Ning Dong

An enthusiastic, adaptive, and fast-learning person with a broad and acute interest in the discovery of new innovative methods. Particularly, a professional enjoying collaborating with scholars from different disciplines to develop new skills and solve new challenges. More specifically, proficient at individualized modeling with strong interpersonal skills and problem-solving techniques.

• Honor Graduate of Shanghai University, 2019
• National First Prize, National College Students' Contest of Marketing and Analysis (8th)
• Individual Scholarship of Shanghai University, 2016, 2018, 2019
• Recipient of Distinguished Graduate Student Assistantship (DGSA) at Texas Tech University, 2020-2024

Development and Optimization of a Condition-Based Maintenance Policy with Sustainability Requirements for Production System

Shanghai University, June 2017 - May 2018

• Collaborated with Dr. Aiping Jiang from Shanghai University and funded by National Natural Science Foundation of China (Grant No. 71302053)
• Built a probabilistic model for Sustainable Maintenance and production and designed simulation experiments and analyzed real case studies
• Completed programming of the model in MATLAB and wrote the manuscript and revision for submission

Bayesian prognosis analysis of human papillomavirus-associated head and neck cancer using hierarchical Dirichlet process mixture models

Texas Tech University, August 2021 - November 2022

• Collaborated with Dr. Ying Liao and Dr. Yisha Xiang
• Led the research progress and conducted literature review on nonparametric Bayesian models
• Built the model and derived all posteriors for sampling and developed the algorithms
• Designed simulation experiments and analyzed real case studies and completed programming in Python

Scalability of Nonparametric Bayesian Models in Large Data Settings (Ongoing)

Texas Tech University, May 2023 - Now

• Aim to enable nonparametric Bayesian models to handle today's large data settings while preserving their interpretability
• Conducted literature review on Bayesian Analysis and MCMC algorithms for large data
• Modified the Dirichlet process mixture model and derived posterior distributions to be sampled from
• Designed scalable MCMC algorithms and completed the programming in Python
• Conducted simulation experiments and analyzed real Heart Disease patient's data
• Presented the work at the INFORMS Annual Conference in October 2023

Advanced Inference Framework in Scalable or Paralleled Nonparametric Bayesian Models in Large Healthcare Data (Ongoing)

Texas Tech University, November 2023 - Now

• Aim to enhance objectives from the previous project, including investigating data structure, density estimation, and clustering information from paralleled posterior samples. Meanwhile, selecting significant explanatory predicting variables if possible
• Develop an advanced and multi-purpose inference framework for scalable or paralleled nonparametric Bayesian models
• Conduct literature review and develop inference for density estimation, clustering inference, and model selection
• Design simulation experiments to demonstrate the performance of proposed inference methods and analyze real healthcare data
• Expected outcome: Journal paper and a Python Package of nonparametric Bayesian survival modeling.

• Jiang, A., Dong, N., Tam, K. L., & Lyu, C. (2018). Development and optimization of a condition-based maintenance policy with sustainability requirements for production system. Mathematical Problems in Engineering, 2018, 1-19.
• Liao, Y., Dong, N., & Xiang, Y. (2023). Bayesian prognosis analysis of human papillomavirus-associated head and neck cancer using hierarchical Dirichlet process mixture models. IISE Transactions, 1-15.
• Dong, N., & Du, D. (2023) Scalable Dependent Dirichlet Process Mixture Model via Parallel MCMC, 2023 INFORMS Annual Conference, Phoenix, AZ