Teaching and Mentoring

Course Summary:

This course is a mixed level introduction to topics at the intersection of computation and language. We will study computational linguistics from both scientific and engineering angles: the use of computational modeling to address scientific questions in linguistics and cognitive science, as well as the design of computational systems to solve engineering problems in natural language processing (NLP). The course will combine analysis and discussion of these approaches with training in the programming and mathematical foundations necessary to put these methods into practice. The course is designed to accommodate students both with and without prior programming experience. Our goal is for all students to leave the course able to engage with and critically evaluate research in cognitive/linguistic modeling and NLP, and to be able to implement intermediate-level computational models for novel computational linguistics research.

Course Summary:

This course provides an in-depth study of advanced topics at the intersection of computation and language, oriented toward linguists and cognitive scientists. The class will focus on both scientific and engineering perspectives - the use of computational modeling to address advanced scientific questions in linguistics and cognitive science, as well as the design of computational systems to solve engineering problems. Topics will include machine translation, parsing, automatic speech recognition, transformer models, and large language models (LLMs), where we will focus on the implementation of these techniques. Students will work towards formulating research questions that can be addressed with complex computational methodology. Students will learn how to generate scripts and implement advanced computational techniques from scratch. Prior knowledge of Python is required.

Course Summary:

This course provides an introduction to how quantitative methods are used in the analysis of linguistic data. This will include a foundation in statistical methods that can be applied to experimental and psycholinguistic data, including probability theory, hypothesis testing, regression models and the use of Bayesian statistics. Further topics will include a brief introduction to the use of basic machine learning algorithms in linguistic research and techniques that can be used in the analysis of large linguistic datasets. The class will be grounded in case studies from a variety of subfields of linguistics and provide hands-on examples through a guided introduction to programming. This class is intended for students who are interested in jump-starting a path into linguistic data science and is designed to be accessible to those with no experience in data science or programming.

Course Summary:

Large Language Models are becoming increasingly prevalent in all aspects of our lives. But what is their place in linguistics? What linguistic phenomena do they exhibit and not exhibit? And what does this even tell us? In this class, we will address the use of LLMs in linguistics and related fields - both their limitations and advantages. This will include a discussion of the models that LLMs are founded on, how they can be probed for linguistic representations, and used as linguistic or psycholinguistic subjects. Ultimately, we will attempt to answer the question of the usefulness of these models and the ways they could be used as tools for linguistic research.

Course Summary:

In this class, we will cover computational techniques for collecting linguistic data. We will also cover various methods for using algorithms to analyze that data and some basic computational theory to understand the complexity and efficiency of our algorithms. We will use the programming language Python and focus on real-world applications to gain experience in gathering, manipulating, and analyzing data from sources such as fieldwork, corpora, or experiments. No previous knowledge of programming is required. This is not recommended for students that already have extensive experience in Python.

Course Summary:

When hearing speech, humans rapidly and robustly map from a continuous acoustic signal to an abstract representation of the sounds of their language. This class will explore models of this acoustic-phonetic perceptual mapping by drawing from a variety of methodologies and perspectives. We will discuss the merits and issues of linguistic, computational, and neuroscientific approaches and draw connections between these disciplines. A background in neuroscience or computational modeling is not required.

Course Summary:

A foundational principle of cognitive science is that the workings of cognitive systems--whether biological, mechanical, or digital--can be productively represented by the operation of formal computational models. This course provides a survey of popular modeling frameworks (such as Bayesian rational agents, connectionist networks, dynamical systems, etc.), as well as the cognitive phenomena that these models have been used to simulate. We will discuss the theoretical commitments of these models, assess strengths and weaknesses of each framework for addressing different types of cognitive questions, and analyze the implications of these models' successes and failures for our understanding of the mind.

Perceptual Models Of Speech [Schedule] [Syllabus]

Fall 2021

When hearing speech, humans rapidly and robustly map from a continuous acoustic signal to an abstract representation of the sounds of their language. This seminar will explore models of this acoustic-phonetic perceptual mapping by drawing from a variety of methodologies and perspectives. We will discuss the merits and issues of linguistic, computational, and neuroscientific approaches and draw connections between these disciplines. A background in neuroscience or computational modeling is not required. 

Phonology I, Fall 2019, Fall 2020, Spring 2021: Teaching Assistant.  Instructor: Dr. Peggy Antonisse.

Introductory Linguistics, Spring 2020: Teaching Assistant/Discussion Section Leader.  Instructor: Dr. Tonia Bleam.

PULSAR Undergraduate Research Seminar, Spring 2022, Fall 2022: Graduate Mentor. Instructors: Tess Wood, Andrea Zukowski

Language at the Museum: Engaging the Public in Language Science, Summer 2022 Graduate Mentor/Assistant. Instructor: Charlotte Vaughn