Han Yu

• Ph.D. in Statistics, Department of Statistics, Florida State University
• M.S. in Statistics, Department of Statistics, Florida State University
• M.S. in Probability and Statistics, Department of Mathematics, Xiamen University
• B.S. in Pure Mathematics, Department of Mathematics, Xiamen University

Joined the University of Northern Colorado (UNC) in 2017. After completing my Ph.D., I taught and developed a variety of statistics and data science courses at universities in the USA, catering to a wide range of academic levels, from undergraduate to doctoral students. At UNC, I am dedicated to teaching both statistics and data science courses, engaging in scholarship, and advising master’s and Ph.D. students on research projects that primarily focus on kernel-based semiparametrics and double machine learning, methodologies that are crucial in the contemporary field of causal inference and missing data. 

I have established a research program focused on modeling, reasoning, inference, discovery, and interpretation of the latent mechanisms underlying complex, high-dimensional network data. Rooted in contemporary measure-theoretic probabilistic reasoning theory, my work explores the functional spaces of probability measures through a minimax coherent paradigm. I have actively pursued various opportunities for interdisciplinary collaborative research projects. These projects largely rely on measure-theoretically sound methodologies and leverage advanced ML/AI algorithms for structural causal learning and discovery. 

I have cultivated insights into a unified measure-theoretic approach to causal inference. This approach leverages functional analysis for product measure-theoretic reasoning applied to random objects in spaces of functions associated with functional co-occurrence data, an expertise fortified through extensive engagement with multidisciplinary research.

My research interests span the following areas:

• Bayesian Nonparametrics
• Empirical Processes Theory
• Hierarchical Latent Variable Models
• Measure-Theoretic Graphical Models
• Modern Causal Inference and Discovery
• Network Computational Statistics and High-Performance Computing
• SPDE-based Spatio-Temporal Statistics
• Stochastic Processes
• Structural/Functional Causal Models

• Rathke, B.H., Yu, H. and Huang, H. (2023) What is left now that fear is gone? Data mining and analysis of COVID-19 pandemic emotions using Twitter, Google Trends, and Public Health data. Disaster Medicine and Public Health Preparedness, 17, E471.
• Agboola, D. O. and Yu, H.  (2023) Neighborhood-Based Cross Fitting Approach to Treatment Effects for High-Dimensional Data. Computational Statistics & Data Analysis, 186, 107780. 
• Huang, H. and Yu, H. and Li, W. (2023) Using Technology Acceptance Model to Analyze the Successful Crowdfunding Learning Game Campaigns. Information Technologies and Learning Tools, 95(3), 25-40
• Owusu, G., Yu, H. and Huang, H. (2022) Temporal dynamics for areal unit-based co-occurrence COVID-19 trajectories. AIMS Public Health, 9(4), 703-717.
• Oduro, M. S., Yu, H. and Huang, H.  (2022) Predicting the Entrepreneurial Success of Crowdfunding Campaigns Using Model-based Machine Learning Methods. International Journal of Crowd Science, 6(1), 7-16.
• Shi, T., Yu, H., Lim, C.L. (2021) Consumer affinity and an extended view of the spillover effects of attitudes toward a cross-border-acquisition event. Journal of Brand Management, 28, 596-608. 
• Yu, H., Jiang, S., and Huang, H. (2021) Spatio-Temporal Parse Network-Based Trajectory Modeling on The Dynamics of Criminal Justice System. Journal of Applied Statistics, 49(8), 1979-2000. 
• Wang, J.Y.  and Yu, H. (2020) The Measure on the Original Space from A Product Measure. Journal of Mathematical Analysis and Applications, 491(1), 124272. 
• Woods, C., Yu, H.  and Huang, H. (2020) Predicting the Success of Entrepreneurial Campaigns in Crowdfunding: A Spatio-Temporal Approach. Journal of Innovation and Entrepreneurship, 9(13), 1-23.  
• Vetter, R. E., Yu, H., Foose, A. K., Adams, P. J. and Dodd, R. K. (2017) Comparison of Training Intensity Patterns for Cardiorespiratory, Speed and Strength Exercise Programs. Journal of Strength and Conditioning Research, 31(12), 3372-3395, December 2017.  

• 2021 NSF travel grant for NSF/CBMS Research Conference on Gaussian Random Fields, Fractals, SPDEs, and Extremes
• 2019 Best Award in model/analysis in the Symposium on Data Science & Statistics Data Challenge 
• 2016 Co-PI:  MRI: Acquisition of the Bartik High-Performance Computing Cluster
  • Sponsor: National Science Foundation (NSF)
  • Years of Proposal Writing: 2014-2016 along with establishing new multidisciplinary data science program (approved) 
  • Amount Funded: $225,607.00
• 2014 NSF Travel Award: the 3rd workshop on Biostatistics and Bioinformatics
  • Sponsors: GSU Research Foundation, National Science Foundation, Institution of Mathematical Statistics, International Chinese Statistical Association and Department of Mathematics and Statistics, Georgia State University
• 2009 Co-PI: The ASA/BJS Small Grants Research Program
  • Sponsors: American Statistics Association (ASA) and Bureau of Justice Statistics (BJS)
  • Amount Funded: $29,517.00
• 2006 First Class Prize of Student Paper Competition, Florida Chapter of the American Statistics Associate (ASA) Annual Meeting
•  2000 Best first-year student in theoretical statistics, Department of Statistics, Florida State University