Program analysis, the process of analyzing source code to derive its properties, has been a prominent research area for decades. Effective program analysis methods have played a pivotal role in ensuring program correctness and optimizing performance. In this talk, I will walk you through a journey centered around program analysis, in particular, it starts with classical symbolic-, logic-based analysis/testing techniques, ventures into the realm of emerging data-driven approaches, and concludes with their applications in machine learning models. I will not only share the results of my research in compiler optimization, bug detection, and model hardening, but more importantly, I will discuss my research vision and plan for building the next-generation programming environment. Bio: Ke Wang is currently a visiting scholar at Stanford University (on leave from his duty as a research scientist at Visa Research). His primary research interests span programming language, program analysis, and machine learning. His work has been featured in premier research conferences in programming language, machine learning and artificial intelligence, including PLDI, OOPSLA, NeurIPs, ICLR, and IJCAI. Notably, Dr. Wang's work received a Distinguished Paper Award at OOPSLA 2020 and an Oral presentation at NeurIPs 2022. He has served on the program committee for PLDI in 2020, 2021 and 2023. Prior to joining Visa Research, Dr. Wang obtained his PhD from UC Davis, where he was awarded twice in 2015 and 2018 an Honorable Mention for Outstanding Graduate Research in the Computer Science Department. He also worked at Microsoft Research, Siemens Corporate Technology/Research and Meta.