I am a seasoned statistician, data scientist, and Al/ML researcher with 10+ years of combined industry and academic experience in coding, and deploying ML models and algorithms with more focus on data fusion, causal inference, anomaly detection, and Bayesian computations using petascale organic data. I earned my PhD in Survey & Data Science from the University of Michigan in July 2021. During my doctoral research, I explored robust and scalable Bayesian approaches for unbiased inference about organic unstructured data as large-scale non-probability samples with applications on sensor-based data from two of the largest naturalistic driving studies, i.e. Strategic Highway Research Program II (SHRP2) and Safety Pilot Model Deployment (SPMD). More importantly, the wide reliance of such methods on flexible prediction modeling and their overlap with the causal inference domain led me to further develop my theoretical knowledge and skills in ML and causal inference. In particular, I studied how to expand ML algorithms to account for unequal sampling weights and prediction uncertainty, which are inevitable for robust and externally valid causal inference, especially in the presence of heterogeneous treatment effects.It has been nearly three years since I began my career at Meta as a Research Scientist where I have continued my research and applied my methodological developments to enhance the quality of our data-driven inferences for business decision-making. My job expertise also encompasses survey sampling, mitigating bias and quantifying uncertainty of ML prediction, feature selection, model evaluation, ANN & DL, NLP, LLM training, fine-tunning and RAG, Monte Carlo simulations, network analysis, time series analysis, experimentation & A/B testing, metrics design, revenue optimization, and responsible AI. These experiences have exposed me to many challenges of analyzing real-world big data and led me to practice a variety of computing management skills such as data partitioning, data compaction, parallelization, and integration of multiple platforms/languages. I have also authored multiple open-source projects and over 40 research articles published in top-tier journals, receiving more than 1,300 citations. Currently, I am passionate about developing scalable systems that establish a unified probabilistic framework for mitigating fairness issues and propagating uncertainty of predictions when training deep generative models using Bayesian non-parametric approaches, aiming to build more responsible and trustworthy AI systems.