Teaching and Mentoring

Teaching and mentoring philosophy

All my courses include a combination of theoretical foundations and hands-on exercises to illustrate how those theories can be applied in practice. My teaching philosophy is strongly influenced by the level of the material in the course. For example in my database course there are a series of hands on activities that develop a student’s cognitive understanding of core principals and active learning that focuses both on the design and use of relational database technologies. At the end of the database course students complete a small project that brings the materials together. In contrast, my text mining course is project-based, where students oscillate between building their processing pipeline on shared data collections and moving their own projects forward. A key component of that course is how to frame your problem a such that it can be addressed as a supervised learning task. Thus I ask students to submit a “dream statement” where they share their idealized project goals and a few existing text collections that might help them achieve those goals, then we work together to transform the passion for their project into a text mining project. Several projects from the text mining course has been subsequently published in conferences.

With respect to mentoring, my strategy varies greatly on the individual being mentored. I have worked with doctoral students who have vast industry experience before joining the program and I help them funnel that expertise into a form that is aligned with academic outcomes. Most of my doctoral students have come straight from previous academic programs . I think it is important that students leave the doctoral program with the ability to discern between challenges that can be overcome in a reasonable amount of time, and challenges that suggest that the current path will not be productive.

I certainly was not the only woman in my computer science undergraduate program, nor was I the only female research scientist working for a mining and heavy manufacturing company, but some of those experiences help me understand challenges faced by students and junior faculty who are not well represented in STEM. I am a strong advocate for data literacy and have published papers on that topic and I am currently collaborating with colleagues and with UIUC extension on the health data literacy ambassador program. This program reaches students at the point where they are most likely to turn away from STEM careers. The Midwest Big Data Hub has also enabled me to reach communities such as the Tribal Nations who are less represented in data science. I see those activities as a long term and sustained commitments, not just one-off activities. From a science perspective we need a diversity of voices to help us understand the context in which data is collected and to engage with us as we set priorities for automated steps within the knowledge discovery process.

Courses designed and taught at UIUC 

Text Mining (IS567, formally LIS590TX)

  • Spring 2010, Fall 2010 (online), Spring 2012, 2013, 2014, 2015 (hybrid), 2016 (hybrid), 2018, 2019, Fall 2020, Fall 2021 (online), Fall 2022
  • List of instructors ranked as excellent Spring 2018, Fall 2021

Database Design and Prototyping (IS455, formerly Introduction to Databases (LIS490DB))

  • Fall 2009, 2010, 2011, 2012, Spring 2010 (online), Spring 2011(online), Fall 2015, Fall 2017 (x2), Spring 2018, Fall 2018, Spring 2019, Fall 2019, Fall 2021
  • List of instructors ranked as excellent Fall 2010, Spring 2019

Courses designed and formally taught at UIUC 

Evidence-based discovery and foundations of socio-technical data analytics were developed as part of the Socio-technical Data Analytics (SODA) specialization and then used as templates for required courses in the Master of Science Information Management degree.

Evidence-based Discovery

  • Core in the SODA specialization Fall 2013 (Inaugural offering), Fall 2014, Fall 2015 (hybrid, both on campus and online)
  • List of instructors ranked as excellent (online) Fall 2014

Foundations in Socio-technical Data Analytics

  • Core in the SODA specialization, Spring 2013 (Inaugural offering), Spring 2014, Spring 2015(Co-taught with V. Stodden)

Socio-technical Data Analytics Practicum

  • Developed practicum requirements for the SODA specialization, coordinated experiences with industry and academic partners
  • Spring 2013, Fall 2013, Summer 2014, Fall 2014, Summer 2015, Fall 2015

Foundations of Information Processing in LIS

  • Introduced a Java section of the course with Wu Zheng