Yehu Chen is a fifth-year Ph.D. candidate at WashU St. Louis major in Computer Science with a focus on political science, co-advised by Prof. Roman Garnett and Prof. Jacob Montgomery. Yehu's research expertise encompasses the intersection of machine learning and social sciences, including causal inference, psychometrics methods, optimal treatment design and election forecasting, and his dissertation focuses on addressing methdology challenges in PoliSci with intepretable machine learning methods. His academic journey has seen him actively contribute to interdisciplinary projects such as causal inference with longitudinal data and time series-based forecasting, resulting in publications in esteemed computer science conferences (AISTATS) and leading political science journals (Political Analysis) with replication archives on github or Harvard Dataverse. Yehu's skill set extends to mastering various programming languages, including C++, Python, MATLAB, and R, as well as deep learning toolboxes like Tensorflow and PyTorch. His commitment to bridging academic insights with practical applications makes Yehu a standout candidate in the realm of applied science.