The doctoral degree, i.e. the PhD, is primarily intended for students desiring a career in research andor collegiate teaching. The focus is on advanced CSE topics, on learning to perform research and to write research papers, and on making fundamental new contributions to a CSE topic. Students take advanced course work and write a doctoral dissertation, also called a thesis. Program overview: Students newly admitted to the doctoral program are classified as precandidates. Upon entering a doctoral program, there is a PhD qualifying process, normally completed during the first two years. After all requirements except the dissertation are completed, students become candidates. Students entering a CSE doctoral program with a bachelor’s degree typically become candidates in the third year and are strongly encouraged to complete the degree within five years. Such students ordinarily complete the requirements for a master’s degree along the way and receive this degree in addition to the PhD A masters thesis is optional. Students who enter a CSE doctoral program with a master’s in the field of their program typically become candidates in their second year and are strongly encouraged to complete the degree within four years. Such students are not ordinarily eligible to receive a CSE master’s degree. Artificial intelligence (AI) researchers at Michigan work to develop the next generation of intelligent systems, blending foundational advances and real-world applications. Our faculty and students explore core areas such as machine learning, deep learning, robotics, natural language processing, perception, reasoning, and trustworthy AI. Projects span automated decision-making, autonomous vehicles, healthcare AI, explainable systems, and collaborative robotics. AI research is highly interdisciplinary, leveraging insights from computer science, psychology, linguistics, economics, and engineering. We lead the development of theory and algorithms as well as practical systems that advance the state of the art in AI technologies. Collaboration across campus and with industry partners enables innovative solutions to societal challenges. Example projects: Improved human-AI collaboration in conservation and health; Advanced reinforcement learning methods for intelligent, adaptive agents; Machine learning systems to deliver predictive analytics in healthcare; Language models for reliable extraction and summarization of real-world events.