Johns Hopkins University 3 credits Computer Science
Programs are the fundamental medium through which humans interact with computers. With the advent of large language models (LLMs), the automated synthesis of programs is rapidly transforming how we build software. Instead of manual code writing, we specify intent through examples, specifications, and natural language.This course explores both the foundations and frontiers of program synthesis, covering traditional symbolic techniques alongside LLM-driven approaches. Students will study a variety of synthesis paradigms, including example-based, type- and specification-guided, and interactive methods. We will examine how LLMs are applied to general-purpose programming tasks as well as to specialized domains such as theorem proving, program repair, planning, and verification.Throughout the course, students will gain exposure to a wide range of programming languages, from widely-used ones like Python and C, to emerging and domain-specific languages such as Rust, Lean, CodeQL, and PDDL. The course offers a research-oriented perspective combined with hands-on assignments and projects, providing students with both conceptual understanding and practical experience at the intersection of programming languages and machine learning.Required course background: Python proficiency and LLM familiarity.
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