teaching |

Courses and volunteering.

Computer Science Practice and Experience: Development Basics (COMPSCI 1XC3)

Winter 2026, McMaster University, Department of Computing & Software

COMPSCI 1XC3 covers fundamental skills for software development, topics including C programming in UNIX/LINUX environments and git version control. More details will come as the semester nears.

Directed Readings in Natural Language Processing for Mental Health Assessment (COMPSCI 4Z03)

Fall 2025, McMaster University, Department of Computing & Software

Natural language processing (NLP) techniques are increasingly used for language-based assessments (LBAs) of mental health. Language data from sources such as clinical notes, social media, interviews, and self-reports often carries subtle linguistic and behavioral signals that can reflect depression, anxiety, suicidality, and other psychological states. Research in this area focuses on detecting these markers to population-level monitoring and mental health screening.

This directed readings course is designed to help the student build foundational and research-oriented competencies in LBAs, with the goal of preparing for graduate-level research in this area. The course will begin with readings focused on technical and methodological foundations of NLP systems used in mental health assessment, with an emphasis on:

psycholinguistic feature extraction and language markers of psychological states;
temporal and time-series analysis to examine and forecast changes in mental health states;
deep learning techniques, including large language models (LLMs), for symptom tracking and adaptive conversational assessments.

The student will then select a focused research question related to LBAs, conduct a literature review, and implement and report on a small-scale experimental study (e.g., through reproduction, ablation testing, or comparison of models). To facilitate this, the student is welcome to select an open community task (e.g., a past CLPsych task) that offers labeled datasets and baselines.

By the end of the course, the successful student will be able to:

summarize the research paradigms and state-of-the-art in NLP approaches to mental health assessment;
discuss open challenges in LBAs, including conceptual, technical and ethical aspects;
conduct a literature review to contextualize an LBA-related research question and identify methodological gaps;
implement an NLP experimental pipeline, including data analysis;
effectively present the research challenge, literature review, and experimental report in writing.

The overall goal, which requires the student achieve these learning objectives, is to prepare the student for graduate-level research in natural language processing or a related domain.

Summer 2023, Stony Brook University, Institute for AI-Driven Discovery and Innovation

Course description: Social Natural Language Processing is a field that combines social science research with computational methods and tools. This interdisciplinary field allows social scientists to conduct research on large-scale data sets, and use machine learning to identify patterns and relationships. Computational tools can be used to analyze large-scale text data, including social media posts, news articles, and historical documents. Text analysis methods include sentiment analysis, topic modeling, and machine learning-based classification, which can provide insights into the attitudes and beliefs of large groups of people. This 2-week workshop brings together two types of audiences: CS AI students and social and psychological researchers interested in language and AI.

Students of computer science will get to learn about the latest techniques specifically for language modeling and dialog, within the context of important social scientific research questions. Students of human-oriented fields, such as psychologists and sociologists, will collaborate with computer science students to explore a wide range of research topics related to language and communication. For example, automated emotion analysis can be useful in studying public opinion, user behavior, or political discourse. By examining patterns of language use, participants can gain insights into social norms, power dynamics, and cultural values. Psychologists and NLP researchers can collaborate to develop algorithms that analyze language use to identify personality traits, which can be useful in predicting behavior in certain contexts.

Participants will learn about the various computational tools and methods used in social NLP, including sentiment analysis, topic modeling, and machine learning-based classification. They will also gain an understanding of how these methods can be used to analyze large-scale text data and gain insights into social norms, power dynamics, and cultural values. Participants will test the effectiveness of various NLP models on social science research questions.

Instructors: Lucie Flek (Professor, University of Bonn, Computer Science), Allie Lahnala (PhD Student, University of Bonn), Ryan Boyd (Professor, Stony Brook University, Psychology & Computer Science).

Organizers: Lucie Flek, H. Andrew Schwartz

Artificial Intelligence Ethics Seminar

Winter Semester 2022/23, University of Marburg and University of Bonn

Course description: In this 15-week seminar, we study artificial intelligence and the ethical dilemmas associated with the research, design, deployment, and interaction with AI systems. Six broad modules structure the seminar: foundations of AI and AI ethics; bias & fairness; privacy & data privacy; social networks & civility of communication; politics & policy; and AI for “social good.” Students learn about the design of ethical and socially responsible systems and gain practice engaging with multidisciplinary perspectives from behavioral and social science. Students develop skills in assessing AI systems, identifying ethical dilemmas and social impacts, reasoning through ethical and social issues, and communicating their reasoning.

IT Summer School

Summer 2022, Hessian AI & University of Marburg

The IT Summer School was a week-long program for high school women in Hessen in which they are introduced to basic programming concepts in MIT Scratch and Python. The curriculum introduces the concepts through video game programming. These materials are available in both English and German on our lab github: IT Summer School.

Intro to NLP for AI4All at University of Michigan

Summer 2020, Michigan AI Lab, AI4All

Introductory Natural Language Processing lessons and projects covering the fundamentals. These materials are available on my github: AI4All2020-Michigan-NLP.

Intro to Computer Science through Video Games

STEM Saturdays, Fall 2015 - Winter 2017, STEM Society, University of Michigan