Carolyn Anderson
Assistant Professor of Computer Science
I use computational, experimental, and symbolic methods to understand meaning in natural language.
My research focuses on understanding how context-sensitive meaning is encoded in natural language. I study words whose meanings change depending on who is using them, and when and where they are used.
I build computational models to understand how conversation participants use knowledge about each other's mental states. How do speakers think about their audience when deciding what to say? How do listeners use their knowledge of the speaker when figuring out the meaning of their utterances?
These aspects of language are among the most difficult for artificial intelligence to grasp, because they are situational, grounded, and interactive. In order to develop language technology that interacts with us in a natural way, we need to develop models that can adjust their language based on users' situations and knowledge states.
Visit my website for more information on my research, teaching, and other interests.
Currently teaching
Artificial Intelligence
CS232
What is artificial intelligence (AI) and should humans fear it as one of "our biggest existential threats"? In this course, we will grapple with these difficult questions and investigate them in different ways. We will discuss the development of the field from the symbolic, knowledge-rich approaches of the 20th century AI (e.g., rule-based systems), to statistical approaches that rely on increasingly large amounts of data, including an overview of contemporary deep learning techniques. We will explore how to apply these techniques in several AI application areas, including robotics, computer vision, and natural language processing, and consider ethical issues around AI in society. By the end of the semester, students should be able to answer the starting questions in-depth and with nuance.
-
An introduction to problem-solving through computer programming. Students learn how to read, modify, design, debug, and test algorithms that solve problems. Programming concepts include control structures, data structures, abstraction, recursion, and modularity. Students explore these concepts in the context of interactive programs, data processing, and graphics or audio, using the Python programming language.Students are required to attend an additional two-hour laboratory section each week.. If you have been previously waitlisted for CS 111, you will be prioritized during initial registration in November. If you have not been previously waitlisted, add yourself to the waitlist during initial registration. During registration changes period (in January 2024), waitlisted students will be offered seats, subject to availability. Students must add themselves to the Workday waitlist; faculty do not have the ability to add students to the Workday waitlist.