frameworks.

Karolos Korkasمشاهده پروفایل

مدرس ارشد

• Data Science
• Statistics
• Time Series Analysis
• +۴ مورد دیگر

Dr. Karolos Korkas is a Senior Lecturer in Data Science at the University of Greenwich's School of Computing and Mathematical Sciences, within the Faculty of Engineering and Science. His academic career is complemented by industry experience in electronic trading, risk management, and modeling at major banking institutions. He holds a PhD in Statistics from the London School of Economics (LSE), alongside advanced degrees in Statistics (LSE), Finance and Investment (Durham University), and Business Management (ASOEE). He was a Fellow in Finance at LSE's Department of Finance. Education : BA in Business Management (Information Systems), ASOEE MSc in Finance and Investment, Durham University Business School MSc in Statistics, London School of Economics (LSE) PhD in Statistics, LSE (funded by EPSRC) Research Interests : Dr. Korkas focuses on statistical methodologies for nonstationary time series, financial risk modeling, and computational approaches to change-point detection. His work bridges theoretical advancements in data science with practical applications in finance and risk management. Key areas include high-dimensional GARCH processes, volatility forecasting, and algorithmic segmentation techniques. Publications : His research emphasizes innovative statistical tools for analyzing dynamic systems, particularly in financial contexts. Recent work explores ensemble segmentation for irregular data, adaptive estimation in autoregressive models, and CAPM applications to industry portfolios. Awards : EPSRC PhD Scholarship Roles & Contributions : As a Senior Lecturer, he integrates academic rigor with industry insights. His prior fellowship at LSE highlights his engagement with financial academia. He has no listed grants or advising roles in available texts. Labs/Teams : No specific lab affiliations or collaborative teams are detailed in provided information.

Davide Raggiمشاهده پروفایل

دانشیار

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

Davide Raggi is an Associate Professor at Ca' Foscari University of Venice's Department of Economics. He serves as Director of the International Master in Economics and Finance and is affiliated with the University Quality Assurance Unit. Office hours are held Thursdays at 10:30 PM CET in San Giobbe campus. His research specializes in time series econometrics applied to financial markets and macroeconomic policy. Core domains include exchange rate forecasting, volatility modeling, rational addiction frameworks, and labor market analysis under information asymmetry. Methodological strengths encompass regime-switching models and structural econometric approaches. Recent publications demonstrate persistent focus on exchange rate dynamics and eurozone fiscal architecture, with methodological progression from volatility modeling to social learning frameworks. Behavioral economics applications expand from consumption patterns to labor motivation. No scientific awards mentioned in source materials Professor Raggi welcomes thesis supervision on econometric topics including financial econometrics and macroeconometric applications. Laboratory affiliations weren't specified in source materials.

• Macroeconomics
• Income Distribution
• Structural Change
• +۶ مورد دیگر

Codrina Rada is a Professor of Economics at the University of Utah, where she has held academic positions since 2007, progressing from Assistant Professor to Associate Professor and now full Professor (since 2022). She earned her PhD in Economics from the New School for Social Research in 2007 and is fluent in Romanian. Her research focuses on macroeconomic dynamics, including growth, income distribution, structural change, and gender economics, employing structuralist modeling and econometric methods. Her primary research interests include: Macroeconomic interactions between inequality and growth Sectoral contributions to labor share decline Distributive cycles and secular stagnation Gender-based labor allocation in developing economies Demographic impacts of aging and migration Recent publications (2021-2025) predominantly analyze US labor share dynamics, structural change, and distributive cycles, with recurring themes of stagnation, productivity-wage decoupling, and policy implications. Methodologically, her work combines econometric analyses (SVAR, Divisia decomposition) with theoretical frameworks from Keynesian, Classical, and structuralist traditions. She has secured external grants including: 'US Labor Share' from the Institute for New Economic Thinking (2018-2020) 'Migration Impacts on Older Parents in Romania' from the Center on Aging (2010) She actively advises PhD students through thesis research courses and teaches macroeconomics, international economics, and advanced research methodologies. Her collaborative projects involve researchers from the University of Utah and international institutions, focusing on labor economics and structural transformation.

