CS 6703 - AI for Science

Fall. 3 credits. Student option grading.

Enrollment is limited to graduate students. Students need to have the approval from the instructor and a permission code to enroll in the course.

C. Gomes.

This is a studio style course that emphasizes collaborative learning. It leverages the AI for Science Schmidt postdoc fellowship program and other programs. Schmidt postdoc fellows will present their research and progress. We will discuss background literature, related research, and possible research directions in class. Throughout the semester, students will learn various AI research methodologies, including AI/ML code, packages, and software, and engage in discussions on current advancements and applications of AI in science and engineering.

Outcome 1: Describe the challenges of AI for Science research.

Outcome 2: Describe examples of how to define and formulate computational problems, as well as algorithms and approaches to solve them, distilling the core computational questions that generalize across different applications.

Outcome 3: Analyze and discuss existing literature on AI for Science and related topics.

Outcome 4: Describe the importance of designing ethical AI systems that can reason in high dimensions and understand tradeoffs among different criteria to address the many challenges we face today including socio, economic, and environmental challenges.