Assistant Professor @ University of Wisconsin, Madison

Teaching

Research Design and Applications for Data Analysis (RDADA)

Masters course, University of California, Berkeley, School of Information, 2022

Introduces the data sciences landscape, with a particular focus on learning data science techniques to uncover and answer the questions students will encounter in industry. Lectures, readings, discussions, and assignments will teach how to apply disciplined, creative methods to ask better questions, gather data, interpret results, and convey findings to various audiences. The emphasis throughout is on making practical contributions to real decisions that organizations will and should make.

LIS/​COMP SCI 611 — USER EXPERIENCE DESIGN 1

Masters course, University of Wisconsin, Madison, The School Information, 2022

Introduction to the user experience design including key stages of the design process, design ethics, and the methods and tools involved at each stage of design. Conduct formative research on clients, users, use contexts and tasks. Gain experience with user research methodologies and learn to create intermediate design tools such as personas. Develop and present a design proposal for a chosen project.

Introduction to Digital Information

Undergraduate course, University of Wisconsin, Madison, The Information School, 2022

Prepares students to use information technologies to solve problems and help people through implementing information infrastructures such as websites, databases and metadata. Students will explore information access, information representation, usability and information policy issues, and increase their understanding of the social impacts and social shaping of information infrastructures.

IST 687 Introduction to Data Science

Masters course, Syracuse University, School of Information Studies, 2022

The course provides students a hands-on introduction to data science, with applied examples of data collection, processing, transformation, management and analysis. Students will explore key concepts related to data science, including applied statistics, information visualization, text mining and machine learning. R, the open source statistical analysis and visualization system, will be used throughout the course. R is reckoned by many to be the most popular choice among data analysts worldwide; having knowledge and skill with using it is considered a valuable and marketable job skill for most data scientists. Students will also learn how to use supervised and unsupervised machine learning techniques. They will focus on structured data, using R (e.g., support vector machines, association rules mining) in conjunction with learning the full life cycle of data science.