• Artificial Intelligence
• Machine Learning
• Data Science
• +۲ مورد دیگر

Dongjie Wang is an Assistant Professor in the Department of Electrical Engineering and Computer Science at the University of Kansas, School of Engineering. His research focuses on artificial intelligence, machine learning, and data science applications in bioinformatics, transportation planning, and data-centric AI. Wang's research interests span several key areas: Development of reinforcement learning frameworks for biological pathway analysis and gene selection Generative AI applications in urban transportation systems Multi-modal representation learning for cross-domain retrieval Neuro-symbolic approaches for feature engineering His recent publications demonstrate a consistent focus on developing novel AI methodologies for real-world applications, particularly in transportation systems optimization, biological data analysis, and tabular data processing. The articles collectively show strong emphasis on reinforcement learning architectures, graph neural networks, and multimodal integration techniques. Wang advises graduate students in computer science and leads research in data-centric AI applications. His laboratory focuses on developing scalable machine learning solutions for complex systems.

Chaewon Baekمشاهده پروفایل

استادیار

• Macroeconomics
• Macro-labor
• Macroeconometrics

Chaewon Baek is an Assistant Professor of Economics at Tufts University, affiliated with the Department of Economics within the School of Arts and Sciences. His research focuses on macroeconomics, macro-labor dynamics, and macroeconometrics. He holds a Ph.D. from the University of California, Berkeley (2021), and degrees from Seoul National University (M.A. 2015, B.A. 2013). His work examines business cycles, monetary policy impacts, and labor market mechanisms. Recent studies include analyzing unemployment effects of pandemic-related stay-at-home orders using high-frequency data and advancing econometric techniques for impulse response analyses. Teaching responsibilities include macroeconomic theory, econometrics, and doctoral supervision. No awards or grants are explicitly listed in available materials.

Zheng Chris Caoمشاهده پروفایل

مدرس

• Tourism Economics
• Tourism Demand Modelling and Forecasting
• Globalisation
• +۳ مورد دیگر

Zheng Chris Cao is a Lecturer in Economics at Aston Business School, Aston University, since 2018. His research focuses on tourism economics, globalisation, and applied econometrics, particularly examining tourism market interdependencies and policy impacts. He has published widely in top-tier journals and contributed invited book chapters, with expertise in macroeconometric and spatial methods. Prior roles include Research Fellow at the University of Surrey (2015–2018) and Research Assistant at the Hong Kong Polytechnic University (2009–2011). He holds a PhD from the University of Surrey, an MSc from the Hong Kong University of Science and Technology, and a BEcon from Shanghai Jiao Tong University. His research interests span tourism demand modelling, economic development, and social network analysis. He serves on editorial boards for the Annals of Tourism Research and Journal of Sustainable Tourism , and is a member of key academic bodies like the International Association of Tourism Economics and American Economic Association. Teaching includes courses on macroeconomics and the global economic environment. Awards include Fellow of the Higher Education Academy (FHEA) and Chartered Membership (CMBE) from the Chartered Association of Business Schools.

• Econometrics
• Financial Economics
• Causal Inference
• +۵ مورد دیگر

Alessio Sancetta is a Professor of Economics at the Department of Economics, Royal Holloway, University of London, and Chair of the Department. He co-founded and co-directs the Centre for Robust Inference in a Digital Economy (RIDE). His research focuses on econometrics, causal inference, financial economics, and machine learning applications in finance. He earned his PhD in Econometrics from the University of Cambridge in 2002. Education: PhD in Econometrics, University of Cambridge (2002). Professional experience includes roles as a University Lecturer at the University of Cambridge and a trader in investment banks and proprietary trading firms, specializing in algorithmic trading strategies. Research interests span high-dimensional estimation, market microstructure, asset pricing, and forecasting methods. He has co-authored influential papers on topics like price drift before macroeconomic news and empirical asset pricing with functional factors. His work has been featured in major financial media such as Bloomberg and the Financial Times. He previously directed the MSc Finance program and co-directed the joint MSc Computational Finance with the Computer Science Department. Current projects include research funded by the Leverhulme Trust on information content in high-frequency trading and market manipulation in cryptocurrencies. His academic contributions include over 35 peer-reviewed articles in journals like the Journal of Econometrics, Journal of Financial Econometrics, and IEEE Transactions on Information Theory.

