CS 568/668 Empirical Research Methods
This course introduces principles of experimental design for empirical research. Topics include the goals and logic of experimental design, research paradigms, levels of measurement, types and attributes of variables, basic elements of experimental design, agreement, reliability, validity, and ethical considerations. Other topics vary from year to year and may include detection theory, item response theory, and experimental design for Amazon Mechanical Turk. The course is fundamental for anyone who plans to conduct independent research or needs to evaluate critically the research of others.
GEN 569/669 Scholarship Skills
This course will make you a better scholar, and a better professional in non-academic venues. It will make you a better writer, a better presenter, and a better reviewer. The course concentrates on your written and oral exposition skills, and also discusses effective reading. You will learn more about both the production and consumption of media used by computer scientists and biomedical engineers to communicate today.
You will learn to write conference and journal articles and theses, and get some tips on effective reading. You will learn how to be an effective reviewer for journals and conferences. You will listen to and deliver oral presentations. You will learn how to prepare yourself for a job hunt in academia or industry when you graduate.
CS/EE 692 Ethics for CS & EE
Computer science research has changed dramatically over the last ten years, both in terms of the ways in which it is conducted as well as the ends to which it is applied. Research in our field is powered by large quantities of data- Tweets, clicks, records, posts, etc.- which, as a rule, were all created by somebody, somewhere. Furthermore, the algorithms and computational techniques that we develop are finding their way into every corner of our lives. They determine what news we see, affect our financial and professional choices, and are widely used as part of our criminal justice system. As they do so, they interact with all aspects of our society, flattening some forms of inequality while amplifying others, often in subtle and surprising ways. Seemingly-minor methodological choices by system designers can have profound consequences. This course will explore these and other issues, with the goal of preparing researchers-in-training with the knowledge they need to responsibly conduct research in this area, and also to prepare them for their professional careers.
Topics will include traditional RCR subjects such as informed consent, ethical reasoning, conflicts of interest, etc., but will address these issues through the lens of modern machine learning and "big data" problems. Additional topics will include issues of anonyminity, social implications of our work, data privacy standards, etc. We will also include several detailed case studies, including the infamous Kramer study on widespread emotional contagion via Facebook and the memorable "gorillas" automated photo-tagging incident.
Weekly seminars in which faculty present their research work.