Loukia Meligkotsidouمشاهده پروفایل

دانشیار

• Bayesian Inference
• Computational Statistics
• Hidden Markov Models
• +۴ مورد دیگر

Loukia Meligkotsidou is an Associate Professor of Statistics at the Department of Mathematics of the National and Kapodistrian University of Athens. She holds a BSc in Statistics from Athens University of Economics and Business (2002) and a PhD in Statistics from Lancaster University (2005), focusing on filtering methods for population genetics, phylogenetics, and mixture models. Her research interests span Bayesian inference, computational statistics, hidden Markov models, biostatistics, and econometrics. She teaches undergraduate courses on Bayesian Inference and postgraduate courses on Time Series analysis. Her work emphasizes methodological advancements in statistical modeling, particularly in longitudinal data analysis, competing risks, and model uncertainty. Recent publications address issues like shared parameter models for longitudinal data, Bayesian unit root testing, and quantile regression approaches in financial forecasting. She is a Fellow of the Royal Statistical Society and active in professional societies like ISBA and IMS. Her research integrates theoretical contributions with applied problems in economics, finance, and biology. Notable contributions include frameworks for handling informative dropout mechanisms and developing efficient Bayesian computational methods for complex models. Her work bridges statistical methodology and real-world applications, particularly in econometrics and biostatistics.

Zongwu Caiمشاهده پروفایل

استاد

• Econometrics
• Quantile Regression
• Macroeconomic Policy Analysis
• +۵ مورد دیگر

Zongwu Cai is the Charles Oswald Professor of Econometrics at the University of Kansas . His research focuses on econometric methodologies for policy evaluation, financial networks, income inequality, and climate change impacts. He holds a Ph.D. in Statistics from the University of California and prior degrees from institutions in China. Key research themes include quantile regression, panel data models, and machine learning applications in economics. Recent work addresses topics like trade war effects, AI-driven labor market shifts, and climate change's economic consequences. His contributions bridge theoretical econometrics with applied policy analysis. He has published extensively on topics such as predictive regression testing, risk assessment models, and structural break analysis. His methodological innovations include functional-coefficient VAR models and time-varying risk aversion frameworks.

Lingjie Liuمشاهده پروفایل

استادیار

• Computer Graphics
• Computer Vision
• Artificial Intelligence
• +۷ مورد دیگر

Lingjie Liu is the Aravind K. Joshi Assistant Professor in the Department of Computer and Information Science at the University of Pennsylvania, leading the Penn Computer Graphics Lab and co-chairing the GRASP Lab. Previously, he was a Lise Meitner Postdoctoral Fellow at the Max Planck Institute for Informatics, and earned his Ph.D. from the University of Hong Kong (2019). His research focuses on neural scene representations, neural rendering, human performance modeling, and 3D reconstruction, blending computer graphics, vision, and AI. He is actively recruiting PhD students in areas like neural representations, generative models, and LLM-guided graphics. Education: Ph.D. in Computer Science (University of Hong Kong, 2019); BEng in Computer Science (Huazhong University of Science and Technology, 2014). Research highlights include NeuS/NeuS2 for 3D reconstruction, DragGAN for interactive image manipulation, and Holodeck for text-driven 3D environments. He has received awards such as the IVA Best Paper Award (2021) and holds editorial roles at IEEE TVCG and program committees for SIGGRAPH/ICCV. Professional activities include organizing workshops on neural fields, generative models, and robotics. His lab emphasizes 3D/4D reconstruction, generative models, and physics-aware systems. Current students include Chen Wang (Meshy AI Fellow), Yiming Huang, and Qiao Feng. His work spans over 50 publications in top venues like SIGGRAPH, CVPR, and NeurIPS.

• Computer Vision
• Artificial Intelligence
• Machine Learning
• +۴ مورد دیگر

Abhinav Shrivastava is an Associate Professor in the Department of Computer Science at the University of Maryland, College Park, with a joint appointment in the Institute for Advanced Computer Studies (UMIACS). His research focuses on AI, particularly computer vision, machine learning, and robotics, with applications in graphics, natural language processing, and cognitive neuroscience. He holds a Ph.D. from Carnegie Mellon University (2017) and an M.S. from the same institution (2011). Research Interests: His work emphasizes temporal understanding, visual perception, and open-world learning. He explores how machines can analyze temporal phenomena like human actions in videos and develop robust models for dynamic environments. Key Projects: Recipient of the NSF CAREER Award (2023) to advance computers' ability to understand temporal phenomena. His research includes developing computational frameworks for video understanding, object discovery, and neural representations for videos. Awards: NSF CAREER (2023), Best Paper Award (WACV 2020), Microsoft Research PhD Fellowship (2014-16), and recognition in media outlets like Wired and Forbes for contributions to AI and computer vision. Labs/Teams: Leads research in the Vision & Learning Center and contributes to the Institute for Trustworthy AI in Law & Society. Active in organizing workshops on open-world vision and novelty handling in AI systems.